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Jou nal o In o ma ion Sys ems Enginee ing and Managemen
2022, 7(2), 14550
e-ISSN:
2468-4376
h ps://www.jisem-jou nal.com/
Augmen ed Reali y o a New Reali y: Using UTAUT-3 o
Assess he Adop ion o Mobile Augmen ed Reali y in
Tou ism (MART)
Agos inho Sousa Pin o1, An ónio Ab eu1, Eusébio Cos a2, Je ónimo Pai a1*
1 ISCAP / Poly echnic o Po o, Po ugal
2 Ins i u e o Highe S udies o Fa e, Po ugal
*
Co esponding Au ho :
je [email protected]
Ci a ion: Pin o A., Ab eu A., Cos a E. and Pai a J. (2022). Augmen ed Reali y o a New Reali y: Using UTAUT-3 o Assess he Adop ion o
Mobile Augmen ed Reali y in Tou ism (MART).
Jou nal o In o ma ion Sys ems Enginee ing and Managemen , 7
(2),
14550
.
h ps://doi.o g/10.55267/iad .07.12012
ARTICLE INFO
ABSTRACT
Recei ed: 26 Jan. 2022
Accep ed: 05 Ap . 2022
Few indus ies we e mo e a ec ed by he COVID-
19 pandemic han ou ism. One o Eu ope´s leading ou is
des ina ions, Po o had unde gone a majo ou i
sm boom un il he s a o pandemic. Mobile Augmen ed Reali y
(MAR) is one o he many eme ging echnologies ha has g ea po en ial o ou is ope a o s. Using his
echnology, hey can c ea e inno a i e ou ism p oduc s ha will help hem eco e om
he p esen c isis. As a
esul , in his s udy, we will empi ically es he la es e sion o he Uni ied Theo y o Accep ance and Use o
Technology (UTAUT) model o explo e he ac o leading o he adop ion Mobile Augmen ed Reali y in Tou ism
(MART) in
Po o. In doing so, we aim o con ibu e o g owing li e a u e on he opic o Mobile Augmen ed Reali y
(MAR). The o iginali y o his s udy lies in he use o an ex ended UTAUT model wi h g ea e p edic i e powe and
he explo a ion o he mode a i e ol
e o gende , age and expe ience. To he da a ob ained om a andom sample
o 201 esponden s who olun a ily answe ed an anonymous online ques ionnai e, we applied s uc u al equa ional
modeling and pa ial leas squa es (SEM-PLS) analysis o es he mod
el. Ou indings show ha habi , hedonic
mo i a ions and acili a ing condi ions a e he de e minan s o he use o MART.
Keywo ds: Vi ual Reali y, UTAUT, Augmen ed eali y, MART, SEM-PLS, Tou ism.
INTRODUCTION
Few indus ies ha e been mo e dis up ed by he pandemic
han ou ism (Libe a o e al., 2019). Du ing he 2010s, Po o
eme ged as one Eu ope´s mos a ac i e des ina ions and
ou ism made up 16,2% o he G oss Domes ic P oduc (GDP)
o Po ugal in 2019. Po o also has he ea u es o a sma
des ina ion: good ee Wi-Fi co e age including on public
anspo a ion; widesp ead p esence o In e ac i e ou is
s ands; good suppo o ou is s h ough websi es and
pe manen cus ome ca e se ice a ailable o cla i ica ion;
widesp ead use o QR Codes and o icial apps o ou ism
(Libe a o e al., 2019). Howe e , he imposi ion o a el
es ic ions and social dis ancing measu es had a d ama ic
impac on he ou ism, an indus y ha McKinsey p edic s may
only ully eco e by 2024 (Espí i o San o e al., 2021).
On he o he hand, he widesp ead adop ion o
sma phones and able s has deli e ed an a o dable and
powe ul pla o m capable o suppo ing augmen ed eali y.
Indeed, mos sma phones ha e buil -in senso s (came a, Wi-Fi,
compass and accele ome e ), as in e ne connec ions and ouch
sc eens ha enable he use o mobile augmen ed eali y (AR)
apps in a eas as a ied as gaming, educa ion, e ail and ou ism.
Vi ual and Augmen ed Reali y (VR/AR) applica ions ha e
become e en mo e widesp ead in he con ex o he COVID-19
pandemic. Indeed, augmen ed eali y is a echnology ha can
help he eco e y o ou ism and imp o e he expe ience isi ing
ou is a ac ions (C anme e al., 2018). In summa y, he aim o
his pape is o empi ically es he UTAUT-3 he adop ion o
MART in he ci y o Po o o answe he ollowing esea ch
ques ions:
Wha ac o s in luence indi iduals o adop MART apps?
Wha ac o s a e he mos impo an in p edic ing he adop ion
o MART?
Pin o A. e al. / J INFORM SYSTEMS ENG, 7(2), 14550
2 / 9
Do age and gende ha e a mode a ing ole in he ela ionship
be ween some o he independen a iables and he in en ion
and use o MART?
A e he e any s a is ically signi ican di e ences in he model
a iables be ween esponden s o di e en age, expe ience
and gende ?
RELATED WORK
Resea che s ha e sough o unde s and he adop ion AR
using a ious me hods. In a li e a u e e iew o he p edic i e
models o echnology accep ance, Gha ibi iden i ied he
Technology Accep ance Model (TAM), Theo y o Planned
Beha iou (TPB) and Uni ied Theo y o Accep ance and Use o
Technology (UTAUT) as he mos used models in he a ea o
ou ism (Gha ibi, 2020). The s udy by Paulo e al. combined
elemen s o UTAUT-2 wi h Technology-Task Fi (TTF) o
explo e he adop ion o AR in ou ism in Po ugal (Paulo e al.,
2018). The au ho s conclude ha he u u e use o MART is
de e mined by Pe o mance Expec ancy (PE), Facili a ing
Condi ions (FC), Hedonic Mo i a ions (HM), Habi (HB) and
Technology-Task Fi (TTF). The mode a o s o age and gende
we e no ound in any cons uc o be s a is ically signi ican in
he model. Gha aibeh e al. used UTAUT-2 o s udy he
adop ion o MART in Jo dan and ound ha pe o mance
expec ancy and aes he ics o be he mos signi ican ac o s
(Gha aibeh e al., 2020). In sum, hese p o ide an impo an
heo e ical basis o he s udy o hese echnologies, bu none
o hem we e ca ied ou a e he pandemic.
CONCEPTUAL FRAMEWORK
UTAUT amewo k
The UTAUT amewo k was i s o mula ed by Venka esh
o explain he adop ion o in o ma ion sys ems by use s by
consolida ing he con ibu ions o 8 models (Venka esh e al.,
2003). The o iginal model was based on 4 key cons uc s:
pe o mance expec ancy (PE), e o expec ancy (EE), social
in luence (SI) and acili a ing condi ions (FC). In 2012, he
o iginal model was la e expanded by adding 3 addi ional
cons uc s o o e come he limi a ions o he o iginal model:
hedonic mo i a ion (HM), p ice alue (PV) and habi (HB)
(Venka esh e al., 2012). In addi ion, age, gende , expe ience
and olun a iness ac as mode a o s be ween FC, HM, PV and
HB and he beha io al in en ion and beha io o use s. Finally,
UTAUT-3 was also chosen because Fa oq added a new
a iable, pe sonal inno a i eness in IT, ha has been ound o
be signi ican (Gunasinghe e al., 2019). Thus, UTAUT-3 was
chosen due o i s supe io p edic i e powe when compa ed o
o he e sions o he model.
Acco ding o Venka esh, olde use s end o ha e g ea e
di icul y in p ocessing new in o ma ion as hei cogni i e
capabili ies decline (Venka esh e al., 2012). As a esul ,
Venka esh hypo hesizes ha olde use s will ha e g ea e
di icul y in adop ing new echnologies and place g ea e
impo ance on he a ailabili y o suppo . In addi ion, men
end o be mo e willing o spend e o in o e coming
di icul ies o each hei goals and depend less on ex e nal
suppo han women. Mo e expe ienced use s a e na u ally mo e
amilia wi h a echnology. As a esul , hey ely less on ex e nal
suppo and ind i easie o lea n how o use new echnologies.
Gende di e ences and oles become mo e p onounced wi h age.
To keep ou model concise, we will only ocus on he mode a ing
e ec s o age and gende .
Now we shall desc ibe he key cons uc s o UTAUT-3 (Fa ooq
e al., 2017; Venka esh e al., 2012, 2003). Pe o mance expec ancy
(PE) is how much an indi idual pe cei es a echnology like
MART o be use ul and b ing bene i s. Since adop ing a new
echnology equi es e o , e o expec ancy (EE) is he pe cei ed
deg ee o ease o using hese mobiles apps in ou ism ac i i ies.
Social in luence (SI) measu es he le el o p essu e om amily,
iends and o he in luen ial people exe o e an indi idual o
adop a MART. Facili a ing condi ions (FC) measu es he
exis ence o echnical and o ganiza ional in as uc u e (mobile
in e ne , sma phones, e c…) ha people belie e a e needed o
use his echnology. Likewise, hedonic mo i a ion (HM)
measu es he pleasu e and un ha an indi idual ob ains om
using MART. P ice alue (PV) measu es he cos s and bene i s
associa ed wi h using MART as consume s will seek o use apps
ha o e “good alue o money”. Habi (HB) ep esen s he sel -
epo ed p e ious expe iences o an indi idual in using a
echnology. Pe sonal Inno a i eness (PT) is a pe sonali y ai
ha e lec s he endency o ce ain indi iduals o y ou and
adop he la es ad ancemen s in IT. Ce ain consume s end o
be “ea ly adop e s” o new gadge s while o he s will only adop
hem when hei use is widesp ead. Finally, while beha io al
in en ion (BI) measu es he epo ed commi men o an
indi idual o use MART, use (U) measu es he sel - epo ed
ac ual beha io o he use .
Mobile Augmen ed Reali y in Tou ism (MART)
Augmen ed Reali y is seen as a a ia ion o VR, hence he use
o e ms like “VR/AR”. AR blends he isual aspec s o compu e
and physical wo ld. By poin ing he mobile de ice o an objec i
p o ides addi ional in o ma ion (Tu ban e al., 2018; Van
K e elen and Poelman, 2010). I synch onizes eal and i ual
objec s wi h each o he , uns in 3D in eal ime and is highly
in e ac i e (Van K e elen and Poelman, 2010). AR can make he
expe ience o ou is s mo e c ea i e, spon aneous and con enien .
I can help ou is s wi h ind pa king, accommoda ion,
es au an s and monumen s, na iga ing public anspo sys ems,
ob aining wea he o ecas s and gi e mo e in o ma ion abou he
su ounding en i onmen (Law e al., 2014). The in eg a ion wi h
social media can allow ou is s o sha e in o ma ion and ips
online. The imposi ion o in e na ional a el es ic ions
a ec ing 90% o he wo ld popula ion jeopa dized he ou ism
and a el indus ies (Singh e al., 2020). Thus he demand o
mobile and web-based AR inc eased d ama ically and is seen as
an impo an ool o help elaunch he ou ism indus y (Mohan y
e al., 2020).
METHODS
This s udy is quan i a i e in na u e. Based on a li e a u e
e iew, we will p opose and empi ically es a model based on
UTAUT-3. To ha end, we will use Wa pPLS 7.01 o un a pa ial
leas squa e eg ession (SEM-PLS) o es ima e he model ha can
Pin o A. e al. / J INFORM SYSTEMS ENG, 7(2), 14550
3 / 9
be seen in Figu e 1. This me hod was chosen because no all
i ems in he da a ollow a no mal dis ibu ion (p<0,05 based on
Kolmogo o -Smi no es ) and he model is complex. To
u he explo e di e ences in mean be ween di e en age and
expe ience g oups we ca ied he independen a iables - es
and a one-way ANOVA es o explo e di e ences be ween age
g oups. In he ollowing sec ion, we will lis he hypo hesis ha
we shall es and p o ide empi ical s udies o suppo hem:
• H1: The ela ionship be ween pe o mance expec ancy
and he beha io al in luence o use MART is posi i e.
(Gio anis e al., 2019; Oli ei a and Bap is a, 2017;
Saxena and Kuma , 2017)
• H2b: The ela ionship be ween e o expec ancy and
pe o mance expec ancy is posi i e. (Paulo e al., 2018;
Venka esh e al., 2012)
• H2b: The ela ionship be ween e o expec ancy and
he beha io al in luence o use MART is posi i e.
(Bha iase i, 2015; Gha aibeh e al., 2020; Tan and Leby Lau,
2016)
• H3: The ela ionship be ween social in luence and he
beha io al in luence o use MART is posi i e.(Gio anis e
al., 2019; Oli ei a and Bap is a, 2017; Paulo e al., 2018; Tan
and Leby Lau, 2016)
• H4a: The ela ionship be ween acili a ing condi ions
and he beha io al in en ion o use MART is posi i e and
mode a ed by age and gende . (Alqah ani and Ka akli, 2017;
Gha aibeh e al., 2020; K ogs ie, 2012; Oli ei a and Bap is a,
2017; Paulo e al., 2018; Saxena and Kuma , 2017; Shang e
al., 2017)
• H4b: The ela ionship be ween acili a ing condi ions
and he ac ual use o MART is posi i e.
• H5: The ela ionship be ween hedonic mo i a ion and
beha io al in en ion o use MART is posi i e and mode a ed
by age and gende . (Ali e al., 2016; A ain e al., 2019;
Heijden, 2004; Jung e al., 2015; Richa ds, 2011)
• H6: The ela ionship be ween p ice alue and
beha io al in en ion o use MART is di ec and mode a ed
by age and gende . (Blaise e al., 2018; de Ke ile e al., 2016;
Sha ma e al., 2017; Slade e al., 2015)
• H7a: The ela ionship be ween habi and beha io al
in en ion o use MART is posi i e and mode a ed by age and
gende . (Kim and Malho a, 2005; Venka esh e al., 2012)
• H7b: The ela ionship be ween habi and ac ual use o
MART is posi i e and mode a ed by age and gende .
• H8a. The ela ionship be ween pe sonal inno a i eness
and beha io al in en ion o use MART is posi i e and
mode a ed by age. (Ramos-de-Luna e al., 2016)
• H8b.The ela ionship be ween pe sonal inno a i eness
and he ac ual use o MART (Ramos-de-Luna e al., 2016)
• H9: The ela ionship be ween beha io al in en ion o
adop MART and ac ual use o MART is posi i e. (Da is, 1989;
Paulo e al., 2018; Venka esh e al., 2003)
• H10a: Gende mode a es he ela ionship be ween habi
and beha io al in en ion o use MART. (Ahmad e al., 2005;
Di in e al., 2019; Paulo e al., 2018; Venka esh e al., 2012)
• H10b: Gende mode a es he ela ionship be ween habi
and ac ual use o MART. (Ahmad e al., 2005; Di in e al., 2019;
Paulo e al., 2018; Venka esh e al., 2012)
• H11: Gende mode a es he ela ionship be ween p ice
alue and beha io al in en ion o use MART. (Ahmad e al.,
2005; Di in e al., 2019; Paulo e al., 2018; Venka esh e al., 2012)
• H12: Gende mode a es he ela ionship be ween hedonic
mo i a ion and beha io al in en ion o use MART. . (Ahmad e
al., 2005; Di in e al., 2019; Paulo e al., 2018; Venka esh e al.,
2012)
• H13: Gende mode a es he ela ionship be ween
acili a ing condi ions and he beha io al in en ion o use
MART. (Ahmad e al., 2005; Di in e al., 2019; Paulo e al., 2018;
Venka esh e al., 2012)
• H14: Age mode a es he ela ionship be ween pe sonal
inno a i eness and beha io al in en ion. (Fa ooq e al., 2017;
Paulo e al., 2018; Sei e and Schlomann, 2021; Venka esh e al.,
2012)
• H15a: Age mode a es he ela ionship be ween habi and
beha io al in en ion o use MART (Paulo e al., 2018; Sei e and
Schlomann, 2021; Venka esh e al., 2012)
• H15b: Age mode a es he ela ionship be ween habi and
ac ual use o MART (Paulo e al., 2018; Sei e and Schlomann,
2021; Venka esh e al., 2012)
• H16: Age mode a es he ela ionship be ween hedonic
mo i a ion and beha io al in en ion o use MART. (Paulo e al.,
2018; Sei e and Schlomann, 2021; Venka esh e al., 2012)
• H17: Age mode a es he ela ionship be ween p ice alue
and beha io al in en ion o use MART (Paulo e al., 2018; Sei e
and Schlomann, 2021; Venka esh e al., 2012)
• H18: Age mode a es he ela ionship be ween acili a ing
condi ions and he beha io al in en ion o use MART. (Paulo e
al., 2018; Sei e and Schlomann, 2021; Venka esh e al., 2012)
Figu e 1. Resea ch Model P oposal
Pin o A. e al. / J INFORM SYSTEMS ENG, 7(2), 14550
4 / 9
Table 1. Analysis o he Sample
n
%
Gende
Male
78
39
Female
123
61
Age
<21
16
8
[21,41[
134
67
[41,61[
27
13
>61
24
12
Expe ience wi h
MART
Yes
172
86
No 29 14
Table 2.
Desc ip i e s a is ics and indi idual eliabili y o all i ems
I em
Mean
S.D.
λ
I em
Mean
S.D.
λ
PE1
4.36
0.99
0.956
PV2
3.38
0.99
0.960
PE2
4.18
1.01
0.946
PV3
3.36
1.01
0.943
PE3
4.13
1.04
0.941
HB1
3.28
1.
37
0.927
EE1
4.28
0.93
0.917
HB2
2.67
1.20
0.817
EE2
4.17
0.92
0.940
HB3
2.90
1.27
0.893
EE3
4.17
0.85
0.902
HB4
3.21
1.32
0.926
EE4
4.18
1.01
0.947
PI1
3.77
1.17
0.914
SI1
3.21
1.19
0.868
PI2
3.66
1.14
0.922
SI2
3.24
1.19
0.829
PI3
2.82
1.27
0.851
SI3
2.75
1.33
0.781
BI1
3.87
1.12
0.953
SI4
2.37
1.27
0.791
BI2
3.57
1.18
0.957
FC1
4.00
1.05
0.811
BI3
3.78
1.17
0.975
FC2
4.06
1.10
0.842
U1
4.39
1.10
0.751
FC3
3.99
1.00
0.860
U2
3.45
1.27
0.774
FC4
3.59
1.10
0.723
U3
4.18
1.11
0.848
HM1
3.75
1.00
0.934
U4
3.84
1.30
0.777
HM2
3.75
1.04
0.947
U5
3.92
1.23
0.785
HM3
3.59
1.09
0.922
U6
3.56
1.30
0.676
PV1
3.32
0.99
0.941
U7
2.91
1.56
0.806
Sample and Da a Collec ion
Based on an ex ensi e li e a u e e iew, we de eloped a
ques ionnai e ha dis ibu ed on social media o an audience o
consume s om he ci y o Po o (see Table 7). Con iden iali y
and anonymi y o pa icipan s was assu ed, all i ems we e
measu ed wi h a 5-poin Like scale and pa icipa ion was
olun a y. A pilo es was ca ied ou wi h 20 answe s and inal
adjus men s we e made. The Ha man´s single ac o es was
ca ied ou and no e idence o common me hod bias was ound.
We ob ained a andom and independen sample o 201
esponden s om he ci y o Po o. Indeed, o a SEM-PLS
analysis, he dimension o he sample should no be below 200
(Boomsma and Hoogland, 2001). O e 95% o esponden s ha e
a sma phone o able and mobile in e ne on hei
sma phones. 86% o esponden s ha e expe ience wi h MART
(see Table 1). In e ms o age and gende , 61% o esponden s
a e emale and 75% a e below he age o 41. Thus, his sample
o mos ly ech-sa y, u ban and young indi iduals mi o s ha
he popula ion o MART use s bu no he gene al popula ion o
he ci y.
RESULTS
Reliabili y and Validi y Analysis
Fo an i em o e lec he cons uc i is ying o measu e,
hei ou e loading (λ) mus be a leas 0,7 (Hussain e al.,
2018). I ems whose λ is unde 0,7 we e elimina ed –
U6(0.676). We used o me ics o con e gen alidi y:
Composi e Reliabili y (CR) and C onbach’s Alpha (
𝜌

)
whose alues end o be simila . Since all cons uc s ha e a
CR and
𝜌

abo e 0.8, we ha e a le el o con e gen alidi y
adequa e o applied esea ch (Ne emeye e al., 2003;
Nunnally and Nunnaly, 1978). Con e gen alidi y is
assu ed as no key cons uc has an a e age a iance
ex ac ed (AVE) below 0.5 (Fo nell and La cke , 1981). The
analysis o desc ip i e s a is ics e eals ha ou sample
displays high le els o PE, EE, FC, HM, BI and U (3,5-5) and
mode a e le els (2,5-3,5) o SI, PV, HB and PI (see
Table 2
).
Di e gen alidi y es ablishes how a ce ain cons uc di e s
om he o he s in ou s udy. All cons uc s ha e
disc iminan (di e gen ) alidi y as he posi i e squa e oo
o he AVE o all ac o s is highe han he highes
co ela ion wi h any o he ac o (see
Table 3
)
(Fo nell and
La cke , 1981).
P edic i e Powe
In
Table 4
, o assess he p edic i e powe o he la en
endogenous a iables we will use wo indica o s: Explained
Va iance and S one-Geisse es . Since he Q
2
alues o all
la en endogenous alues a e abo e 0 and he explained
a iance is abo e 0,1, he a iables ha e easonable
p edic i e powe (Falk and Mille , 1992). Since all la en
endogenous a iables ha e an R
2
abo e 0,1, hese cons uc s
ha e a easonable p edic i e powe .
Pin o A. e al. / J INFORM SYSTEMS ENG, 7(2), 14550
5 / 9
Table 3.
Measu es o alidi y and eliabili y o all key cons uc s
𝝆
𝑻
CR
AVE
PE
EE
SI
FC
HM
PV
HB
PI
BI
U
Gende
Age
PE
0.943
0.964
0.898
.95
EE
0.945
0.960
0.858
.69
.93
SI
0.834
0.890
0.669
.01
.04
.82
FC
0.824
0.884
0.657
.54
.63
.19
.81
HM
0.927
0.954
0.873
.58
.64
.30
.5
5
.93
PV
0.944
0.964
0.899
.50
.46
.21
.53
.54
.95
HB
0.913
0.939
0.795
.45
.47
.39
.42
.59
.59
.89
PI
0.877
0.925
0.804
.45
.62
.18
.57
.58
.43
.43
.90
BI
0.959
0.974
0.925
.51
.56
.29
.49
.68
.58
.80
.52
.96
U
0.885
0.913
0.
637
.41
.49
.32
.48
.52
.38
.57
.49
.67
.78
Gende
-
.29
-
.09
.26
-
.17
-
.06
-
.18
-
.02
-
.00
-
.03
.06
1.0
Age .47 -.46
.18
-.26
-.27
-.22
-.14
-.28
-.15
-.01
.35 1.0
Table 4.
P edic i e Powe : Explained Va iance and S one-Geisse es alues
La en e
ndogenous a iables
R
2
Q
2
Pe o mance Expec ancy (PE)
0.527
0.534
Beha io In en ion (BI)
0.748
0.753
Use (U)
0.576
0.580
Table 5.
Resul s o he es s o hypo hesis
2
Hypo hesis
β
p
-
alue
Hypo hesis
β
T
p
-
alue
H1
–
PE
→
BI
0.03
0.4
0.34
H9
–
BI
→
U
0.
28
4.18
<0.001***
H2a
–
EE
→
PE
0.73
11.8
<0.001***
H10a
–
Gende *HB
→
BI
-
0.07
-
1.07
0.144
H2b
–
EE
→
BI
-
0.05
-
0.73
0.23
H10b
–
Gende *HB
→
U
-
0.01
-
0.07
0.471
H3
–
SI
→
BI
-
0.02
-
0.23
0.41
H11
–
Gende *PV
→
BI
-
0.07
-
0.99
0.161
H4a
–
FC
→
BI
0.16
2.37
0.009**
H12
–
G
ende *HM
→
BI
0.06
0.88
0.190
H4b
–
FC
→
U
0.19
2.73
0.004**
H13
–
Gende *FC
→
BI
-
0.07
-
1.01
0.157
H5
–
HM
→
BI
0.18
2.63
0.005**
H14
–
Age*PI
→
U
0.19
2.86
0.002**
H6
–
PV
→
BI
0.08
1.21
0.114
H15a
-
Age*HB
→
BI
0.12
1.68
0.047*
H7a
–
HB
→
BI
0.52
8.10
<0.001***
H15b
-
A
ge*HB
→
U
0.06
0.85
0.198
H7b
–
HB
→
U
0.2
3.00
<0.002**
H16
–
Age*HM
→
BI
0.06
0.89
0.189
H8a
–
PI
→
BI
0.03
0.44
0.331
H17
–
Age*PV
→
BI
-
0.08
-
1.09
0.140
H8b – PI→U 0.00 0.03
0.490
H18– Age*FC→BI
-0.05
-0.65
0.257
Resul s o he hypo hesis es s
Fo a one-sided S uden ´s dis ibu ion wi h 200 deg ees o
eedom, he c i ical alues a e: (95%)=1.6525*,
(99%)=2.3451**, (99,9%)=3.131***. In a o al o 24 hypo hesis,
only 9 we e no ejec ed wi h 95% con idence. H1, H2b, H3, H6,
H8a, H8b, H10a, H10b, H11-13, H15b-18 can be ejec ed wi h
95% con idence because hei alues a e below 1,652 and hei
p- alue is highe han 0,05. H2a, H4a, H4b, H5, H7a, H7b, H9
and H14-15a canno be ejec ed wi h 95% con idence because
hei alues a e abo e 1,652 and hei p- alue is lowe han
0,05 (see Table 5).
Unlike wha was ini ially hypo hesized, wi h 95%
con idence, we can say ha he only a iables ha in luence
he use o MART a e acili a ing condi ions, hedonic
mo i a ions and habi . Beha io al in en ion in luences he
ac ual use o MART, hence, all a iables ha in luence BI ha e
an indi ec e ec on U. People who in en o use MART end up
using his echnology. HM only plays and indi ec ole
( h ough BI) while FC and HB ha e bo h a di ec and indi ec
e ec on U. Habi is he mos impo an de e minan o he use
o MART – o al e ec = indi ec e ec + di ec e ec = 𝛽 ∗
𝛽 + 𝛽= 0,52*0,28+0,2 = 0,346. We can also b eak down he
o al e ec o FC on he use o MART in indi ec (𝛽 ∗ 𝛽=
0,16*0,28= 0,045) and di ec e ec s (𝛽 = 0,19). Finally, a
95% con idence, con a y o ou ini ial hypo hesis, he e is no
e idence ha gende mode a es he ela ionship be ween any
o he a iables. Age only seems o mode a e he ela ionship
be ween pe sonal inno a i eness and use and habi and
beha io al in en ion. As people g ow olde , he link be ween HB
and BI and PI and U g ows s onge . Wi h 95% con idence, a e
unning an independen samples - es , he e a e no s a is ically
signi ican di e ences be ween men and women excep wo
a iables: pe o mance expec ancy and social in luence. On
a e age, emale esponden s exhibi highe le els o
pe o mance expec ancy and lowe le els o social in luence han
male esponden s. On he o he hand, be ween he g oup o
esponden s wi h and wi hou expe ience, he e a e signi ican
di e ences all a iables excep SI. On a e age, use s wi h
expe ience exhibi signi ican ly highe le els o pe o mance and
e o expec ancy, acili a ing condi ions, hedonic mo i a ions,
p ice alue, habi , pe sonal inno a ion, beha io al in en ion and
use o MART han use s wi hou expe ience. To explo e he
di e ences be ween di e en age g oups we an a one-way
ANOVA es wi h 95% con idence. The e a e s a is ically
conside able di e ences be ween age g oups in all a iables. On
a e age, he only a iable ha s eadily inc eases wi h age is
social in luence. The g oup aged be ween 21 and 41 yea s old
(Millennials and some Gen-Z) is he g oup wi h he highes le els
o BI, PI, HB, PV, HM, FC, EE and PE. The g oup o e he age o
61 (Baby Boome s and Silen Gene a ion) exhibi s he lowes
a e age sco e in all a iables excep SI in which hey ha e he
highes a e age sco e. As a esul , in line wi h he ideas o
Venka esh, all a iables excep SI end o decline wi h age.
2p<0.05 (*), p<0.01 (**), p<0.001 (***)

Pin o A. e al. / J INFORM SYSTEMS ENG, 7(2), 14550
6 / 9
Table 6.
Model i indica o s (Kock, 2010)
Indica o s Values Accep able alues
GoF 0.748
≥0.36
AFVIF 3.229
≤5
SPR 0.708
≥0.7
RSCR 0.930
≥0.9
SSR 1
≥0.7
NLBCDR 1
≥0.7
STDSR 0.824
≥0.7
STDCR 0.942
≥0.7
SMAR 0.076
≤0.1
SRMR 0.097 ≤0.1
Table 7.
I ems and sou ces o he ques ionnai e
I em
Ques ion
Sou ce
PE
PE1
I belie e ha
mobile in e ne and apps a e use ul in ou ism
(Fa ooq e al., 2017;
Paulo e al., 2018;
Saxena and Kuma ,
2017; Venka esh e al.,
2012, 2003)
PE2
I belie e ha mobile in e ne and apps enhance my chances o ge ing hings ha a e impo an
PE3
I belie e ha MAR makes ou ism mo e con enien
EE
EE1
Lea ning how o us
e mobile in e ne and apps o ou is ic ac i i ies is easy
EE2
My in e ac ion wi h MART is clea and unde s andable
EE3
I ind mobile in e ne and apps a e easy o use in ou is ic ac i i ies
EE4
I is easy o become adep a using mobile in e n
e and apps in ou ism
SI
SI1
People who in luence me hink ha I should use MART
SI2
People who a e impo an o me hink ha I should use MART
SI3
People in my en i onmen who use MART a e mo e p ominen
SI4
Using MART is a s a us symbol
FC
FC1
I ha e he necessa y esou ces o ake ad an age o MART
FC2
I ha e he necessa y knowledge o use MART
FC3
Mobile in e
ne is compa ible wi h o he echnologies I use in ou ism
FC4
I can ge help om o he s when I ha e di icul ies wi h MART
HM
HM1
Using mobile in e ne and apps in ou is ic ac i i ies is un
(Fa ooq e al., 2017; Paulo
e al., 2018; Saxena and
Kuma , 2017; Venka esh
e al., 2012)
HM2
Using mobile in e ne
and apps in ou is ic ac i i ies is enjoyable
HM3
Using mobile in e ne and apps in ou is ic ac i i ies is cap i a ing
PV
PV1
Mobile in e ne and apps o ou is ic ac i i ies a e easonably p iced
PV2
Mobile in e ne and apps o ou is ic ac i
i ies a e good alue o money
PV3
A he cu en p ice, MART p o ides good alue o money
HB
HB1
The use o mobile in e ne and apps in ou is ic ac i i ies is a habi
HB2
I am addic ed o using mobile in e ne and apps in ou is ic ac i i ies
HB3
I mus use mobile in e ne and apps in ou is ic ac i i ies
HB4
Using mobile in e ne and apps in ou is ic ac i i ies is na u al
PI
PI1
I like o expe imen new apps, gadge s and echnologies
(Fa ooq e al., 2017)
PI2
When a mobile app in oduc
es a new ea u e, I am keen o y i ou
PI3
Usually I am he i s o use new gadge s and echnologies among my pee s
BI
BI1
I in end o con inue using mobile in e ne and apps in he u u e
(Paulo e al., 2018;
Venka esh e al., 2003)
BI2
I will always y o use mobile in e ne and apps in ou is ic ac i i i
es
BI3
I plan o con inue o use mobile in e ne and apps equen ly in ou ism
U
U1
Maps
(Paulo e al., 2018)
U2
Museums and Tou is A ac ions
U3
Finding es au an s, ba s and ca és
U4
Find accommoda ion
U5
T anspo s
U6
E en s
U7
Vi ual T ips and E
-
ou ism
Pin o A. e al. / J INFORM SYSTEMS ENG, 7(2), 14550
7 / 9
Resul s o he hypo hesis es s
Finally, we sough o assess he i ou model has a good
i by p esen ing se e al indica o s in able 6. As we can see,
since all he alues o he indica o s a e wi hin he accep able
alues, his model is easonably consis en wi h he da a. No
e-speci ica ion o he model is equi ed. The A e age ull
collinea i y VIF (AFVIF) is below 3.3 which is ideal and
shows ha he e a e no signs o mul icollinea i y. In igu e
2, we p esen a summa y o he es ima ed empi ical model.
CONCLUSION AND FUTURE WORKS
In sum, a e empi ically es ing he UTAUT3 model we can
e eal impo an indings abou he s a e o adop ion o MART
in Po o. In con as o ini ial p emises, he only a iables ha
ha e an impac on he use o MART a e acili a ing condi ions,
hedonic mo i a ions and habi . Thus, esponden s seem no o
be in luenced by p ices, he opinions o o he s ( ela i es,
iends and colleagues), he e o ha i akes o use MART,
hei own expec a ions o he bene i s hey can ob ain om
using his echnology no do hey seem o be in luenced by
hei endency o be “ea ly adop e s” o new echnologies. The
independen a iables o UTAUT-3 can explain 75% o
a ia ion in BI. In u n, EE can explain 53% o a ia ion in PE.
BI, FC, HB and PI explain 58% o a ia ion in he use o MART.
Ou s udy has se e al impo an implica ions. Fi s , ech
and ou ism companies should s udy he ma ke ha hey a e
ying o each and segmen he ma ke . Ou analysis o
mode a o s showed ha only age has a signi ican in luence in
he ela ionships be ween pe sonal inno a ion and use and
habi and beha io al in en ion. The e a e signi ican
di e ences in beha io and a i udes o a ious age g oups.
Mos no ably, he g oups unde 21 and be ween 21 and 41 (Gen
Z and Millennials) exhibi he highes le els o in en ion o use
MART, bu he g oup ha shows he highes le el o use is he
aged be ween 41 and 61 (Gen X and some Baby Boome s). As a
esul , he younge gene a ions a e he ones ha a e d i ing he
g ow h in he use o mobile echnologies. As a esul , he needs
o consume s a y wi h age. Fo example, Gen Z and
Millennial ou is s a e usually e y keen on using maps o ge
a ound he ci y, apps o ind a place o ea and o s ay bu use
apps o cul u al e en s and i ual ips a less o en han Gen
X use s. Indeed, he adop ion o his echnology makes ou is
ac i i ies mo e con enien , un and enjoyable. Howe e , special
a en ion needs o be gi en o olde ou is s as hey equi e
g ea e suppo han mos . Because he elde ly end o su e
mo e om heal h condi ions, app de elope s need o make
accessibili y a op p io i y o each his g owing ma ke segmen
in Po ugal. Gende plays no mode a ing ole and gende
di e ences be ween men and women a e only el in wo
a iables. In con as , he e a e e y la ge di e ences be ween
expe ienced and non-expe ienced use s in nea ly all indica o s.
Consume s a e also becoming inc easingly demanding and
ocused on expe iences. The use o MART is la gely a ma e o
habi . Main aining a loyal use base should be a g ea e p io i y
han simply boos ing he numbe o downloads o an app. E en
be o e he begging o he ou is ips, use s can use apps o help
hem plan a ip (places o isi , places o ea , booking ho els,
e c…). In designing he apps, he mos impo an hing is o make
hem pleasan o use and able o p o ide momen s o enjoymen
o a ele s. Facili a ing condi ions is s a is ically signi ican
because o use MART equi es ha use s adop o he
echnologies (sma phones, able s, 4G in e ne , e c…)
be o ehand. Though he use o sma phones and able s has
become widesp ead and Po o has an good public Wi- i ne wo k,
i is essen ial no o ake o g an ed ha all consume s ha e he
necessa y equipmen and suppo o use MART. Finally, i is
impo an o emembe ha hese echnologies a e s ill
de eloping and he in oduc ion o 5G echnology may open
many new applica ions o MART.
Ou esea ch pape has some limi a ions ela ed o he size
and composi ion o he sample. The sample is comp ised o
consume s om Po o and i s composi ion is di e en om ha
o he gene al popula ion o he ci y. In gene al, he g oup o
esponden s is, on a e age, signi ican ly younge han he
popula ion o he ci y. Howe e , use s o MART end o be
mos ly u ban, ech-sa y and young jus like ou sample. Thus,
one mus be ca e ul be o e gene alizing he indings o his s udy
o o he con ex s. Ano he limi a ion is ha we elied on sel -
epo ed use beha io . Fu u e esea che s should seek o
moni o how much pa icipan s use a ce ain app in p ac ice.
Figu e 2. Summa y o Es ima ed Resea ch Model
8 / 9
Pin o A. e al. / J INFORM SYSTEMS ENG, 7(2), 14550
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