Generative AI as a tourism actor: Reconceptualising experience co-creation, destination governance and responsible innovation in the synthetic experience economy

Ch is ou, E angelos; Fo iadis, Anes is; Giannopoulos, An onios
A icle — Published Ve sion
Gene a i e AI as a ou ism ac o : Reconcep ualising expe ience co-
c ea ion, des ina ion go e nance and esponsible inno a ion in he
syn he ic expe ience economy
Jou nal o Tou ism, He i age & Se ices Ma ke ing
Sugges ed Ci a ion: Ch is ou, E angelos; Fo iadis, Anes is; Giannopoulos, An onios (2025) :
Gene a i e AI as a ou ism ac o : Reconcep ualising expe ience co-c ea ion, des ina ion go e nance
and esponsible inno a ion in he syn he ic expe ience economy, Jou nal o Tou ism, He i age &
Se ices Ma ke ing, ISSN 2529-1947, In e na ional Hellenic Uni e si y, Thessaloniki, Vol. 11, Iss. 2,
pp. 16-41,
h ps://doi.o g/10.5281/zenodo.16562068 ,
h ps://zenodo.o g/ eco ds/16562068
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/322477
S anda d-Nu zungsbedingungen:
Die Dokumen e au EconS o dü en zu eigenen wissenscha lichen
Zwecken und zum P i a geb auch gespeiche und kopie we den.
Sie dü en die Dokumen e nich ü ö en liche ode komme zielle
Zwecke e iel äl igen, ö en lich auss ellen, ö en lich zugänglich
machen, e eiben ode ande wei ig nu zen.
So e n die Ve asse die Dokumen e un e Open-Con en -Lizenzen
(insbesonde e CC-Lizenzen) zu Ve ügung ges ell haben soll en,
gel en abweichend on diesen Nu zungsbedingungen die in de do
genann en Lizenz gewäh en Nu zungs ech e.
Te ms o use:
Documen s in EconS o may be sa ed and copied o you pe sonal
and schola ly pu poses.
You a e no o copy documen s o public o comme cial pu poses, o
exhibi he documen s publicly, o make hem publicly a ailable on he
in e ne , o o dis ibu e o o he wise use he documen s in public.
I he documen s ha e been made a ailable unde an Open Con en
Licence (especially C ea i e Commons Licences), you may exe cise
u he usage igh s as speci ied in he indica ed licence.
h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0/deed.en
Jou nal o Tou ism, He i age & Se ices Ma ke ing, Vol. 11. No. 2, 2025, pp. 16-41 16
C Copy igh
© 200
© 2025 Au ho s. Published by In e na ional Hellenic Uni e si y
ISSN: 2529-1947. UDC: 658.8+338.48+339.1+640(05)
h p://doi.o g/10.5281/zenodo.16562068
Published online: 1 Decembe 2025
www.j hsm.g py igh © 200
Some igh s ese ed.
Excep o he wise no ed, his wo k is licensed unde
h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0
Gene a i e AI as a ou ism ac o :
Reconcep ualising expe ience co-c ea ion,
des ina ion go e nance and esponsible
inno a ion in he syn he ic expe ience economy
E angelos Ch is ou
In e na ional Hellenic Uni e si y, G eece
Anes is Fo iadis
Zayed Uni e si y, Uni ed A ab Emi a es
An onios Giannopoulos
In e na ional Hellenic Uni e si y, G eece
Abs ac :
Pu pose: This concep ual s udy examines how Gene a i e A i icial In elligence (GenAI) eshapes alue co c ea ion,
des ina ion go e nance, and esponsible inno a ion in ou ism. I seeks o eposi ion GenAI om a backs age ool o a ou ism
ac o and o p esen he Syn he ic Expe ience Sys em, a iadic amewo k connec ing Tou is , GenAI, and Place/Communi y
h ough da a, con en , and emo ion laye s.
Me hods: The pape ollows an in eg a i e heo y building app oach. I abduc i ely syn hesises ou ism li e a u e, in o ma ion
sys ems, ma ke ing, psychology and e hics o su ace ecu ing cons uc s, si ua es hem wi hin he se ice dominan logic and
he ac o –ne wo k heo y, and i e a i ely e ines a model h ough compa ison o GenAI applica ions ocusing on esponsible
esea ch and inno a ion.
Resul s: Analysis e eals h ee con inuous co c ea ion loops ha ci cula e agency among ac o s and ou bounda y
condi ions—au hen ici y, bias, sus ainabili y, p i acy— ha de e mine sys em iabili y. The Syn he ic Expe ience Sys em
cla i ies whe e alue eme ges, iden i ies poin s o po en ial alue co des uc ion, and yields i een esea ch p oposi ions
spanning ou is cogni ion, i m capabili ies, des ina ion policy, and plane a y ca bon limi s.
Implica ions: The amewo k p o ides a oadmap o des ina ion managemen o ganisa ions, pla o m designe s, and
egula o s o audi algo i hms, design pa icipa o y p omp s, and adop ca bon awa e deploymen . By naming ac o s, laye s,
and bounda ies, he s udy o e s a sha ed ocabula y ha can ancho empi ical in es iga ions and s imula e c oss disciplina y
ci a ions in ou ism, in o ma ion sys ems, and sus ainabili y esea ch.
Keywo ds: gene a i e a i icial in elligence, syn he ic ou ism, expe ience co-c ea ion, se ice-dominan logic, ac o –ne wo k
heo y, algo i hmic go e nance, esponsible esea ch, esponsible inno a ion
JEL Classi ica ion: L83, O33, L86, M31
Ci a ion: Ch is ou, E., Fo iadis, A., & Giannopoulos, A. (2025). Gene a i e AI as a ou ism ac o : Reconcep ualising
expe ience co-c ea ion, des ina ion go e nance and esponsible inno a ion in he syn he ic expe ience economy. Jou nal o
Tou ism, He i age & Se ices Ma ke ing, 11(2), 16-41. h p://doi.o g/10.5281/zenodo.16562068
Biog aphical no e: E angelos Ch is ou is a P o esso o Tou ism Ma ke ing a In e na ional Hellenic Uni e si y, Thessaloniki,
G eece. Anes is Fo iadis is a P o esso a Zayed Uni e si y, Abu Dhabi, UAE (Anes is.Fo i[email p o ec ed].ae). An onios
Giannopoulos is an Assis an P o esso o Se ices Ma ke ing & Managemen in Hospi ali y & Tou ism a In e na ional
Hellenic Uni e si y, Thessaloniki, G eece ([email p o ec ed]). Co esponding au ho : E angelos Ch is ou
([email p o ec ed])
1 INTRODUCTION: FROM DIGITAL TO SYNTHETIC
TOURISM
O e he pas wen y yea s, ou ism has expe ienced successi e
wa es o digi al dis up ion, each p omising iche , mo e
seamless expe iences ye o en deli e ing only inc emen al
e iciency gains. In he p e In e ne e a, a ele s elied on
pape b ochu es, elephone based a el agen s, and s a ic
guidebooks. The ise o online booking pla o ms in he ea ly
2000s cen alised in en o y and paymen s, while “sma
ou ism” ini ia i es in oduced senso -based way inding and
con ex -awa e mobile guides (Buhalis & Ama anggana, 2015;
Neuho e , Buhalis & Ladkin, 2015). By he mid 2010s, ule-
based cha bo s appea ed ac oss ho el websi es and des ina ion
GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 17
po als, au oma ing ou ine asks— oom a ailabili y checks,
wea he o ecas s, basic des ina ion FAQs— ia decision ees
and sc ip ed empla es (I ano  & Webs e  2017). Al hough
hese sys ems deli e ed 24/7 esponsi eness and educed
s a ing cos s, hey le he co e isi o expe ience un ouched:
exchanges emained ansac ional, cons ained by ixed
esponse ma ices ha could nei he lea n om in e ac ion no
spa k genuine dialogue.
Tou ism heo y long mi o ed his ins umen al s ance.
Se ice-dominan logic, which unde pins much ou ism
esea ch, posi ioned echnology as a passi e ope and esou ce
wi hin human-led alue co-c ea ion sys ems
(Va go & Lusch, 2004). Ac o –ne wo k esea ch likewise
ea ed digi al a e ac s as ine in e media ies a he han
agen s wi h hei own in en ionali y (Xiang e al., 2017).
C ea i i y, an icipa ion, and adap i e beha iou we e ese ed
o human hos s, while machines simply execu ed p ede ined
ins uc ions.
A e iew o he mos ecen li e a u e show he ex en o which
his pe spec i e s ill pe sis s. Me a-analyses ha e igo ously
ca alogued ea ly GenAI expe imen s—use -accep ance
su eys o Cha GPT (Zheng e al., 2024), s udies on
hallucina ion con ol in AI-gene a ed des ina ion con en
(Chen & Lee, 2024), and op imisa ion ac ics o AI-d i en
ecommende sys ems (Ga cía-Sánchez e al., 2025). Valuable
as hese assessmen s a e, hey emain desc ip i e and siloed.
The ield s ill lacks a cohesi e, heo y-d i en oadmap ha
simul aneously olds GenAI in o i s dominan concep ual
adi ions, ecognises echnology as a genuine co-c ea i e
ac o , and con on s he a endan e hical, egula o y, and
en i onmen al s akes.
Tha gap has widened since he ad en o ounda ion models—
massi ely p e- ained neu al ne wo ks such as GPT-3
(B own e  al., 2020) and BERT (De lin e  al., 2019). These
models, along wi h mul imodal successo s like CLIP and
DALL·E (Ramesh e  al., 2021), a e no shackled o igid
decision ees; hey gene a e o iginal ou pu s by sampling
ac oss as p obabili y spaces lea ned om ex , images, and
code. In ou ism, ounda ion models in oduce h ee
in e wined dynamics. Fi s , hey con e c ea i e au onomy,
allowing, o example, AI o wea e a a elle ’s sus ainabili y
alues and gas onomic p e e ences in o a bespoke des ina ion
i ine a y (e.g. he case o Ba celona as ound in he li e a u e)
ha eels cu a ed a he han compu ed (Sigala, 2019;
Tussyadiah & Mille , 2021). Second, hei seamless
mul i-modali y means a single p omp can e u n, o example,
an English-Indonesian my h abou a Balinese emple, o e lay
hidden ca ings in augmen ed eali y, and s eam an e oca i e
soundscape ha conju es he si e’s i ual ambience (G e zel e
al.,  2022; Neuho e  e  al., 2015). Thi d, he models enable
adap i e eal- ime pe sonalisa ion: li e wea he eeds, a ic
da a, and e en biome ic signals can igge on- he- ly i ine a y
adjus men s— e ou ing a hike o a shaded lookou when
co isol le els spike o shi ing a museum isi indoo s when
ain app oaches (Ran ala, 2023; Xiang & Fuchs, 2017).
Collec i ely, hese capabili ies inaugu a e wha we call
syn he ic ou ism, an ecosys em in which GenAI mig a es om
backs age ool o dis ibu ed, co-c ea ing ac o .
Acknowledging his mig a ion o ces a iple concep ual pi o .
Se ice-dominan logic mus e ol e o g an non-human
in elligences pa ial agency in he ‘cho eog aphy’ o
alue-c ea ion, echoing he collabo a i e e hos championed by
Va go & Lusch (2017) and o eshadowed by
P ahalad & Ramaswamy’s (2004) dialogues on co-c ea ion.
Ac o –ne wo k maps mus be ed awn o cap u e he luid
cons ella ions o ou is s, AI agen s, local communi ies, and
egula o s, acknowledging—as La ou (2005) a gued— ha
agency is a p ope y o ne wo ks a he han indi idual nodes.
Finally, Responsible Resea ch & Inno a ion (Eu opean
Commission, 2021) mus ancho he con e sa ion, b inging
algo i hmic bias, da a-go e nance isk, and he ca bon
in ensi y o model aining (Flo idi e  al., 2018) in o he same
ame as isi o deligh and des ina ion compe i i eness.
C ucially, he a gumen s ad anced he e a e no con ined o
ou ism s udies. They ake hei cues om
in o ma ion-sys ems esea ch on socio- echnical assemblages
(O likowski & Iacono, 2001), ma ke ing science on
cus ome -jou ney o ches a ion and expe ien ial alue
(Lemon & Ve hoe , 2016), psychological wo k on need
sa is ac ion and emo ion egula ion (Deci & Ryan, 2000),
esponsible-AI li e a u e on ai ness and anspa ency
(Flo idi & Cowls, 2019), and inno a ion-managemen insigh s
in o open ecosys ems and di usion (Chesb ough, 2003). By
wea ing hese s ands oge he , he discussion posi ions
gene a i e ou ism as a node in a much wide scien i ic
con e sa ion—one ha in i es ci a ion and deba e ac oss
mul iple domains.
Agains his backd op, he es o he pape pu sues i e
in e locking objec i es. I i s e isi s he expe ience
economy, se ice-dominan logic, ac o –ne wo k heo y, and
Responsible Resea ch and Inno a ion (RRI) o build a
concep ual sca old obus enough o syn he ic ou ism. I
hen su eys he apidly g owing body o GenAI applica ions
ac oss p e- ip planning, on-si e augmen a ion, and back-o ice
op imisa ion, dis illing pa e ns and exposing blind spo s.
Building on hese insigh s, i in oduces he Syn he ic
Expe ience Sys em, a iadic model ha h eads Tou is ,
GenAI, and Place/Communi y h ough da a, con en , and
emo ion laye s while lagging bounda y condi ions such as
au hen ici y, bias, ene gy use, and in ellec ual-p ope y igh s.
A o wa d-looking esea ch agenda ollows, posing i een
high-impac ques ions ha ange om mic o-le el issues o
a elle wellbeing h ough meso-le el i m capabili ies and
wo k o ce u u es o mac o-le el go e nance and he plane a y
sus ainabili y o AI in as uc u e. Finally, he pape ansla es
heo y in o ac ion, ou lining AI-li e acy oolki s o
des ina ion-managemen o ganisa ions, isk-audi p o ocols
o pla o m ope a o s, pa icipa o y-design guidelines o
communi ies, and egula o y oadmaps consonan wi h he
EU AI Ac (Eu opean Commission, 2021) and UNWTO
AI-E hics Guidance (UNWTO, n.d.).
By acing he a c om s a ic au oma ion o dynamic
co-c ea ion—and by using ou ism li e a u e wi h insigh s
om IS, ma ke ing, psychology, e hics, and inno a ion
s udies— his s udy o e s bo h an analy ic lens and a p ac ical
oadmap o schola s and p ac i ione s na iga ing he new e a
o Gen AI in ou ism.
2 CONCEPTUAL FOUNDATIONS
The a gumen ha GenAΙ is becoming ou ism ac o es s on
i e s ands o esea ch ha ha e shaped, and con inue o
eshape, ou ism hough : he expe ience economy, alue co
c ea ion, ac o –ne wo k heo y (ANT), algo i hmic
go e nance, and Responsible Resea ch & Inno a ion (RRI).
Ra he han o e ing i e pa allel summa ies, his sec ion
shows how each adi ion mo es he deba e o wa d, whe e i s
18 E angelos Ch is ou, Anes is Fo iadis and An onios Giannopoulos
explana o y powe now s ains unde GenAI’s weigh , and
wha concep ual ex ensions—o en d awn om adjacen
disciplines—a e needed o a nex gene a ion esea ch agenda.
2.1. F om s aged expe iences o p og ammable “syn he ic
momen s”
The expe ience economy hesis o Pine and Gilmo e (1999)
eposi ioned ou ism as a hea e in which economic alue
s ems om cho eog aphed memo ies a he han om he
exchange o physical goods. Subsequen li e a u e deepened
he me apho : Ca ù and Co a (2003) unpacked mul isenso y
imme sion; Neuho e , Buhalis and Ladkin (2014) aced he
usion o on si e and digi al ouch poin s; Punpeng and
Yodnane (2024) highligh ed how gues s s ep “on s age” as co
p oduce s; Coudouna is e al. (2025) in es iga ed he in luence
o he ‘Big Fi e’ pe sonali y ai s on memo able ou ism
expe iences. Ye he s age emained squa ely human
di ec ed— he guide, designe o hos was p esumed o hold he
sc ip , while echnology me ely ex ended he scene y.
GenAI des abilises ha hie a chy; o ins ance, when a isi o
co-au ho s an o igin my h o a Balinese emple wi h a la ge
language model ine uned on Ja anese epics, he d ama u gy
is no longe lesh and blood bu a p obabilis ic a chi ec u e
whose “c ea i e well” is a illion oken co pus. Au hen ici y
wo k—long analysed as he hos –gues dialec ic o exis en ial
meaning making (Wang, 1999)—is ans o med in o a iadic
nego ia ion among hos , gues and algo i hmic weigh s. The
s age becomes a cloud endpoin , and he “p ops” a e eal- ime
da a eeds.
This shi om s aging singula e en s o p og amming
syn he ic momen s has a leas ou concep ual implica ions.
Fi s , empo ali y comp esses. Classic expe ience design
assumed phases o an icipa ion, on si e enac men and
ecollec ion (Tung & Ri chie, 2011); ounda ion models
collapse hose phases by i e a ing na a i es in milliseconds.
Second, au ho ship blu s. In e ac ion design esea ch shows
ha use s end o o e a ibu e agency o con e sa ional agen s
(Nass & Moon 2000), complica ing long s anding ou ism
deba es abou au ho ship, in e p e a ion and powe . Thi d,
alue me ics di e si y. Whe eas sa is ac ion and
memo abili y domina ed he expe ience economy oolki ,
Human-Compu e In e ac ion (HCI) esea ch now calls o
acking emo ional g anula i y, a ousal cu es and e en
eudaimonic ou comes such as meaning and sel -g ow h
(Hassenzahl, 2010; McCa hy & W igh , 2004). Fou h, he
poli ics o cu a ion in ensi y. Algo i hm s udies li e a u e
wa ns ha gene a i e models may ep oduce hegemonic
na a i es o hallucina e cul u ally insensi i e con en
(Bende  e  al. 2021), aising new s akes o des ina ions
seeking o sa egua d in angible he i age.
C i ical ou ism esea che s ha e begun o sense hese emo s.
Gu en ag (2022) a gues ha VR and AI a e mo ing
expe iences om place based o code based, decoupling alue
om physical p oximi y. Ga alas e al. (2024) and Halde e
al. (2024) show how algo i hmic i ine a y builde s al eady
shape expec a ions be o e a elle s a i e. Ye we s ill lack a
amewo k cla i ying how human in en ionali y and machine
c ea i i y in e sec , and who is accoun able when an au o-
gene a ed s o yline mis ep esen s li ing cul u e.
A way o wa d lies in b idging ou ism wi h design o ien ed
disciplines. In e ac ion design schola s p opose “co expe ience
p o o yping” in which humans and AI i e a i ely adjus each
o he ’s ou pu s in si u (Rezwana & Fo d, 2025). HCI wo k on
explainable AI (Abdul e  al., 2018) o e s me hods o
exposing a model’s na a i e pa hways, le ing guides audi o
o e ide p oblema ic a cs. Meanwhile, ma ke ing science is
expe imen ing wi h “algo i hmic d ama u gy”—dynamic
s o y elling ha adap s o biome ic eedback (P i i e a e al.,
2025). Impo ing hese insigh s would le ou ism mo e om
pos -hoc e alua ion o on s age design o syn he ic momen s.
Fu u e esea ch, hen, should ea expe ience design as an
un olding con e sa ion a he han a p e-w i en sc ip .
Longi udinal ield expe imen s in li ing labs—whe e ou is s,
guides and GenAI agen s co-cons uc s o ylines unde
con olled bu na u alis ic condi ions—could e eal how
agency, au ho ship and accoun abili y shi o e ime (Wu e
al., 2019). Agen -based simula ions, g ounded in empi ical
in e ac ion da a, can po en ially assess he impac o sub le
changes in p omp s on a ange o measu es including
sa is ac ion, lea ning, and cul u al in luence (Dwi edi e al.,
2021). Finally, quali a i e s udies mus c i ically analyse
whose oices a e ampli ied and whose a e supp essed when
models syn hesise local knowledge a scale.
Essen ially, Pine and Gilmo e's ‘ hea e’ is s ill in p og ess;
howe e , i s backs age has been augmen ed wi h gene a i e
echnologies ha gene a e new dialogue du ing he play.
Tou ism schola s now ace he dual challenge o heo ising his
p og ammable d ama u gy and o equipping p ac i ione s wi h
design p inciples ha ha ness machine c ea i i y wi hou
o ei ing cul u al in eg i y.
2.2. Value co-c ea ion and i s da ke win
Se ice-dominan logic (S-D Logic) eshaped how ou ism
schola s pe cei e alue— om a s a ic en i y exchanged a
ma ke poin s o a dynamic phenomenon ealised du ing
consump ion, whe e ou is s ac i ely combine hei skills,
knowledge, and si ua ional con ex s o c ea e memo able
expe iences (Va go & Lusch, 2008, 2016). This pa adigm has
no ably illumina ed di e se ou ism p ac ices, including
c owdsou ced ou e planning, pee - o-pee accommoda ion,
and pa icipa o y cul u al in e p e a ions (Buhalis & Foe s e,
2015; P ebensen, 2014; Shaw e al., 2011).
Howe e , he ise o GenAI, speci ically h ough sophis ica ed
ounda ion models, challenges and ex ends he heo e ical
ounda ions o S-D Logic beyond i s o iginal human-cen ic
assump ions. These ad anced models eme ge as ac i e ope an
esou ces a he han passi e ools (Maglio & Spoh e , 2013),
capable o au onomously in eg a ing di e se da a s eams—
such as eal- ime a ic condi ions, use p e e ences, and
cul u al insigh s— o dynamically co-c ea e highly
pe sonalised ou ism expe iences (B eidbach & B odie, 2017).
Fo ins ance, a la ge language model c a ing a bespoke
gas onomic ou in Lisbon is e ec i ely pe o ming esou ce
in eg a ion independen ly, unc ioning as a p oac i e agen
a he han a eac i e in e ace. Despi e he impo ance
highligh ed by B eidbach and Maglio (2016), ou ism esea ch
has only jus begun o empi ically in es iga e he
ans o ma i e po en ial o AI as an ac i e co-c ea o wi hin S-
D Logic amewo ks, ep esen ing a c i ical esea ch gap in he
cu en academic esea ch.
Concu en wi h his is a g owing need o examine u he he
lesse -s udied bu highly pe inen opposi e o se ice-
dominan logic— alue co-des uc ion. Inconsis encies
be ween s akeholde goals can s ongly inhibi p ocesses o
alue gene a ion (Jä i e al., 2018). Fo GenAI, sou ces o
goal inconsis encies can include en enched algo i hms
con aining ha m ul s e eo ypes o disc imina o y conduc ,
a o ing ce ain indi iduals o popula ions (Bolukbasi e al.,
2016; Caliskan e al., 2017), as well as subs an ial
en i onmen al consequences ela ed o powe -hung y model
GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 19
aining a a iance wi h sus ainabili y goals o many
des ina ions (Luccioni e al., 2023; S ubell e al., 2019).
Addi ionally, Paluch and Wünde lich (2016) highligh how
echnology-enabled se ice ailu es ueled by social media can
gene a e widesp ead dissa is ac ion and lead o subs an ial
epu a ion and ope a ional damage.
In ackling he in icacies o alue co-c ea ion and co-
des uc ion, ans o ma i e se ice esea ch calls o inclusi e
and in eg a i e me ics o well-being ha conside all-a ound
indi idual, socie al, and ecological e ec s (Ande son e al.,
2011). Empi ical esea ch using longi udinal "li ing labs,"
including he Helsinki Sma Tou ism Lab (Ci y o Helsinki,
n.d.), o e s a p agma ic esea ch amewo k o enable eal-
ime examina ion o dynamic in e dependencies be ween
ou is s, esiden s, and GenAI echnology. Embedding
inno a i e agen -based simula ions in o he empi ical esea ch
design allows o he c ea ion o sophis ica ed modeling ools
o iden i y key sys emic ipping poin s, whe e algo i hmic bias
o sus ainabili y comp omises b eak equilib ium and cause
alue deg ada ion, h ea ening he esilience o he en i e
ou ism sys em (Gajdošík, 2022; Jo zik e al., 2024).
In conclusion, in eg a ion o GenAI in o Sus ainable
De elopmen al Lea ning equi es mo e han supe icial
adjus men s bu a comp ehensi e and in insic concep ual shi
ha akes explici no e o algo i hmic agency, e hical
implica ions, inclusi i y, and plane a y bounda ies (Yang &
Lee, 2024). These issues mus be add essed o ou ism o
p omo e genuinely ans o ma i e and equi able expe iences
and no pe pe ua e exploi a i e endencies.
2.3. Ac o –ne wo k heo y: Recon igu ing he
socioma e ial assemblage
I is a gued by ac o –ne wo k heo y (ANT) ha agency a ises
om ela ional in e ac ion among a ied en i ies, including
bo h non-human and human ac o s, which cons an ly
econ igu e social eali ies (La ou , 2005). In he ou ism
li e a u e, ANT has been used ex ensi ely o show how
ou ism expe iences and go e nance a e cons uc ed oge he
h ough ma e ial-discu si e ne wo ks. Fo example, F anklin
(2004) illus a ed how Maasai guides, sa a i ehicles, and
pho og aphic equipmen cons uc Kenya’s amous “looked-a
landscape,” unde mining simplis ic opposi ion be ween
passi e en i onmen s and ac i e obse e s. Simila ly, Van de
Duim (2007) con incingly a gued ha seemingly un oman ic
sp eadshee calcula ions and no cha isma ic alk o g ea
speeches ac ually suppo he p ac ice o Du ch des ina ion
managemen . Such de ailed analyses howe e la gely
ep esen echnologies as s able in e media ies—en i ies ha
enable ela ionships a he han being ac i e agen s cons an ly
emapping hem (Ren e al., 2012).
On he con a y, Gene a i e A i icial In elligence (GenAI)
se e ely unde mines such s abili y. GPT-4 and simila
ounda ion models a e luid "obliga o y passage poin s" wi h
pa ame e se ings con inuously being in ansi ion, eac ing o
new p omp s, so wa e upda es, and he i e a i e p ocesses
in ol ed wi h ein o cemen lea ning (Schneide e al., 2024).
GenAI is no s a ic like i s en i ies bu dynamically e ol es and
changes eal- ime, hus con inuously econ igu ing ou ism
p ac ice (Huang e al., 2025). Fo ins ance, a seemingly
negligible adjus men in a de elope 's command o a p omp
in San F ancisco can quickly sp ead h ough cloud-based app
p og amming in e aces and a ec ou is s' in e ac ions wi h
a ied cul u es in Bali, and quickly al e local adi ions and
in e p e a ions.
The speed wi h which such ans o ma ions occu equi es new
app oaches (Thees e al., 2021). Digi al mul i-si ed
e hnog aphy o e s me hodological app oaches h ough which
esea che s can ack changes be ween algo i hms and s eams
o da a, acing in e ac ions ha s e ch om code hubs loca ed
in No h Ame ica, h ough se e s loca ed in Eas Asia, and
down o angible e ec s el by sma phone use s loca ed in
Indonesia (Pink e al., 2022). T ace e hnog aphy o e s ano he
pe spec i e h ough c i ical examina ion o log iles and online
aces and how mino changes wi hin algo i hms—such as
changes o neu al ne wo k weigh s—can ha e p o ound e ec s
on anking and isibili y o cul u ally p ominen si es (Geige ,
2017). Walk h ough me hods also shed ligh on how in e ace
con igu a ions guide people h ough pa icula p omp s o
s o ies and he eby e eal unde lying dynamics o cul u al
o ien a ion and go e nance (Ligh e al., 2018).
I is c i ical ha he e is an ho ough elabo a ion o cu en
li e a u e on aining da a aimed a demys i ying how such
sou ces in o m he pe pe ua ion o impac ul discou ses. Such
aining se s as LAION-5B and Common C awl ine i ably
ep esen Eu ocen ism and sexism (Bi hane & P abhu, 2021)
ha can p ese e inequali ies upon hei deploymen wi hin
ou ism ecommenda ion pla o ms. I is also a gued by
C aw o d and Paglen (2021) ha such ep esen a ion poli ics
a e embedded wi hin he ou pu s o algo i hms and hus
pe pe ua e ongoing hie a chies. A he same ime, Ki chin
(2017) inds ha such da a in as uc u es hemsel es expose
geog aphical inequali ies ha lead o la ency di e en ials and
en i onmen al ex e nali ies a ec ing non-Wes e n digi al
landscapes.
Fu u e ANT-based in es iga ions ough o ca e ully ace ou
he shi ing socio echnical a angemen s o GenAI, which
include no jus con en ional ac o s like ou is s and ou
guides bu also allegedly mino ac o s such as Gi Hub issues
and e sion upda es (And es e al., 2024; Li & Zhu, 2024).
Such ich mappings cla i y how meanings, alues, and powe
dynamics wi hin he ou ism indus y cons an ly c ys allise and
disin eg a e wi hin an inc easingly syn he ic eali y.
2.4. Algo i hmic go e nance: F om pla o ms o
ounda ion models
Ea ly explo a ions o algo i hmic go e nance ha e desc ibed
online pla o ms as au ho i a i e ac o s ha go e n ma ke
p ocesses and se up p i a e egula o y models h ough
complex so wa e me hods (Gillespie, 2014; Yeung, 2018).
S udies on ou ism ha e iden i ied pla o ms such as
T ipAd iso and Ai bnb as pe o ming sub le and conside able
o ms o go e nance. Fo ins ance, Sma P icing on Ai bnb
sub ly mo i a es hos s owa d adop ing no ma i e beha io s
ha maximise pla o ms' e enues and shi hei beha io s in
p ac ically unappa en ways (Bouchon & Rausche , 2019).
Simila ly, T ipAd iso ankings om use -gene a ed a ings
compu ed h ough non- anspa en algo i hms cons an ly
a ec ou ism mobili y and guide local economic p ocesses
(G e zel, 2011; Sco & O likowski, 2021). These ini ial
unde s andings based on de e minis ic iews on algo i hms
ha e in e ed ixed egula o y models and p e-es ablished
ou comes, bu hese a e hidden h ough a lack o anspa ency
(Singh & Sibi, 2023). Bu he ad en o ounda ion models
unde mines he ea lie p emise o de e minis ic s abili y.
Unlike adi ional pla o m algo i hms, ounda ion models like
GPT-4 gene a e ou pu s p obabilis ically and demons a e
dynamic esponsi eness o luc ua ions in da a inpu s, nuanced
changes in p omp s, and egula upda es (Bende e al., 2021;
Bommasani e al., 2021). Go e nance hus shi s om clea ly

20 E angelos Ch is ou, Anes is Fo iadis and An onios Giannopoulos
de ined pla o m in e aces o complex "cloud s acks" ha
include da a cu a ion p ac ices, holis ic aining
me hodologies, ine- uning p ocesses, and ad anced p omp
enginee ing cul u es (Amoo e, 2019). Algo i hmic opaci y
le els in his se up a e unp eceden ed: e en wi hin eams
esponsible o de eloping he models, i becomes ha de o
link speci ic gene a ed ou pu s o speci ic ules o inpu s
(Bu ell, 2016; Pasquale, 2015).
This shi gene a es h ee signi ican implica ions. Fi s , le els
o opaci y and ambigui y ise, calling o igo ous
documen a ion p ac ices like "model ca ds" (Mi chell e al.,
2019) and "da ashee s o da ase s" (Geb u e al., 2021).
Al hough hey may be bene icial, s akeholde s in he ou ism
indus y a ely gi e hese anspa ency ools p io i y, lea ing
signi ican knowledge gaps un esol ed. Second,
accoun abili y is b oadly dispe sed ac oss a ange o
s akeholde s—including da a p o ide s, algo i hm de elope s,
pla o m in e media ies, des ina ion manage s, and ou is s
hemsel es— hus closely esona ing wi h Ananny and
C aw o d's (2018) seminal amewo k o dis ibu ed mo al
esponsibili y. Thi d, geopoli ical asymme ies a e
exace ba ed, as a i icial in elligence models ained on
p edominan ly Anglo-Ame ican da ase s ep oduce and
ein o ce biased Global No h pe spec i es, exace ba ing "da a
colonialism" conce ns and en enching exploi a i e ou ism
dynamics in he Global Sou h (Could y & Mejias, 2019; Milan
& T e é, 2019).
To deal wi h such complexi ies on a comp ehensi e le el,
ou ism esea ch mus p io i ise a de ailed explo a ion o
model s acks and no jus pla o ms and use in e aces.
Ad anced explainable AI me hods—such as SHAP alue
assessmen (Lundbe g & Lee, 2017) and coun e ac ual
ai ness es ing (Kusne e al., 2017)—can empi ically iden i y
which ac o s eliably a ec algo i hmic-gene a ed i ine a ies
and con en . Fu he mo e, deploymen o igo ous algo i hmic
audi s—a g owing end among AI e hics esea ch (Raji e al.,
2020)—can sys ema ically assess AI-gene a ed ou ism
con en on he basis o se in e na ional no ms such as
UNESCO he i age guidelines o en i onmen al sus ainabili y.
Finally, schola ly esea ch on egula ion mus ca e ully
examine how he de eloping ansna ional go e nance
en i onmen —speci ically isk-based equi emen s se ou by
he EU AI Ac —mee s wi h o con adic s in e na ional digi al
ade libe alisa ion guidelines managed by he Wo ld T ade
O ganisa ion (WTO) and hus impac s on he p ospec i e
go e nance egime o syn he ic ou ism.
2.5. E hics in esponsible esea ch and inno a ion: A global
pe spec i e
Responsible Resea ch and Inno a ion (RRI) i s eme ged in
ou ism esea ch o add ess majo con o e sies su ounding
o e ou ism, communi y accep ance, and social license o
ope a e in he indus y (Be selli e al.. 2022). Howe e , he
apid de elopmen o GenAI signi ican ly expands he e hical
scope, aising key mo al challenges ac oss cul u al,
en i onmen al, and sys emic dimensions.
Fi s and o emos , GenAI poses se ious issues conce ning
epis emic jus ice. The in insic disc imina o y p ejudice ound
wi h con en ional ecommende algo i hms is u he
augmen ed o an unp eceden ed ex en by he gene a i e na u e
o GenAI. La ge language models, o ins ance, ha e
demons a ed a p opensi y owa d gene a ion o cul u ally
insensi i e o o ensi e con en known as "hallucina ions,"
which a e di ec ly and un ep esen a i ely gene a ed om
biased o poo ly ep esen ed aining ma e ials (Bende e al.,
2021; Bi hane & P abhu, 2021). These inciden s isk
pe pe ua ing epis emic ha m h ough sp eading biased and
la gely ha m ul depic ions o ma ginalised cul u es, he eby
e lec ing he wide social p ejudices embedded wi hin
algo i hmic p og amming (Hagendo , 2020; Noble, 2018).
Second, he en i onmen al ex e nali ies o GenAI p esen
signi ican challenges o sus ainable ou ism. The aining o
complex ans o me models, including di e en e sions o
GPT, can lead o ca bon dioxide emissions equi alen o he
amoun emi ed by mul iple ansa lan ic ligh s (S ubell e al.,
2019). Recen comp ehensi e li ecycle assessmen s also epo
ha ongoing in e ence— he con inual use and ope a ional
deploymen o hese models ac oss di e se applica ions—
cons i u es an e en la ge con ibu o o en i onmen al
deg ada ion han he aining p ocess i sel (Luccioni e al.,
2023; Pa e son e al., 2021). Gi en he al eady sizeable ca bon
oo p in o ou ism, he un egula ed use o GenAI can h ea en
he clima e pledges o many des ina ions, hus c ea ing
ensions wi h se in e na ional sus ainabili y no ms (Gössling
& Higham, 2020).
The double use po en ial o GenAI equi es s ingen e hical
o e sigh . While i has he po en ial o pe pe ua e ha m ul
s e eo ypes, such gene a i e echnologies also show g ea
po en ial o pushing o wa d on egene a i e ou ism's
goals—designing ou p og ams aimed a minimizing
en i onmen al impac s, p omo ing o -season ou ism, and
enabling local ecosys em and si e es o a ion and e i al
(Gallego & Fon , 2020; Higgins-Desbiolles, 2018).
Con en ional Responsible Resea ch and Inno a ion (RRI)
amewo ks—an icipa e, e lec , engage, ac —p o ide
essen ial s a ing poin s o add essing hese challenges, ye
hey equi e speci ic adjus men s ha a e exp essly ailo ed o
he ou ism indus y (S ilgoe e al., 2013). Audi s o
algo i hmic bias, o ins ance, mus go beyond simple
demog aphic pa i y o closely examine he cul u al au hen ici y
o AI-gene a ed con en agains es ablished in e na ional
he i age no ms, as ou lined by UNESCO’s con en ions on
in angible he i age, o ins ance (UNESCO, 2024). Fede a ed
lea ning echniques may also acili a e e hical p ac ices
h ough enabling ou ism des ina ions o in e nally de elop
and e ine localised language models, hus sa egua ding
sensi i e communi y knowledge and ad ancing Indigenous
da a so e eign y (Bosca ino e al., 2022; Kai ouz e al., 2021;
Kuku ai & Taylo , 2016). Las ly, while li e-cycle ca bon
accoun ing has been ad oca ed o in compu e science
li e a u e (Schwa z e al., 2020), i seems c i ical ha his
p ac ice becomes an in eg al aspec o p ocu emen policies
wi hin des ina ion managemen o ganisa ions.
Success ul execu ion o hese amewo ks equi es p oac i e
and in e disciplina y coo dina ion. Compu e science-de i ed
ools, including exhaus i e audi s o explainabili y and bias-
mi iga ing s a egies (Raji e al., 2020), can complemen
insigh s om en i onmen al psychology on he eco- eedback
mechanisms in luencing beha io al changes among ou is s
(Schmuck & Vlek, 2003). Legal expe ise will also be needed
o maneu e h ough complex egula o y s uc u es, including
he isk- ie ed manda es de eloped by he Eu opean Union’s
AI Ac , ha in e sec wi h digi al se ices egula ions upheld
by he Wo ld T ade O ganisa ion (Veale e al., 2021). Tou ism
li e a u e will also be impo an , as i en ails es ing mul iple
in e en ions in a ied eal-wo ld se ings and assessing hei
impac in ad ancing dis ibu i e equi y, sa egua ding cul u al
iden i ies and hei in eg i y, and enhancing plane a y
sus ainabili y. I is h ough such comp ehensi e and
GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 21
collabo a i e app oaches ha GenAI can indeed be an e hical
enable and no a ha m ul o ce in pu suing sus ainable ou ism
u u es.
2.6. Syn hesis: Taking s ock and looking o wa d
The con empo a y concep ual hemes ha a e being
examined—s aged expe iences, alue (co-)dynamics, ac o -
ne wo k assemblages, go e nance by algo i hms, and
Responsible Resea ch and Inno a ion (RRI)—o e a ich
in ellec ual agenda ha can build upon and ad ance syn he ic
ou ism esea ch. Each o e s a dis inc i e analy ical lexicon
s essing dis inc i e ye complemen a y p ocesses:
pe o mance (Pine & Gilmo e, 1999), esou ce in eg a ion
(Va go & Lusch, 2008), ne wo k building (La ou , 2005), ule
building (Gillespie, 2014), and e hical esponsibilisa ion
(S ilgoe e al., 2013). Howe e , hese heo e ical models a e
p esen ly ecei ing challenging empi ical e idence om he
de eloping capabili ies o GenAI. O likowski’s (2007) ea ly
suppo o socioma e ial analysis has become inc easingly
pe inen : GenAI inc easingly en angles code, capi al, cul u e,
and cogni ion such ha any dis inc ions ecede in o a bi a y
and poli ically eigh ed analy ical ou comes (Ki chin, 2014;
Leona di, 2011). Hence, esea che s mus de elop— a he
han disca d— hei cu en heo e ical models.
A majo de elopmen o hese heo e ical amewo ks equi es
a e ision o expe iences as pe o mances ha a e no only
conduc ed by human planne s bu also by p obabilis ic,
algo i hmically-con olled agen s. This equi es econside ing
alue as being bo h co-c ea ed, co-e oded, and co-managed by
lea ning models (Paluch & Wünde lich, 2021). Addi ionally, i
demands eimagining ne wo ks as dynamic, eac i e
con igu a ions cons an ly e o med by eal- ime API
in e ac ions (Ki chin, 2017). Addi ionally, he p ocess
in ol es an explo a ion o algo i hmic go e nance h ough he
lens o s ochas ic, con ex -dependen models whose ou pu s
challenge con en ional de e minis ic go e nance s a egies
(Bu ell, 2016). This s udy also widens e hical conside a ion
om only localised ou ism impac s o include he global da a
ecologies and sys emic inequali ies o digi al in as uc u es
(C aw o d, 2021).
Na iga ing hese complex dimensions calls o inno a i e
me hods h ough he combina ion o mixed-me hod esea ch
oolki s. AI-ex ended e hnog aphy ha combines s anda d
pa icipan obse a ion wi h eal- ime algo i hmic logging
o e s ich insigh s abou how ou is s and GenAI join ly
gene a e meaning (Pink e al., 2022). T ace e hnog aphy
(Geige , 2017) when combined wi h big-da a e hnog aphic
app oaches (Va is & Hou, 2020) can ace sub le algo i hmic
adjus men s om de elopmen en i onmen s h ough global
da a ne wo ks and highligh how small changes can ha e la ge
consequences wi hin local ou ism en i onmen s. Agen -based
simula ion, a s anda d me hodological s a egy wi hin he
social sciences (Eps ein, 1999; Gilbe & T oi zsch, 2005), can
d aw upon empi ical p omp - esponse da a in simula ing
c i ical poin s a which pe sonalised pleasu e suddenly shi s
o con usion o dissonance. In addi ion, mixed-me hod
explainabili y audi s combining algo i hmic in e p e abili y
me hods like SHAP o LIME (Ribei o e al., 2016) wi h
phenomenological esea ch (Weick, 1995) ha e he po en ial
o deli e deepe insigh s no jus on wha GenAI sugges s bu
on how a ele s p ocess hese sugges ions cogni i ely and
a ec i ely.
In eali y, answe ing hese ques ions equi es a join e o
combining se e al disciplines. In o ma ion sys ems p o ide
es ed and p o en models ela ed o socio- echnical
go e nance (Leona di & Ba ley, 2008); ma ke ing p o ides
deep insigh s in o cus ome jou ney complexi y (Lemon &
Ve hoe , 2016; Machado e al., 2025); psychology-based
amewo ks p o ide u he in e p e a ions h ough emo ion
egula ion heo ies (G oss, 2015); inno a ion esea ch o e s
ich knowledge on echnology adop ion and pa icipa ion
wi hin ecosys ems (Chesb ough, 2003); and compu e science
e hics p o ides de ailed me hods o e alua ing algo i hmic
bias (Jobin e al., 2019). Tou ism schola s who d aw upon
hese mul i-discipline insigh s place hei wo k on pa wi h
wha Geo ge e al. (2016) de ine as “g and challenge”
esea ch—an inqui y combining academic igo and socie al
ele ance and ans o ma i e e ec .
In summa y, by sys ema ically expanding i s heo e ical
wo ld iew and adop ing a plu alis ic s ance, ou ism esea ch
can ac as a ib an labo a o y o expe imen s seeking o
answe ou mos bu ning echnological ques ions: Who
ac ually b ings ou ism expe iences o li e in an age
dis inguished by gene a i e in elligence? How a e cul u al
alues con inuously nego ia ed and con es ed h ough
algo i hms? And c ucially, how can global sus ainabili y be
embedded wi hin he e y digi al in as uc u es now
o ches a ing global ou is mobili y?
3 STATE OF THE ART MAPPING (2018 – 2025)
Al hough esea ch on gene a i e o la ge-scale AI emains a a
nascen s age compa ed o adi ional ecommende sys ems
esea ch, ecen de elopmen s clea ly demons a e an
eme ging h ee-s eam a chi ec u e e lec ing GenAI's
inc easingly ac i e ole in ou ism (illus a ed in Figu e 1). The
i s s eam ocuses on p e- ip decision-making, documen ing
he e olu ion om simple sc ip ed FAQ bo s owa ds
sophis ica ed, la ge language model-d i en i ine a y "co-
au ho s." These AI-d i en agen s ac i ely engage wi h
a ele s, shaping ip decisions h ough collabo a i e,
con e sa ional in e ac ions. A second s eam emphasises
GenAI's eal- ime, in si u augmen a ion capabili ies,
highligh ing mul imodal models ha powe digi al concie ges,
ins an aneous ansla ion se ices, and emo ionally a uned
augmen ed eali y expe iences. He e, GenAI di ec ly impac s
ou is s' expe iences by in luencing hei pe cep ions and
engagemen le els in an ac i e way. The hi d s eam, which is
impe cep ible o ou is s, examines enhancemen s o back-
o ice p ocesses, whe e gene a i e echnologies like GPT-
powe ed demand o ecas ing and ein o cemen lea ning-
based s a scheduling ac ually imp o e ou ism se ices in a
s eal hy way.
This c i ical examina ion goes beyond a cu so y ch onicle,
poin ing ou pa icula p esump ions in he ex an li e a u e.
Empi ical esea ch on a egula basis a ge s pilo p ojec s
based in da a- ich, English-speaking u ban a eas, hus
o e looking ele an u al and Global Sou h se ings.
Mo eo e , s udy designs cu en ly emphasise posi i is
measu es o pe o mance, which ul ima ely deg ades he
p ominence o e hnog aphic and longi udinal app oaches ha
can shed ligh on c i ical issues su ounding powe dynamics,
au hen ici y and cul u e, and en i onmen al ou comes.
Concep ually, much esea ch s ill ep esen s GenAI as a
passi e ool, wi h li le conside a ion o deepe no ions o AI
agency, possible co-des uc i e ou comes, and o e a ching
plane a y e hical ques ions. These la gely neglec ed
conside a ions highligh he impo ance o ecognizing GenAI
22 E angelos Ch is ou, Anes is Fo iadis and An onios Giannopoulos
as an ac i e agen o ou ism—a posi ion ha is co e o he
ollowing concep ual deba e.
Figu e 1. Eme ging h ee-s eam a chi ec u e e lec ing
GenAI's inc easingly ac i e ole in ou ism
3.1. Consume decision making: F om sc ip ed cha bo s o
co c ea i e i ine a y engines
Ea ly empi ical s udies on AI in ou ism decision-making o en
elega ed con e sa ional in e aces o he ole o glo i ied FAQ
eposi o ies. Pillai and Si a hanu (2020) demons a ed ha
a ele accep ance o ho el cha bo s was p ima ily mo i a ed
by pe cei ed use ulness and ease o use, echoing Da is’s
(1989) Technology Accep ance Model and subsequen
e inemen s such as UTAUT (Venka esh e al., 2003). These
ini ial cha bo s, limi ed by igid decision ees, e ec i ely
con eyed in o ma ion bu ailed o ac i ely shape ou is
p e e ences o os e deepe engagemen (I ano & Webs e ,
2019a).
A pa adigm shi eme ged as na u al language p ocessing
(NLP) sys ems powe ed by deep lea ning gained ac ion. Lu,
Cai and Gu soy (2019) expanded he accep ance amewo k by
alida ing a "se ice obo in eg a ion willingness" scale
among Ame ican a ele s, e ealing us and hedonic
enjoymen —no me ely u ili y—as c i ical de e minan s o AI
accep ance. This shi owa d mo e emo ionally and
expe ien ially ancho ed accep ance c i e ia aligns wi h
indings by I ano and Webs e (2017) wi hin Eu opean
hospi ali y con ex s, indica ing a ansi ion om u ili a ian
in e ac ions o iche , expe ien ial co-design possibili ies.
The a ailabili y o publicly accessible ounda ion models has
g ea ly accele a ed his de elopmen . I asciuc, Cand ea, and
Ispas (2025) oge he wi h Ghesh, Alexande , and Da is
(2024) compa ed ou i ine a ies gene a ed by GPT wi h hose
composed manually. Thei s udy e eals a gene al endency
owa ds AI ecommenda ions despi e ins ances o ac ual
e o s and logis ical e o s, he la e co esponding o wide
c i iques exp essed by Bende e al. (2021) ha iew la ge
language models as "s ochas ic pa o s" ha a o plausible
easoning o e ac uali y and, by ex ension, highligh an
inhe en ension be ween c ea i i y and u h ulness in con en
gene a ed a i icially.
Adding o hese in icacies, Flo ido-Bení ez and del Alcáza
Ma ínez (2024) used eye- acking me hods o show ha
ecommenda ions made by gene a i e language models
(LLMs) signi ican ly in luence use s' u u e online sea ch
beha io , hus sub ly bu conclusi ely limi ing he ange o
op ions conside ed. Such algo i hmic guidance inds echoes in
he beha io al "nudge" heo y o Thale and Suns ein (2008),
enabled by agen s ha unc ion in a non- anspa en and
undisclosed en i onmen .
Despi e hese de elopmen s, cu en li e a u e is gene ally
ocused on indi idual use engagemen wi h limi ed
examina ion o b oade ma ke impac . Fouad, Salem, and
Fa hy (2024) a e a no able excep ion, u ilizing simula ion
s udies o illus a e how GPT-based pla o ms signi ican ly
inc ease isibili y o u ban des ina ions wi h ich da a. A he
same ime, equi y-o ien ed schola s like Benjamin (2019) and
Noble (2018) cau ion ha da ase s may esul in acialised o
neo-colonial bias in AI-gene a ed na a i es in he ou ism
con ex ; ye , ull audi s ailo ed o he ou ism sec o a e
ex emely uncommon.
The e o e, u u e s udies need o go beyond single beha io al
expe imen s o include es s a he ma ke le el as well as in-
dep h in es iga ions o aining da ase s (Yu & Meng, 2025).
This will enable de ailed analysis o asce ain i gene a i e
i ine a y sys ems ac ually enhance he democ a isa ion o
ou ism o only pe pe ua e p e ailing s uc u al inequali ies in
he name o pe sonalisa ion.
3.2. On-si e augmen a ion: Digi al ep esen a ions in eal
ime, linguis ic ansla ion and emo ional ecogni ion
In he ield o a el-planning apps, li le esea ch has ocused
on how he applica ion o GenAI (GenAI) a ec s he ac ual
expe ience o a eling; howe e , he echnologies do ha e
g ea ans o ma i e po en ial (Zhu e al., 2024). On-si e
augmen a ion, encompassing digi al a a a s, eal- ime
language ansla ion, and a ec i e sensing, adically ede ines
a ele s' in e ac ions wi h des ina ions, deepening imme sion
and ecalib a ing cul u al encoun e s (Liu and & Hao, 2024).
The debu o "digi al humans"—pho o ealis ic, oice-
esponsi e a a a s—du ing he 2018 PyeongChang Win e
Olympics o e ed a glimpse in o his new expe ien ial
landscape (Sylaiou & Fidas, 2022). Ko ea’s MBC’s sis e
channel MBN AI launched an AI ancho , syn hesizing
highligh s ins an aneously, exempli ied he immediacy and
ealism now achie able (Ko ea JoongAng Daily, 2020).
Ex ending hese capabili ies in o he i age con ex s, Yo che a,
Buhalis, Ga zidis and an Elzakke (2014) obse ed a
signi ican inc ease in isi o ecall when his o ical si es we e
enhanced wi h mobile augmen ed eali y (AR), con i ming
Ree es and Nass’s (1996) ea ly heo isa ion on mul isenso y
memo y augmen a ion.
Subsequen esea ch has been di ided in o wo p ominen
s ands. The i s in ol es in e ac i e a a a concie ges. Fo
example, Velasco, Va gas and Pe i (2024) demons a ed
no ewo hy ise in e ail con e sions h ough deploying a
highly ealis ic AI sommelie in wine ies. Howe e , c i ical
quali a i e ollow-up e ealed isi o s mis akenly a ibu ing
au ho i y o a a a s a he han human expe s, esona ing wi h
he "media equa ion" e ec —indi iduals esponding socially
and emo ionally o media agen s as i hey we e human (Nass
& Moon, 2000). Mo eo e , he unse ling ealism inhe en in
hese a a a s isks alling in o Mo i’s (1970) "uncanny alley,"
a phenomenon suppo ed by ecen empi ical e idence, no ing
how hype ealis ic i ual guides e oked discom o and
skep icism among consume s (Thale e al., 2021). This e ec ,
ex ensi ely subs an ia ed in psychological esea ch, desc ibes
a phenomenon whe eby agen s ha closely esemble
humans—bu possess sub le impe ec ions— igge eelings
o unease, dis us , o e en e ulsion (Ma hu & Reichling,
2016). In he ou ism con ex , ecen empi ical s udies ha e
iden i ied his same pa e n: ou is s in e ac ing wi h highly
ealis ic i ual hos s o guides o en epo discom o and
On-si e Augmen a ion
(Digi al A a a s, Real- ime
T ansla ion)
• Emo ional sensing and
biome ics
• P i acy and e hical issues
Back-O ice Op imiza ion
(S a ing, Re enue, Fo ecas ing)
• GPT-enhanced o ecas ing
• Rein o cemen lea ning
• E iciency s in e p e abili y
•
Labo and su eillance issues
Cus ome Decision-making
(P e- ip Planning)
• Sc ip ed FAQ bo s as so
co-au ho s
• T us and hedonism
asymme y
• No el y s accu acy
• Algo i hmic s ee ing
Gene a i e AI as Ac i e Tou ism Ac o
C oss-cu ing Themes:
• Longi udinal blind spo s
• Me hodological gaps
• AI as co-c ea i e ac o
• E hical impe a i es
GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 23
suspicion, pa icula ly when he agen s’ mo emen s o
exp essions all jus sho o na u al human beha io (Alipou
e al., 2025). Howe e , comp ehensi e la ge-N e hnog aphic
s udies emain sca ce, lea ing he nuanced in e ac i e
dynamics be ween isi o s and a a a s unde explo ed
(Pad icelli e al., 2021). In ou ism, whe e isi o imme sion
and us in he guide a e c i ical (Sihombing e al., 2024), hese
uncanny alley e ec s a e pa icula ly p oblema ic. I is
he e o e c i ical ha des ina ion manage s and echnologis s
ind a balanced ha mony be ween ealism and sub le
abs ac ion such ha AI gene a i e guides p omo e
engagemen wi hou c ea ing uneasy o e humanisa ion ha
unde mines au hen ici y and de ac s om ou is s'
expe iences.
The second e e s o eal- ime linguis ic media ion.
De elopmen s like OpenAI’s Whispe ma k he dawn o an
age o seamless ansla ion wi h he po en ial o o e come
con en ional communica ion ba ie s. G aham and Roll’s
(2023) esea ch has al eady p o en ha Whispe ’s
pe o mance can compa e a o ably wi h ha o human
in e p e e s in ansac ional con e sa ions; he e a e s ill
signi ican weaknesses, howe e , in i s ende ing o idioma ic
exp essions and cul u ally ich me apho s. In acco dance wi h
C onin’s (2013) imely wa ning, machine ansla ion is p one
o ade-o co e "con ex ual hickness," hus h ea ening
insidious cul u al nuances ha a e i al o au hen ic
in e cul u al unde s anding. Mo eo e , as O’Hagan (2016)
con ends, eal- ime machine ansla ion isks c ea ing a " alse
luency," which can mask unde lying powe imbalances in
cul u al exchange—a mo al haza d ha is la gely unadd essed
ac oss ou ism li e a u e.
A nascen domain, a ec i e sensing, le e ages ad ances in
ision-language-ac ion sys ems o adap s o ies in eal- ime
based on a ec i e indica o s. Expanding on Pica d's (1997)
ini ial wo k on a ec i e compu ing, ecen p ac ical
applica ions show g ea p omise. Liu and Shin (2025, in p ess)
in oduce an augmen ed eali y p o o ype ha adap s s o y
empo o i wi h ou is s' ace exp essions and hus e ec i ely
encou ages a ec i e engagemen . Howe e , widesp ead
biome ic moni o ing caused by his echnology poses eal
p i acy conce ns. D awing on Solo e's (2006) axonomy o
p i acy ha ms, we can see hese echnologies pose se ious
isks a ound "agg ega ion" (cap u ing la ge-scale pe sonal
emo ional in o ma ion) and "exclusion" (changing access on
basis o a ec i e s a e); howe e , s ong deba e on egula o y
con ol is no iceably absen wi hin ou ism esea ch (Yeung e
al., 2019).
Wi hin hese di e en ields o augmen a ion wo main
knowledge gaps a ise. Fi s ly, he e is a signi ican gap in
longi udinal esea ch on he long- e m cul u al implica ions o
na a i es cons uc ed h ough a i icial in elligence. Do
epea ed exposu es o algo i hmically cu a ed con en p omo e
cul u al li e acy, o does i cons ain in e p e a i e skills by
c ea ing echo chambe s h ough algo i hms (Zubo , 2019;
Milan & T e é, 2019)? Secondly, en i onmen al implica ions
a e oo commonly igno ed; as edge in e ence echnologies
lowe la ency, hey mig a e compu a ional complexi y o
consume de ices and hus consume mo e powe and con lic
wi h sus ainabili y goals ela ed o sus ainable ou ism
(Luccioni e al., 2023; Mo ley, Widdicks, & Hazas, 2021).
Explaining hese complexi ies in ol es using mul i-me hod
esea ch designs ha include e hnog aphic inqui y,
assessmen s o ene gy oo p in s, and ansla ion c i ical
analyses, hus exhaus i ely s udying how GenAI ede ines he
concep o "being he e" as digi ally co-c a ed cul u al
in e ac ions.
3.3. Back-o ice ope a ions op imisa ion: Pe sonnel
managemen , e enue op imisa ion, and demand
o ecas ing
While a a a s and cha bo s mesme ise popula imagina ion in
he con ex o ou ism business, i is a guable ha mo e
p o ound ope a ional changes w ough by GenAI me ely occu
a mo e sub le le els. The ea lies app oaches owa d
au oma ing wo k ini ially concen a ed p ima ily on physical
and angible se ice obo s. I ano and Webs e (2017) had
solid e idence demons a ing economic iabili y h ough he
use o elay obo s as eplacemen s o ho el nigh -shi
ecep ionis s, especially o geog aphies ha con inue o ace
ongoing unde s a ing. I ano (2020) also d ew simila
conclusions on cos sa ings while also discussing conce ns
abou a possible long- e m dependence on obo ic solu ions.
Howe e , as a en ion mo ed om physical o i ual sys ems
in he me a e se e a (Assiou as e al., 2024a, 2024b; Sousa e
al., 2024), algo i hmic managemen became he p ime d i ing
o ce o imp o ed p oduc i i y.
Ea ly algo i hmic me hods, including dynamic p icing models,
achie ed quan i iable bu limi ed quali y imp o emen .
Signi ican oom yield gains we e expe ienced due o ai ly
simple AI-based p icing p ocesses by Guo e al. (2023).
Ne e heless, he use o complex ein o cemen lea ning
me hods based on undamen al e enue managemen
pa adigms concei ed by Tallu i and an Ryzin (2004) has
signi ican ly supplemen ed hese bene i s. New e idence by
Tuncay e al. (2023) sugges s ha ein o cemen lea ning
models can cu en ly imp o e on complex p icing schemes on
hei own abo e and beyond s a ic o con en ional heu is ic
models de eloped wi h his aim by a wide ma gin bu a a cos
o diminished anspa ency and in e p e abili y (Molna ,
2022).
Demand o ecas ing is an e e mo e dynamic domain in he
con ex o AI-d i en inno a ion. Con en ional ARIMA
me hods had been dominan un il he e olu iona y
in oduc ion o a en ion-based neu al ne wo ks di ec ly
inspi ed by he landma k T ans o me a chi ec u e in oduced
by Vaswani e al. (2017). In hei esea ch, Law e al. (2019)
employed such neu al ne wo ks o p edic ou is a i als in
Macau, and hey epo ed conside able imp o emen s in e ms
o accu acy by dec easing mean absolu e pe cen age e o s by
abou 12%. Meanwhile, Menzel e al. (2022) demons a ed he
conside able p edic i e powe in ol ed in le e aging Google
T ends coupled wi h g adien boos ing algo i hms, unco e ing
COVID-19- ela ed demand shocks weeks in ad ance o
o icial public heal h announcemen s. The Long Sho Te m
Memo y deep lea ning a i icial neu al ne wo k by Polyzos
Sami as and Spy idou (2020) s essed he need o adap i e
o ecas ing models p o icien in iden i ying and esponding o
"black swan" e en s, a pe sis en weakness iden i ied in
con en ional me hodologies (Taleb, 2007).
Despi e such ad ances, he e exis se ious ope a ional and
e hical issues. The use o ene gy, hi he o igno ed, has quickly
g own as a op p io i y. S ubell, Ganesh, and McCallum
(2019) es ima ed ha aining a single T ans o me model
gene a es ca bon oo p in equaling se e al ansa lan ic
ligh s. Addi ionally, Pa e son e al. (2021) highligh ed he
unexpec ed spike in ene gy demands a in e ence phases o
ope a ional deploymen and hus posed challenges o ou ism
o ganisa ions ega ding hei sus ainabili y and ne -ze o
ca bon ini ia i es (Gössling & Higham, 2020).
30 E angelos Ch is ou, Anes is Fo iadis and An onios Giannopoulos
deploymen s migh eshape employees' skill ajec o ies,
emphasizing models o human-AI complemen a i y a he han
subs i u ion (Da enpo & Ronanki, 2018).
RP6. Wha o ganisa ional capabili ies and AI li e acies do
ou ism en e p ises need o manage gene a i e sys ems
e hically and e ec i ely?
While echnological adop ion amewo ks emphasise
eadiness and capabili y building, managing GenAI e hically
in oduces addi ional complexi y, equi ing nuanced
unde s anding beyond adi ional digi al compe encies
(Leona di & Neeley, 2022; Ch is ou e al., 2025). Recen
li e a u e unde sco es he u gency o de eloping
o ganisa ional li e acies ha encompass no only echnical
p o iciency bu also e hical o esigh , in e p e i e capabili ies,
and obus go e nance p ac ices (Wes e man e al., 2014).
Employing mixed-me hod o ganisa ional s udies combining
quali a i e in e iews and quan i a i e su eys can
sys ema ically unco e he c i ical capabili ies needed o
e hically ope a ionalise GenAI, he eby enabling p oac i e,
esponsible inno a ion wi hin ou ism i ms (Baum, 2015;
Mo ley e al., 2021).
RP7. In wha ways does GenAI-d i en au oma ion in luence
wo k o ce pe cep ions o job sa is ac ion, p eca i y, and
p o essional iden i y?
This ques ion di ec ly ollows om he g owing discussion on
he psychosocial implica ions o wo kplace au oma ion,
including job insecu i y, diminished au onomy, and changing
p o essional iden i ies (B ynjol sson & Mi chell, 2017; F ey &
Osbo ne, 2017). Employees in he ou ism indus y a e
pa icula ly ulne able o hese changes because o he
p eca ious wo k a angemen s and he high emo ional labo
demands ypical o he sec o (Hochschild, 2012).
E hnog aphic and mixed-me hod in es iga ions o he
wo k o ce ha e he po en ial o clea ly explain how wo ke s
pe cei e and cope wi h a i icial in elligence-d i en
au oma ion, hus o e ing impo an insigh s in o sus aining
heal hy wo k en i onmen s and e aining digni y and
meaning ul wo k amids echnological p og ess.
RP8. How do ou is s pe cei e i m c edibili y and us when
expe iences a e co-c ea ed by GenAI e sus human s a ?
This ield o esea ch s udy examines he implica ions o
inco po a ing GenAI on pe cei ed au hen ici y and us o
consume s— ac o s pi o al in se ice-in ensi e ou is
se ings (Lu e al., 2019; Maye e al., 1995; Nechoud e al.,
2021). Since he e is ex ensi e li e a u e po aying he high
le el o consume ecep i i y o se ice au hen ici y and us -
based ela ionships (G ayson & Ma inec, 2004), expe imen al
scena io app oaches along wi h sys ema ic su ey esea ch a e
necessa y o ca e ully es ou is consume s' eac ions o
si ua ions whe e hey a e wo king alongside AI.
5.3. Mac o-le el: Des ina ion go e nance and policy
amewo k
Mo e b oadly, he esea ch agenda di ec ly add esses
go e nance and policy issues s emming om he in eg a ion o
GenAI in o des ina ion-le el s a egies. These esea ch
p oposi ions a ise om pe cei ed sho comings in
unde s anding he complex in e play be ween echnological
inno a ion, cul u al ep esen a ion, isi o managemen , and
e hics ac oss di e en scales o des ina ions.
RP9. How can des ina ion managemen o ganisa ions
(DMOs) es ablish e ec i e go e nance mechanisms o AI-
gene a ed con en and cul u al ep esen a ion?
This ques ion a ises om he c i ical unde s anding ha DMOs
adi ionally se e as cus odians o cul u al au hen ici y and
image managemen (Bui e al., 2024) ye cu en ly lack
adequa e go e nance models o AI-gene a ed ou pu s. P io
esea ch unde sco es ha ungo e ned AI-gene a ed con en
may isk i ializing o mis ep esen ing local cul u es, causing
long- e m epu a ional damage (Zhu e al., 2024; Luong, 2024;
Lan e al., 2025). Employing compa a i e case s udies and
de ailed policy documen analysis, esea ch he e aims o
iden i y bes p ac ices and c i ical go e nance amewo ks ha
DMOs can adop o manage AI-media ed cul u al
ep esen a ions esponsibly and e hically, he eby p ese ing
des ina ion au hen ici y and in eg i y (Dangi & Jamal, 2016).
RP10. Wha policy amewo ks a e equi ed o mi iga e
algo i hmic bias and disc imina ion embedded in GenAI
applica ions a a des ina ion le el?
The necessi y o his inqui y o igina es om es ablished
conce ns su ounding sys emic biases embedded in
algo i hmic decision-making p ocesses, no ably in luencing
des ina ion b anding (Giannopoulos e al., 2021; Csapó &
Kusumaning um, 2025) and isi o expe iences h ough
disc imina o y p ac ices (Noble, 2018; O'Neil, 2016). Gi en
documen ed cases o algo i hmic bias in luencing ou ism
ma ke ing and des ina ion po ayal, c i ical policy analysis
and igo ous algo i hmic audi s become essen ial ools o
p oac i ely iden i ying, mi iga ing, and p e en ing biases ha
pe pe ua e acial, cul u al, o socio-economic inequi ies.
Resea ch ou comes would ideally in o m conc e e policy
amewo ks o os e equi able and inclusi e AI go e nance
p ac ices a he des ina ion scale (Eubanks, 2018).
RP11. How can des ina ions esponsibly use GenAI o balance
isi o low and success ully mi iga e he impac s o badly-
managed ou ism?
This ques ion d aws on ex ensi e li e a u e ac oss academics
on o e ou ism and he need o sus ainable app oaches o
managing ou is numbe s (McKe che & P ideaux, 2014). The
p edic i e po en ial o GenAI can enable dynamic and eal-
ime managemen o ou is mo emen , hus mi iga ing
o e c owding and en i onmen al deg ada ion conce ns
(Bollenbach e al., 2024; Viñals e al., 2024). Howe e , he
e hical use o he ools equi es ad anced unde s anding o
isi o beha io pa e ns (Misi lis e al., 2021), ela ed ade-
o s, and po en ial unin ended e ec s (OECD, 2024). Agen -
based simula ion and scena io modeling o e solid ools o
e alua ing and p ojec ing he e icacy o GenAI in eal-li e
isi o managemen si ua ions (Bollenbach e al., 2022),
enabling DMOs o c ea e o wa d- hinking, da a-led s a egies
balancing he bene i s o ou ism and he demands o
sus ainable managemen (Koens e al., 2018).
RP12. Wha egula o y s eps should be aken o p o ec he
p i acy and au onomy o ou is s in a i icial in elligence-
enhanced en i onmen s?
The ele ance o he s udy a ises om moun ing conce ns o
p i acy e osion and iola ions o pe sonal libe ies due o
la ge-scale da a ga he ing and a i icial in elligence moni o ing
in he ou ism indus y (Nissenbaum, 2010; Zubo , 2019). I
is necessa y o use p i acy impac assessmen s in conjunc ion
wi h pa icipa o y go e nance models o main ain ou is
con ol o pe sonal da a and in o med consen . An in-dep h

GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 31
analysis o such egula o y measu es can shed ligh on he way
in which ou is a eas can e icien ly balance p i acy
p o ec ion, isi o agency, and echnology, hus enhancing
e hical egula ion o ou ism in he mode n echnology-d i en
en i onmen (Wach e e al., 2017).
5.4. Global amewo k: Ca bon emissions, sus ainable and
egene a i e ou ism
The plane a y agenda clea ly ou lines he global en i onmen al
impac s ela ed o he in eg a ion o GenAI in o ou ism
sys ems, in cong uence wi h sus ainabili y science and he
explo a ion o plane a y bounda ies. E e y esea ch agenda
a ises om an inc eased awa eness o he esou ce-demanding
na u e o GenAI and i s po en ial abili y o os e egene a i e
app oaches in he ou ism indus y.
RP13. Wha a e he en i onmen al implica ions o GenAI
adop ion in ou ism and o wha ex en can ca bon-conscious
AI p ac ices be applied app op ia ely?
The high-ene gy equi emen s o aining la ge-scale a i icial
in elligence models (S ubell e al., 2019) highligh an
impo an gap in he echnology implemen a ion dimensions o
he ou ism indus y's sus ainabili y. Li ecycle analysis (LCA)
app oaches, as explained by Pa e son e al. (2021), p o ide an
o ganised me hod o he in-dep h conside a ion o he
en i onmen al oo p in s ac oss he whole li ecycle o AI—
co e ing da a p ocessing, model aining, in e ence, and
ul ima e disposal. B idging his impo an gap will p o ide
s akeholde s in he a el indus y wi h essen ial in o ma ion
ega ding en i onmen al ade-o s, hus enabling he use o
ca bon-minimised p ac ices, such as he use o enewable
powe -based da a cen e s, model a chi ec u e minimisa ion,
and he use o localised da a hos ing mechanisms o limi he
p oduc ion o ca bon emissions (Hil y & Aebische , 2015;
Jones, 2018).
RP14. Can AI-gene a ed scena ios e ec i ely acili a e
egene a i e ou ism p ac ices, enhancing en i onmen al
eju ena ion a des ina ions?
This ques ion aligns wi h he g owing p omo ion o
egene a i e ou ism ha aims no me ely o educe nega i e
impac s bu ac i ely con ibu e o ecological es o a ion and
communi y wellbeing (Gibbons, 2020; Reed, 2007). In ligh o
ou ism's pas en i onmen al impac s, i is impe a i e o
examine how AI-d i en p edic i e scena ios can con ibu e
p ac ically owa ds ad ancing ecological es o a ion s a egies.
The applica ion o pa icipa o y ac ion esea ch app oaches,
combined wi h ecological scena io modeling, can empi ically
de e mine whe he AI can e ec i ely suppo des ina ion
s akeholde s in ealizing es o a i e esul s, such as
biodi e si y eco e y, habi a conse a ion, and socio-
ecological esilience enhancemen , and consequen ly add ess
essen ial knowledge gaps ega ding he p ac ical applica ions
o egene a i e concep s (Sha pley, 2020; La iza Co al-
Gonzalez e al., 2023).
RP15. Which sys em changes a e needed in ou ism alue
chains o o mally include plane a y bounda ies in inno a ion
s a egies led by gene a i e a i icial in elligence?
The plane a y bounda ies app oach, as concep ualised by
Rocks öm e al. (2009), p o ides a cogen a ionale o he
delibe a e in eg a ion o ecological limi s in o inno a ion
planning. This componen o c i ical esea ch explici ly
add esses he u gen need o e aming ou ism de elopmen
a ound sus ainable ecological s anda ds o ensu e ha
inno a ions do no inad e en ly exace ba e exis ing
en i onmen al issues. The use o sys ems hinking, coupled
wi h Delphi analysis among sus ainabili y expe s, allows o
he sys ema ic speci ica ion o needed changes ac oss di e en
s ages o ou ism alue chains, including supply chains,
esou ce ex ac ion, anspo a ion, in as uc u e cons uc ion,
and was e disposal. Add essing hese sys emic changes makes
i possible o ensu e ha he in eg a ion o GenAI suppo s
global sus ainabili y goals ins ead o unde mining hem
(S e en e al., 2015; Whi eman e al., 2013).
5.5. Expanding ho izons: De eloping an inclusi e
amewo k o explo e GenAI in he ou ism sec o
The sys ema ic esea ch app oach inco po a ing mic o, meso,
mac o, and plane a y le els o analysis is he bes way o
answe he heo e ical gap p esen in he la es schola ly deba e
su ounding he use o GenAI, expanding p e ious esea ch on
he ou ism ecosys em including he mic o, meso, and mac o-
le els (Giannopoulos e al., 2020, 2021). To he bes o ou
knowledge, he li e a u e is domina ed by sys ema ic e iews
agg ega ing ea ly empi ical esul s o in-dep h s udies o
pa icula gene a i e ools. Academic ci cles a e in e es ed in
he use o pa icula GenAI ools like Cha GPT (Shawal e al.,
2017), dealing wi h he issue o con en hallucina ion
(Ch is ensen e al., 2025), and imp o ing ecommende
algo i hms o gain supe io accu acy (Kzaz e al., 2025).
Al hough he exis ing li e a u e is use ul, i la gely p o ides
desc ip i e in o ma ion ha is limi ed o pa icula con ex s
and somewha isola ed om la ge heo e ical cons uc s. As a
esul , i ails o p esen a comp ehensi e examina ion o he
a - eaching implica ions o GenAI ac oss he complex socio-
echnical sys ems o ou ism (Dwi edi e al., 2023). In
con as , he newly in oduced esea ch agenda skill ully
pushes hese bounded ho izons by heo e ically assimila ing
GenAI in o well-es ablished academic adi ions. By
igo ously applying GenAI o exempla y ou ism heo ies like
S-D Logic (Va go & Lusch, 2016), expe ience economy
heo y (Pine & Gilmo e, 1999), alue co-c ea ion and co-
des uc ion amewo ks (Eche e i & Skålén, 2011; Jä i e al.,
2018), and heo e ical in es iga ions o algo i hmic
go e nance (Yeung e al., 2019), his agenda g ea ly expands
i s heo e ical ho izons.
Signi ican ly, in doing so, his app oach econcep ualises
GenAI as an ac i e pa icipan endowed wi h dis ibu ed
agency in ela ional encoun e s, ins ead o educing i o he
le el o being passi e echnology. Doing so unde mines
an h opocen ic assump ions and en iches heo e ical
discussions conce ning powe a angemen s in ou ism s udies
(Leona di & Ba ley, 2008; O likowski & Sco , 2008).
Addi ionally, by ep esen ing GenAI as an in e ac i e pa ne
some imes subs i u ing o human agen s and no only
augmen ing he ope a ions o he la e , his app oach
encou ages in-dep h explo a ion o po en ial social-cul u al
and o ganisa ional changes. I also s imula es esea ch deba e
in o e hinking p o essional iden i ies, eassessing
conno a ions o au hen ici y and ep esen a ion o cul u es, and
he e hical pi alls o aised au onomy in AI-media ed
encoun e s (Benjamin, 2019; Flo idi & Cowls, 2019).
This change calls o exhaus i e empi ical in es iga ion in o
he powe dynamics a ising om a i icial in elligence's
inhe en capabili ies, which eshape he ela ionships be ween
ou is s, se ice indus ies, and hos communi ies. This
p esages he p essing need o sa egua d human agency and
digni y in algo i hmically in e media ed ou is expe iences
(And eje ic & Selwyn, 2020; Pica d, 1997). Secondly, he
amewo k p esen ed he ein p onounces a clea syn hesis o
32 E angelos Ch is ou, Anes is Fo iadis and An onios Giannopoulos
e hical, egula o y, and en i onmen al conce ns. I no ably
emphasises an icipa o y go e nance egimes ha en ail
igo ous p i acy-by-design app oaches (Acquis i e al., 2015),
ex ensi e audi s o unco e ing algo i hmic biases
(Buolamwini & Geb u, 2018), and he c ea ion o echnologies
wi h ega ds o ca bon emissions, aligned wi h global clima e
pledges (Pa e son e al., 2021; Gössling e al., 2021).
Placing ou ism esea ch wi hin he la ge con ex o plane a y
bounda ies (Rocks öm e al., 2009; S e en e al., 2015)
highligh s he need o sus ainable inno a ion s a egies ha
conside he some imes neglec ed en i onmen al
consequences o GenAI. Add essing he social and
en i onmen al e ec s o a i icial in elligence in ou ism
e ec i ely equi es ongoing in e disciplina y coope a ion
among compu e science esea che s, sus ainabili y schola s,
and policy esea che s (Jobin e al., 2019). Finally, he holis ic
esea ch amewo k no only ills a ele an concep ual lacuna
bu also p o ides a sound heo e ical and e hical unde pinning
o ensuing schola ly in es iga ion. I calls o an ad anced,
c oss-disciplina y app oach o esea ch ha c i ically explo es
he ans o ma i e capabili ies o GenAI while concu en ly
dealing wi h ela ed e hical, cul u al, and en i onmen al
conce ns. Pa icipa ion in his ambi ious amewo k allows
ou ism schola s o make meaning ul con ibu ions o he
b oade schola ly con e sa ion, hus acili a ing an inclusi e,
equi able, and uly sus ainable ad ance in GenAI.
6 MANAGEMENT AND POLICY IMPLICATIONS
To success ully go e n he apidly e ol ing en i onmen o
GenAI, Des ina ion Managemen O ganisa ions (DMOs),
ou ism pla o ms, and policymake s equi e holis ic and
p ac ical oolki s and egimes o go e nance app op ia e o
add ess he a ious e hical, legisla i e, and ope a ional
dilemmas p esen ed by GenAI. Lacking an unde pinning o
sys emic eadiness, s akeholde s isk exace ba ing al eady-
exis ing inequali ies, iola ing p i acy, and en enching
cul u al s e eo ypes ypical o AI deploymen s (Dwi edi e al.,
2023).
6.1. Toolki o DMOs and Pla o ms
AI li e acy
Based on he alues o AI li e acy, Des ina ion Managemen
O ganisa ions (DMOs) and hei ela ed pla o ms should
ac i ely enligh en s akeholde s—execu i e decision-make s,
on -line employees, and ou is s—abou he po en ials,
limi a ions, e hical aspec s, and a ange o e ec s o GenAI on
cul u al au hen ici y and he isi o expe ience (Buhalis &
Sina a, 2019; Long & Mage ko, 2020). Based on Wes e man,
Bonne , and McA ee (2014), digi al li e acy is mo e han jus
basic echnical skills, bu includes highe -le el in e p e i e,
e hical, and analy ical skills. As such, o mal AI li e acy
aining mus go beyond echnical desc ip ions o include in-
dep h unde s anding o algo i hmic bias, possible p i acy
ulne abili ies, and he dange s o AI-d i en cul u al
uni o mi y and s anda disa ion (Benjamin, 2019; Noble, 2018;
Buolamwini & Geb u, 2018). P io i izing c i ical AI li e acy
enables s akeholde s in he ou ism indus y o o esee and
success ully esol e e hical challenges, inc ease anspa ency,
and acili a e ai human-AI in e ac ions.
Risk audi ing
Gi en he well-documen ed issues o algo i hmic opaci y,
inhe en biases, and p o ound p i acy ulne abili ies c ea ed
by he use o a i icial in elligence (Pasquale, 2015; O'Neil,
2016; Yeung e al., 2019), he implemen a ion o obus isk
audi ing p ocedu es is essen ial. Des ina ion Managemen
O ganisa ions (DMOs) mus in eg a e sys ema ic algo i hmic
audi s wi h anspa ency epo ing sys ems (Raji e al., 2020),
he eby enabling s akeholde s o es algo i hmic ai ness,
cul u al sensi i i y, and ope a ional anspa ency s ingen ly.
E idence-based esea ch in in o ma ion sys ems p o ides
es ablished amewo ks o ecognise algo i hmic bias, quan i y
p i acy isks, and assess e hical implica ions o enhance he
success ul applica ion o AI egula ion (Flo idi & Cowls,
2019; Mi els ad e al., 2016). Applying hese amewo ks
acili a es he empowe men o s akeholde s o p eemp i ely
add ess he ha ms caused by algo i hms in acco dance wi h
e hical bes p ac ices.
Pa icipa o y design
Pa icipa o y design is he e o e c ucial o inclusi e and
cul u ally esponsi e deploymen o GenAI (Sande s &
S appe s, 2008). Des ina ion managemen o ganisa ions and
pla o ms need o ac i ely engage a di e se ange o
s akeholde g oups—including communi ies, cul u al he i age
ep esen a i es, and on line se ice p o ide s—in he co-
de elopmen o AI p omp s, cul u ally esponsi e na a i es,
and e hical go e nance s uc u es. Such pa icipa o y
app oaches no only encou age s onge s akeholde buy-in bu
also educe he isks o cul u al e asu e, digi al colonialism,
and exclusion o mino i y oices (Benjamin, 2019; Could y &
Mejias, 2019). E ec i e pa icipa o y design u he adds o
he legi imacy o he sys em and encou ages sus ainable
cul u al engagemen .
6.2. Roadmap o egula o s
P i acy-by-design
The egula o s should embed s ong p i acy-by-design
p inciples wi hin legal policies o enable he imely
implemen a ion o p i acy p o ec ion mechanisms in a i icial
in elligence sys ems wi hou downg ading hese conce ns o
seconda y p io i y (Ca oukian, 2009; Nissenbaum, 2010).
Manda o y end- o-end comp ehensi e p i acy impac
assessmen s (PIAs) should be implemen ed, unde sco ing he
alue o p oac i e iden i ica ion and mi iga ion o po en ial
p i acy iola ions, isks o commodi ica ion o da a, and
ulne abili ies o emo ional manipula ion in algo i hmic
ou ism si ua ions (Acquis i e al., 2015; Solo e, 2006).
P oac i e egula ion os e s he independence, digni y, and
us o he ou is h ough he assu ance o he adop ion o AI
in acco dance wi h he cu en e hical no ms in he ou ism
indus y.
Algo i hmic anspa ency
Go e nance amewo ks should equi e high le els o
anspa ency and accoun abili y om ou ism pla o ms using
GenAI echnology. Regula ions should in ol e clea
p o isions o he aim o he objec i es pu sued by he AI,
de ailed desc ip ions o he aining da ase s used, he decision
algo i hms adop ed, and ecogni ion o he possibili y o
inhe en bias and limi a ions (Pasquale, 2015; Mi els ad e al.,
2016; Jobin e al., 2019). O e sigh bodies mus p omo e he
obliga o y use o model ca ds and da ashee s, which a e
s anda dised documen a ion empla es (Mi chell e al., 2019;
Geb u e al., 2021). The use o hese ools g ea ly inc eases he
le el o s akeholde awa eness, p omo es p ope public
o e sigh , and encou ages accoun abili y in he complex socio-
echnical a angemen s ele an o ou ism.
GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 33
Toge he , he abo e policy and manage ial in e en ions a e
he c i ical measu es o success ully guiding GenAI in he
ou ism indus y. These s a egies p o ide Des ina ion
Managemen O ganisa ions, pla o ms, and policymake s wi h
no jus clea and ac ionable oolki s bu also e hical, cul u ally
esponsi e, and sus ainable ad ances, hence sa egua ding he
in eg i y o ou ism in a u u e wi h GenAI.
7 CONCLUSION: TOWARD A RESPONSIBLE
“SYNTHETIC EXPERIENCE ECONOMY”
This s udy has es ablished an in o med and comp ehensi e
ounda ion o unde s anding and e hically emb acing he
subs an ial impac o GenAI in he ou ism indus y in enabling
he shi owa ds an e hical "Syn he ic Expe ience Economy."
Desc ibing he ole o GenAI no only as an ine echnological
acili a o o single ope a o , bu also as an ac i e pa icipan
and d i e , he Syn he ic Expe ience Sys em (SES)
p oblema ises and expands es ablished ou ism pa adigms
gene ally based on expe ien ial co-c ea ion by humans (Pine &
Gilmo e, 1999; Va go & Lusch, 2016). This shi highligh s
he enhanced complexi y and agency o GenAI, calling o
conside a ion o i s po en ial o p o oundly eshape in e ac ion,
na a i e c ea ion, and alue exchange in ou ism sys ems.
The esea ch agenda, de eloped h ough di e se analy ical
lenses—mic o ( ou is cogni ion and well-being), meso
(o ganisa ional capabili ies and labo p ocesses), mac o
(go e nance and egula o y en i onmen s), and plane a y
(en i onmen al consequences and egene a i e app oaches)—
discloses conside able knowledge gaps alongside equally
ema kable oppo uni ies o in ellec ual ad ancemen .
Signi ican ly, howe e , his agenda challenges and expands he
se ice-dominan logic concep ual amewo k by posi ioning
he ole o GenAI as pa o he co-c ea i e ac i i y o
expe ience c ea ion, as opposed o con ining i o he ole o
passi e ool o esou ce (Leona di, 2011; O likowski & Sco ,
2008; Va go & Lusch, 2016). GenAI's in eg a ion in o
heo e ical amewo ks in ou ism esea ch no only
s eng hens exis ing models bu also encou ages he
de elopmen o no el heo e ical cons uc s able o adequa ely
cap u e issues a ound non-human agency and e hical conce ns.
In addi ion, he inclusion o e hical, egula i e, and
en i onmen al aspec s in he en isioned amewo k iden i ies
c i ical and commonly neglec ed issues like algo i hmic bias,
su eillance capi alism, da a commodi ica ion, cul u al
ep esen a ion, and sus ainabili y (Could y and Mejias, 2019;
Noble, 2018; Zubo , 2019;). By aking hese key de e minan s
in o explici conside a ion, he use o GenAI becomes linked
o he b oade sus ainabili y goals o in e na ional agendas,
including he Glasgow Decla a ion on Clima e Ac ion in
Tou ism (Gössling e al., 2021). No ably, he mul idisciplina y
amewo k combines heo e ical cohe ence wi h empi ical
co ec ness and, as a esul , enables he de elopmen o
ou ism science o be di ec ed owa ds esponsible inno a ion
p ac ices.
The SES amewo k o e s Des ina ion Managemen
O ganisa ions (DMOs), ou ism pla o ms, and egula o y
bodies a comp ehensi e and ac ionable oolki o esponsible
go e nance o GenAI. The ecommenda ions ou lined in his
pape —including measu es o p omo e AI li e acy, pu sue
igo ous isk assessmen s, ins i u e inclusi e design p ocesses,
enac egula ions g ounded in p i acy-by-design
undamen als, and gua an ee p ac ices acili a ing algo i hmic
anspa ency— ep esen conc e e and ac ionable measu es o
p omo e he e hical, cul u ally esponsi e, and
en i onmen ally sus ainable use o gene a i e echnologies
(Flo idi & Cowls, 2019; Mi chell e al., 2019; Pasquale, 2015;
Wes e man e al., 2014). These s eps a e in ended o p e-
emp i ely mi iga e isks o he misuse o pe sonal da a,
homogenisa ion o cul u al exp essions, and exace ba ion o
global en i onmen al p oblems, while also allowing ou ism
s akeholde s o ully le e age he ans o ma i e po en ial o
GenAI.
In sho , he pa h o a esponsible syn he ic expe ience
economy equi es con inuous, collabo a i e, and mul i-
disciplina y in e ac ion among ou ism expe s, p ac i ione s,
policymake s, e hicis s, echnologis s, and schola s o
sus ainabili y (Buhalis & Sina a, 2019; Dwi edi e al., 2023;
Jobin e al., 2019). The impo ance o ansdisciplina i y and
in eg a i e app oaches canno be o e emphasised in
app oaching he inno a i e possibili ies and e hical objec i es
o GenAI. The SES app oach speci ically ad oca es hese
in eg a i e app oaches, unde s anding he ou ism indus y as
a c i ical case o wide socie al dialogue on echnology
s ewa dship, digi al e hics, and sus ainable inno a ion. I is
only h ough such all-encompassing, c oss-disciplina y
collabo a ions ha he ou ism sec o can p ope ly and
esponsibly ha ness he immense po en ial o GenAI, and
hence p oac i ely mi iga e i s socie al, e hical, and
en i onmen al impac s, he eby ensu ing uly sus ainable,
equi able, and meaning ul u u e ad ances in ou ism.
REFERENCES
Abdul, A., Ve meulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M.
S. (2018). T ends and ajec o ies o explainable, accoun able
and in elligible sys ems: An HCI esea ch agenda. In
P oceedings o he 2018 CHI Con e ence on Human Fac o s in
Compu ing Sys ems (pp. 1–18). ACM.
h ps://doi.o g/10.1145/3173574.3174156
Acquis i, A., B andima e, L., & Loewens ein, G. (2015). P i acy
and human beha io in he age o in o ma ion. Science,
347(6221), 509–514. h ps://doi.o g/10.1126/science.aaa1465
Alipou , A., Ha mann, T., & Alima dani, M. (2025). Would you
ely on an ee ie agen ? A sys ema ic e iew o he impac o he
uncanny alley e ec on us in human-agen in e ac ion.
a Xi . h ps://doi.o g/10.48550/a Xi .2505.05543
Amoo e, L. (2019). Cloud e hics: Algo i hms and he a ibu es o
ou sel es and o he s. Duke Uni e si y P ess.
h ps://doi.o g/10.1215/9781478009276
Ananny, M., & C aw o d, K. (2018). Seeing wi hou knowing:
Limi a ions o he anspa ency ideal and i s applica ion o
algo i hmic accoun abili y. New Media & Socie y, 20(3), 973–
989. h ps://doi.o g/10.1177/1461444816676645
Ande son, L., Os om, A. L., & Bi ne , M. J. (2011). Su ounded by
Se ices: A New Lens o Examining he In luence o Se ices
as Social S uc u es on Well-Being. Wo king S udy, WP Ca ey
School o Business, A izona S a e Uni e si y.
And eassen, T. W., Le ik-Olsen, L., Snyde , H., Van Riel, A. C.,
Sweeney, J. C., & Van Vae enbe gh, Y. (2018). Business
model inno a ion and alue-c ea ion: The iadic way. Jou nal
o Se ice Managemen , 29(5), 883-906.
h ps://doi.o g/10.1108/JOSM-05-2018-0125
And eje ic, M., & Selwyn, N. (2020). Facial ecogni ion echnology
in schools: C i ical ques ions and conce ns. Lea ning, Media
and Technology, 45(2), 115–128.
h ps://doi.o g/10.1080/17439884.2020.1686014
And es, J., Dan a, C., Bianchi, A., Hong, S., Li, Z., Sando al, E. B.,
Ma in, C., & Coope , N. (2024). Unde s anding and shaping
34 E angelos Ch is ou, Anes is Fo iadis and An onios Giannopoulos
human– echnology assemblages in he age o gene a i e AI
[Wo kshop]. a Xi . h ps://doi.o g/10.48550/a Xi .2404.18405
Assiou as, I., Giannopoulos, A., Ma agani, E., & Buhalis, D.
(2024a). Vi ual eali y and men al image y owa ds a el
inspi a ion and isi in en ion. In e na ional Jou nal o Tou ism
Resea ch, 26(2), e2646. h ps://doi.o g/10.1002/j .2646
Assiou as, I., Giannopoulos, A., Ma agani, E., & Buhalis, D.
(2024b). Vi ual eali y acili a ed a el inspi a ion: he ole o
pleasu e and a ousal. Cu en Issues in Tou ism, 1-14.
h ps://doi.o g/10.1080/13683500.2024.2406412
Assiou as, I., Skou is, G., Giannopoulos, A., Buhalis, D., &
Ka aosmanoglu, E. (2023). Tes ing he ela ionship be ween
alue co-c ea ion, pe cei ed jus ice and gues s’ enjoymen .
Cu en Issues in Tou ism, 26(4), 587-602.
Au o , D. H. (2015). Why a e he e s ill so many jobs? The his o y
and u u e o wo kplace au oma ion. Jou nal o Economic
Pe spec i es, 29(3), 3–30. h ps://doi.o g/10.1257/jep.29.3.3
A loni is, G. J., & Giannopoulos, A. A. (2012). Balanced ma ke
o ien a ion: quali a i e indings on a agile
equilib ium. Managing Se ice Quali y: An In e na ional
Jou nal, 22(6), 565-579.
Baum, T. (2015). Human esou ces in ou ism: S ill wai ing o
change?—A 2015 ep ise. Tou ism Managemen , 50, 204–212.
h ps://doi.o g/10.1016/j. ou man.2015.02.001
Bende , E. M., Geb u, T., McMillan-Majo , A., & Shmi chell, S.
(2021). On he dange s o s ochas ic pa o s: Can language
models be oo big? In P oceedings o he 2021 ACM
Con e ence on Fai ness, Accoun abili y, and T anspa ency (pp.
610–623). ACM. h ps://doi.o g/10.1145/3442188.3445922
Bende , E. M., Geb u, T., McMillan-Majo , A., & Shmi chell, S.
(2021). On he dange s o s ochas ic pa o s: Can language
models be oo big? P oceedings o he 2021 ACM Con e ence
on Fai ness, Accoun abili y, and T anspa ency, 610–623.
h ps://doi.o g/10.1145/3442188.3445922
Benjamin, R. (2019). Race a e echnology: Aboli ionis ools o
he new Jim Code. Poli y P ess.
Be selli, C., Pe ei a, L. A., Pe ei a, T., & Limbe ge , P. F. (2022).
O e ou ism: Residen s’ pe cei ed impac s o ou ism
sa u a ion. Tou ism Analysis, 27(2), 161–172.
h ps://doi.o g/10.3727/108354221X16187814403074
Bigné, J. E., Ma ila, A. S., & And eu, L. (2008). The impac o
expe ien ial consump ion cogni ions and emo ions on
beha io al in en ions. Jou nal o Se ices Ma ke ing, 22(4),
303–315. h ps://doi.o g/10.1108/08876040810881704
Bi hane, A., & P abhu, V. U. (2021). La ge image da ase s: A
py hic win o compu e ision? P oceedings o he IEEE/CVF
Win e Con e ence on Applica ions o Compu e Vision
(WACV), 1536–1546.
h ps://doi.o g/10.1109/WACV48630.2021.00158
Bollenbach, J., Neubig, S., Hein, A., Kelle , R., & K cma , H.
(2024). Enabling ac i e isi o managemen : Local, sho - e m
occupancy p edic ion a a ou is ic poin o in e es . In o ma ion
Technology & Tou ism, 26, 521–552.
h ps://doi.o g/10.1007/s40558-024-00291-2
Bollenbach, J., Neubig, S., Hein, A., Kelle , R., & K cma , H.
(2022). Using machine lea ning o p edic POI occupancy o
educe o e c owding. INFORMATIK 2022.
h ps://doi.o g/10.18420/INF2022_34
Bolukbasi, T., Chang, K. W., Zou, J. Y., Salig ama, V., & Kalai, A.
T. (2016). Man is o compu e p og amme as woman is o
homemake ? Debiasing wo d embeddings. P oceedings o he
30 h In e na ional Con e ence on Neu al In o ma ion
P ocessing Sys ems (NIPS 2016), 4356–4364.
h ps://doi.o g/10.48550/a Xi .1607.06520
Bommasani, R., Hudson, D. A., Adeli, E., Al man, R., A o a, S.,
on A x, S., … Liang, P. (2021). On he oppo uni ies and isks
o ounda ion models. a Xi p ep in .
h ps://doi.o g/10.48550/a Xi .2108.07258
Bosca ino, N., Ca w igh , R. A., Fox, K., & Tsosie, K. S. (2022).
Fede a ed lea ning and Indigenous genomic da a so e eign y.
Na u e Machine In elligence, 4(11), 909–911.
h ps://doi.o g/10.1038/s42256-022-00551-y
Bouchon, F., & Rausche , M. (2019). Ci ies and ou ism, a lo e and
ha e s o y: Towa ds a concep ual amewo k o u ban
o e ou ism managemen . In e na ional Jou nal o Tou ism
Ci ies, 5(4), 598–619. h ps://doi.o g/10.1108/IJTC-06-2019-
0080
B eidbach, C. F., & B odie, R. J. (2017). Engagemen pla o ms in
he sha ing economy: Concep ual ounda ions and esea ch
di ec ions. Jou nal o Se ice Theo y and P ac ice, 27(4), 761–
777. h ps://doi.o g/10.1108/JSTP-04-2016-0071
B eidbach, C. F., & Maglio, P. P. (2016). Technology-enabled alue
co-c ea ion: An empi ical analysis o ac o s and esou ces.
Indus ial Ma ke ing Managemen , 56, 73–85.
h ps://doi.o g/10.1016/j.indma man.2016.03.011
B oude , P. (2020). Rese edux: Possible e olu iona y pa hways
owa ds he ans o ma ion o ou ism in a COVID-19 wo ld.
Tou ism Geog aphies, 22(3), 484-490.
h ps://doi.o g/10.1080/14616688.2020.1760928
B own, T. B., Mann, B., Ryde , N., Subbiah, M., Kaplan, J.,
Dha iwal, P., … Amodei, D. (2020). Language models a e ew-
sho lea ne s. Ad ances in Neu al In o ma ion P ocessing
Sys ems, 33, 1877–1901.
h ps://doi.o g/10.48550/a Xi .2005.14165
B ynjol sson, E., & Mi chell, T. (2017). Wha can machine lea ning
do? Wo k o ce implica ions. Science, 358(6370), 1530–1534.
h ps://doi.o g/10.1126/science.aap8062
Buchmann, A., Moo e, K., Månsson, M., & Fishe , D. (2021).
Au hen ici y in ou ism heo y and expe ience: P ac ically
indispensable and heo e ically mischie ous? Annals o
Tou ism Resea ch, 89, 103208.
h ps://doi.o g/10.1016/j.annals.2021.103208
Buhalis, D., & Foe s e, M. (2015). SoCoMo ma ke ing o a el and
ou ism: Empowe ing co-c ea ion o alue. Jou nal o
Des ina ion Ma ke ing & Managemen , 4(3), 151–161.
h ps://doi.o g/10.1016/j.jdmm.2015.04.001
Buhalis, D., & Sina a, Y. (2019). Real- ime co-c ea ion and
nowness se ice: Lessons om ou ism and hospi ali y. Jou nal
o T a el & Tou ism Ma ke ing, 36(5), 563-582.
h ps://doi.o g/10.1080/10548408.2019.1592059
Buhalis, D., Ama anggana, A. (2015). Sma Tou ism Des ina ions
Enhancing Tou ism Expe ience Th ough Pe sonalisa ion o
Se ices. In: Tussyadiah, I., In e sini, A. (eds) In o ma ion and
Communica ion Technologies in Tou ism 2015. Sp inge ,
Cham. h ps://doi.o g/10.1007/978-3-319-14343-9_28
Bui, H. T., Filimonau, V., & Seze el, H. (2024). AI- hen ici y:
Explo ing he e ec o pe cei ed au hen ici y o AI-gene a ed
isual con en on ou is pa onage in en ions. Jou nal o
Des ina ion Ma ke ing & Managemen , 32, A icle 100956.
h ps://doi.o g/10.1016/j.jdmm.2024.100956
Buolamwini, J., & Geb u, T. (2018). Gende shades: In e sec ional
accu acy dispa i ies in comme cial gende classi ica ion.
P oceedings o Machine Lea ning Resea ch, 81, 77–91.
h p://p oceedings.ml .p ess/ 81/buolamwini18a.h ml
Bu ell, J. (2016). How he machine ' hinks': Unde s anding opaci y
in machine lea ning algo i hms. Big Da a & Socie y, 3(1).
h ps://doi.o g/10.1177/2053951715622512
Caliskan, A., B yson, J. J., & Na ayanan, A. (2017). Seman ics
de i ed au oma ically om language co po a con ain human-
like biases. Science, 356(6334), 183–186.
h ps://doi.o g/10.1126/science.aal4230
Ca ù, A., & Co a, B. (2003). Re isi ing consump ion expe ience: A
mo e humble bu comple e iew o consump ion. Ma ke ing
Theo y, 3(2), 267-286.
h ps://doi.o g/10.1177/14705931030032004
Cassani, A., Rube l, M., Salis, A., Giannese, G., & Boanelli, G.
(2024). zIA: A GenAI-powe ed local aun ie assis s ou is s in
I aly. a Xi . h ps://doi.o g/10.48550/a Xi .2407.11830
Ca oukian, A. (2009). P i acy by design: The 7 ounda ional
p inciples. In o ma ion and P i acy Commissione o On a io.
h ps://www.ipc.on.ca/en/media/1826/download?a achmen
GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 35
Chesb ough, H. (2003). Open Inno a ion: The New Impe a i e o
C ea ing and P o i ing om Technology. Ha a d Business
School P ess.
Chesb ough, H. W. (2003). Open inno a ion: The new impe a i e
o c ea ing and p o i ing om echnology. Ha a d Business
School P ess.
Ch is ensen, J., Hansen, J. M., & Wilson, P. (2024). Unde s anding
he ole and impac o Gene a i e A i icial In elligence (AI)
hallucina ion wi hin consume s’ ou ism decision-making
p ocesses. Cu en Issues in Tou ism, 28(4), 545–560.
h ps://doi.o g/10.1080/13683500.2023.2300032
Ch is ou, E., Giannopoulos, A., & Simeli, I. (2025). The E olu ion
o Digi al Tou ism Ma ke ing: F om Hash ags o AI-Imme si e
Jou neys in he Me a e se E a. Sus ainabili y, 17(13), 6016.
h ps://doi.o g/10.3390/su17136016
Ci y o Helsinki. (n.d.). Sma ou ism in Helsinki. Re ie ed June
28, 2025, om h ps://www.hel. i/en/business-and-
wo k/de elop- ou ism-and-e en s/sma - ou ism-in-helsinki
Cohen, S. A., & Cohen, E. (2019). New di ec ions in he sociology
o ou ism. Cu en Issues in Tou ism, 22(2), 153-172.
h ps://doi.o g/10.1080/13683500.2017.1347151
Coudouna is, D. N., Bjö k, P., T i ono a Ma ino a, S., Ja a guliye ,
F., K aso a, O., S hapi , E., Va blane, U., & Talias, M. A.
(2025). ‘Big-5’ pe sonali y ai s and e isi in en ions: The
media ing e ec o memo able ou ism expe iences. Jou nal o
Tou ism, He i age & Se ices Ma ke ing, 11(1), 46–60.
h ps://doi.o g/10.5281/zenodo.15655588
Could y, N., & Mejias, U. A. (2019). Da a colonialism: Re hinking
big da a’s ela ion o he con empo a y subjec . Tele ision &
New Media, 20(4), 336-349.
h ps://doi.o g/10.1177/1527476418796632
Could y, N., & Mejias, U. A. (2019). The cos s o connec ion: How
da a is colonizing human li e and app op ia ing i o
capi alism. S an o d Uni e si y P ess.
h ps://doi.o g/10.1515/9781503609754
C aw o d, K. (2021). A las o AI: Powe , poli ics, and he plane a y
cos s o a i icial in elligence. Yale Uni e si y P ess.
h ps://doi.o g/10.2307/j.c 1gh 45
C aw o d, K., & Paglen, T. (2021). Exca a ing AI: he poli ics o
images in machine lea ning aining se s. AI & Socie y, 36(4),
1105–1116. h ps://doi.o g/10.1007/s00146-021-01162-8
C onin, M. (2013). T ansla ion in he digi al age. Rou ledge.
h ps://doi.o g/10.4324/9780203073599
Csapó, J., & Kusumaning um, S. D. (2025). Unco e ing ends in
des ina ion b anding and des ina ion b and equi y esea ch:
Resul s o a opic modelling app oach. Jou nal o Tou ism,
He i age & Se ices Ma ke ing, 11(1), 34–45.
h ps://doi.o g/10.5281/zenodo.15652600
Cummings, M. L. (2014). Man e sus machine o man + machine?
IEEE In elligen Sys ems, 29(5), 62-69.
h ps://doi.o g/10.1109/MIS.2014.87
Dangi, T. B., & Jamal, T. (2016). An in eg a ed app oach o
"sus ainable communi y-based ou ism". Sus ainabili y, 8(5),
475. h ps://doi.o g/10.3390/su8050475
Da enpo , T. H., & Ronanki, R. (2018). A i icial in elligence o
he eal wo ld. Ha a d Business Re iew, 96(1), 108–116.
Da is, F. D. (1989). Pe cei ed use ulness, pe cei ed ease o use, and
use accep ance o in o ma ion echnology. MIS Qua e ly,
13(3), 319–340. h ps://doi.o g/10.2307/249008
Deci, E. L., & Ryan, R. M. (2000). The “wha ” and “why” o goal
pu sui s: Human needs and he sel -de e mina ion o beha io .
Psychological Inqui y, 11(4), 227–268.
h ps://doi.o g/10.1207/S15327965PLI1104_01
De lin, J., Chang, M.-W., Lee, K., & Tou ano a, K. (2019). BERT:
P e- aining o deep bidi ec ional ans o me s o language
unde s anding. In P oceedings o NAACL-HLT (pp. 4171–
4186).
Doan Do, T. T. M., Sil a, J. A. M., Del Chiappa, G., & Pe ei a, L.
N. (2024). The mode a ing ole o sense o powe and
psychological isk on he e ec o eWOM and pu chase
in en ions o Ai bnb. Jou nal o Tou ism, He i age & Se ices
Ma ke ing, 10(2), 3–14.
h ps://doi.o g/10.5281/zenodo.14535723
D edge, D., & Jamal, T. (2015). P og ess in ou ism planning and
policy: A pos -s uc u al pe spec i e on knowledge p oduc ion.
Tou ism Managemen , 51, 285–297.
h ps://doi.o g/10.1016/j. ou man.2015.06.002
Dwi edi, Y. K., Hughes, L., Baabdullah, A. M., Ribei o-Na a e e,
S., Giannakis, M., & Al-Debei, M. M. (2023). Me a e se
beyond he hype: Mul idisciplina y pe spec i es on eme ging
challenges, oppo uni ies, and agenda o esea ch, p ac ice and
policy. In e na ional Jou nal o In o ma ion Managemen , 66,
102542. h ps://doi.o g/10.1016/j.ijin omg .2022.102542
Dwi edi, Y. K., Hughes, L., Ismagilo a, E., Aa s, G., Coombs, C.,
C ick, T., Duan, Y., Dwi edi, R., Edwa ds, J., Ei ug, A.,
Galanos, V., Ila a asan, P. V., Janssen, M., Jones, P., Ka , A.
K., Kizgin, H., K onemann, B., Lal, B., Lucini, B., Medaglia,
R., & Williams, M. D. (2021). A i icial in elligence (AI):
Mul idisciplina y pe spec i es on eme ging challenges,
oppo uni ies, and agenda o esea ch, p ac ice and policy.
In e na ional Jou nal o In o ma ion Managemen , 57, A icle
101994. h ps://doi.o g/10.1016/j.ijin omg .2019.08.002
Eche e i, P., & Skålén, P. (2011). Co-c ea ion and co-des uc ion:
A p ac ice- heo y based s udy o in e ac i e alue o ma ion.
Ma ke ing Theo y, 11(3), 351–373.
h ps://doi.o g/10.1177/1470593111408181
Eps ein, J. M. (1999). Agen -based compu a ional models and
gene a i e social science. Complexi y, 4(5), 41–60.
h ps://doi.o g/10.1002/(SICI)1099-
0526(199905/06)4:5%3C41::AID-CPLX9%3E3.0.CO;2-F
Eubanks, V. (2018). Au oma ing inequali y: How high- ech ools
p o ile, police, and punish he poo . S . Ma in’s P ess.
Eu opean Commission. (2021). P oposal o a egula ion laying
down ha monised ules on a i icial in elligence (A i icial
In elligence Ac ). COM(2021) 206 inal. Re ie ed May 17
2025 om h ps://eu -lex.eu opa.eu/legal-
con en /EN/TXT/?u i=CELEX%3A52021PC0206
Fleming, P. (2017). The human capi al hoax: Wo k, deb and
insecu i y in he e a o Ube iza ion. Poli y P ess.
Flo idi, L. (2016). Faul less esponsibili y: on he na u e and
alloca ion o mo al esponsibili y o dis ibu ed mo al ac ions.
Philosophical T ansac ions o he Royal Socie y A
374:20160112.
Flo idi, L., & Cowls, J. (2019). A uni ied amewo k o i e
p inciples o AI in socie y. Ha a d Da a Science Re iew,
1(1). h ps://doi.o g/10.1162/99608 92.8cd550d1
Flo ido-Bení ez, L., & del Alcáza Ma ínez, B. (2024). How
A i icial In elligence (AI) Is Powe ing New Tou ism
Ma ke ing and he Fu u e Agenda o Sma Tou is
Des ina ions. Elec onics, 13(21), 4151.
h ps://doi.o g/10.3390/elec onics13214151
Fouad, A. M., Salem, I. E., & Fa hy, E. A. (2024). Gene a i e AI
insigh s in ou ism and hospi ali y: A comp ehensi e e iew
and s a egic esea ch oadmap. Tou ism and Hospi ali y
Resea ch, 0(0). h ps://doi.o g/10.1177/14673584241293125
F anklin, A. (2004). Tou ism as an o de ing: Towa ds a new
on ology o ou ism. Tou is S udies, 4(3), 277-301.
h ps://doi.o g/10.1177/1468797604057328
F ey, C. B., & Osbo ne, M. A. (2017). The u u e o employmen :
How suscep ible a e jobs o compu e isa ion? Technological
Fo ecas ing and Social Change, 114, 254–280.
h ps://doi.o g/10.1016/j. ech o e.2016.08.019
Fuchs, M., Höpken, W., & Lexhagen, M. (2014). Big da a analy ics
o knowledge gene a ion in ou ism des ina ions–A case om
Sweden. Jou nal o Des ina ion Ma ke ing & Managemen ,
3(4), 198-209. h ps://doi.o g/10.1016/j.jdmm.2014.08.002
Gallego, I., & Fon , X. (2020). Changes in ai passenge demand as
a esul o he COVID-19 c isis: Using Big Da a o in o m
ou ism policy. Jou nal o Sus ainable Tou ism, 29(9), 1470–
1489. h ps://doi.o g/10.1080/09669582.2020.1773476
Ga alas, D., Pan ziou, G., Kons an opoulos, C., & Vans eenwegen,
P. (2024). Tou is ip planning: Algo i hmic ounda ions.

36 E angelos Ch is ou, Anes is Fo iadis and An onios Giannopoulos
Applied So Compu ing, 166, A icle 112280.
h ps://doi.o g/10.1016/j.asoc.2024.112280
Geb u, T., Mo gens e n, J., Vecchione, B., Vaughan, J. W., Wallach,
H., Daumé, H., & C aw o d, K. (2021). Da ashee s o da ase s.
Communica ions o he ACM, 64(12), 86–92.
h ps://doi.o g/10.1145/3458723
Geige , R. S. (2017). Beyond opening up he black box:
In es iga ing he ole o algo i hmic sys ems in Wikipedian
o ganiza ional cul u e. Big Da a & Socie y, 4(2).
h ps://doi.o g/10.1177/2053951717730735
Geo ge, G., Howa d-G en ille, J., Joshi, A., & Tihanyi, L. (2016).
Unde s anding and ackling socie al g and challenges h ough
managemen esea ch. Academy o Managemen Jou nal,
59(6), 1880–1895. h ps://doi.o g/10.5465/amj.2016.4007
Ge ais, Daniel J., The Machine As Au ho (Ma ch 25, 2019). Iowa
Law Re iew, Vol. 105, 2019, 2053-2106, Vande bil Law
Resea ch Pape No. 19-35, A ailable a SSRN:
h ps://ss n.com/abs ac =3359524
Ghesh, N., Alexande , M., & Da is, A. (2024). The a i icial
in elligence-enabled cus ome expe ience in ou ism: A
sys ema ic li e a u e e iew. Tou ism Re iew, 79(5), 1017–
1037. h ps://doi.o g/10.1108/TR-04-2023-0255
Giannopoulos, A., Li as, C., Simeli, I., & Achlada, C. (2022). Is
des ina ion image ins ag ammable? Visi in en ions and alue
co-c ea ion h ough social media con en . In e na ional Jou nal
o Technology Ma ke ing, 16(4), 349-369.
Giannopoulos, A., Piha, L., & Skou is, G. (2021). Des ina ion
b anding and co-c ea ion: a se ice ecosys em pe spec i e.
Jou nal o P oduc & B and Managemen , 30(1), 148-166.
h ps://doi.o g/10.1108/JPBM-08-2019-2504
Giannopoulos, A., Skou is, G., Kalliga, A., Don as-Ch ysis, D. M.,
& Paschalidis, D. (2020). Co-c ea ing high- alue hospi ali y
se ices in he ou ism ecosys em: owa ds a pa adigm shi ?.
Jou nal o Tou ism, He i age & Se ices Ma ke ing, 6(1), 3-11.
h p://dx.doi.o g/10.5281/zenodo.3822065
Gibbons, L. V. (2020). Regene a i e—The new sus ainable?
Sus ainabili y, 12(13), 5483.
h ps://doi.o g/10.3390/su12135483
Gige enze , G., & Gaissmaie , W. (2011). Heu is ic decision
making. Annual Re iew o Psychology, 62(1), 451–482.
h ps://doi.o g/10.1146/annu e -psych-120709-145346
Gilbe , N., & T oi zsch, K. G. (2005). Simula ion o he social
scien is . McG aw-Hill Educa ion.
Gillespie, T. (2014). The ele ance o algo i hms. Media
Technologies: Essays on Communica ion, Ma e iali y, and
Socie y, 167–194.
h ps://doi.o g/10.7551/mi p ess/9780262525374.003.0009
Gössling, S., & Higham, J. (2020). The Low-Ca bon Impe a i e:
Des ina ion Managemen unde U gen Clima e Change.
Jou nal o T a el Resea ch, 60(6), 1167-1179.
h ps://doi.o g/10.1177/0047287520933679
Gössling, S., Sco , D., & Hall, C. M. (2021). Pandemics, ou ism
and global change: A apid assessmen o COVID-19. Jou nal
o Sus ainable Tou ism, 29(1), 1-20.
h ps://doi.o g/10.1080/09669582.2020.1758708
Go e s, R., & Go, F. M. (2016). Place b anding: Glocal, i ual and
physical iden i ies, cons uc ed, imagined and expe ienced.
Palg a e Macmillan.
G aham, C., & Roll, N. (2024). E alua ing OpenAI's Whispe ASR:
Pe o mance analysis ac oss di e se accen s and speake ai s.
JASA Exp ess Le e s, 4, A icle 025206.
h ps://doi.o g/10.1121/10.0024876
G ayson, K., & Ma inec, R. (2004). Consume pe cep ions o
iconici y and indexicali y and hei in luence on assessmen s o
au hen ic ma ke o e ings. Jou nal o Consume Resea ch,
31(2), 296–312. h ps://doi.o g/10.1086/422109
G e zel, U. (2011). In elligen sys ems in ou ism: A social science
pe spec i e. Annals o Tou ism Resea ch, 38(3), 757–779.
h ps://doi.o g/10.1016/j.annals.2011.04.014
G e zel, U., Fuchs, M., Baggio, R., Hoepken, W., Law, R.,
Neidha d , J., ... & Xiang, Z. (2020). e-Tou ism beyond
COVID-19: A call o ans o ma i e esea ch. In o ma ion
Technology & Tou ism, 22(2), 187-203.
h ps://doi.o g/10.1007/s40558-020-00181-3
G oss, J. J. (2015). Emo ion egula ion: Cu en s a us and u u e
p ospec s. Psychological Inqui y, 26(1), 1–26.
h ps://doi.o g/10.1080/1047840X.2014.940781
Guo, Q., Zhu, D., Lin, M.-T. (B)., Li, F. (S)., Kim, P. B., Du, D., &
Shu, Y. (2023). Hospi ali y employees’ echnology adop ion a
he wo kplace: E idence om a me a-analysis. In e na ional
Jou nal o Con empo a y Hospi ali y Managemen , 35(7),
2437–2464. h ps://doi.o g/10.1108/IJCHM-06-2022-0701
Gu en ag, D. (2022). Vi ual Reali y and he End o Tou ism? A
Subs i u ion Accep ance Model," Sp inge Books, in: Zheng
Xiang & Ma hias Fuchs & Ul ike G e zel & Wol am Höpken
(ed.), Handbook o e-Tou ism, chap e 79, pages 1901-1919,
Sp inge .
Hagendo , T. (2020). The e hics o AI e hics: An e alua ion o
guidelines. Minds and Machines, 30(1), 99–120.
h ps://doi.o g/10.1007/s11023-020-09517-8
Halde , S., Lim, K. H., Chan, J., & Zhang, X. (2024). A su ey on
pe sonalized i ine a y ecommenda ion: F om op imisa ion o
deep lea ning. Applied So Compu ing, 152, A icle 111200.
h ps://doi.o g/10.1016/j.asoc.2023.111200
Hassenzahl, M. (2010). Expe ience design: Technology o all he
igh easons. Syn hesis Lec u es on Human‐Cen e ed
In o ma ics, 3(1), 1–95.
h ps://doi.o g/10.2200/S00261ED1V01Y201003HCI008
Higgins-Desbiolles, F. (2018). Sus ainable ou ism: Sus aining
ou ism o some hing mo e? Tou ism Managemen
Pe spec i es, 25, 157–160.
h ps://doi.o g/10.1016/j. mp.2017.11.017
Hil y, L. M., & Aebische , B. (Eds.). (2015). ICT inno a ions o
sus ainabili y. Sp inge . h ps://doi.o g/10.1007/978-3-319-
09228-7
Hochschild, A. R. (2012). The managed hea : Comme cializa ion o
human eeling (3 d ed.). Uni e si y o Cali o nia P ess.
Huang, J., Xu, Y., Wang, Q., Wang, Q. (C.), Liang, X., Wang, F.,
Zhang, Z., Wei, W., Zhang, B., Huang, L., Chang, J., Ma, L.,
Ma, T., Liang, Y., Zhang, J., Guo, J., Jiang, X., Fan, X., An, Z.,
Li, T., … Fei, A. (2025). Founda ion models and in elligen
decision-making: P og ess, challenges, and pe spec i es. The
Inno a ion, 6(6), A icle 100948.
h ps://doi.o g/10.1016/j.xinn.2025.100948
In e na ional Labou O ganiza ion (ILO). (2021). Wo ld
Employmen and Social Ou look 2021: The ole o digi al
labou pla o ms in ans o ming he wo ld o wo k. Gene a:
ILO.
I ano , S. (2020). The impac o au oma ion on ou ism and
hospi ali y jobs. In o ma ion Technology & Tou ism, 22(2),
205–215. h ps://doi.o g/10.1007/s40558-020-00175-1
I ano , S., & Webs e , C. (2017). Adop ion o obo s, a i icial
in elligence and se ice au oma ion by a el, ou ism and
hospi ali y companies—a cos -bene i analysis. P epa ed o he
In e na ional Scien i ic Con e ence "Con empo a y Tou ism –
T adi ions and Inno a ions", So ia Uni e si y, 19-21 Oc obe
2017, A ailable a SSRN: h ps://ss n.com/abs ac =3007577
I ano , S., & Webs e , C. (2019a). Pe cei ed app op ia eness and
in en ion o use se ice obo s in ou ism. In Pesonen, J., &
Neidha d , J. (Eds.), In o ma ion and Communica ion
Technologies in Tou ism 2019 (pp. 237–248). Sp inge .
h ps://doi.o g/10.1007/978-3-030-05940-8_19
I ano , S., & Webs e , C. (2019b). Economic undamen als o he
use o obo s, a i icial in elligence, and se ice au oma ion in
a el, ou ism, and hospi ali y. In S. I ano & C. Webs e
(Eds.), Robo s, a i icial in elligence, and se ice au oma ion in
a el, ou ism and hospi ali y (pp. 39–55). Eme ald Publishing
Limi ed. h ps://doi.o g/10.1108/978-1-78756-687-320191002
I asciuc, I.-S., Cand ea, A. N., & Ispas, A. (2025). Explo ing
Tou ism Expe iences: The Vision o Gene a ion Z Ve sus
A i icial In elligence. Adminis a i e Sciences, 15(5), 186.
h ps://doi.o g/10.3390/admsci15050186
GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 37
Jä i, H., Kähkönen, A. K., & To inen, H. (2018). When alue co-
c ea ion ails: Reasons ha lead o alue co-des uc ion.
Scandina ian Jou nal o Managemen , 34(1), 63–77.
h ps://doi.o g/10.1016/j.scaman.2018.01.002
Jo, Y., & Shin, H. (2025). Can Gami ica ion and Augmen ed Reali y
(AR) Re i alize Declining Des ina ions? In es iga ing i s
Impac on Tou is A i udes and Beha io . Jou nal o T a el
Resea ch, 0(0). h ps://doi.o g/10.1177/00472875251332961
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape o
AI e hics guidelines. Na u e Machine In elligence, 1(9), 389–
399. h ps://doi.o g/10.1038/s42256-019-0088-2
Jones, N. (2018). How o s op da a cen es om gobbling up he
wo ld’s elec ici y. Na u e, 561(7722), 163–166.
h ps://doi.o g/10.1038/d41586-018-06610-y
Jo zik, P., Klein, S. P., Kanbach, D. K., & K aus, S. (2024). AI-
d i en business model inno a ion: A sys ema ic e iew and
esea ch agenda. Jou nal o Business Resea ch, 182, A icle
114764. h ps://doi.o g/10.1016/j.jbus es.2024.114764
Kahneman, D. (2011). Thinking, as and slow. Fa a , S aus and
Gi oux.
Kai ouz, P., McMahan, H. B., A en , B., Belle , A., Bennis, M.,
Bhagoji, A. N., Bonawi z, K., Cha les, Z., Co mode, G.,
Cummings, R., D’Oli ei a, R. G. L., Eichne , H., El Rouayheb,
S., E ans, D., Ga dne , J., Ga e , Z., Gascón, A., Ghazi, B.,
Gibbons, P. B., G u ese , M., … Zhao, S. (2021). Ad ances and
open p oblems in ede a ed lea ning. Founda ions and T ends®
in Machine Lea ning, 14(1–2), 1–210.
h ps://doi.o g/10.1561/2200000083
Kim, J., & Fesenmaie , D. R. (2015). Measu ing emo ions in eal
ime: Implica ions o ou ism expe ience design. Jou nal o
T a el Resea ch, 54(4), 419–429.
h ps://doi.o g/10.1177/0047287514550100
Ki chne , S., Meye , S. & Tisch, A. (2023). “Digi al Taylo ism” o
some, “digi al sel -de e mina ion” o o he s? Inequali y in job
au onomy ac oss di e en ask domains. Zei sch i ü
Sozial e o m, 69(1), 57-84. h ps://doi.o g/10.1515/zs -2022-
0101
Ki illo a, K., Leh o, X. Y., & Cai, L. (2017). Exis en ial au hen ici y
and anxie y as ou comes: The ou is in he expe ience
economy. In e na ional Jou nal o Tou ism Resea ch, 19(1),
13–26. h ps://doi.o g/10.1002/j .2080
Ki chin, R. (2014). The da a e olu ion: Big da a, open da a, da a
in as uc u es and hei consequences. Sage.
h ps://doi.o g/10.4135/9781473909472
Ki chin, R. (2017). Thinking c i ically abou and esea ching
algo i hms. In o ma ion, Communica ion & Socie y, 20(1), 14–
29. h ps://doi.o g/10.1080/1369118X.2016.1154087
Koens, K., Pos ma, A., & Papp, B. (2018). Is o e ou ism o e used?
Unde s anding he impac o ou ism in a ci y con ex .
Sus ainabili y, 10(12), 4384.
h ps://doi.o g/10.3390/su10124384
Kuku ai, T., & Taylo , J. (2016). Indigenous da a so e eign y:
Towa d an agenda. ANU P ess.
h ps://doi.o g/10.22459/CAEPR38.11.2016
Kusne , M. J., Lo us, J. R., Russell, C., & Sil a, R. (2017).
Coun e ac ual ai ness. Ad ances in Neu al In o ma ion
P ocessing Sys ems, 30, 4066–4076.
h ps://doi.o g/10.48550/a Xi .1703.06856
Kzaz, L., Dakhchoune, D., & Dahab, D. (2018). Tou ism
ecommende sys ems: An o e iew o ecommenda ion
app oaches. In e na ional Jou nal o Compu e Applica ions,
180(20), 40–46. h ps://doi.o g/10.5120/ijca2018916467
La iza Co al-Gonzalez, L., Judi h Ca azos-A oyo, J., & Ga cía-
Mes anza, J. (2023). Regene a i e ou ism: A bibliome ic
analysis. Jou nal o Tou ism, He i age & Se ices Ma ke ing,
9(2), 41–54. h ps://doi.o g/10.5281/zenodo.10539722
La ou , B. (2005). Reassembling he Social: An In oduc ion o
Ac o –Ne wo k–Theo y. Ox o d Uni e si y P ess.
Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tou ism demand
o ecas ing: A deep lea ning app oach. Annals o Tou ism
Resea ch, 75, 410–423.
h ps://doi.o g/10.1016/j.annals.2019.01.014
Lee, J. L.-M., Lau, C. Y.-L., & Wong, C. W.-G. (2023).
Reexamining b and loyal y and b and awa eness wi h social
media ma ke ing: A collec i is coun y pe spec i e. Jou nal o
Tou ism, He i age & Se ices Ma ke ing, 9(2), 3–10.
h ps://doi.o g/10.5281/zenodo.10539175
Lemon, K. N., & Ve hoe , P. C. (2016). Unde s anding Cus ome
Expe ience Th oughou he Cus ome Jou ney. Jou nal o
Ma ke ing, 80(6), 69-96. h ps://doi.o g/10.1509/jm.15.0420
Lenzen, M., Sun, Y.-Y., Fa u ay, F., Ting, Y.-P., Geschke, A., &
Malik, A. (2018). The ca bon oo p in o global ou ism.
Na u e Clima e Change, 8(6), 522–528.
h ps://doi.o g/10.1038/s41558-018-0141-x
Leona di, P. M. (2011). When lexible ou ines mee lexible
echnologies. MIS Qua e ly, 35(1), 147–168.
h ps://doi.o g/10.2307/23043493
Leona di, P. M., & Ba ley, S. R. (2008). Ma e iali y and change:
Challenges o building be e heo y abou echnology and
o ganizing. In o ma ion and O ganiza ion, 18(3), 159–176.
h ps://doi.o g/10.1016/j.in oando g.2008.03.001
Leona di, P. M., & Neeley, T. (2022). The digi al mindse : Wha i
eally akes o h i e in he age o da a, algo i hms, and AI.
Ha a d Business Re iew P ess.
Li, X., Law, R., Vu, H. Q., & Rong, J. (2013). Disco e ing he ho el
selec ion p e e ences o Hong Kong inbound a ele s using he
Choque In eg al. Tou ism Managemen , 36, 321–330.
h ps://doi.o g/10.1016/j. ou man.2012.10.017
Li, Y., & Zhu, J. (2024). An e hical s udy o gene a i e AI om he
Ac o –Ne wo k Theo y pe spec i e. a Xi .
h ps://doi.o g/10.48550/a Xi .2406.11860
Ligh , B., Bu gess, J., & Duguay, S. (2018). The walk h ough
me hod: An app oach o he s udy o apps. New Media &
Socie y, 20(3), 881–900.
h ps://doi.o g/10.1177/1461444816675438
Liu, B., Ge, J., & Wang, J. (2025). Vaiage: A mul i-agen solu ion o
pe sonalized a el planning. a Xi .
h ps://doi.o g/10.48550/a Xi .2505.10922
Liu, S., & Hao, F. (2024). Engaging wi h a a a in i ual
egene a i e ou ism. Jou nal o T a el & Tou ism Ma ke ing,
41(6), 864–879.
h ps://doi.o g/10.1080/10548408.2024.2350053
Long, D., & Mage ko, B. (2020). Wha is AI li e acy? Compe encies
and design conside a ions. P oceedings o he 2020 CHI
Con e ence, 1-16. h ps://doi.o g/10.1145/3313831.3376727
Lu, L., Cai, R., & Gu soy, D. (2019). De eloping and alida ing a
se ice obo in eg a ion willingness scale. In e na ional Jou nal
o Hospi ali y Managemen , 80, 36–51.
h ps://doi.o g/10.1016/j.ijhm.2019.01.005
Luccioni, A. S., Viguie , S., & Ligoza , A.-L. (2023). Es ima ing he
ca bon oo p in o BLOOM, a 176B pa ame e language
model. Jou nal o Machine Lea ning Resea ch, 24, 1–15.
h ps://hal.science/hal-04288059 1
Lundbe g, S. M., & Lee, S.-I. (2017). A uni ied app oach o
in e p e ing model p edic ions. Ad ances in Neu al In o ma ion
P ocessing Sys ems, 30, 4765–4774.
h ps://doi.o g/10.48550/a Xi .1705.07874
Luong, T.-B. (2024). The impac o uses and mo i a ion
g a i ica ions on ou is beha io al in en ion: The media ing
ole o des ina ion image and ou is s’ a i udes. Jou nal o
Tou ism, He i age & Se ices Ma ke ing, 10(1), 3–13.
h ps://doi.o g/10.5281/zenodo.13771484
Machado, M., Dias, Á., Pa uleia, M., & Pe ei a, L. (2025). A model
o ma ke ing-d i en inno a ion in li es yle ou ism businesses.
Jou nal o Tou ism, He i age & Se ices Ma ke ing, 11(1), 21–
33. h ps://doi.o g/10.5281/zenodo.15651896
Maglio, P. P., & Spoh e , J. (2013). A se ice science pe spec i e on
business model inno a ion. Indus ial Ma ke ing Managemen ,
42(5), 665–670.
h ps://doi.o g/10.1016/j.indma man.2013.05.007
38 E angelos Ch is ou, Anes is Fo iadis and An onios Giannopoulos
Ma hu , M. B., & Reichling, D. B. (2016). Na iga ing a social wo ld
wi h obo pa ne s: A quan i a i e ca og aphy o he uncanny
alley. Cogni ion, 146, 22–32.
h ps://doi.o g/10.1016/j.cogni ion.2015.09.008
Maye , R. C., Da is, J. H., & Schoo man, F. D. (1995). An
in eg a i e model o o ganiza ional us . Academy o
Managemen Re iew, 20(3), 709–734.
h ps://doi.o g/10.5465/am .1995.9508080335
McCa hy, J., & W igh , P. (2004). Technology as expe ience. MIT
P ess.
McKe che , B., & P ideaux, B. (2014). Academic my hs o ou ism.
Annals o Tou ism Resea ch, 46, 16-28.
h ps://doi.o g/10.1016/j.annals.2014.02.003
Menzel, S., Sp inge , S., Ziege , M., & S zelecki, A. (2023).
Google T ends con i ms (COVID)-19 impac on ou is
indus y. Tou ism Cul u e & Communica ion, 23(2–3), 97–102.
h ps://doi.o g/10.3727/109830422X16600594683418
Michael, N., Chunawala, M. A., & Fus é-Fo né, F. (2025).
Ins ag ammable des ina ions: he use o pho og aphs in digi al
ou ism ma ke ing in he Uni ed A ab Emi a es. Jou nal o
Tou ism, He i age & Se ices Ma ke ing, 11(1), 3–10.
h ps://doi.o g/10.5281/zenodo.15650944
Michael, N., & Fo iadis, A. (2022). Employee u no e : The ho el
indus y pe spec i e. Jou nal o Tou ism, He i age & Se ices
Ma ke ing, 8(1), 38-47. h ps://zenodo.o g/ eco ds/6583698
Milan, S., & T e é, E. (2019). Big Da a om he Sou h(s): Beyond
da a uni e salism. Tele ision & New Media, 20(4), 319–335.
h ps://doi.o g/10.1177/1527476419837739
Misi lis, N., Elsho , M., & Vlachopoulou, M. (2021). Modeling
Facebook use s ’ beha io owa ds he use o pages ela ed o
heal hy die and spo ac i i ies. Jou nal o Tou ism, He i age &
Se ices Ma ke ing, 7(2), 49–57.
h ps://doi.o g/10.5281/zenodo.5549911
Mi chell, M., Wu, S., Zaldi a , A., Ba nes, P., Vasse man, L.,
Hu chinson, B., Spi ze , E., Raji, I. D., & Geb u, T. (2019).
Model ca ds o model epo ing. In P oceedings o he 2019
Con e ence on Fai ness, Accoun abili y, and T anspa ency
(FAT ’19)* (pp. 220–229). Associa ion o Compu ing
Machine y. h ps://doi.o g/10.1145/3287560.3287596
Mi chell, M., Wu, S., Zaldi a , A., Ba nes, P., Vasse man, L.,
Hu chinson, B., & Geb u, T. (2019). Model ca ds o model
epo ing. In P oceedings o he 2019 Con e ence on Fai ness,
Accoun abili y, and T anspa ency (pp. 220–229). Associa ion
o Compu ing Machine y.
h ps://doi.o g/10.1145/3287560.3287596
Mi els ad , B. D., Allo, P., Taddeo, M., Wach e , S., & Flo idi, L.
(2016). The e hics o algo i hms: Mapping he deba e. Big Da a
& Socie y, 3(2), 2053951716679679.
h ps://doi.o g/10.1177/2053951716679679
Mkono, M. (2013). A ican and Wes e n ou is s: Objec au hen ici y
ques ? Annals o Tou ism Resea ch, 41, 195–214.
h ps://doi.o g/10.1016/j.annals.2013.01.002
Molna , C. (2022). In e p e able Machine Lea ning (2nd ed.).
Leanpub.
Mo i, M. (1970). The uncanny alley. Ene gy, 7(4), 33–35.
h ps://doi.o g/10.1109/MRA.2012.2192811 ( ep in DOI)
Mo ley, J., Elhalal, A., & Ga cia, F. (2021). E hics as a se ice: A
p agma ic ope a ionalisa ion o AI e hics. Minds and Machines,
31(2), 239–256. h ps://doi.o g/10.1007/s11023-021-09563-w
Mo ley, J., Widdicks, K. V., & Hazas, M. D. (2018). Digi alisa ion,
ene gy and da a demand: The impac o In e ne a ic on
o e all and peak elec ici y consump ion. Ene gy Resea ch &
Social Science, 38, 128–137.
h ps://doi.o g/10.1016/j.e ss.2018.01.018
Nass, C., & Moon, Y. (2000). Machines and mindless social
a ibu ions: Compu e s as social ac o s. Jou nal o Social
Issues, 56(1), 81–103. h ps://doi.o g/10.1111/0022-4537.00153
Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social
esponses o compu e s. Jou nal o Social Issues, 56(1), 81–
103. h ps://doi.o g/10.1111/0022-4537.00153
Nechoud, L., Ghidouche, F., & Se aphin, H. (2021). The in luence
o eWOM c edibili y on isi in en ion: An in eg a i e
mode a ed media ion model. Jou nal o Tou ism, He i age &
Se ices Ma ke ing, 7(1), 54–63.
h ps://doi.o g/10.5281/zenodo.4521314
Neuho e , B., Buhalis, D., & Ladkin, A. (2015). Sma echnologies
o pe sonalized expe iences: A case s udy in he hospi ali y
domain. Elec onic Ma ke s, 25(3), 243–254.
h ps://doi.o g/10.1007/s12525-015-0182-1
Nissenbaum, H. (2010). P i acy in con ex : Technology, policy, and
he in eg i y o social li e. S an o d Uni e si y P ess.
Noble, S. U. (2018). Algo i hms o opp ession: How sea ch engines
ein o ce acism. NYU P ess.
h ps://doi.o g/10.2307/j.c 1pw 9w5
Nu ski, L., & Ho man, M. (2022). The impac o a i icial
in elligence on he na u e and quali y o jobs (Wo king Pape
14/2022). B uegel. Re ie ed om
h ps://www.b uegel.o g/sys em/ iles/2022-
07/WP%2014%202022.pd
O’Hagan, M. (2016). Massi ely open ansla ion. In e na ional
Jou nal o Communica ion, 10, 929–946.
O'Neil, C. (2016). Weapons o ma h des uc ion: How big da a
inc eases inequali y and h ea ens democ acy. C own
Publishing G oup.
O ganisa ion o Economic Co-ope a ion and De elopmen (OECD).
(2024). A i icial In elligence and Tou ism: G7/OECD Policy
Pape . OECD Publishing.
O likowski, W. J. (2007). Socioma e ial p ac ices: Explo ing
echnology a wo k. O ganiza ion S udies, 28(9), 1435–1448.
h ps://doi.o g/10.1177/0170840607081138
O likowski, W. J., & Iacono, C. S. (2001). Resea ch commen a y:
Despe a ely seeking he “IT” in IT esea ch—a call o
heo izing he IT a i ac . In o ma ion Sys ems Resea ch, 12(2),
121–134. h ps://doi.o g/10.1287/is e.12.2.121.9700
O likowski, W. J., & Sco , S. V. (2008). Socioma e iali y:
Challenging he sepa a ion o echnology, wo k, and
o ganiza ion. Academy o Managemen Annals, 2(1), 433–474.
h ps://doi.o g/10.5465/19416520802211644
Pad icelli, G. M., Punziano, G., & Sa acino, B. (2021). Vi ual s.
digi al: Examples o ne nog aphy and digi al e hnog aphy in
ou ism s udies o a compa ison be ween me hods. A hens
Jou nal o Social Sciences, 8(4), 259–278.
h ps://doi.o g/10.30958/ajss.8-4-4
Paluch, S., & Wünde lich, N. V. (2016). Con as ing isk
pe cep ions o echnology-based se ice inno a ions in in e -
o ganiza ional se ings. Jou nal o Business Resea ch, 69(7),
2424–2431. h ps://doi.o g/10.1016/j.jbus es.2016.01.012
Pasquale, F. (2015). The black box socie y: The sec e algo i hms
ha con ol money and in o ma ion. Ha a d Uni e si y P ess.
h ps://doi.o g/10.4159/ha a d.9780674736061
Pa e son, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M.,
Ro hchild, D., So, D., Texie , M., & Dean, J. (2021). Ca bon
emissions and la ge neu al ne wo k aining. a Xi .
h ps://doi.o g/10.48550/a Xi .2104.10350
Pé ez-Rica do, E. del C., & Ga cía-Mes anza, J. (2023). Booking
in en ion esea ch p og ess: Eme ging ends and esea ch
agenda. Jou nal o Tou ism, He i age & Se ices Ma ke ing,
9(2), 11–27. h ps://doi.o g/10.5281/zenodo.10539221
Pe necky, T. (2023). Mobilizing ela ional on ology: mee ing he
plu i e sal challenge in ou ism s udies. Cu en Issues in
Tou ism, 27(22), 3623–3635.
h ps://doi.o g/10.1080/13683500.2023.2281599
Pica d, R. W. (1997). A ec i e compu ing. MIT P ess.
Pillai, R., & Si a hanu, B. (2020). Adop ion o AI-based cha bo s
o hospi ali y and ou ism. In e na ional Jou nal o
Con empo a y Hospi ali y Managemen , 32(10), 3199–3226.
h ps://doi.o g/10.1108/IJCHM-04-2020-0259
Pine, J. B., & Gilmo e, J. H. (1999). The expe ience economy: Wo k
is hea e & e e y business a s age. Ha a d Business School
P ess.
GENAI AS A TOURISM ACTOR: RECONCEPTUALISING CO-CREATION, DESTINATION GOVERNANCE & RESPONSIBLE INNOVATION 39
Pink, S., Ho s , H., Pos ill, J., Hjo h, L., & Lewis, T. (2022). Digi al
e hnog aphy: P inciples and p ac ice (2nd ed.). SAGE
Publica ions.
Pi akaso, R., Golinska-Dawson, P., Luesak, P., S ichok, T., &
Khonjun, S. (2025). Emb acing open inno a ion in hospi ali y
managemen : Le e aging AI-d i en dynamic scheduling
sys ems o complex esou ce op imiza ion and enhanced gues
sa is ac ion. Jou nal o Open Inno a ion: Technology, Ma ke ,
and Complexi y, 11(1), A icle 100487.
h ps://doi.o g/10.1016/j.joi mc.2025.100487
Polyzos, S., Sami as, A., & Spy idou, A. E . (2020). Tou ism
demand and he COVID-19 pandemic: an LSTM app oach.
Tou ism Rec ea ion Resea ch, 46(2), 175–187.
h ps://doi.o g/10.1080/02508281.2020.1777053
P ahalad, C. K., & Ramaswamy, V. (2004). Co-c ea ion
expe iences: The nex p ac ice in alue c ea ion. Jou nal o
In e ac i e Ma ke ing, 18(3), 5–14.
h ps://doi.o g/10.1002/di .20015
P ebensen, N. K., Chen, J. S., & Uysal, M. (Eds.). (2014). C ea ing
expe ience alue in ou ism. CABI Publishing.
h ps://doi.o g/10.1079/9781780643489.0000
P i i e a, A.G., Fon ana, F., & Ge onazzo, M. (2025). The Role o
Audio in Imme si e S o y elling: a Sys ema ic Re iew in
Cul u al He i age. Mul imedia Tools & Applica ions, 84,
16105–16143. h ps://doi.o g/10.1007/s11042-024-19288-4
Punpeng, G., & Yodnane, P. (2024). The ou e o imme sion: a
concep ual amewo k o c oss-disciplina y imme si e hea e
and expe iences. Humani ies & Social Sciencies
Communica ions, 11, 1477. h ps://doi.o g/10.1057/s41599-
024-03971-w
Pym, A. (2014). Explo ing ansla ion heo ies (2nd ed.). Rou ledge.
Raji, I. D., e al. (2020). Closing he AI accoun abili y gap.
P oceedings o FAT 2020*, 33–44.
h ps://doi.o g/10.1145/3351095.3372873
Raji, I. D., Sma , A., Whi e, R. N., Mi chell, M., Geb u, T.,
Hu chinson, B., Smi h-Loud, J., The on, D., & Ba nes, P.
(2020). Closing he AI accoun abili y gap: De ining an end- o-
end amewo k o in e nal algo i hmic audi ing. In
P oceedings o he 2020 Con e ence on Fai ness,
Accoun abili y, and T anspa ency (FAT* ’20) (pp. 33–44).
Associa ion o Compu ing Machine y.
h ps://doi.o g/10.1145/3351095.3372873
Reed, M. S. (2007). Pa icipa o y echnology de elopmen o
ag o o es y ex ension: An inno a ion-decision app oach.
A ican Jou nal o Ag icul u al Resea ch, 2(8), 334–341.
Ree es, B., & Nass, C. (1996). The media equa ion. Camb idge
Uni e si y P ess.
Ren, C., Jóhannesson, G. T., & an de Duim, R. (Eds.). (2012).
Ac o -ne wo k heo y and ou ism: O de ing, ma e iali y and
mul iplici y (1s ed.). Rou ledge.
h ps://doi.o g/10.4324/9780203122976
Ren, C., Jóhannesson, G. T., & an de Duim, R. (Eds.). (2017). Co-
c ea ing ou ism esea ch: Towa ds collabo a i e ways o
knowing (1s ed.). Rou ledge.
h ps://doi.o g/10.4324/9781315393223
Rezwana, J., & Fo d, C. (2025). Human-cen e ed AI communica ion
in co-c ea i i y: An ini ial amewo k and insigh s. In C&C '25:
P oceedings o he 2025 Con e ence on C ea i i y and
Cogni ion (pp. 651–665).
h ps://doi.o g/10.1145/3698061.3726932
Ribei o, M. T., Singh, S., & Gues in, C. (2016). “Why should I us
you?”: Explaining he p edic ions o any classi ie . P oceedings
o he 22nd ACM SIGKDD In e na ional Con e ence on
Knowledge Disco e y and Da a Mining, 1135–1144.
h ps://doi.o g/10.1145/2939672.2939778
Rickly, J. M., & Vidon, E. S. (Eds.). (2018). Au hen ici y & ou ism:
Ma e iali ies, pe cep ions, expe iences. Eme ald Publishing.
Rocks öm, J., S e en, W., Noone, K., Pe sson, Å., Chapin III, F. S.,
Lambin, E. F., Len on, T. M., Sche e , M., Folke, C.,
Schellnhube , H. J., Nyk is , B., de Wi , C. A., Hughes, T., an
de Leeuw, S., Rodhe, H., Sö lin, S., Snyde , P. K., Cos anza,
R., S edin, U., Falkenma k, M., … Foley, J. A. (2009). A sa e
ope a ing space o humani y. Na u e, 461(7263), 472–475.
h ps://doi.o g/10.1038/461472a
Rocks öm, J., S e en, W., Noone, K., Pe sson, Å., Chapin, F. S.,
III, Lambin, E. F., Len on, T. M., Sche e , M., Folke, C.,
Schellnhube , H. J., Nyk is , B., de Wi , C. A., Hughes, T., an
de Leeuw, S., Rodhe, H., Sö lin, S., Snyde , P. K., Cos anza,
R., S edin, U., … Foley, J. A. (2009). A sa e ope a ing space
o humani y. Na u e, 461(7263), 472–475.
h ps://doi.o g/10.1038/461472a
Samuelson, P. (2023). Gene a i e AI mee s copy igh . Science,
381(6654), 158–161. h ps://doi.o g/10.1126/science.adi0656
Sande s, E. B. N., & S appe s, P. J. (2008). Co-c ea ion and he new
landscapes o design. CoDesign, 4(1), 5–18.
h ps://doi.o g/10.1080/15710880701875068
Schmuck, P., & Vlek, C. (2003). Psychologis s can do much o
en i onmen al sus ainabili y. Eu opean Psychologis , 8(2), 66–
76. h ps://doi.o g/10.1027//1016-9040.8.2.66
Schneide , J., Meske, C., & Kuss, P. (2024). Founda ion models.
Business & In o ma ion Sys ems Enginee ing, 66(2), 221–231.
h ps://doi.o g/10.1007/s12599-024-00851-0
Schwa z, R., Dodge, J., Smi h, N. A., & E zioni, O. (2020). G een
AI. Communica ions o he ACM, 63(12), 54–63.
h ps://doi.o g/10.1145/3381831
Sco , D. (2014). The ou is : a new heo y o he leisu e class.
Annals o Leisu e Resea ch, 17(1), 113–114.
h ps://doi.o g/10.1080/11745398.2014.890513
Sha pley, R. (2020). Tou ism, sus ainable de elopmen and he
heo e ical di ide: 20 yea s on. Jou nal o Sus ainable Tou ism,
28(11), 1932–1946.
h ps://doi.o g/10.1080/09669582.2020.1779732
Shaw, G., Bailey, A., & Williams, A. M. (2011). Se ice dominan
logic and i s implica ions o ou ism managemen . Tou ism
Managemen , 32(2), 207–214.
h ps://doi.o g/10.1016/j. ou man.2010.05.020
Shawal, N. S. M., Bakh ia , M. F. S., Nu zaman, M. A. A. K.,
Kedin, N. A., & Talib, A. H. (2023). Explo ing Use
Accep ance, Expe ience and Sa is ac ion owa ds Cha bo s in
an Online T a el Agency (OTA). In e na ional Jou nal o
Academic Resea ch in Business and Social Sciences, 13(5),
196 – 206. h p://dx.doi.o g/10.6007/IJARBSS/ 13-i5/17015
Sigala, M. (2018). Implemen ing social cus ome ela ionship
managemen : A p ocess amewo k and implica ions in ou ism
and hospi ali y. In e na ional Jou nal o Con empo a y
Hospi ali y Managemen , 30(7), 2698-2726.
h ps://doi.o g/10.1108/IJCHM-10-2015-0536
Singh, R., & Sibi, P.S. (2023). E-loyal y o ma ion o Gene a ion Z:
Pe sonal cha ac e is ics and social in luences. Jou nal o
Tou ism, He i age & Se ices Ma ke ing, 9(1), 3–14.
h ps://doi.o g/10.5281/zenodo.8054004
Solo e, D. J. (2006). A axonomy o p i acy. Uni e si y o
Pennsyl ania Law Re iew, 154(3), 477–560.
h ps://doi.o g/10.2307/40041279
Sousa, N., Alén González, M. E., Losada, N., & Melo, M. (2024).
The adop ion o Vi ual Reali y echnologies in he ou ism
sec o : In luences and pos -pandemic pe spec i es. Jou nal o
Tou ism, He i age & Se ices Ma ke ing, 10(2), 47-57.
h ps://zenodo.o g/ eco ds/14524968
S e en, W., Richa dson, K., Rocks öm, J., Co nell, S. E., Fe ze , I.,
Benne , E. M., ... & Sö lin, S. (2015). Plane a y bounda ies:
Guiding human de elopmen on a changing plane . Science,
347(6223), 1259855. h ps://doi.o g/10.1126/science.1259855
S ilgoe, J., Owen, R., & Macnagh en, P. (2013). De eloping a
amewo k o esponsible inno a ion. Resea ch Policy, 42(9),
1568–1580. h ps://doi.o g/10.1016/j. espol.2013.05.008
S ubell, E., Ganesh, A., & McCallum, A. (2019). Ene gy and policy
conside a ions o deep lea ning in NLP. P oceedings o he
57 h ACL, 3645–3650. h ps://doi.o g/10.18653/ 1/P19-1355
Sylaiou, S., & Fidas, C. (2022). Vi ual Humans in Museums and
Cul u al He i age Si es. Applied Sciences, 12(19), 9913.
h ps://doi.o g/10.3390/app12199913