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In e na ional Jou nal o Social and Educa ional Inno a ion
Vol. 12, Issue 24, 2025
ISSN (p in ): 2392 – 6252
eISSN (online): 2393 – 0373
DOI: 10.5281/zenodo.17278219
DIGITAL LIBRARY SERVICES AND USER BEHAVIOR
IN NIGERIAN UNIVERSITIES:
AN EXPECTATION-CONFIRMATION MODEL ANALYSIS
Adedeji Daniel GBADEBO
Depa men o Accoun ing Science
Wal e Sisulu Uni e si y, M ha ha, Sou h A ica
[email p o ec ed]
Abs ac
This s udy in es iga es he beha io al ac o s in luencing uni e si y s uden s’ con inued use o
digi al lib a y se ices in Nige ia, applying he Expec a ion Con i ma ion Model (ECM) as he
heo e ical amewo k. A s uc u al equa ion modeling app oach was employed o es a
concep ual model de eloped om ECM cons uc s, such as he pe cei ed use ulness,
con i ma ion, sa is ac ion, and con inuance in en ion, augmen ed by sys em quali y and
pe cei ed ease o use. P ima y da a we e collec ed ia an online su ey dis ibu ed ac oss
mul iple uni e si ies in Lagos S a e, Nige ia, using andom sampling echniques. The empi ical
indings demons a e ha con i ma ion signi ican ly a ec s bo h pe cei ed use ulness and
sa is ac ion, which in u n in luence s uden s’ in en ion o con inue using digi al lib a y
se ices. Addi ionally, sys em quali y and pe cei ed ease o use eme ged as signi ican
p edic o s o sa is ac ion. The s udy con ibu es o he li e a u e on digi al se ice adop ion in
de eloping con ex s by o e ing e idence-based insigh s ha in o m he design,
implemen a ion, and policy su ounding academic digi al in as uc u es. Recommenda ions
a e p o ided o enhancing sys em quali y, managing use expec a ions, and ensu ing equi able
digi al access in highe educa ion.
Keywo ds: Digi al Lib a ies; Use Beha io ; Expec a ion Con i ma ion Model; S uc u al
Equa ion Modeling; Highe Educa ion; Nige ia.
JEL Codes: C51; I23; O33; D83; L86.
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1. In oduc ion
The digi al ans o ma ion o academic ins i u ions has signi ican ly ede ined how in o ma ion
is accessed, p ocessed, and u ilized. Among he mos a ec ed a e academic lib a ies, which a e
inc easingly in eg a ing sma echnologies o enhance he e iciency, accessibili y, and quali y
o lib a y se ices. This e olu ion has b ough abou he p oli e a ion o digi al se ices such
as online ca alogues, emo e access o e- esou ces, digi al lending, and AI-powe ed in o ma ion
e ie al sys ems, all aimed a os e ing use -cen ic expe iences and imp o ing s uden
engagemen wi h schola ly con en (Zhou & Li, 2021). In he con ex o de eloping economies
like Nige ia, whe e access o con en ional lea ning esou ces emains une en, digi al lib a y
se ices p esen a c i ical oppo uni y o b idge gaps in academic esou ce p o ision and
democ a ize knowledge access. Howe e , despi e he g owing p esence o hese se ices in
Nige ian uni e si ies, he e emains a lack o empi ical e idence conce ning how s uden s
engage wi h hem o e ime, especially beyond ini ial adop ion.
Unde s anding use beha io in digi al en i onmen s necessi a es an explo a ion o no only
adop ion pa e ns bu also he de e minan s o con inued use. Resea ch in in o ma ion sys ems
has demons a ed ha use s’ pos -adop ion expe iences a e i al o he long- e m success o
any echnology in e en ion (Bha ache jee, 2001). The Expec a ion Con i ma ion Model
(ECM), widely used in digi al se ice esea ch, o e s a comp ehensi e lens h ough which o
assess his phenomenon. Roo ed in cogni i e dissonance heo y, he ECM sugges s ha use s
o m expec a ions p io o using a sys em, which a e hen ei he con i med o discon i med
based on hei expe iences. These ou comes in luence use s’ pe cep ions o use ulness,
sa is ac ion, and hei subsequen in en ion o con inue using he se ice (Roca e al., 2006;
Mensah & Mi, 2020). Wi hin he academic lib a y con ex , he ECM se es as a aluable
amewo k o unde s and he e ol ing dynamics o s uden in e ac ion wi h digi al se ices o
sa is ac ion and loyal y.
While nume ous s udies ha e applied ECM in a eas such as mobile banking, e-lea ning, and
cloud compu ing (Lin e al., 2019; Bawack e al., 2023), ela i ely ew ha e examined i s
applica ion in academic lib a ies, pa icula ly wi hin Sub-Saha an A ica. This is a no able gap
gi en he unique in as uc u al, pedagogical, and sociocul u al dynamics ha cha ac e ize
digi al se ice deli e y in his egion. In Nige ia, academic lib a ies a e g adually ansi ioning
om manual o digi al and sma sys ems, bu challenges such as inadequa e digi al li e acy,
in e mi en in e ne connec i i y, and limi ed ins i u ional suppo con inue o hinde ull
u iliza ion (Ojedokun & Okewale, 2020). Consequen ly, while s uden s may ini ially adop
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hese echnologies, hei willingness o con inue usage o en depends on hei subjec i e
e alua ions o sys em pe o mance and se ice quali y.
To add ess hese gaps, his s udy adop s he Expec a ion Con i ma ion Model o examine how
s uden s in Nige ian uni e si ies engage wi h sma lib a y se ices. Speci ically, i e alua es
how expec a ion, con i ma ion, pe cei ed use ulness, and sa is ac ion in e ac o in luence
con inuance in en ion. A concep ual model wi h ou hypo heses, g ounded in ECM li e a u e,
was de eloped o guide he empi ical analysis. The s udy employed S uc u al Equa ion
Modeling (SEM) o es hese hypo heses using su ey da a collec ed om a di e se sample o
uni e si y s uden s in Lagos S a e, Nige ia. A andom sampling s a egy was applied o ensu e
a iabili y in demog aphic and ins i u ional cha ac e is ics, he eby enhancing he
gene alizabili y o indings. The da a collec ion ins umen consis ed o alida ed ECM
measu emen i ems adap ed om p e ious empi ical esea ch, ensu ing cons uc alidi y and
eliabili y (Al aja e al., 2023; Roca e al., 2006).
This s udy con ibu es o heo y by ex ending he applicabili y o ECM o he unde explo ed
domain o sma lib a y se ices in a de eloping coun y con ex . I also p o ides a s uden -
cen e ed pe spec i e ha illumina es he psychological and cogni i e p ocesses unde pinning
digi al se ice engagemen in highe educa ion. P ac ically, he indings o e s a egic insigh s
o lib a y adminis a o s, academic policymake s, and echnology de elope s aiming o
op imize use sa is ac ion and ensu e long- e m iabili y o digi al lib a y sys ems. Key
implica ions include he impo ance o aligning digi al se ices wi h use expec a ions,
enhancing pe cei ed alue h ough aining and in e ace design, and es ablishing eedback
mechanisms o acili a e con inuous se ice imp o emen .
As digi al lib a y ecosys ems con inue o e ol e, pa icula ly in esou ce-cons ained
educa ional se ings, he e is a p essing need o g ound se ice design and implemen a ion in
empi ical insigh s abou use beha io . This s udy answe s ha call by explo ing he cogni i e
an eceden s o digi al se ice con inuance h ough he lens o he Expec a ion Con i ma ion
Model. In doing so, i en iches he li e a u e on academic lib a y mode niza ion and p o ides
an e idence-based amewo k o p omo ing sus ained use engagemen in digi al academic
en i onmen s ac oss Nige ia and compa able con ex s.
2. Li e a u e Re iew
The e olu ion o digi al lib a y se ices in academic ins i u ions has been ex ensi ely s udied
o e he pas decade, wi h nume ous empi ical in es iga ions explo ing ac o s in luencing
s uden adop ion, usage, and sa is ac ion. The Expec a ion Con i ma ion Model (ECM) has
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been a p e alen amewo k in hese s udies, emphasizing he ole o use expec a ions and
pe cei ed pe o mance in de e mining con inued usage in en ions. Complemen ing ECM, he
Technology Accep ance Model (TAM) and he Uni ied Theo y o Accep ance and Use o
Technology (UTAUT) ha e been widely applied o unde s and use accep ance o digi al
lib a y se ices. A me a-analysis by Ali and Wa aich (2024) syn hesized indings om
mul iple s udies, highligh ing ha pe cei ed use ulness and ease o use a e signi ican
p edic o s o digi al lib a y adop ion among s uden s.
In he A ican con ex , se e al s udies ha e examined digi al lib a y usage among uni e si y
s uden s. Fo ins ance, Kiana e al. (2023) in es iga ed p edic o s o digi al lib a y usage among
unde g adua e s uden s in Namibia, inding ha lib a y aining signi ican ly in luences
pe cei ed use ulness and ac ual usage. Umuko o and Tiamiyu (2017) explo ed de e minan s o
e-lib a y se ice use among Nige ian uni e si y s uden s, iden i ying ac o s such as sys em
quali y, se ice quali y, and use sa is ac ion as c i ical o usage beha io .
The impac o digi al lib a y se ices on use sa is ac ion has also been a ocal poin in ecen
s udies. Azib e al. (2025) e alua ed use sa is ac ion wi h digi al lib a y se ices in Malaysian
highe educa ion ins i u ions, e ealing ha in o ma ion quali y, sys em quali y, and se ice
quali y signi ican ly a ec o e all sa is ac ion. Mo eo e , he in eg a ion o eme ging
echnologies in o lib a y se ices has been explo ed o enhance use engagemen . Wei e al.
(2024) in oduced an augmen ed eali y sys em designed o en ich physical lib a y expe iences,
demons a ing i s po en ial o inc ease s uden engagemen .
In e ms o accessibili y, Paul and Chauhan (2024) examined he ole o AI-powe ed assis i e
echnologies in enhancing lib a y access o pa ons wi h disabili ies, highligh ing he
impo ance o inclusi e digi al se ices. S udies ha e also add essed he challenges o digi al
lib a y se ice u iliza ion. Ade ayo e al. (2024) in es iga ed uni e si y s uden s' lib a y
engagemen , no ing ha ac o s such as eading habi s and gende dynamics in luence usage
pa e ns. Fu he mo e, he ole o social media in engaging lib a y use s has been analyzed.
Zou e al. (2020) explo ed s a egies o using social media o build pa icipa o y lib a y
se ices, emphasizing he need o lib a ies o adop use -cen e ed engagemen app oaches.
In he Nige ian con ex , Isa e al. (2025) conduc ed a compa a i e s udy o digi al e e ence
se ice u iliza ion be ween Nige ian and Malaysian uni e si y lib a ies, iden i ying di e ences
in usage pa e ns and sugges ing s a egies o op imiza ion. Al-Suq i and Al-Au i (2019)
highligh ed ha in addi ion o pe cei ed use ulness, cons uc s such as digi al li e acy and
cul u al con ex signi ican ly a ec he in en ion o use digi al lib a y se ices among Gul
Coope a ion Council (GCC) uni e si y s uden s. This suppo s he g owing ecogni ion ha
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egional, sociocul u al, and in as uc u al condi ions modula e echnology accep ance pa e ns
in academic se ings, pa icula ly in low- and middle-income coun ies. Such insigh s ha e
p omp ed a ee alua ion o one-size- i s-all adop ion models, ad oca ing o con ex -sensi i e
implemen a ions ha add ess localized ba ie s o usage.
Fu he , se e al s udies ha e explo ed how use aining and o ien a ion p og ams can enhance
he u iliza ion o digi al esou ces. Fo example, Mwan imwa and Ndenje-Sichalwe (2021)
demons a ed ha s uc u ed digi al li e acy ini ia i es signi ican ly imp o e he pe cei ed ease
o use and ac ual usage equency o e- esou ces among Tanzanian uni e si y s uden s. Thei
indings align wi h hose o Abdul Ka im and Da us (2020), who no ed ha s uden s exposed
o egula digi al lib a y aining sessions epo ed highe sa is ac ion and con inuance in en ion
sco es. These empi ical insigh s sugges ha beyond echnological a ailabili y, use
p epa edness and compe ence play a cen al ole in digi al lib a y success.
In addi ion, sys em design and use in e ace unc ionali y ha e eme ged as c i ical
de e minan s o digi al se ice engagemen . A s udy by Mon a edzadeh and K ishnan (2023)
indica ed ha sys em quali y had a signi ican posi i e impac on bo h pe cei ed use ulness
and sa is ac ion among pos g adua e use s in I anian academic lib a ies. This esona es wi h
indings om Asemi and Riyazi (2020), who showed ha echnical issues such as poo sea ch
algo i hms o sys em lags can nega i ely in luence con inued usage, e en when digi al se ices
a e o he wise a ailable and p omo ed. Thus, echnical in as uc u e, including back-end
sys em eliabili y, mus align wi h on -end usabili y o suppo sus ained engagemen .
Mo eo e , he ole o ins i u ional suppo and policy amewo ks canno be unde s a ed.
Empi ical s udies such as hose by Onyegbule e al. (2022) ha e shown ha uni e si ies wi h
clea ly de ined digi al lib a y policies, adequa e unding, and ongoing echnical suppo exhibi
highe s uden sa is ac ion and usage a es. This indica es ha ins i u ional commi men —
mani es ed h ough budge a y alloca ion, con inuous se ice imp o emen , and esponsi e use
eedback mechanisms—signi ican ly enhances he success o digi al lib a y se ices. Such
indings a e pa icula ly pe inen o Nige ian uni e si ies, whe e in as uc u al unde unding
and policy agmen a ion equen ly unde mine he sus ainabili y o digi al lib a y p ojec s.
Finally, ecen in e disciplina y esea ch has highligh ed he impo ance o emo ional and
psychological a iables in de e mining s uden s’ in e ac ion wi h digi al lib a y en i onmen s.
Fo example, a s udy by Chang and Fang (2022) in eg a ed he concep o digi al anxie y in o
he ECM amewo k, e ealing ha s uden s expe iencing high le els o anxie y we e less
likely o pe cei e digi al lib a ies as use ul o sa is ying. Simila ly, Nguyen and Ha (2021)
ound ha posi i e emo ional engagemen , os e ed h ough gami ica ion and pe sonalized
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con en deli e y, led o s onge con inuance in en ions. These eme ging pe spec i es u ge
schola s and p ac i ione s o mo e beyond pu ely cogni i e models and conside a ec i e
dimensions o digi al se ice usage in educa ional se ings.
3. Me hodology
This s udy adop s a quan i a i e esea ch app oach o e alua e he beha io al de e minan s o
s uden s’ con inued usage o digi al se ices in academic lib a ies, le e aging he heo e ical
ounda ion o he Expec a ion Con i ma ion Model (ECM). The ECM amewo k, ini ially
de eloped by Bha ache jee (2001), posi s ha use s’ in en ion o con inue using a sys em is
in luenced p ima ily by hei sa is ac ion and pe cei ed use ulness, bo h o which a e
condi ioned by he deg ee o which ini ial expec a ions a e con i med a e ac ual usage. To
adap his model o he con ex o sma digi al lib a y se ices in Nige ian uni e si ies, he
cu en s udy in eg a es addi ional cons uc s such as pe cei ed ease o use and sys em quali y,
d awing om es ablished ex ensions in in o ma ion sys ems esea ch (DeLone & McLean,
2003; Venka esh e al., 2012).
To o mally a icula e he hypo hesized ela ionships, a s uc u al model is speci ied wi h
mul iple endogenous cons uc s. Expec a ion con i ma ion (EC) se es as a c i ical an eceden
a iable in luencing pe cei ed use ulness (PU) and use sa is ac ion (SAT). These, in u n, a e
hypo hesized o de e mine con inuance in en ion (CI) owa d digi al lib a y echnologies. In
addi ion, pe cei ed ease o use (PEOU) and sys em quali y (SQ) a e in oduced as exogenous
p edic o s o con inuance in en ion, ex ending he ECM o cap u e sys em- ela ed dimensions.
The s uc u al ela ionships among he cons uc s a e ep esen ed by he ollowing equa ions:
𝑃𝑈 = 𝛽1𝐸𝐶 + 𝜀1 (1)
𝑆𝐴𝑇 = 𝛽2𝐸𝐶 + 𝛽3𝑃𝑈 + 𝜀2 (2)
𝐶𝐼 = 𝛽4𝑃𝑈 + 𝛽5𝑆𝐴𝑇 + 𝛽6𝑃𝐸𝑂𝑈 + 𝛽7𝑆𝑄 + 𝜀3 (3)
In hese equa ions, 𝑃𝑈 deno es pe cei ed use ulness, 𝑆𝐴𝑇 ep esen s use sa is ac ion, and 𝐶𝐼
is he beha io al in en ion o con inue using digi al lib a y se ices. 𝐸𝐶 s ands o expec a ion
con i ma ion, de ined as he deg ee o which s uden s’ expe iences align wi h hei ini ial
expec a ions. 𝑃𝐸𝑂𝑈 cap u es pe cei ed ease o use, e e ing o he ex en o which s uden s
ind he digi al se ices use - iendly, while 𝑆𝑄 e lec s sys em quali y, encompassing ac o s
such as sys em eliabili y, accessibili y, and esponse ime. The coe icien s 𝛽1 h ough 𝛽7
deno e he s uc u al pa h coe icien s, and 𝜀1, 𝜀2, and 𝜀3 ep esen he co esponding e o
e ms. Figu e 1 p o ides he lowcha o he expec a ion con i ma ion model.
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Figu e 1: Expec a ion Con i ma ion Model Flowcha
All la en a iables in he model a e measu ed h ough mul iple indica o s adap ed om p io
alida ed ins umen s. Expec a ion con i ma ion and pe cei ed use ulness a e measu ed using
scale i ems om Bha ache jee (2001). Sa is ac ion is ope a ionalized using i ems e lec ing
o e all con en men wi h he digi al se ice expe ience, while pe cei ed ease o use is assessed
using measu es adap ed om he Technology Accep ance Model (Venka esh e al., 2003).
Sys em quali y cons uc s a e based on DeLone and McLean’s (2003) IS success model,
inco po a ing i ems on eliabili y, sys em up ime, and use in e ace unc ionali y. Con inuance
in en ion is measu ed by s uden s’ s a ed likelihood o u u e use and ecommenda ion o he
digi al se ices.
The s udy sample consis s o uni e si y s uden s en olled ac oss i e academic ins i u ions in
Lagos S a e, Nige ia. A s a i ied andom sampling echnique was adop ed o ensu e
ep esen a i e co e age ac oss public and p i a e uni e si ies, acul ies, and academic le els.
The da a we e collec ed ia an online ques ionnai e dissemina ed h ough ins i u ional email
lis s and s uden o ums. Responses we e sc eened o comple eness, esul ing in 452 usable
obse a ions. All i ems we e a ed on a i e-poin Like scale anging om 1 (“S ongly
Disag ee”) o 5 (“S ongly Ag ee”).
Da a analysis was conduc ed using SEM implemen ed in Sma PLS 4.0. The me hod was
chosen due o i s capaci y o handle complex models in ol ing la en a iables and i s
obus ness in analyzing e lec i e measu emen models in small o medium samples. The
eliabili y and alidi y o he measu emen model we e assessed using composi e eliabili y,
C onbach’s alpha, and a e age a iance ex ac ed (AVE), all o which me he ecommended
h esholds. Pa h coe icien s we e e alua ed h ough boo s apping wi h 5,000 subsamples o
es he s a is ical signi icance o he hypo hesized ela ionships. The o e all model i was
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assessed using he s anda dized oo mean squa e esidual (SRMR), wi h alues below 0.08
conside ed accep able, as well as he no med i index (NFI) and R-squa ed alues o
endogenous cons uc s.
The esea ch design adhe es s ic ly o e hical s anda ds o s udies in ol ing human subjec s.
In o med consen was ob ained elec onically om all pa icipan s, and ins i u ional e hical
app o al was secu ed p io o da a collec ion. Pa icipan anonymi y and da a con iden iali y
we e main ained h oughou , and he da ase was s o ed secu ely o academic pu poses only.
The me hodological amewo k, he e o e, p o ides a igo ous empi ical ounda ion o
e alua ing how s uden s' expec a ions, sa is ac ion, and pe cep ions o digi al lib a y se ices
in e ac o in luence hei long- e m usage beha io s.
4. Resul s and Implica ions
4.1. Resul s
Table 1 shows he eliabili y and con e gen alidi y o he measu emen model. eliabili y and
con e gen alidi y we e assessed using C onbach’s alpha, composi e eliabili y (CR), a e age
a iance ex ac ed (AVE), and indica o loadings. All cons uc s demons a ed high in e nal
consis ency eliabili y, wi h C onbach’s alpha alues anging om 0.817 o 0.880, exceeding
he commonly ecommended h eshold o 0.70 (Nunnally & Be ns ein, 1994). Composi e
eliabili y sco es we e all abo e he accep able cu o o 0.70, u he con i ming in e nal
consis ency (Hai e al., 2019).
The AVE alues anged om 0.603 o 0.691, su passing he 0.50 h eshold, indica ing ha
mo e han 50% o he a iance in each cons uc is explained by i s espec i e indica o s.
Fu he mo e, he indica o loadings o all i ems we e abo e he ecommended 0.70, signi ying
s ong indi idual i em eliabili y. Collec i ely, hese esul s con i m ha he measu emen
model exhibi s adequa e con e gen alidi y and eliabili y o all la en cons uc s.
Disc iminan alidi y (in Table 2) was assessed using he Fo nell-La cke c i e ion. The squa e
oo o each cons uc ’s AVE was g ea e han he co esponding in e -cons uc co ela ions,
as e idenced by he bold diagonal alues in Table 2. Fo example, he squa e oo o AVE o
Con inuance In en ion (0.832) was g ea e han i s co ela ion wi h Pe cei ed Use ulness
(0.719) and Sa is ac ion (0.655), indica ing su icien disc iminan alidi y (Fo nell & La cke ,
1981). The cons uc Expec a ion Con i ma ion demons a ed disc iminan alidi y wi h i s
highes co ela ion being 0.589 (wi h PU), which is lowe han he squa e oo o i s AVE
(0.808). This pa e n was consis en ac oss all cons uc s, con i ming ha each la en a iable
in he model is empi ically dis inc om he o he s.
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Table 1: Measu emen Model Assessmen
Cons uc
C onbach's
Alpha
Composi e
Reliabili y
(CR)
A e age
Va iance
Ex ac ed (AVE)
Indica o
Loadings
(Range)
Expec a ion
Con i ma ion
(EC)
0.842
0.889
0.652
0.730 – 0.880
Pe cei ed
Use ulness (PU)
0.865
0.906
0.678
0.740 – 0.900
Sa is ac ion
(SAT)
0.830
0.876
0.615
0.710 – 0.870
Pe cei ed Ease o
Use (PEOU)
0.817
0.868
0.603
0.700 – 0.860
Sys em Quali y
(SQ)
0.849
0.890
0.642
0.730 – 0.885
Con inuance
In en ion (CI)
0.880
0.914
0.691
0.760 – 0.910
Sou ce: Au ho (2025)
Table 2: Disc iminan Validi y (Fo nell-La cke C i e ion)
Cons uc
EC
PU
SAT
PEOU
SQ
CI
EC
0.808
PU
0.589
0.823
SAT
0.532
0.678
0.784
PEOU
0.470
0.510
0.455
0.777
SQ
0.525
0.574
0.497
0.613
0.801
CI
0.564
0.719
0.655
0.498
0.551
0.832
No e: Bold alues on diagonal a e squa e oo s o AVE.
Sou ce: Au ho (2025)
The s uc u al model esul s (in Table 3) indica e s ong empi ical suppo o he p oposed
hypo heses. Expec a ion Con i ma ion had a signi ican and subs an ial e ec on Pe cei ed
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