In e na ional Jou nal o Social Science and Human Resea ch
ISSN (p in ): 2644-0679, ISSN (online): 2644-0695
Volume 08 Issue 10 Oc obe 2025
DOI: 10.47191/ijssh / 8-i10-39, Impac ac o - 8.007
Page No: 7865-7878
IJSSHR, Volume 08 Issue 10 Oc obe 2025 www.ijssh .in Page 7865
S udy on The In en ion o Use Pe sonalized Men al Heal h Ca e Applica ions
Among Vie namese High School S uden s
Ph.D. T inh Khanh Chi1, Nguyen Minh Quynh2
1Uni e si y o Labou and Social A ai s
2Vie nam Aus alia In e na ional School
ABSTRACT: The men al heal h o high school s uden s in Vie nam is acing nume ous challenges due o academic p essu e,
impo an examina ions, socie al expec a ions, and he impac o he digi al en i onmen . In his con ex , he applica ion o digi al
echnology, especially pe sonalized men al heal h apps, is conside ed a po en ial solu ion o help s uden s manage s ess, enhance
concen a ion, and imp o e hei quali y o li e. This s udy aims o analyze he ac o s (i) “Pe cei ed Need o Men al Heal h Ca e”,
(ii) “Pe cei ed Use ulness”, (iii) “Pe cei ed Ease o Use”, and (i ) “Subjec i e No m” in luencing high school s uden s' in en ion
o use pe sonalized men al heal h apps. The esul s indica e ha Pe cei ed Use ulness (PU) and Subjec i e No m (SN) signi ican ly
in luence BI, wi h PU being he s onges in luencing ac o . This implies ha s uden s a e only willing o use he apps i hey clea ly
pe cei e p ac ical bene i s and ecei e encou agemen om amily, iends, and school. The esea ch indings con ibu e empi ical
e idence o he applicabili y o TPB and TAM in he con ex o school men al heal h in Vie nam.
KEYWORDS: Apps, men al heal h, pe sonalized apps, high school s uden s, TAM, TPB
1. INTRODUCTION
Men al heal h among adolescen s is one o he signi ican public-heal h challenges bo h globally and in Vie nam. The Wo ld Heal h
O ganiza ion de ines a men al diso de as a signi ican dis u bance in an indi idual’s cogni ion, emo ional egula ion, o beha io .
Among hese diso de s, dep ession and anxie y a e he mos common (WHO, 2022). Wo ldwide, dep ession a ec s 28% o he
popula ion, while anxie y a ec s 26%. The p e alence o men al heal h p oblems among adolescen s ( ypically anxie y and
dep ession) is app oxima ely 10–20% (WHO, 2019). No ably, hese condi ions a e o en o e looked o no add essed in a imely
manne , esul ing in nume ous nega i e consequences o s uden s’ academic, social, and physical de elopmen (NIMH, 2017).
In his con ex , mobile men al-heal h applica ions ha e a ac ed inc easing a en ion because o hei po en ial o suppo
moni o ing, p e en ion, and ea ly in e en ion o psychological diso de s. Wi h mo e han 10,000 apps de eloped globally
(Rubano ich e al., 2017), hese digi al ools allow indi iduals o access se ices any ime and anywhe e, educe cos s, and lowe
s igma- ela ed ba ie s o seeking help (Koh, Tng & Ha an o, 2022). Se e al sys ema ic e iews sugges ha such applica ions can
enhance quali y o li e, imp o e esilience, and educe le els o anxie y and dep ession (K use e al., 2022; Se ano-Ripoll e al.,
2022). Alongside hese bene i s, conce ns emain ega ding sa e y, p i acy, and sus ained use engagemen (Weisel e al., 2019;
Koh e al., 2022).
Fo high-school s uden s in Vie nam, he need o pe sonalized men al-heal h ca e is inc easingly u gen . A s udy by Ha
N.T. & Minh N.H. (2025), based on da a om 541 nin h-g ade s uden s in lowe seconda y schools in Hanoi, ound ha 58.4% o
s uden s exhibi ed dep essi e symp oms a a ious le els: minimal (26.1%), mild (13.9%), mode a e (10.5%), and se e e (7.9%).
Fac o s s a is ically associa ed wi h dep ession p e alence included gende , academic p essu e, physical bullying, cybe bullying,
and p oblema ic mobile-phone use (p < 0.01). This indica es a signi ican gap in access o men al-heal h se ices, and sugges s ha
applica ions could play an essen ial ole in closing ha gap. Technologies such as mobile applica ions a e expec ed o na ow
dispa i ies in access o ca e (Henson P. e al., 2019, p.1).
Al hough mHeal h holds conside able po en ial, ac ual usage a es among pa ien s wi h men al diso de s ange only om
12.51% o 34.07%. In con as , in e es in using hese applica ions is e y high, a 74.48%-83.46% (Gu acho e al., 2023). This
disc epancy e eals a “ echnology accep ance gap,” e lec ing use s’ willingness o adop hese ools ha is no ye ma ched by
su icien us , use -cen e ed design, and cul u al i . In es iga ing he in en ion o use pe sonalized men al-heal h applica ions will
no only iden i y s uden s’ eal needs bu also guide he de elopmen o app op ia e digi al ools, con ibu ing o na owing he gap
in access o adolescen men al-heal h se ices.
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On his basis, he p esen s udy was conduc ed o analyze ac o s in luencing he in en ion o use pe sonalized men al heal h
applica ions among high school s uden s in Vie nam, d awing on he Theo y o Planned Beha io (TPB - Ajzen, 1991) and he
Technology Accep ance Model (TAM). The s udy aims o p o ide scien i ic e idence o he de elopmen o e ec i e digi al
p oduc s and o con ibu e o he in e na ional discou se on he applica ion o digi al echnologies in adolescen men al heal h ca e.
2. THEORETICAL FRAMEWORK AND LITERATURE REVIEW
2.1. Pe sonalized Men al-Heal h Applica ions
De ini ion: In he con ex o indi idual men al heal h in gene al and adolescen men al heal h-speci ically high-school s uden s- in
pa icula , becoming a global conce n, he de elopmen o digi al echnologies has opened a new app oach o o e coming ba ie s
in men al-heal h ca e. Pe sonalized men al-heal h applica ions a e de ined as digi al pla o ms, p ima ily deployed on sma phones,
ha suppo use s in he p e en ion, moni o ing, and in e en ion o psychological p oblems based on each indi idual’s needs,
s a es, and cha ac e is ics (Rubano ich, Moh , & Schuelle , 2017).
Cha ac e is ics: Pe sonalized men al-heal h applica ions ha e h ee co e cha ac e is ics:
(i) Symp om sel -managemen capabili ies h ough ools such as emo ion acking, mood dia ies, o b ie assessmen s ia a ing
scales. Acco ding o Council NAMH (2018), digi al echnologies, including mobile apps, can s eng hen he men al-heal h ca e
sys em by encou aging sel -help beha io s and alle ia ing bu dens on heal h se ices. Recen s udies indica e ha he use o men al
heal h apps yields mul iple bene i s: imp o ed quali y o li e, inc eased esilience, be e sleep quali y, and educ ions in dep ession,
anxie y, and ange (K use e al., 2022; Se ano-Ripoll e al., 2022; Le in e al., 2017).
(ii) In eg a ion o digi al in e en ions such as mind ulness medi a ion, online cogni i e beha io al he apy (CBT), o b ea hing-
elaxa ion exe cises. Th ough pe sonalized apps, men al heal h ca e can educe s igma- ela ed ba ie s o seeking di ec suppo
owing o anonymi y and lexibili y o use (Fuh e al., 2024). Howe e , one impo an challenge o men al-heal h apps is building
a sense o engagemen and us (Bo din, 1979; A di o & Rabellino, 2011). In he digi al en i onmen , he he apeu ic alliance can
be econs uc ed by: (i) a use - iendly, easy- o-use in e ace; (ii) in e ac i e, pe sonalized eedback; and (iii) a sense o connec ion,
whe eby use s eel he app “unde s ands” and “accompanies” hem (Mackie e al., 2017).
(iii) Pe sonaliza ion o con en h ough analysis o use da a o p o ide ecommenda ions app op ia e o he use ’s emo ional s a e,
ci cums ances, o cul u e (Schuelle e al., 2019). A dis inguishing ea u e compa ed wi h con en ional men al-heal h apps is he
deg ee o cus omiza ion o indi idual cha ac e is ics, he eby inc easing he apeu ic e ec i eness. Pe sonaliza ion is conside ed a
cen al de e minan o use s’ in en ion o adop and hei engagemen le el wi h men al-heal h apps (Rubano ich e al., 2017). Fo ms
o pe sonaliza ion include: (i) con en cus omiza ion; (ii) eal- ime eedback based on beha io al-da a analysis o emo ion-dia y
inpu s using a i icial in elligence; and (iii) in eg a ion o cul u al, linguis ic, and eligious-belie elemen s app op ia e o he use .
The a o emen ioned bene i s o pe sonalized men al-heal h applica ions a e suppo ed by empi ical e idence. Apps such
as ACHESS and Ginge .io ha e been epo ed o yield posi i e ou comes in symp om moni o ing, inc eased use sa is ac ion, and
imp o ed communica ion wi h heal h p o essionals (Fo chuk e al., 2016; Baue e al., 2018). Clinical ials also indica e ha mobile
apps suppo ing dep ession ea men deli e supe io e ec i eness compa ed wi h con en ional in e en ions wi hou digi al ools
(Bae e al., 2023). Ne e heless, some s udies epo limi a ions: use s some imes lack a sense o sa e y and exp ess conce ns abou
suppo a ailabili y when apps expe ience echnical aul s o uns able pe o mance (Mackie e al., 2017). This highligh s he pi o al
ole o sa e y, secu i y, and eliabili y ea u es in app design.
Co e ea u es: Based on su eys and usabili y es ing, he ea u es deemed essen ial in pe sonalized men al-heal h apps include: (i)
p o ision o educa ional in o ma ion abou dep essi e and anxie y diso de s; (ii) sel -ca e s a egies such as mind ulness, mood
jou naling, and b ea hing echniques; (iii) sel -assessmen ools (e.g., PHQ-9, GAD-7); and (i ) medical eminde s o medica ion
o appoin men schedules. These ea u es help inc ease pe cei ed beha io al con ol (PBC) acco ding o he Theo y o Planned
Beha io (TPB), while also ein o cing use s’ con idence and p oac i i y.
Gi en hese co e ea u es, pe sonalized men al heal h applica ions a e playing an inc easingly impo an ole amid
sho ages o heal hca e se ices, ising school- ela ed psychological p essu e, and demand o lexible, p i a e solu ions. They o e
ad an ages in access, cos , and s igma educ ion, while acing challenges ela ed o engagemen , secu i y, and clinical e ec i eness.
Pa icula ly o high school s uden s, pe sonaliza ion is no only a echnological end bu also a key ac o ha can make an app
genuinely use ul and sus ainable.
2.2. High-School S uden s and he Need o Use Pe sonalized Men al-Heal h Applica ion
De ini ion o high-school s uden s
Acco ding o he Law on Educa ion o Vie nam (2019), uppe seconda y educa ion is deli e ed o e h ee academic yea s, om
g ade 10 h ough g ade 12. En ance o g ade 10 equi es a lowe -seconda y (lowe seconda y school) g adua ion ce i ica e,
ypically a age 15, and s uden s comple e uppe seconda y educa ion a app oxima ely 17–18 yea s o age. This le el is he inal
s age o gene al educa ion, se ing o consolida e knowledge, skills, and cha ac e , while also o ien ing s uden s owa d ca ee pa hs
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be o e hey en e highe educa ion such as uni e si y, college, o oca ional schools (A icle 28 - Law on Educa ion, 2019). Thus,
“high-school s uden s” a e unde s ood he e as adolescen s aged 15 o 18 who a e en olled in g ades 10, 11, and 12, wi h he
admission equi emen o a lowe -seconda y g adua ion ce i ica e.
P ominen psychophysiological cha ac e is ics o high-school s uden s
A his s age, s uden s en e la e adolescence and ea ly adul hood, a c i ically impo an pe iod in human de elopmen . They mus
abso b a la ge olume o academic con en while also con on ing subs an ial psychophysiological, pe sonali y, and social changes.
The salien cha ac e is ics can be analyzed ac oss h ee dimensions:
(i) Physical and physiological cha ac e is ics: Du ing he high-school yea s, s uden s’ bodies gene ally each a ela i ely ma u e
le el o de elopmen compa ed wi h ea ly adolescence. Inc eases in heigh , weigh , muscula u e, and neu al ma u a ion enable
g ea e capaci y o lea ning, wo k, and pa icipa ion in mo e complex social ac i i ies. Howe e , une en de elopmen may lea e
many s uden s wi h di icul ies in emo ion egula ion o main aining physiological s abili y. This pe iod also ies physical and men al
heal h closely oge he : a igue, sleep dep i a ion, o p olonged academic s ess can di ec ly a ec emo ional s a es and cogni i e
unc ioning.
(ii) Cogni i e and easoning cha ac e is ics: A no able ea u e o high-school s uden s is he ansi ion om conc e e o mo e abs ac
and logical hinking. They become be e able o analyze, compa e, syn hesize, and gene alize, and begin o de elop c i ical hinking
and sel -e alua ion skills. This s age is also ma ked by a g owing need o sel -a i ma ion h ough academic pe o mance,
ex acu icula achie emen s, o social s a us wi hin pee g oups. Ne e heless, adolescen s’ easoning emains ulne able o
emo ional in luences and pe sonal expe ience; many end o absolu ize hei own iews o idealize social issues, which may lead o
con lic s wi h pa en s and eache s. This e lec s a need o espec and au onomy in bo h lea ning and pe sonal decision-making.
(iii) Emo ional cha ac e is ics: High-school s uden s a e suscep ible o in ense and apidly luc ua ing emo ional s a es. They may
eel exhila a ed, op imis ic, and con iden a one momen , and anxious, pessimis ic, o disappoin ed he nex when aced wi h
se backs. The de elopmen o mo al sen imen s, humani a ian conce n, pee a achmen s, and adolescen oman ic eelings becomes
mo e p onounced. These emo ional changes p esen bo h oppo uni ies and challenges: s ong emo ions can mo i a e s uden s’
e o s bu may also des abilize hem in he absence o adequa e coping skills. Uppe seconda y is he pe iod when s uden s make
c i ical decisions abou hei u u e-pa icula ly hei choice o ield and pa hway a e high school- hus many expe ience s ess and
con usion i amily, eache , and school suppo a e lacking.
The necessi y o men al heal h ca e
The psychophysiological p o ile o high-school s uden s indica es ha hey cons i u e a dis inc g oup cha ac e ized by subs an ial
de elopmen al changes ha pose bo h isks and oppo uni ies o holis ic de elopmen . Co ec ly iden i ying hese cha ac e is ics
helps eache s and pa en s adop app op ia e educa ional s a egies and se es as a basis o designing men al heal h suppo
solu ions, including pe sonalized men al heal h applica ions. In e na ional epo s show ha he p e alence o any men al illness
(AMI) is highes among young adul s aged 18–25, eaching 36.2%, compa ed wi h 29.4% among hose aged 26–49 and 13.9%
among hose aged 50 and o e (NIMH, 2024). This demons a es ha adolescen s and young people, including high-school s uden s,
a e among he g oups a g ea es isk o men al-heal h p oblems. Despi e his, only abou 43.1% o adul s wi h men al heal h issues
in he Uni ed S a es ecei ed ea men in he mos ecen yea (NIMH, 2017). The igu e o de eloping coun ies like Vie nam
may be subs an ially lowe due o sho ages o school psychologis s, limi ed se ice a ailabili y, and low men al-heal h li e acy
among s uden s, while social ba ie s and s igma de e many om seeking di ec help. Deploying pe sonalized men al-heal h
applica ions enables s uden s o access suppo any ime and anywhe e, educes cos s ela i e o adi ional he apy, and c ea es a
disc ee , sa e en i onmen . Mo eo e , apps designed o i Vie namese cul u e and language, o example, by in eg a ing
mind ulness p ac ices in o med by Buddhis adi ions o school-based inspi a ional na a i es, inc ease use accep ance and up ake.
2.3. Fac o s In luencing High-School S uden s’ In en ions o Use Pe sonalized Men al-Heal h Applica ions
In esea ch on echnology adop ion in gene al and men al-heal h applica ions in pa icula , beha io al and echnology-accep ance
heo e ical models ha e been widely applied o explain and p edic use beha io . Fo high-school s uden s, use o pe sonalized
men al-heal h apps is in luenced by mul iple con ex -speci ic ac o s, including age- ela ed psychological cha ac e is ics, he
lea ning en i onmen , social p essu es, and access o echnology. Wi hin he scope o his s udy, he au ho s ocus on wo p incipal
heo e ical amewo ks— he Technology Accep ance Model (TAM) and he Theo y o Planned Beha io (TPB)—and supplemen
hese wi h he pe sonaliza ion ac o o cla i y de e minan s o in en ion o use.
The TAM, p oposed by Da is (1989), asse s ha beha io al in en ion o use a echnology is p ima ily a ec ed by
Pe cei ed Use ulness (PU) and Pe cei ed Ease o Use (PEOU). In he con ex o men al-heal h applica ions, PU e lec s he deg ee
o which s uden s belie e ha using he app can imp o e hei men al s a e, educe s ess, o enhance concen a ion. PEOU e e s o
he ease o ope a ion and he use - iendliness o he app in e ace.
Meanwhile, Ajzen’s (1991) TPB posi s ha beha io al in en ion is de e mined by h ee ac o s: A i ude owa d he
beha io (ATT), Subjec i e No m (SN), and Pe cei ed Beha io al Con ol (PBC). TPB is conside ed an app op ia e heo e ical lens
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o explaining he use o pe sonalized men al-heal h apps among high-school s uden s because i encompasses bo h indi idual and
social in luence ac o s.
The ac o s conside ed in his a icle as a ec ing high-school s uden s’ in en ion o use pe sonalized men al heal h
applica ions include:
- Pe cei ed need o men al heal h ca e. Acco ding o Ajzen (1991), a i ude owa d a beha io esul s om pe sonal belie s abou
he cos s and bene i s o pe o ming ha beha io . In his con ex , i high school s uden s clea ly pe cei e a need o men al heal h
ca e, hey will o m mo e posi i e a i udes owa d using an app. When s uden s ecognize ha s ess, anxie y, o exam p essu e can
impac hei lea ning and o e all well-being, hey will iew a men al heal h app as a necessa y and aluable esou ce. Highe
pe cei ed need leads o mo e posi i e a i udes because s uden s ega d he app as a p ac ical solu ion o hei pe sonal needs
(Rubano ich e al., 2017).
- Subjec i e no m (SN). This ac o e lec s he in luence o pee s, amily, eache s, and socie y. In he Vie namese cul u al con ex ,
encou agemen o guidance om amily and eache s u he inc eases s uden s’ in en ion o use men al-heal h apps.
- Pe cei ed use ulness (PU). Acco ding o TAM, his ac o is especially impo an . S uden s will be willing o use an app i hey
belie e i can deli e p ac ical bene i s such as imp o ed s udy concen a ion, s ess managemen be o e exams, o be e sleep
quali y. Sys ema ic e iews (K use e al., 2022; Se ano-Ripoll e al., 2022) ha e demons a ed ha mobile men al-heal h apps can
indeed imp o e quali y o li e and educe psychological symp oms.
- Pe cei ed ease o use (PEOU). Among de e minan s o echnology adop ion, PEOU is a undamen al ac o . Fo high school
s uden s, simplici y, ease o ope a ion, and in ui i e in e ace design a e c ucial. I an app is complex, equi es many egis a ion
s eps, o demands ad anced echnical skills, s uden s a e likely o abandon i despi e i s help ul ea u es.
- Beha io al in en ion (BI). Beha io al in en ion is unde s ood as he deg ee o s uden s’ willingness o adop a pe sonalized
men al-heal h app in hei daily li es o in he nea u u e. Ajzen (1991) a i ms ha in en ion is he s onges p edic o o beha io .
To ous e al. (2018) also show ha in en ion o use men al heal h apps is an impo an indica o o long- e m adop ion. In gene al,
high school s uden s’ in en ions o use pe sonalized men al heal h applica ions a ise om a combina ion o pe sonal pe cep ions and
social in luence (SN). When s uden s belie e an app is easy o use, use ul, and socially suppo ed by pee s and amily, hey will
o m posi i e a i udes and exhibi g ea e in en ion o use.
2.4. Resea ch Model and P oposed Measu emen Scales
On he basis o cla i ying heo ies o consume beha io al in en ion, he Theo y o Planned Beha io (TPB), and he Technology
Accep ance Model (TAM), he esea ch eam p oposes he ollowing esea ch model:
Figu e 1: P oposed esea ch model
Sou ce: P oposed by he au ho g oup
Pe cei ed Men al Ca e
Need (PMCN)
Pe cei ed Use ulness
(PU)
Pe cei ed Ease o Use
(PEOU)
Subjec i e No ms (SN)
Beha io al In en ion
(BI).
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Resea ch Hypo heses: Independen a iables
H1: Pe cei ed men al ca e need (PNMC) has a posi i e e ec on he in en ion o use pe sonalized men al-heal h applica ions.
H2: Pe cei ed use ulness (PU) has a posi i e e ec on he in en ion o use pe sonalized men al-heal h applica ions.
H3: Pe cei ed ease o use (PEOU) has a posi i e e ec on he in en ion o use pe sonalized men al-heal h applica ions.
H4: Subjec i e no m (SN) has a posi i e e ec on he in en ion o use pe sonalized men al-heal h applica ions.
Table 1: P oposed Measu emen Scale
Va iable
Code
Measu emen Scale
Pe cei ed
Men al Ca e
Need
PNMC1
I belie e ha high school s uden s need o ecei e men al-heal h ca e.
PNMC2
I eel ha I need a ool o suppo my pe sonal men al well-being.
PNMC3
Academic s ess and p essu e make me eel he need o men al-heal h ca e p oduc s.
PNMC4
S ess and p essu e in daily li e make me eel he need o men al-heal h ca e p oduc s.
Pe cei ed
Use ulness
PU1
I hink ha a pe sonalized men al-heal h ca e p oduc would be use ul o me.
PU2
I belie e ha a pe sonalized men al-heal h ca e p oduc can help me manage s ess mo e
e ec i ely.
PU3
I eel in e es ed in he idea o using a pe sonalized men al-heal h ca e applica ion.
Pe cei ed
Ease o Use
PEOU1
I hink ha using a men al-heal h ca e applica ion would be e y easy.
PEOU2
I eel ha I can quickly lea n how o use a men al-heal h ca e applica ion.
PEOU3
I hink ha accessing he unc ions o he applica ion is simple and easy o unde s and.
Subjec i e
No m
SN1
My iends suppo he use o pe sonalized men al-heal h ca e applica ions.
SN2
My amily encou ages pe sonalized men al-heal h ca e h ough he use o such
applica ions.
SN3
My school is conce ned abou p o iding pe sonalized men al-heal h ca e o s uden s.
SN4
Socie y in gene al suppo s he use o pe sonalized men al-heal h ca e applica ions o
s uden s.
Beha io al
In en ion
BI1
I in end o use a pe sonalized men al-heal h ca e applica ion in he nea u u e.
BI2
I he e is a sui able applica ion a ailable, I am willing o egis e and use i .
BI3
I am willing o ecommend a pe sonalized men al-heal h ca e applica ion o my iends.
Sou ce: P oposed by he au ho g oup
3. RESEARCH METHODOLOGY
3.1. Da a Collec ion Me hod
Based on heo e ical ounda ions and a e iew o s udies on ac o s in luencing he in en ion o use pe sonalized men al heal h ca e
p oduc s, he a iables included in he esea ch model consis o : (i) Pe cei ed Need o Men al Heal h Ca e (PN), (ii) Pe cei ed
Use ulness (PU), (iii) Pe cei ed Ease o Use (PEOU), (i ) Subjec i e No m (SN), which in luence ( ) Beha io al In en ion o use
pe sonalized men al heal h ca e p oduc s (BI).
A quan i a i e esea ch me hod was employed o collec opinions om high school s uden s. A e de eloping he ques ionnai e, a
pilo su ey was conduc ed wi h 5 andomly selec ed s uden s. The p elimina y esul s indica ed ag eemen on he inclusion o he
p oposed ac o s in he model. The ques ionnai e con en was based on Table 1, using a Like 5-poin scale as ollows:
1. S ongly disag ee
2. Disag ee
3. Neu al
4. Ag ee
5. S ongly ag ee
Due o limi a ions in ime and esou ces, a con enience sampling me hod was applied. The minimum equi ed sample size was
calcula ed using he o mula n = 50 + 8*m (m: numbe o independen a iables) (Tabachnick & Fidell, 1996). Wi h i e a iables
in his s udy, he minimum equi ed sample size was 50 + 8*5 = 90 esponden s. To ensu e g ea e eliabili y and s abili y o he
e ec s, mo e da a we e collec ed h ough an online Google Fo m dis ibu ed ia he ollowing link:
h ps:// o ms.gle/jYkoXao6p 2eeQik9. A o al o 222 alid esponses we e collec ed o analysis.
3.2. Da a Analysis Me hod
The quan i a i e esea ch me hod was employed o p ocess da a collec ed om he high school s uden su ey. The gene al
s uc u al eg ession equa ion is exp essed as:
𝐁𝐈 = 𝐚 ∗ 𝐏𝐍𝐌𝐂 + 𝐛 ∗ 𝐏𝐔 + 𝐜 ∗ 𝐏𝐄𝐎𝐔 + 𝐝 ∗ 𝐒𝐍
The SMARTPLS so wa e was used o es he hypo heses and assess he e ec s o he in luencing ac o s.
S ep 1: E alua ing Measu emen Model
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E alua ing measu emen model based on examining alues o eliabili y, quali y o obse ed a iable, con e gence, and
disc iminan
- Tes ing he quali y o obse ed a iables (Ou e Loadings)
Ou e Loadings o obse ed a iables a e indica o s showing he deg ee o associa ion be ween obse ed a iables and la en
a iables (p oxy a iables). Basically, ou e loadings in SMARTPLS a e he squa e oo o he absolu e alue o R2 linea eg ession
om he la en a iables o he sub-obse ed a iables.
Hai e al. (2016) sugges ha he ou e loadings should be g ea e han o equal o 0.708 obse ed a iables ha a e quali y. To
make i easie o emembe , he esea che s ounded o he h eshold o 0.7 ins ead o he numbe 0.708.
- E alua ing Reliabili y
E alua ing he eliabili y h ough SMARTPLS by wo main indica o s, C onbach's Alpha and Composi e Reliabili y (CR).
Composi e Reliabili y (CR) is p e e ed by many esea che s o e C onbach’s Alpha because C onbach’s Alpha unde es ima es he
eliabili y compa ed wi h CR. Chin (1998) claims ha in explo a o y esea ch CR mus be o e 0.6. Fo con i med s udies, he 0.7
h eshold is he app op ia e le el o CR (Hensele & Sa s ed , 2013). O he esea che s ag ee ha 0.7 is he app op ia e h eshold
o he as majo i y o cases such as Hai e al. (2010), and Bagozzi & Yi (1988).
Thus, he eliabili y h ough SMARTPLS is shown by C onbach's Alpha ≥ 0.7 (DeVellis, 2012); Composi e Reliabili y CR
≥ 0.7 (Bagozzi& Yi, 1988).
- Tes ing Con e gence
E alua ing Con e gence on SMARTPLS is based on A e (A e age Va iance Ex ac ed). Hock & Ringle (2010) claim ha a scale
eaches a con e gence alue i AVE eaches 0.5 o highe . This le el o 0.5 (50%) means ha he a e age la en a iable will explain
a leas 50% o he a ia ion o each sub-obse ed a iable. Thus, con e gence is e alua ed by A e age Va iance Ex ac ed AVE ≥
0.5 (Hock & Ringle, 2010).
- Tes ing Disc iminan Validi y
Disc iminan alue is used o conside whe he a esea ch a iable is eally di e en om o he esea ch a iables in he model. To
e alua e he disc iminan alidi y, Sa s ed & e al (2014) said ha conside ing wo c i e ia including c oss-loadings and he
measu emen o Fo nell and La cke (1981).
C oss-loading coe icien s a e o en he i s app oach o e alua ing he disc iminan alidi y o indica o s (obse ed a iables)
(Hai , Hul , e al., 2017). The load ac o o he obse ed a iable (indica o ) linked in he ac o (la en a iable) should be g ea e
han any o i s c oss-load ac o s (i s co ela ion) in he o he ac o s.
Fo nell and La cke (1981) ecommend ha disc iminan is ensu ed when he squa e oo o AVE o each la en a iable
is highe han all co ela ions be ween la en a iables. In addi ion, Hensele & e al (2015) used simula ion s udies o demons a e
ha disc iminan alidi y is be e e alua ed by he HTMT index ha hey de eloped.
Wi h he HTMT index, Ga son (2016) said ha he disc iminan alidi y be ween wo la en a iables is gua an eed when
he HTMT index is less han 1. Hensele & e al (2015) p opose ha i his alue is below 0.9, he disc iminan alidi y will be
gua an eed. Meanwhile, Cla k, L. A., & Wa son, D. (1995) used a s ic e s anda d h eshold o 0.85. SMARTPLS p e e ed a
h eshold o 0.85 in he e alua ion.
- Tes ing Mul icollinea i y
In his s udy, he au ho uses a scale ela ed o mul icollinea i y as a a iance magni ica ion ac o (VIF). Ve y high le els o
mul icollinea i y a e indica ed by VIF alues >= 5; he model does no ha e mul icollinea i y when VIF indica o s < 5 (Hai e al.,
2016).
S ep 2: E alua ing S uc u al Model
A e e alua ing he sa is ac o y measu emen model, e alua e he s uc u al model h ough he impac ela ionship, pa h coe icien ,
R squa ed, and squa ed.
- E alua ing impac ul ela ionships
To e alua e impac ela ionships, use he esul s o Boo s ap analysis. Based mainly on wo columns (1) O iginal Sample
(no malized impac ac o ) and (2) P Values (sig alue compa ed o 0.05 signi icance le el).
● O iginal Sample: S anda dized impac ac o o he o iginal da a. SMARTPLS ha e no uns anda dized impac ac o .
● Sample Mean: The a e age s anda dized impac ac o o all samples om Boo s ap.
● S anda d De ia ion: S anda d de ia ion o he s anda dized impac ac o (acco ding o he o iginal sample).
● T S a is ics: Tes alue ( es s uden he meaning o he impac ).
● P Values: The signi icance le el o he T S a is ics. This signi icance le el is conside ed wi h compa a i e h esholds such as
0.05, 0.1, o 0.01 (usually used as 0.05).
E alua ing he le el o in e p e a ion o he independen a iable o he dependen a iable by R2 coe icien (R squa e). To e alua e
he R2 coe icien , we will use he esul s o he PLS Algo i hm analysis. The R2 alue e alua es he p edic i e accu acy o he
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model and shows he le el o in e p e a ion o he independen a iable o he dependen a iable. R squa e is be ween 0 and 1, he
close o 1 indica es he mo e independen a iables ha accoun o he dependen a iable (Hai , Hul , e al, 2017).
Addi ionally, du ing ac o e alua ion, da a collec ed we e agg ega ed, compu ed, and isualized h ough cha s, ables, and igu es
using Mic oso Excel.
Fo cons uc s measu ed using he 5-poin Like scale, he mean sco e o each ac o was calcula ed o de e mine he le el o
ag eemen and he s eng h o in luence based on mean alue anges.
In e al alue= (Maximum - Minimum) / n = (5-1)/5 = 0.8
E alua ion h esholds based on mean sco es:
+ 1.00 -1.80: S ongly disag ee
+ 1.81 - 2.60: Disag ee
+ 2.61 - 3.40: Neu al
+ 3.41 - 4.20: Ag ee
+ 4.21 - 5.00: S ongly ag ee
4. RESEARCH RESULTS
4.1. Cha ac e is ics o Responden s
A o al o 222 alid esponses we e collec ed om he dis ibu ed su ey. De ails ega ding he gende and age dis ibu ion o
esponden s a e p esen ed below.
Table 2. Desc ip i e S a is ics o Su ey Pa icipan s
Occpa ion
Age
G ade Le el
Numbe o
pa icipan s
Pe cen age
(%)
Gende
Numbe o
pa icipan s
Pe cen age
(%)
G ade 10
71
31,8%
Male
97
43,7%
G ade 11
101
45,5%
Female
123
55,4%
G ade 12
50
22,7%
Do no wan o
be speci ic
2
0,9%
To al
222
100%
To al
222
100%
Sou ce: Su ey esul s
The majo i y o su ey esponden s we e emale, accoun ing o 123 indi iduals (55.4%), while 97 esponden s (43.7%) we e
male, and 2 esponden s (0.9%) p e e ed no o speci y hei gende .
Since he esponden s we e high school s uden s, hei ages anged om 15 o 18 yea s, co esponding o he ollowing
g ade le els: 71 s uden s in G ade 10 (31.8%), 101 s uden s in G ade 11 (45.5%), and 50 s uden s in G ade 12 (22.7%). The
pa icipan s we e mainly om p i a e schools (50%), ollowed by public schools (27.3%), as illus a ed in Figu e 2.
Figu e 2. Classi ica ion o School Types A ended by Su eyed S uden s
Sou ce: Su ey esul s
27.30%
50%
9.10%
13.60%
Public school P i a e school Semi-public school O he
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4.2. Su ey Resul s
4.2.1. Assessmen o Obse ed Va iable Quali y
The quali y o obse ed a iables was e alua ed h ough he ou e loadings coe icien s.
The quali y o he obse ed a iables in luencing he in en ion o use pe sonalized men al-heal h ca e p oduc s is p esen ed in Table
3.
Table 3. Ou e Loadings o Fac o s A ec ing he In en ion o Use Pe sonalized Men al-Heal h Ca e P oduc s
BI
PEOU
PNMC
PU
SN
BI1
0,877
BI2
0,868
BI3
0,885
PEOU1
0,883
PEOU2
0,908
PEOU3
0,925
PNMC2
0,773
PNMC3
0,754
PNMC4
0,854
PU1
0,861
PU2
0,879
PU3
0,813
SN1
0,827
SN2
0,842
SN3
0,894
SN4
0,850
PNMC1
0,790
Sou ce: Da a analysis conduc ed by he esea ch eam
The esul s om Table 3 indica e ha he ou e loadings o all co ela ion coe icien s be ween he obse ed a iables and he o al
a iables a ec ing he in en ion o use pe sonalized men al heal h ca e applica ions a e all g ea e han 0.7 (Hai e al., 2016),
demons a ing ha he obse ed a iables a e s a is ically signi ican .
Reliabili y es ing o measu emen scales
The eliabili y o he measu emen scales o ac o s in luencing he in en ion o use pe sonalized men al heal h ca e applica ions
was assessed in PLS-SEM h ough wo main indica o s: C onbach’s Alpha and Composi e Reliabili y (CR).
Table 4. C onbach’s Alpha and Composi e Reliabili y o ac o s a ec ing he in en ion o use pe sonalized men al heal h
ca e applica ions
C onbach's Alpha
ho_A
Composi e Reliabili y
A e age Va iance Ex ac ed (AVE)
BI
0,850
0,853
0,909
0,768
PEOU
0,890
0,892
0,932
0,820
PNMC
0,806
0,817
0,872
0,630
PU
0,809
0,811
0,887
0,725
SN
0,876
0,877
0,915
0,729
Sou ce: Da a analysis conduc ed by he esea ch eam
Acco ding o Table 4, he esul s o he eliabili y analysis using C onbach’s Alpha indica e ha : “Pe cei ed Need o Men al Ca e”
(PNMC) achie ed 0.806, “Pe cei ed Use ulness” (PU) achie ed 0.809, “Pe cei ed Ease o Use” (PEOU) achie ed 0.890,
“Subjec i e No m” (SN) achie ed 0.876, and “Beha io al In en ion o Use Pe sonalized Men al Heal h Ca e Applica ions” (BI)
achie ed 0.850. The e o e, all measu emen scales sa is y he condi ion o being g ea e han 0.7 (DeVellis, 2012) and do no iola e
any ule equi ing a iable emo al. Hence, no a iable was elimina ed, and all a e conside ed eliable.
The Composi e Reliabili y (CR) o all obse ed a iables is also g ea e han 0.7 (Bagozzi & Yi, 1988) (Table 4). Consequen ly, he
measu emen scales a e eliable, analy ically alid, and sui able o subsequen ac o analysis.
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Con e gen Validi y
Acco ding o he da a analysis esul s p esen ed in Table 4, he A e age Va iance Ex ac ed (AVE) alues o each cons uc a e as
ollows: “Pe cei ed Need o Men al Ca e” (PNMC) = 0.630, “Pe cei ed Use ulness” (PU) = 0.725, “Pe cei ed Ease o Use”
(PEOU) = 0.820, “Subjec i e No m” (SN) = 0.729, and “Beha io al In en ion o Use Pe sonalized Men al Heal h Ca e
Applica ions” (BI) = 0.768. The e o e, since all AVE alues exceed he 0.5 h eshold (Hock & Ringle, 2010), he model sa is ies
he condi ions o con e gen alidi y.
Disc iminan Validi y
The esul s in Table 5, based on he Fo nell–La cke c i e ion, indica e ha all cons uc s in he esea ch model in luencing
Beha io al In en ion o Use Pe sonalized Men al Heal h Ca e Applica ions (BI)—including (i) Pe cei ed Need o Men al Ca e,
(ii) Pe cei ed Use ulness, (iii) Pe cei ed Ease o Use, and (i ) Subjec i e No m—mee he disc iminan alidi y equi emen s. This
is e idenced by he ac ha all squa e oo s o AVE alues along he diagonal a e highe han hei co esponding o -diagonal
co ela ions. The e o e, acco ding o bo h he c oss-loading c i e ion and he Fo nell–La cke c i e ion, he model sa is ies he
equi emen s o disc iminan alidi y (Fo nell & La cke , 1981).
Table 5. Fo nell–La cke C i e ion o he Resea ch Model on Fac o s In luencing he In en ion o Use Pe sonalized Men al
Heal h Ca e Applica ions
BI
PEOU
PNMC
PU
SN
BI
0,876
PEOU
0,676
0,905
PNMC
0,630
0,614
0,794
PU
0,748
0,673
0,648
0,851
SN
0,706
0,766
0,728
0,706
0,854
Sou ce: Da a analysis conduc ed by he esea ch eam
The alue o he unc ion 2
The ² alue ep esen s he deg ee o in luence o a cons uc ( ac o ) when i is emo ed om he model. Acco ding o Cohen
(1988), ² alues o 0.02, 0.15, and 0.35 co espond o small, medium, and la ge e ec sizes o exogenous a iables, espec i ely.
I he e ec size < 0.02, i is conside ed o ha e no impac .
Table 6. Summa y o ² Values
BI
PEOU
PNMC
PU
SN
BI
PEOU
0,033
PNMC
0,014
PU
0,214
SN
0,030
Sou ce: Da a analysis conduc ed by he esea ch eam
In his model, as shown in Table 6, he ac o “Pe cei ed Need o Men al Ca e” (PNMC) has ² < 0.02, indica ing no signi ican
in luence. Meanwhile, “Pe cei ed Ease o Use” (PEOU) and “Subjec i e No m” (SN) ha e 0.02 < ² <0.15, ep esen ing medium
e ec sizes, whe eas “Pe cei ed Use ulness” (PU) has 0.15< ² < 0.35, indica ing a signi ican e ec on “Beha io al In en ion o
Use Pe sonalized Men al Heal h Ca e Applica ions” (BI).
E alua ion o he In luence Le el Using he S uc u al Model
Assessmen o he Rela ionships and Thei E ec s
The ela ionships and e ec magni udes o ac o s in luencing he in en ion o use pe sonalized men al heal h ca e applica ions in
he SMARTPLS model a e illus a ed in Figu e 3.
