Medical teleconsultation from the patient’s perspective. A demographic segmentation

Vol.:(0123456789)
The Eu opean Jou nal o Heal h Economics
h ps://doi.o g/10.1007/s10198-024-01753-4
ORIGINAL PAPER
Medical eleconsul a ion om hepa ien ’s pe spec i e.
Ademog aphic segmen a ion
Jo geA enas‑Gai án1 · Pa icioE.Ramí ez‑Co ea2 · PabloLedesma‑Cha es1 · LuisJ.Calla isaFiol3
Recei ed: 28 Sep embe 2023 / Accep ed: 16 Decembe 2024
© The Au ho (s) 2025
Abs ac
Medical eleconsul a ion is a ool ha is he e o s ay among he se ices o e ed by heal h sys ems. The e o e, i is impo an
o unde s and he p ocess o adop ing his echnology. Howe e , mos s udies ha e endo sed he poin o iew o heal h
p o essionals. Ou esea ch adop s he pa ien ’s poin o iew wi h a sample o 1500 pa ien s who ha e used eleconsul a ion
in Spain be ween May and No embe 2022, he e o e, in a pos -COVID-19 scena io. We s a ed om a echnology accep -
ance model, UTAUT, and applied a no el segmen a ion echnique: Pa hmox. As a esul , we ha e ob ained six segmen s o
pa ien s using eleconsul a ion wi h di e en ia ed echnology accep ance p ocesses, and we also p opose s a egies adap ed
o each o hem.
Keywo ds Telemedicine· Teleconsul a ion· Demog aphic segmen a ion· Technology accep a ion· Pa hmox
In oduc ion
One o he p io i y objec i es o he Eu opean Union is o
ensu e ha i s ci izens ha e access o he heal h sys ems o
i s Membe S a es. EU coun ies aim o ensu e ha hei
heal h sys ems p o ide a o dable, equi able and high-qual-
i y medical ca e, emphasising ha heal h ca e is a undamen-
al human igh (Eu opean [26]). In o de o achie e hese
goals, i is essen ial o ad ance he p ocess o digi alisa ion
o heal h sys ems [51]. F om an economic pe spec i e, he
Eu opean Commission i sel es ima es ha imp o ed access
o, and exchange o , heal h da a could sa e €5.5 billion o e
he nex 10yea s (Eu opean [27]).
Heal hca e sys ems ha e used ad ances in in o ma ion
and communica ion echnologies since hei incep ion. Fo
example, he i s s eps in elemedicine da e back o he
mid- wen ie h cen u y, wi h he use o he elephone as a
means o medical consul a ion. La e , in he 1970s, du -
ing he space ace, NASA de eloped elemedicine ools o
p o ide heal h ca e o i s as onau s. Howe e , he ecen
COVID-19 pandemic was a u ning poin in he de elopmen
o hese ools [62]. Today, in a socie y hea ily in luenced by
echnology a all le els, all kinds o de ices such as mobile
phones, came as o wea able biosenso s ha e been inco po-
a ed o ob ain clinical in o ma ion [1]. This echnological
ad ance a ec s indi iduals and socie y as a whole, implying
a digi al ans o ma ion [32] in he ield o heal h sys ems.
Howe e , his digi al ans o ma ion does no a ec e e yone
equally. The e a e impo an di e ences be ween indi idu-
als and social g oups when i comes o coping wi h he use
o digi al echnologies [13, 39]. In his digi al con ex , he
pa ien plays an inc easingly ac i e ole [85]. And among
all he digi al se ices a ailable, a key se ice is he medical
consul a ion as a momen o in e ac ion be ween doc o and
pa ien . The digi al en i onmen has gi en way o his [36].
The s udy by [22] sugges s ha di ec - o-consume el-
emedicine use migh lead o inc eased medical se ice u i-
lisa ion in he sho and in e media e e m. This end could
* Jo ge A enas-Gai án
[email p o ec ed]
Pa icio E. Ramí ez-Co ea
[email p o ec ed]
Pablo Ledesma-Cha es
[email p o ec ed]
Luis J. Calla isa Fiol
[email p o ec ed]
1 Depa amen o de Adminis ación de Emp esas y Ma ke ing,
Facul ad de Ciencias Económicas y Emp esa iales,
Uni e sidad de Se illa, 41018Se ille, Spain
2 Escuela de Ingenie ía, Uni e sidad Ca ólica del No e,
Coquimbo, Chile
3 Depa amen o de Adminis ación de Emp esas y Ma ke ing,
Uni e sidad Jaume I, 12071Cas ellódelaPlana, Spain
J.A enas-Gai án e al.
esul om he ease o access and he a ailabili y o echnol-
ogy bu could also encou age o e use o unnecessa y use
i a s ong connec ion o p ima y ca e is no es ablished.
To add ess hese isks, he au ho s unde sco e he need o
iden i y which diagnoses and ea men s a e sui able o
di ec - o-consume elemedicine, aiming o maximise i s
cos -e ec i eness while cu ailing i s use whe e his could
be inapp op ia e. This equi es a ho ough examina ion o
beha iou pa e ns among elemedicine use s, which could
o e insigh s o e ine public policies and os e e icien
p ac ices. Meanwhile, pos -COVID-19, Eu ope is u ning o
elemedicine o b idge gaps in access o p ima y heal hca e
se ices. A key componen o his s a egy is he es ablish-
men o in eg a ed p ima y ca e cen es ha ha ness ech-
nology o manage elec onic medical eco ds and p omo e
collabo a i e clinical documen a ion. This model is pa -
icula ly bene icial o heal hca e p o essionals who p ac-
ise elemedicine. These cen es can subs an ially enhance
coo dina ion among p o essionals and ensu e high-quali y
ca e o pa ien s, e en in emo e o unde se ed a eas [30].
To achie e his objec i e, a ho ough unde s anding o el-
econsul a ion use s’ p ac ices and p e e ences is essen ial, as
his in o ma ion could d i e he e ec i e managemen and
op imisa ion o hese cen es.
The e a e a signi ican numbe o s udies on medical
eleconsul a ion [31], mos o hem om he poin o iew
o heal h ca e p o essionals. Howe e , he e a e s ill ew
s udies om he pa ien s’ poin o iew [7]. Mo eo e , we
know om o he highly digi ised sec o s, such as elec onic
banking [79] o online social ne wo ks (Villa ejo-Ramos,
Pe al-Pe al, and A enas-Gai án 2019) ha he p ocess o
accep ance o new echnologies is no homogeneous among
all use s. The e is he e ogenei y, di e ences, be ween seg-
men s o use s o online se ices. We belie e ha medical
eleconsul a ion is no excep ion.
The aim o his pape is o analyse he accep ance o el-
econsul a ion echnology by di e en ia ing be ween di e en
ypes o use s. In o de o achie e his objec i e, we will
ansla e i in o se e al esea ch ques ions. The i s esea ch
ques ion examines he accep ance p ocess o eleconsul a-
ion om he pa ien ’s pe spec i e. To add ess his, we will
u ilise he Uni ied Theo y o Accep ance and Use o Tech-
nology (UTAUT) [77] allows o an adequa e analysis o
he echnological accep ance p ocess o medical elecon-
sul a ion. This model has been widely used in he ield o
elemedicine [31], al hough he e a e s ill ew s udies o he
case o medical eleconsul a ion. None heless, we unde -
s and ha his accep ance p ocess may be in luenced by he
cha ac e is ics o indi iduals. The e o e, he second esea ch
ques ion is whe he he socio-demog aphic cha ac e is ics
o he use s, such as hei age, le el o educa ion o income,
in luence he p ocess o accep ance o eleconsul a ion. To
answe his ques ion, we will use he Pa hmox echnique
[44], which allows us o dis inguish di e en g oups, in he
o m o a ee, using dis inc segmen a ion c i e ia a he
same ime. Gi en ha each segmen has i s own cha ac e is-
ics, we will analyse he simila i ies and di e ences be ween
he di e en segmen s ob ained abo e wi h espec o hei
medical eleconsul a ion accep ance p ocess. To es his, we
will use he PLS-MGA mul ig oup analysis [65].
The pape is o ganised in a manne ha i s p o ides
a comp ehensi e e iew o he ele an li e a u e. Subse-
quen ly, he me hodology employed in he s udy is explained.
The main indings o he analysis a e hen p esen ed, ol-
lowed by a discussion o he esul s in compa ison o o he
s udies. The academic, manage ial, and social implica ions
o he esea ch a e also highligh ed. The pape concludes
wi h an assessmen o he limi a ions o he esea ch and
sugges ions o u u e esea ch a enues.
Theo e ical amewo k
Telemedicine andmedical eleconsul a ion
Telemedicine, as de ined by he Wo ld Heal h O ganisa ion
(WHO), is a heal hca e p ac ice ha le e ages in e ac i e
audio- isual and da a communica ions echnologies o p o-
ide medical se ices, including diagnosis, ea men , con-
sul a ion, heal h educa ion and he exchange o medical da a
[2]. The use o elemedicine has been widely accep ed as an
e ec i e solu ion o emo e heal hca e, pa icula ly in a eas
wi h limi ed o inaccessible heal hca e acili ies [56]. Wi h
he widesp ead adop ion o echnology, elemedicine has
gained popula i y by enabling medical specialis s o o e
hei se ices o pa ien s wi hou he need o physical a el
[56]. Telemedicine ep esen s he p og ession o heal hca e
in o he digi al age and is poised o shape he u u e o medi-
cal p ac ices [18].
Telemedicine, as i is p ac ised oday, u ilises he com-
pu ing de ices o ei he he pa ien o heal hca e p o es-
sional, along wi h low-cos p op ie a y equipmen such
as sma phones, biosenso s and lap ops, o ga he clinical
da a, hus ob ia ing he need o ex ensi e aining [54].
This has esul ed in a educ ion o a el expenses and
sa ed ime, while also lowe ing medical cos s and acili-
a ing g ea e access o specialis medical p ac i ione s
o he gene al public wi hou he need o in e up hei
daily ac i i ies, he eby inc easing o e all p oduc i i y.
Addi ionally, i has alle ia ed he wo kload o heal hca e
p o essionals by dec easing missed appoin men s and can-
cella ions, which in u n has inc eased e enue and pa ien
h oughpu , and has led o imp o ed ollow-up ca e and
o e all heal h ou comes [6]. The ad en o elemedicine
has spa ked a mig a ion o heal hca e om adi ional clin-
ics and hospi als o he home en i onmen [49]. As can be
Medical eleconsul a ion om hepa ien ’s pe spec i e. Ademog aphic segmen a ion
seen, elemedicine is a b oad concep . The e o e, Tables1,
2 summa ise he main ypes o elemedicine acco ding o
di e en c i e ia and hei main applica ions oday.
The implemen a ion o elemedicine in heal hca e has
aced nume ous challenges, hinde ing i s widesp ead adop-
ion. These challenges include a lack o awa eness among
pa ien s, he high cos o implemen a ion, ope a ional ine -
iciencies, di icul ies in conduc ing physical examina ions,
a gene al pe cep ion ha i ual ca e is no as e ec i e as
in-pe son ca e, inancial implica ions, legal and egula o y
hu dles, and conce ns abou medical liabili y [52].
In his con ex , i is c ucial o unde s and medical el-
econsul a ion as a pi o al componen wi hin he ealm o
elemedicine. Ad ances in echnology, such as compu e s,
sma phones and able s, ha e ele a ed he adi ional doc o -
pa ien and doc o -doc o communica ion me hods beyond
jus audi o y means [48]. The abili y o ansmi labo a o y
esul s, diagnos ic images, ideos, and e en conduc ideo
consul a ions o examine a pa ien ’s own pa hology, has been
demons a ed h ough nume ous s udies o be highly e ec-
i e. Resea che s like Augus e e e al. [5] and Mishkin
e al. [55] ha e analysed eleconsul a ions in he ields o
psychology and psychia y, and despi e acknowledging he
Table 1 Telemedicine ypes
Own elabo a ion based on Chellaiyan, Ni upama, and Taneja [18]
Ca ego y Telemedicine ype Desc ip ion
Acco ding o he
iming o he in o -
ma ion ansmi ed
Real ime o synch onous elemedicine Whe e he sende and ecei e a e online a he same ime and in o -
ma ion is ans e ed li e
S o e-and- o wa d o asynch onous elemedicine Whe e he sende s o es in o ma ion in da abases and sends i o he
ecei e o e iew a hei con enience
Remo e moni o ing ype o elemedicine Uses a ious echnological de ices o emo ely moni o a pa ien ’s
heal h and clinical signs
Acco ding o he
in e ac ion be ween
he indi iduals
in ol ed
Heal h p o essional o heal h p o essional P o ides easie access o speciali y ca e, e e al, and consul a ion
se ices
Heal h p o essional o pa ien Deli e s heal hca e o unde se ed popula ions by p o iding di ec
access o a medical p o essional
Table 2 Telemedicine applica ions
Own elabo a ion based on Chellaiyan, Ni upama, and Taneja [18]
Ca ego y Telemedicine applica ion Desc ip ion
Educa ional Tele-educa ion In e ac i e long-dis ance lea ning p og amme o aining and upda es on ecen
medical ad ances
Tele-con e encing Vi ual discussions and in e ac ions be ween doc o s du ing wo kshops, con e -
ences, and con inuing medical educa ion p og ammes
Tele-p oc o ing Remo e men o ing and e alua ion o su gical ainees using ad anced ideo
con e encing equipmen
Heal hca e deli e y School-based heal h cen es Manages ch onic condi ions like as hma, diabe es, and obesi y by p o iding
school nu ses emo e access o specialis medical opinions
Co ec ional acili ies Add esses inma es’ heal hca e needs wi hou he cos s and isks o inma e ans-
po a ion o equi ing specialis isi s
Mobile heal h clinics P o ides quick access o emo e physicians o medical specialis s
Shipping and anspo a ion Helps a oid e acua ions and unscheduled di e sions du ing medical eme gencies
Indus ial heal h O e s on-si e medical managemen and iage ad ice
Heal hca e managemen Tele-heal h ca e Uses ICT o p e en i e and p omo i e heal hca e, including eleconsul a ion and
ele ollow-up
Tele-home heal h ca e Moni o s pa ien s emo ely wi h a Compu e Telephone In eg a ed (CTI) sys em
o 24-h i al signs moni o ing
Special ies Includes ele-oph halmology, ele-psychia y, ele-ca diology, ele-su ge y, e c
Diagnos ic se ices P o ides ele- adiology and ele-endoscopy se ices
Eme gency and p e en i e ca e Disas e managemen Ideal o disas e -s icken egions wi h dis up ed connec i i y, using sa elli e and
cus omised elemedicine so wa e
Sc eening o diseases Uses elemedicine echnologies o ea ly de ec ion and sc eening o diseases
J.A enas-Gai án e al.
need o p o essional aining, hey highligh he bene i s and
po en ial o g ow h. Simila ly, W igh and Honey [84] s ud-
ied he applica ion o elemedicine in home ca e, while Vu al
and Ramadan (2019) explo ed i s e ec i eness in eme gency
si ua ions. Fu he mo e, i has been applied o add ess he
consequences o gende -based iolence [80] and has been
u ilised in consul a ions ela ed o he COVID-19 pandemic
wi h high le els o pa ien sa is ac ion (Blanco [11]).
V anda and Cicil [80] posi ha he success o consul a-
ion ools is con ingen upon a numbe o ac o s, including
easibili y, de ice size, enhancemen o ace- o- ace in e -
ac ion, capabili y o ansmi ing pa ien da a, da a ype,
use - iendly in e ace and po abili y, and a synch onous o
asynch onous mode o ope a ion. The in e ac ion can occu
h ough a a ie y o means, such as mobile applica ions,
ex -based me hods using specialised sma phone applica-
ions o cha -based sys ems and pla o ms (e.g., Wha sApp,
Google Hangou s, Facebook Messenge ), ideo cha pla -
o ms (e.g., Skype, Face ime), and e en asynch onous media
like emails and axes. While each consul a ion ool has i s
own unique ad an ages and disad an ages, i is howe e e i-
den ha he ield o medical eleconsul a ion is unde going
con inuous e olu ion due o echnological ad ancemen s
(Vu al and Ramadan 2019b).
F om he pa ien ’s pe spec i e, medical eleconsul a ion
has been s udied h ough heo ies o echnology adop ion,
wi h heo e ical amewo ks such as he Technology Accep -
ance Model (TAM) and UTAUT [8, 50, 61]. The TAM was
de eloped o be applied o echnologies a he wo kplace
le el [23, 25]. The TAM model is based on he idea ha he
accep ance o a echnology depends on i s use ulness and
ease o use [24]. Subsequen ly, he UTAUT model [77] is a
e ision and ex ension o he TAM wi h con ibu ions om
o he ela ed heo ies, inco po a ing concep s such as social
in luence o acili a ing condi ions. We belie e ha UTAUT
o e s a sui able concep ual amewo k o s udy he p ocess
o adop ion o eleconsul a ion.
Mo e speci ically, UTAUT [77] p oposed ou la en a i-
ables ha de e mine use accep ance and usage beha iou
(USE): Pe o mance Expec ancy (PE), E o Expec ancy
(EE), Social In luence (SI), and Facili a ing Condi ions
(FCs). PE is de ined as he deg ee o which a pe son using
an applica ion belie es ha his will help him o he achie e
a be e job pe o mance. EE is de ined as he deg ee o
pe cei ed ease o use o an applica ion. SI is de ined as he
deg ee o which an indi idual pe cei es ha impo an o h-
e s belie e ha he o she should use an applica ion. FCs a e
de ined as he ex en o which an indi idual belie es ha
an o ganisa ional/ echnical in as uc u e exis s o assis he
use o an applica ion. These ou cons uc s di ec ly a ec
beha iou al in en ion (BI). In addi ion, beha iou al in en-
ion di ec ly in luences he use o he echnology, and he
acili a ing condi ions di ec ly de e mining use beha iou o
echnology. I can be posi ed ha an inc ease in pe cep ions
o PE, EE, SI and FC will esul in an inc ease in he u ilisa-
ion o a echnology h ough BI [77]. Based on his idea, we
o mula e he ollowing hypo hesis:
H1: The UTAUT model p o ides a obus heo e ical
basis o examining he adop ion p ocesso medical el-
econsul a ion om he use ’s pe spec i e.
Explo ing hein e sec ion o  amily s uc u es,
socio‑demog aphics, and echnology adop ion
T adi ionally, he applica ion o models o analyse a eal-
i y has assumed ha he da a unde analysis comes om a
homogeneous popula ion. Howe e , in he ield o social
sciences, and mo e speci ically in he s udy o human beha -
iou , his p emise is o en un ealis ic, gi en ha indi iduals
a e likely o exhibi he e ogenei y in hei pe cep ions and
e alua ions o a phenomenon [53, 64, 66]. This asse ion
applies o he use o medical eleconsul a ion se ices as
well. Family cha ac e is ics, such as size and composi ion,
as well as indi iduals’ le el o echnology li e acy, play a
c ucial ole in he adop ion o hese new echnologies [21].
Educa ion, aining, he numbe o household membe s,
and he geog aphic loca ion also in luence he up ake and
usage o new echnologies, and some s udies e en conside
ace and income [58]. Fo ins ance, amilies wi h child en
o olde membe s end o exhibi di e en beha iou s com-
pa ed o amilies wi hou such membe s [37, 46, 72]. In his
ega d, we p esen some demog aphic a iables ha ha e
been shown o a ec he beha iou o echnology use s:
Age
Nume ous s udies ha e highligh ed age as he mos in lu-
en ial demog aphic ac o a ec ing echnology adop ion
[9, 10, 47]. This phenomenon o en esul s om he na u al
decline in cogni i e abili ies o olde adul s’ sel -pe cep ion
o eeling aged. The belie ha hei cogni i e skills ha e
diminished becomes a ba ie o adop ing new echnology
[28]. Addi ionally, he li e a u e e iew sugges s ha sen-
io s’ pe cep ion o he complexi y o in e ac i e echnol-
ogy signi ican ly impac s hei adop ion decisions. In his
espec , elemedicine applica ions can be complex.
Educa ional le el
Indeed, esea ch consis en ly highligh s he impac o he
educa ion le el on echnology adop ion [9, 10]. The heo y
o lea ning sugges s ha indi iduals wi h lowe educa ion
le els may s uggle due o simple cogni i e s uc u es,
hinde ing hei abili y o adap o new en i onmen s [57].
In con as , hose wi h highe educa ion end o app oach
echnology wi h less app ehension [68]. Thei awa eness
Medical eleconsul a ion om hepa ien ’s pe spec i e. Ademog aphic segmen a ion
and posi i e a i udes owa ds new echnology con ibu e
o highe adop ion a es.
Income
The e is a signi ican body o esea ch indica ing ha
income signi ican ly a ec s he p ocess o echnology
adop ion [9, 10]. Lowe -income consume s end o be
mo e cos -sensi i e and esis in es ing in new echnol-
ogy [69]. Highe income le els a e associa ed wi h g ea e
sel -con idence, leading o a highe pe cei ed abili y o
use new echnology. Lowe -income indi iduals o en iew
new echnology as useless. Adop e s o new echnology
gene ally ha e highe income le els han non-adop e s.
Howe e , he analysis o hese a iables sepa a ely
gi es a pa ial iew o he eali y. The e may be connec-
ions be ween hese socio-demog aphic a iables a di -
e en le els which need o be aken in o accoun . Fo
example, despi e he g owing in e es in unde s anding
he up ake o in o ma ion echnologies in he heal hca e
sec o om he pa ien ’s pe spec i e, he majo i y o s ud-
ies ocus speci ically on he elde ly demog aphic. This
is demons a ed by Ka andi and Jaana’s [37] indings
which indica e ha 51% o esea ch on he adop ion o
Heal h In o ma ion Technologies (HIT) by olde adul s
ails o conside an adop ion model o amewo k. The e
a e con lic ing esul s ega ding he impac o socio-
demog aphic a iables, such as age, gende , and educa-
ion, on HIT adop ion in olde adul s, sugges ing ha a
b oade age ange should be s udied o ully unde s and
hese e ec s [20]. On he o he hand, Chimen o-Díaz e al.
[19] ound, in hei s udy o echnology adop ion in hose
o e 64yea s old based on he TAM, ha ac o s such as
younge age, highe educa ion, and zes o li e posi i ely
in luence accep ance o echnology use in his socio-demo-
g aphic. Las ly, Tse sidis, Kolkowska, and Heds öm [75]
obse ed ha echnology accep ance pos -implemen a ion
is in luenced by mul iple ac o s, including age, wi h iews
on echnology changing o he be e as olde adul s eal-
ise i s a ious bene i s in hei daily li es.
In summa y, he cu en body o li e a u e ega ding he
accep ance o heal h echnologies, pa icula ly eleconsul a-
ion, lacks su icien explo a ion om he pa ien ’s pe spec-
i e, especially wi h ega ds o a mo e comp ehensi e analy-
sis o he in luence o socio-demog aphic ac o s. Based on
he analysis o he abo e li e a u e, we p opose he ollowing
hypo hesis:
Hypo hesis 2: The combina ion o socio-demog aphic
cha ac e is ics, including age, educa ion, and income le -
els, among use s o medical eleconsul a ion allows o he
iden i ica ion o s a is ically dis inc segmen s based on hei
echnology accep ance beha io s.
Me hodology
Scales o measu emen
The measu emen scales used in ou in es iga ion had
been igo ously e alua ed and alida ed in p io esea ch.
The scales used o assess Pe o mance Expec ancy, E o
Expec ancy, Facili a ing Condi ions, Social In luence and
Beha iou In en ion we e adap ed om he UTAUTmodel
p oposed by Venka esh e al. [77] and Venka esh, Thong,
and Xu [78]. Meanwhile, he scale o measu ing he u i-
lisa ion o eleconsul a ion was de i ed om [40]. The
a iables pe aining o he UTAUT model we e quan i ied
using i e-poin Like scales.
In addi ion, a comp ehensi e examina ion o a ious
socio-demog aphic ac o s associa ed wi h he pa icipan s
was ca ied ou , including hei age and le el o educa-
ion. Fu he mo e, inqui ies ega ding he composi ion
o hei household we e conduc ed, including he size o
he household, he p esence o mino s, and he numbe o
indi iduals o e 70yea s o age esiding wi hin he house-
hold. These socio-demog aphic a iables may be ei he
quali a i e, such as educa ional a ainmen , o nume ical,
such as age.
To ensu e ha he esul s o ou analysis a e ee om
Common Me hod Bias, we ha e employed he [38] es o
assess he Va iance In la ion Fac o (VIF) o ou a iables.
Ou indings indica e ha all he VIF le els a e below
3.3, he eby gua an eeing he absence o Common Me hod
Bias in ou s udy.
Sample
We u ilised a non-p obabili y sample o elemedicine
use s. The da a we e collec ed o Spain as a whole
be ween May and No embe 2022. This pe iod ollowed
he manda o y con inemen in Spain due o he Co id-19
pandemic om Ma ch o June 2020. To ensu e he sui a-
bili y o pa icipan s, we equi ed hem o be o e 18yea s
old and o ha e used medical eleconsul a ion se ices
wi hin he pas yea . We engaged a company specialising
in elec onic ques ionnai e da a collec ion. In o al, we
ob ained a sample o 1500 indi iduals. Addi ionally, we
implemen ed a il e ing p ocess o ensu e sample quali y.
Speci ically, we excluded ques ionnai es comple ed in less
han h ee minu es, conside ing his du a ion insu icien
o eliable esponses. The a e age comple ion ime o
he ques ionnai e was jus o e i e minu es. Simila ly, we
emo ed ques ionnai es wi h inconsis en esponses. As a
esul o his p ocess, we ob ained a inal sample o 1412
indi iduals o ou analyses. A p io i, his is a su icien

J.A enas-Gai án e al.
sample [74, 83] o he p oposed s uc u al model, wi h
an an icipa ed e ec size o 0.11 and a desi ed s a is ical
powe le el o 0.8. O he esponden s, 64.4% we e male
and 35.5% we e emale. The majo i y o he pa icipan s,
68.2%, esided in u ban a eas wi h a popula ion o mo e
han 50,000 while 31.8% li ed in u al loca ions. In e ms
o educa ion, 1.7% had no o basic educa ion, 59.3% had
seconda y educa ion, and 39% had a uni e si y educa ion.
Fu he demog aphic de ails on he sample can be ound
in he accompanying Table3.
The Spanish heal h sys em ollows he Be e idge model.
This model is cha ac e ised by ax-based inancing, uni e sal
access, sala ied o capi a ed doc o s, a mino ole o he
p i a e sec o and a s ong s a e in ol emen in manage-
men . In he Eu opean con ex , o he s a es ha ollow his
model include Po ugal, I aly, he Uni ed Kingdom, I eland,
Denma k, Finland, and Sweden.
S a is ical ools
To conduc ou s udy, we employed se e al s a is ical me h-
ods. Fi s ly, we used s uc u al equa ion modelling, speci i-
cally PLS-SEM, o alida e ou UTAUT model. This model
se es as a ounda ion o he c ea ion o Pa hmox [44],
which was u ilised o analyse he di e si y in he esponses,
leading o a segmen a ion o non- ace- o- ace consul a ion
use s. The Pa hmox echnique elies on cons uc ing a bina y
ee o de ec popula ion segmen s wi h di e se beha iou s
in ela ion o he p oposed s uc u al equa ion model, PLS.
Finally, we employed mul i-g oup analysis, MGA-PLS
[17, 45], o assess and examine he beha iou al di e ences
be ween he iden i ied segmen s.
Resul s
PLS‑SEM
The Pa ial Leas Squa es S uc u al Equa ion Modelling
(PLS-SEM) me hodology encompasses wo dis inc s ages:
he examina ion o measu emen scales, ollowed by he
e alua ion o he s uc u al model. To p esen he ou comes
o his analysis, in ou epo ing we shall adhe e o he
guidelines pu o wa d by Hai e al. [33].
A he onse o ou analysis o he measu emen scales,
i is impe a i e o emphasise ha all he cons uc s we e
deemed o be e lec i e in ou s udy. To e i y he eliabili y
o he indi idual i ems, we e alua ed each i em’s loading
alue and con i med ha hey all exceeded he ecommended
0.7. The eliabili y o he cons uc s was u he es ablished
h ough Composi e Reliabili y and C onbach’s Alpha, bo h
o which e ealed alues abo e 0.7, in line wi h he es ab-
lished li e a u e [33]. The con e gen alidi y was es ab-
lished h ough he Analysis o Va iance Ex ac ion (AVE),
yielding alues abo e he h eshold o 0.5. These indings
a e p esen ed in Table4. To ensu e disc iminan alidi y, we
employed he Fo nell and La cke [29] and HTMT es s [35],
as p esen ed in Tables4, 5. O e all, ou esul s demons a e
he sui abili y o he measu emen scales used in he s udy.
The objec i e o analysing he s uc u al model is o
de e mine he pa hs and R2 alues. The pa hs e lec he
magni ude o he co ela ion be ween he dependen and
independen a iables, while he R2 alues demons a e he
p opo ion o a iance explained by each dependen a i-
able in he model. A boo s apping app oach wi h 10,000
subsamples was employed in his analysis. The esul s o
hese measu emen s a e p esen ed in Fig.1. Fu he mo e,
he SRMR was used as a me ic o he model’s goodness o
i , yielding a alue o 0.064, which is lowe han he bench-
ma k o 0.08 es ablished by Hai e al. [33]. The combina-
ion o he SRMR and R2 alues sugges s ha he s uc u al
model has a sa is ac o y explana o y capabili y.
I should be no ed ha when conside ing pa h alues,
Pe o mance Expec ancy and Social In luence a e signi ican
p edic o s o Beha iou al In en ion. The e is a obus co -
ela ion be ween Beha iou al In en ion and Use. Howe e ,
E o Expec ancy and Facili a ing Condi ions do no ha e
a signi ican impac on ei he cu en use o he in en ion o
use non- ace- o- ace consul a ions among he ull sample o
1412 esponden s. These o e all esul s may no accu a ely
depic he complex and di e se beha iou s o di e en seg-
men s. Th ough he e ogenei y analysis, we can s udy hese
unique beha iou s, which may no be e ealed in he o e all
esul s [4] To unco e hese segmen s, Pa hmox analysis will
be applied.
Pa hmox
Pa hmox is an o iginal idea by Gas ón Sánchez [63] o dis-
co e he e ogenei y in a s uc u al equa ion model based on
decision ees. Decision ees can unco e hidden decision
ules and allow high in e p e abili y o explain eal applica-
ions [86].
Gene ally, he e ogenei y is associa ed wi h a mix u e o
popula ions ha o m di e en ia ed segmen s [43]. This
Table 3 Sample demog aphic cha ac e is ics
Min Max A e age
Age 18 74 38.6
Household size 1 10 3.6
Numbe o child en
unde 18 in he house-
hold
0 6 1.1
Numbe o people o e
70 in he household
0 5 0.2
Medical eleconsul a ion om hepa ien ’s pe spec i e. Ademog aphic segmen a ion
Table 4 Indica o s o measu emen scales
AVE a e age a iance ex ac ed, CR composi e eliabili y, CA C onbach’s alpha
E o expec ancy (EE) Global Nod 4 Nod 5 Nod12 Nod13 Nod14 Nod15
AVE 0.748 0.668 0.701 0.825 0.850 0.685 0.785
CR 0.922 0.889 0.903 0.950 0.958 0.896 0.936
CA 0.862 0.834 0.857 0.929 0.941 0.845 0.909
Lea ning how o use eleconsul ing is easy o me 0.851 0.805 0.796 0.903 0.925 0.834 0.858
My in e ac ion wi h eleconsul ing is clea and unde s andable 0.891 0.858 0.865 0.919 0.933 0.909 0.911
I ind eleconsul ing easy o use 0.884 0.846 0.862 0.939 0.931 0.812 0.911
I is easy o me o become skil ul a using eleconsul ing 0.832 0.755 0.822 0.871 0.898 0.747 0.863
Pe o mance expec ancy (PE)
AVE 0.830 0.772 0.800 0.852 0.890 0.885 0.851
CR 0.900 0.910 0.923 0.945 0.960 0.959 0.945
CA 0.830 0.853 0.875 0.914 0.938 0.935 0.913
I ind eleconsul ing use ul in my daily li e 0.915 0.891 0.908 0.929 0.936 0.932 0.927
Using eleconsul ing inc eases my chances o achie ing hings ha a e
impo an o me
0.903 0.843 0.902 0.901 0.942 0.934 0.921
Using eleconsul ing helps me accomplish hings mo e quickly 0.916 0.900 0.873 0.940 0.952 0.957 0.920
Social in luence (SI)
AVE 0.883 0.833 0.840 0.950 0.923 0.909 0.923
CR 0.958 0.937 0.940 0.983 0.973 0.968 0.973
CA 0.934 0.900 0.905 0.974 0.959 0.950 0.958
People who a e impo an o me hink ha I should use eleconsul ing 0.937 0.912 0.906 0.972 0.965 0.918 0.960
People who in luence my beha iou hink ha I should use eleconsul ing 0.939 0.909 0.911 0.983 0.959 0.970 0.957
People whose opinions I alue p e e ha I use eleconsul ing 0.943 0.917 0.932 0.969 0.959 0.972 0.965
Facili a ing condi ions (FC)
AVE 0.705 0.657 0.632 0.821 0.781 0.617 0.711
CR 0.905 0.884 0.873 0.948 0.934 0.866 0.908
CA 0.862 0.827 0.807 0.927 0.906 0.808 0.870
I ha e he esou ces necessa y o use eleconsul ing 0.853 0.854 0.770 0.922 0.894 0.771 0.834
I ha e he knowledge necessa y o use eleconsul ing 0.857 0.835 0.816 0.917 0.912 0.779 0.833
Teleconsul ing is compa ible wi h o he echnologies I use 0.839 0.763 0.805 0.903 0.902 0.827 0.863
I can ge help om o he s when I ha e di icul ies wi h eleconsul ing 0.810 0.788 0.790 0.881 0.824 0.764 0.843
Beha iou al in en ion (BI)
AVE 0.812 0.762 0.790 0.816 0.853 0.861 0.839
CR 0.928 0.906 0.918 0.930 0.946 0.949 0.940
CA 0.884 0.844 0.867 0.887 0.914 0.919 0.904
I in end o con inue using eleconsul ing in he u u e 0.880 0.856 0.865 0.869 0.898 0.916 0.898
I will always y o use eleconsul ing in my daily li e 0.909 0.877 0.901 0.907 0.934 0.927 0.927
I plan o con inue o use eleconsul ing equen ly 0.914 0.886 0.900 0.934 0.938 0.941 0.923
Use
AVE 0.812 0.782 0.786 0.785 0.832 0.792 0.853
CR 0.928 0.915 0.917 0.916 0.937 0.920 0.946
CA 0.882 0.860 0.864 0.864 0.899 0.870 0.914
I end o use eleconsul ing equen ly 0.906 0.871 0.883 0.907 0.921 0.914 0.940
I spend a lo o ime on eleconsul ing 0.885 0.898 0.866 0.836 0.886 0.857 0.895
I ge in ol ed a lo in eleconsul ing 0.912 0.883 0.910 0.913 0.929 0.898 0.935
J.A enas-Gai án e al.
cha ac e is ic be ween uni s is a signi ican p oblem in da a
analysis. When a sample is he e ogeneous bu is ea ed as
homogeneous, he quali y o he s udy can be a ec ed, gen-
e a ing biases in he in e p e a ion [15].
Speci ically, Pa hmox is o ien ed o look o di e ences
a he s uc u al le el. Tha is, i sea ches o segmen s ha
di e in he e ec s be ween he la en a iables o he model.
Fo his pu pose, Pa hmox uses a ecu si e algo i hm based
on bina y decision ee lea ning [59] o analyse he models
associa ed wi h di e en da a segmen s. These da a seg-
men s a e gene a ed by di iding he sample based on addi-
ional analysis a iables. The analysis a iables a e ex e -
nal o he s uc u al equa ion model. Gi en he objec i e
o Pa hmox, he p ocedu e uses he F- es o de e mine he
disc epancy o he ue coe icien s in wo linea eg essions
associa ed wi h di e en da a segmen s.
The Pa hmox algo i hm can be summa ised as ollows.
All possible pai s o segmen s based on he analysis a i-
ables a e gene a ed, and hen he pai o segmen s wi h he
mos signi ican disc epancy in hei e ec s is selec ed. Nex ,
a p ocedu e like he p e ious one is execu ed o each seg-
men o his pai , and so on, un il he di e ences a e no
signi ican , he size o he segmen s is iny, o he numbe o
segmen a ion le els is many. Finally, he algo i hm deli e s
he disco e ed segmen s and he a iables based on his seg-
men a ion. As he e a e mo e analysis a iables, i is possible
o es mo e po en ial segmen con igu a ions. Howe e , he
ime complexi y o he algo i hm inc eases [86].
In he p esen s udy, se en analysis a iables we e used o
execu e he Pa hmox: educa ional le el, place o esidence,
income, age, numbe o household membe s unde 18yea s,
and numbe o elde ly household membe s. The algo i hm
was un in he R en i onmen [60] using he genpa hmox
package [42]. The esul o he p ocedu e is shown in Fig.2.
As can be seen, Pa hmox disco e ed six segmen s wi h di -
e ences a he s uc u al le el, as de ailed below.
In gene al, Pa hmox selec s he analysis a iables age,
educa ional le el, and income. Age is he i s a iable ha
disc imina es. Then, he educa ional le el de e mines he
di e ence in indi iduals unde 39yea s o age. In indi-
iduals aged 39 and o e , he educa ional le el and income
de e mine his di e ence. Speci ically, he node labelled
ou comp ises 432 indi iduals be ween 18 and 38yea s
old wi h a K-12 educa ion le el. This segmen is he la ges .
Node i e has 252 indi iduals be ween 18 and 38yea s old
wi h a uni e si y educa ion le el. The node labelled wel e
Table 5 Disc iminan alidi y
No e: Diagonal elemen s (bold) a e he squa e oo o he a iance sha ed be ween he cons uc s and hei measu es (AVE). Unde he diagonal
elemen s a e he co ela ions be ween cons uc s [29]. The elemen s abo e he diagonal a e HTML es alues
Beha iou al
in en ion
E o expec ancy Facili a ing
condi ions
Pe o mance
expec ancy
Social in luence Use
Beha iou al in en ion 0.901 0.555 0.468 0.674 0.714 0.723
E o expec ancy 0.623 0.865 0.739 0.707 0.500 0.412
Facili a ing condi ions 0.528 0.847 0.840 0.566 0.436 0.333
Pe o mance expec ancy 0.756 0.789 0.637 0.911 0.600 0.540
Social in luence 0.785 0.544 0.472 0.654 0.940 0.709
Use 0.812 0.455 0.365 0.600 0.777 0.901
Fig. 1 UTAUT global esul
Medical eleconsul a ion om hepa ien ’s pe spec i e. Ademog aphic segmen a ion
comp ises 106 indi iduals be ween 39 and 74yea s old wi h
a K-12 educa ion le el and low income. Node hi een has
232 indi iduals be ween 39 and 74yea s old wi h a K-12
educa ion le el and non-low income. The node labelled ou -
een is made up o 85 indi iduals be ween 39 and 74yea s
old wi h a uni e si y educa ion le el and low income. This
segmen is he smalles . Finally, node i een is o med o
214 indi iduals be ween 36 and 74yea s old wi h a uni e -
si y educa ion le el and non-low income.
We calcula ed he s a is ic powe using G*Powe so -
wa e; he minimum s a is ic powe calcula ed was 0.764.
The 10- imes ule (Hai e al. 2011) has been applied, see
Table6.
The main ea u es o he segmen s a e summa ised in
Table7, based on he suppo ed ela ionships in he esea ch
model we p oposed labels o .
Following he Pa hmox esul s, six segmen s ha e been
iden i ied (see Table7). On he one hand, he e a e wo seg-
men s made up o young people. The i s segmen , Cu ious
Explo e s (Node 4), comp ises young indi iduals below
39yea s o age wi h no uni e si y educa ion and is he la g-
es among he segmen s. Analysing he eleconsul a ion
adop ion p ocess o his segmen compa ed o he es , we
ind ha i is cha ac e ised by pe o mance expec ancy (PE)
signi ican ly a ec ing he in en ion o use his ool (BI) bu
less so han o he segmen s. This esul could be explained
by he idea ha in a segmen made up o young people who
a e e y amilia wi h new echnologies in all aspec s o hei
li es, hey may ha e no malised he ad an ages o digi al
media, so hei expec a ions o use ulness a e lowe when
no compa ed o o line al e na i es. These esul s a e also
applicable o Node 5. Dynamic Educa ed (Node 5), he sec-
ond segmen , is made up o young people below 39yea s
wi h a uni e si y educa ion. This segmen , in addi ion o i s
signi ican bu low PE o BI a io (as in Cu ious Explo e s),
is pa icula ly cha ac e ised by he ac ha he acili a ing
condi ions (FC) a e signi ican . In o he wo ds, o his seg-
men o uni e si y-educa ed young people, i is impo an o
ha e sui able de ices o ca y ou medical eleconsul a ion.
This las esul may be ela ed o disposable income in his
Fig. 2 Pa hmox esul
Table 6 Es ima ion o he
s a is ic powe Global Node 4 Node 5 Node 12 Node 13 Node 14 Node 15
Sample size 1412 432 252 106 323 85 214
10- imes ule 40 40 40 40 40 40 40
S a is ic powe 1.000 0.999 0.999 0.868 0.998 0.764 0.997
Table 7 Summa y o Pa hmox segmen cha ac e is ics
Node Label Age Educa ional le el Income Di e en ial cha ac e is ics com-
pa ed o he UTAUT global model
4 Cu ious Explo e s 18–38 No Uni e si y PE- > BI signi ican , bu low
5 Dynamic Educa ed 18–38 Uni e si y FC- > BI signi ican
12 Expe ienced Expe imen e s 39–74 No Uni e si y Ve y low PE- > BI no signi ican
13 Resilien Adap e s 39–74 No Uni e si y Low o High Simila o he global model
14 Insigh ul G adua es 39–74 Uni e si y Low and e y low PE- > BI signi ican , high
15 C i ical Use s 39–74 Uni e si y Medium o High FC- > USE, signi ican and nega i e
J.A enas-Gai án e al.
17 Cheah, J.H., Thu asamy, R., Memon, M.A., Chuah, F., Ting, H.:
Mul ig oup analysis using sma pls: s ep-by-s ep guidelines o
business esea ch. Asian J. Bus. Res. (2020). h ps:// doi. o g/ 10.
14707/ ajb . 200087
18. Chellaiyan, V.G., Ni upama, A.Y., Taneja, N.: Telemedicine in
India: whe e do we s and? J. Fam. Med. P im. Ca e 8(6), 1872
(2019)
19. Chimen o-Díaz, S., Sánchez-Ga cía, P., F anco-An onio, C., San-
ano-Mogena, E., Espino-Ta o, I., Co do illa-Gua dia, S.: Fac o s
associa ed wi h he accep ance o new echnologies o ageing in
place by people o e 64 yea s o age. In . J. En i on. Res. Public
Heal h 19(5), 2947 (2022). h ps:// doi. o g/ 10. 3390/ ije p h1905
2947
20. Chua, S.L., Chem, D., Wong, P.: Compu e anxie y and i s co e-
la es: a me a analysis. Compu . Hum. Beha . 14, 559–577 (1999).
h ps:// doi. o g/ 10. 1016/ S0747- 5632(99) 00039-4
21 C ease , A.V., Hall, J., Cos a, S., Bingham, D.D., Clemes, S.A.:
Explo ing amilies’ accep ance o wea able ac i i y acke s: a
mixed-me hods s udy. In . J. En i on. Res. Public Heal h (2022).
h ps:// doi. o g/ 10. 3390/ ije p h1906 3472
22. Dahlg en, C., Spånbe g, E., S e éus, S., Dackehag, M., Wändell,
P., Rehnbe g, C.: Sho -and in e media e- e m impac o DTC el-
emedicine consul a ions on subsequen heal hca e consump ion.
Eu . J. Heal h Econ. 25(1), 157–176 (2024). h ps:// doi. o g/ 10.
1007/ s10198- 023- 01572-z
23. Da is, F. D. 1985. A echnology accep ance model o empi ically
es ing new end-use in o ma ion sys ems: heo y and esul s.
24. Da is, F.D.: Pe cei ed use ulness, pe cei ed ease o use, and use
accep ance o in o ma ion echnology. MIS Q. 13(3), 319–340
(1989). h ps:// doi. o g/ 10. 2307/ 249008
25. Da is, F.D., Bagozzi, R.P., Wa shaw, P.R.: Use accep ance o
compu e echnology: a compa ison o wo heo e ical models.
Manage. Sci. 35(8), 982–1003 (1989). h ps:// doi. o g/ 10. 1287/
mnsc. 35.8. 982
26. Eu opean Commision. 2024a. EU Heal h Policy. O e iew. h ps://
heal h. ec. eu opa. eu/ eu- heal h- policy/ o e iew_ en# p io i ies- o -
2021- 2027. Re ie ed 20 Sep 2024 (h ps:// heal h. ec. eu opa. eu/
eu- heal h- policy/ o e iew_ en# p io i ies- o - 2021- 2027)
27. Eu opean Commision. 2024b. Fac shee on Eu opean Heal h Da a
Space. B ussels
28 Fa i a , S., Abouzah a, M., Ghasemaghaei, M.: Wea able de ice
adop ion among olde adul s: a mixed-me hods s udy. In . J. In .
Manag. (2020). h ps:// doi. o g/ 10. 1016/j. ijin omg . 2020. 102209
29. Fo nell, C., La cke , D.F.: E alua ing s uc u al equa ion models
wi h unobse able a iables and measu emen e o . J. Ma k. Res.
18(1), 39–50 (1981)
30. Ga a ini, L., Ma ini, M.B., Zane i, M.: Mo e oom o el-
emedicine a e COVID-19: lessons o p ima y ca e? Eu . J.
Heal h Econ. 22(2), 183–186 (2021). h ps:// doi. o g/ 10. 1007/
S10198- 020- 01248-Y
31 Ga a and, A., Aslani, N., Nad i, H., Abedini, S., Dehghan, S.:
Accep ance o elemedicine echnology among physicians: a sys-
ema ic e iew. In . Med. Unlocked (2022). h ps:// doi. o g/ 10.
1016/j. imu. 2022. 100943
32. G adillas, M., Thomas, L.D.W.: Dis inguishing digi iza ion and
digi aliza ion: a sys ema ic e iew and concep ual amewo k. J.
P od. Inno . Manag. (2023). h ps:// doi. o g/ 10. 1111/ jpim. 12690
33. Hai , J.F., Rishe , J.J., Sa s ed , M., Ringle, C.M.: When o use
and how o epo he esul s o PLS-SEM. Eu . Bus. Re . 31(1),
2–24 (2019)
34 Ha s , L., Lan zsch, H., Scheibe, M.: Theo ies p edic ing end-
use accep ance o elemedicine use: sys ema ic e iew. J. Med.
In e ne Res. (2019). h ps:// doi. o g/ 10. 2196/ 13117
35. Hensele , J., Ringle, C.M., Sa s ed , M.: A new c i e ion o
assessing disc iminan alidi y in a iance-based s uc u al equa-
ion modeling. J. Acad. Ma k. Sci. 43(1), 115–135 (2015)
36. He nández-Zamb ano, S.M., Cas iblanco-Mon añez, R.A., Gue -
e o, P.M.Á., Mo eno, M.A.G., Sac is án, L.G.M., Rod íguez-Va -
gas, G.S., Villa eal, L., Rubio, J.A., Quin e o, E., Gómez, M.A.,
Chá ez-Chá ez, J., T iana, D.P.R., Casano a, R., Rojas-Villa aga,
A., San os-Mo eno, P.: Expe ience o ele consul ing ca e o
heal h ca e p o essionals and pa ien s wi h heuma oid a h i is,
in he heal h eme gency due o co id-19. J. Clin. Rheuma ol. 27,
53 (2021)
37. Ka andi, H., Jaana, M.: Fac o s ha a ec heal h in o ma ion ech-
nology adop ion by senio s: a sys ema ic e iew. Heal h Soc. Ca e
Commun. 28(6), 1827–1842 (2020). h ps:// doi. o g/ 10. 1111/ hsc.
13011
38. Kock, N.: Common me hod bias in PLS-SEM: a ull collinea i y
assessmen app oach. In . J. E-Collab. 11(4), 1–10 (2015). h ps://
doi. o g/ 10. 4018/ ijec. 20151 00101
39. K aus, S., Schia one, F., Pluzhniko a, A., In e nizzi, A.C.: Digi-
al ans o ma ion in heal hca e: analyzing he cu en s a e-o -
esea ch. J. Bus. Res. 123, 557–567 (2021). h ps:// doi. o g/ 10.
1016/j. jbus es. 2020. 10. 030
40. Kwon, O., Wen, Y.: An empi ical s udy o he ac o s a ec ing
social ne wo k se ice use. Compu . Hum. Beha . 26(2), 254–263
(2010)
41 Lakoma, S., Pasanen, H., Lahdensuo, K., Pehkonen, J., Viini-
kainen, J., To kki, P.: Quali y o he digi al gp isi s and cha -
ac e is ics o he use s: e ospec i e obse a ional s udy. Scand.
J. P im. Heal h Ca e (2024). h ps:// doi. o g/ 10. 1080/ 02813 432.
2024. 23809 21
42. Lambe i, G., Lambe i, M. G. 2014. Genpa hmox.
43 Lambe i, G., Aluja, T.B., Sanchez, G.: The pa hmox app oach o
PLS pa h modeling segmen a ion. Appl. S och. Models Bus. Ind.
(2016). h ps:// doi. o g/ 10. 1002/ asmb. 2168
44. Lambe i, G., Bane -Aluja, T., Sanchez, G.: The pa hmox
app oach o PLS pa h modeling segmen a ion. Appl. S och.
Model. Bus. Ind. 32(6), 453–468 (2017). h ps:// doi. o g/ 10. 1002/
asmb. 2168
45. La an, H., Noonan, R.: Pa ial leas squa es pa h modelling. Basic
concep s, me hodological issues and applica ions. Sp inge In e -
na ional Publishing, Cham (2017)
46. Lindsay, J.S.B.: Types o amily and amily ypes. Fam. P ocess
7(1), 51–66 (1968). h ps:// doi. o g/ 10. 1111/j. 1545- 5300. 1968.
00051.x
47. Liu, C., Hung, K., Wang, D., Wang, S.: De e minan s o sel -
se ice echnology adop ion and implemen a ion in ho els: he
case o China. J. Hosp. Ma ke . Manag. 29(6), 636–661 (2020).
h ps:// doi. o g/ 10. 1080/ 19368 623. 2020. 16892 16
48. Liu, C., Lim, R., Taylo , S., Cal o, R.A.: S uden s’ beha iou al
engagemen in e iewing hei ele-consul a ion eedback wi hin
an online clinical communica ion skills pla o m. Compu . Hum.
Beha . 94, 35–44 (2019)
49. Lopez, A.M., Lam, K., Tho a, R.: Ba ie s and acili a o s o el-
emedicine: can you hea me now? Am. Soc. Clin. Oncol. Educ.
Book 41, 25–36 (2021)
50. Lu, W., Wei, D., Li, C., Gao, P., Ma, R., Zhai, Y., Wang, C.: How
o p omo e elemedicine pa ien adop ion beha io o g eene
heal hca e? J. Clean. P od. 434, 139884 (2024). h ps:// doi. o g/
10. 1016/J. JCLEP RO. 2023. 139884
51. Majche ek, D., Hege y, S.W., Kowalski, A.M., Lewandowska,
M.S., Diko a, D.: Oppo uni ies o heal hca e digi aliza ion in
Eu ope: compa a i e analysis o inequali ies in access o medical
se ices. Heal h Policy 139, 104950 (2024). h ps:// doi. o g/ 10.
1016/j. heal hpol. 2023. 104950
52. Makhni, M.C., Riew, G.J., Suma hipala, M.G.: Telemedicine in
o hopaedic su ge y: challenges and oppo uni ies. JBJS 102(13),
1109–1115 (2020)
53. Ma hews, L.M., Sa s ed , M., Hai , J.F., Ringle, C.M.: Iden i-
ying and ea ing unobse ed he e ogenei y wi h FIMIX-PLS.

Medical eleconsul a ion om hepa ien ’s pe spec i e. Ademog aphic segmen a ion
Eu . Bus. Re . 28(2), 208–224 (2016). h ps:// doi. o g/ 10. 1108/
EBR- 09- 2015- 0095
54. Mazzuca, D., Bo selli, M., G a e i, S., Zampogna, G., Feola, A.,
Co e, M.D., Gua na, F., Sco cia, V., Giannacca e, G.: Applica-
ions and cu en medico-legal challenges o elemedicine in oph-
halmology. In . J. En i on. Res. Public Heal h 19(9), 5614 (2022)
55. Mishkin, A.D., Cheung, S., Capo e, J., Fan, W., Muskin, P.R.:
Su ey o clinician expe iences o elepsychia y and ele-con-
sul a ion-liaison psychia y. J. Acad. Consul . Liaison Psychia y
63(4), 334–344 (2022)
56. Ni a i, G., Khuman, R., Baldoni, S., Pallo a, G., Ba ineni, G.,
Si ignano, A., Amen a, F., Ricci, G.: Telemedicine p ac ice:
e iew o he cu en e hical and legal challenges. Telemed.
E-Heal h 26(12), 1427–1437 (2020)
57. Paas, F., an Me iënboe , J.J.G.: Cogni i e-load heo y: me hods
o manage wo king memo y load in he lea ning o complex asks.
Cu . Di . Psychol. Sci. 29(4), 394–398 (2020). h ps:// doi. o g/ 10.
1177/ 09637 21420 922183
58. Po e , C.E., Don hu, N.: Using he echnology accep ance model
o explain how a i udes de e mine in e ne usage: he ole o
pe cei ed access ba ie s and demog aphics. J. Bus. Res. 59(9),
999–1007 (2006). h ps:// doi. o g/ 10. 1016/j. jbus es. 2006. 06. 003
59. Quinlan, J.R.: Induc ion o decision ees. Mach. Lea n. 1(1),
81–106 (1986). h ps:// doi. o g/ 10. 1007/ b 001 16251
60. R De elopmen Co e Team. 2021. R: a language and en i onmen
o s a is ical compu ing. R Found. S a is ical Compu .
61 Rouidi, M., Elouadi, A.E., Hamdoune, A., Chouj ani, K., Cha i,
A.: TAM-UTAUT and he accep ance o emo e heal hca e
echnologies by heal hca e p o essionals: a sys ema ic e iew.
In . Med. Unlocked (2022). h ps:// doi. o g/ 10. 1016/j. imu. 2022.
101008
62 Gee anjali, S., Sha ma, M., Kapu , A.: E olu ion o sma heal h-
ca e: elemedicine du ing COVID-19 pandemic. J. Ins . Eng.
India Se . B 102(6), 1319–1324 (2021). h ps:// doi. o g/ 10. 1007/
s40031- 021- 00568-8
63. Sanchez, G.: Pa hmox app oach: segmen a ion ees in pa ial leas
squa es pa h modeling. Uni e si a Poli ècnica de Ca alunya, Ba -
celona (2009)
64. Sa s ed , M.: A e iew o ecen app oaches o cap u ing he e o-
genei y in pa ial leas squa es pa h modelling. J. Model. Manag.
3(2), 140–161 (2008). h ps:// doi. o g/ 10. 1108/ 17465 66081 08901
26
65 Sa s ed , M., Ringle, C.M., Hai , J.F.: Pa ial leas squa es s uc-
u al equa ion modeling. In: Handbook o ma ke esea ch, pp.
1–40. Sp inge , Cham (2017)
66 Sa s ed , M., Ringle, C.M., Hai , J.F.: T ea ing unobse ed he -
e ogenei y in PLS-SEM. In: Pa ial leas squa es pa h modeling,
pp. 361–81. Sp inge , Cham (2017)
67 Scheu ele, E., Hodo , B., Popa, G., Wang, S., Kassle , W.: Popu-
la ion segmen a ion using a no el socio-demog aphic da ase .
Online J. Public Heal h In . (2022). h ps:// doi. o g/ 10. 5210/ ojphi.
14i1. 11651
68. Schönaue , A.L., Glanz, S.: Hyd ogen in u u e ene gy sys ems:
social accep ance o he echnology and i s la ge-scale in a-
s uc u e. In . J. Hyd ogen Ene gy 47(24), 12251–12263 (2022).
h ps:// doi. o g/ 10. 1016/j. ijhyd ene. 2021. 05. 160
69 Schue z, S., Venka esh, V.: Blockchain, adop ion, and inancial
inclusion in India: esea ch oppo uni ies. In . J. In . Manag.
(2020). h ps:// doi. o g/ 10. 1016/j. ijin omg . 2019. 04. 009
70. Sempe e, M.M., Cala ayud, C.R.: G an s p og amme and ui ion
ees policy in he Spanish public uni e si y sys em: is i eally
e ec i e? Re is a de Educacion 2022(398), 135–160 (2022).
h ps:// doi. o g/ 10. 4438/ 1988- 592X- RE- 2022- 398- 555
71. Shannon, R., S hien apapayu , T., Moschis, G.P., Teiche , T.,
Balikcioglu, B.: Family li e cycle and he li e cou se pa adigm:
a ou -coun y compa a i e s udy o consume expendi u es. J.
Glob. Sch. Ma ke . Sci. 30(1), 34–44 (2020). h ps:// doi. o g/ 10.
1080/ 21639 159. 2019. 16139 13
72. Sha ma, R.: The amily and amily s uc u e classi ica ion ede-
ined o he cu en imes. J. Fam. Med. P im. Ca e 2(4), 306
(2013). h ps:// doi. o g/ 10. 4103/ 2249- 4863. 123774
73. Shin, S.H., Yun, E.K.: Unde s anding he online heal h in o ma-
ion use p o iles in Ko ea: om a psychological pe spec i e. Tel-
emed. E-Heal h 17(5), 341–347 (2011). h ps:// doi. o g/ 10. 1089/
mj. 2010. 0170
74. Sope , D. S. 2023. A-p io i sample size calcula o o s uc u al
equa ion models. h ps:// www. danie lsope . com/ s a c alc
75. Tse sidis, A., Kolkowska, E., Heds öm, K.: Fac o s in luencing
senio s’ accep ance o echnology o ageing in place in he pos -
implemen a ion s age: a li e a u e e iew. In . J. Med. In o ma ics
129, 324–333 (2019). h ps:// doi. o g/ 10. 1016/j. ijmed in . 2019. 06.
027
76. Venka esh, M., Da is, and Da is.: Use accep ance o in o ma-
ion echnology: owa d a uni ied iew. MIS Q. 27(3), 425 (2003).
h ps:// doi. o g/ 10. 2307/ 30036 540
77. Venka esh, V., Mo is, M.G., Da is, G.B., Da is, F.D.: Use
accep ance o in o ma ion echnology: owa d a uni ied iew. MIS
Q. 27(3), 425–478 (2003)
78. Venka esh, V., Thong, J.Y.L., Xu, X.: Consume accep ance and
use o in o ma ion echnology: ex ending he uni ied heo y o
accep ance and use o echnology. MIS Q. 36(1), 157–178 (2012)
79 Villa ejo-Ramos, Á.F., Pe al-Pe al, B., A enas-Gai án, J.: La en
segmen a ion o olde adul s in he use o social ne wo ks and
e-banking se ices. Uni e si y o Bo as, Sweden (2019)
80. V anda, M.N., Cicil, V.R.: Tele-consul a ion o he su i o s o
in ima e pa ne iolence: guidelines o men al heal h p o ession-
als. Indian J. Psychol. Med. 44(4), 349–353 (2022)
81. Vuik, S.I., Maye , E., Da zi, A.: A quan i a i e e idence base o
popula ion heal h: applying u iliza ion-based clus e analysis o
segmen a pa ien popula ion. Popul. Heal h Me ics 14(1), 44
(2016). h ps:// doi. o g/ 10. 1186/ s12963- 016- 0115-z
82. Vu al, S., Ramadan, H.: A sho e iew on he compa ison o con-
sul a ion sys ems and ools in he eme gency depa men p ac ice:
ele-consul a ion. Cumhu iye Med. J. 41(2), 239–243 (2019)
83. Wes land, Ch.J.: Lowe bounds on sample size in s uc u al equa-
ion modeling. Elec on. Comme . Res. Appl. 9(6), 476–487
(2010). h ps:// doi. o g/ 10. 1016/J. ELERAP. 2010. 07. 003
84 W igh , Jane, Honey, Michelle: New Zealand nu ses’expe ience o
ele-consul a ion wi hin seconda y and e ia y se ices o p o ide
ca e a a dis ance. Nu s. P ax. N. Z. 32(2), 30 (2016)
85 Yang, H., Guo, X., Peng, Z., Lai, K.H.: Pa ien empowe men in an
online heal h pla o m: explo ing he quad a ic e ec s o pa ien s’
conscious-compe ence on pe cei ed heal h s a us. Compu . Hum.
Beha . (2022). h ps:// doi. o g/ 10. 1016/j. chb. 2022. 107346
86 Zhao, X., Nie, X.: Spli ing choice and compu a ional complexi y
analysis o decision ees. En opy (2021). h ps:// doi. o g/ 10. 3390/
e2310 1241
Publishe 's No e Sp inge Na u e emains neu al wi h ega d o
ju isdic ional claims in published maps and ins i u ional a ilia ions.