May , Alexande ; S ahmann, Philip; Nebel, Maximilian; Janiesch, Ch is ian
A icle — Published Ve sion
S ill doing i you sel ? In es iga ing de e minan s o he
adop ion o in elligen p ocess au oma ion
Elec onic Ma ke s
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Sp inge Na u e
Sugges ed Ci a ion: May , Alexande ; S ahmann, Philip; Nebel, Maximilian; Janiesch, Ch is ian
(2024) : S ill doing i you sel ? In es iga ing de e minan s o he adop ion o in elligen p ocess
au oma ion, Elec onic Ma ke s, ISSN 1422-8890, Sp inge , Be lin, Heidelbe g, Vol. 34, Iss. 1,
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RESEARCH PAPER
S ill doing i you sel ? In es iga ing de e minan s o headop ion
o in elligen p ocess au oma ion
Alexande May 1· PhilipS ahmann2 · MaximilianNebel2· Ch is ianJaniesch2
Recei ed: 23 Ap il 2024 / Accep ed: 14 Oc obe 2024 / Published online: 14 No embe 2024
© The Au ho (s) 2024
Abs ac
In elligen p ocess au oma ion (IPA) augmen s symbolic p ocess au oma ion using a i icial in elligence. Emula ing human
decision-making, IPA enables he execu ion o complex p ocesses equi ing decision-making capaci ies. IPA p omises g ea
economic po en ial as i enables mo e e icien use o he human wo k o ce. Howe e , he adop ion a e in p ac ice alls
behind hese po en ials. Ou s udy aims o in es iga e easons and iden i y a eas o ac ion owa ds IPA adop ion. To his
end, we iden i ied 13 de e minan s and c ea ed an ex ended UTAUT model. We es ed he model wi h pa ial leas squa es
s uc u al equa ion modeling o signi ican in luen ial ela ionships be ween he de e minan s based on a use s udy. We
con ibu e o heo y and p ac ice inding a special ole o us and anspa ency o he adop ion o IPA. Likewise, we show
ha o ganiza ions should cul i a e a posi i e a i ude owa ds IPA di usion. Fu he , ou esul s con ibu e wi h a ocus on
he po en ial adop e s as IPA adop ion is con ingen upon hei cha ac e is ics, such as expe ience and job le el.
Keywo ds In elligen p ocess au oma ion· Business p ocess managemen · Robo ic p ocess au oma ion· UTAUT ·
Technology adop ion
JEL Classi ica ion C9· M15
In oduc ion
The idea o au oma ion has cha ac e ized e icien wo k
design o decades. Along wi h echnological ad ancemen s,
asks o iginally pe o med by humans ha e been delega ed
o new echnology (Rin a-Kahila e al., 2023). Fo example,
machines ha e been designed o au oma e epe i i e manu-
ac u ing asks o o ice wo k. Delega ion o echnology has
le e aged wo kinds o ad an ages. On he one hand, wo k-
e s’ capaci ies ha we e in es ed in epe i i e asks could be
used o he wise. On he o he hand, au oma ion s eamlined
ask execu ion leading o educ ions in ope a ional ailu es
and manu ac u ing a ia ions. Opposed o physical labo ,
knowledge-in ensi e asks ha e emained mos ly un ouched
by au oma ion as hey equi ed human cogni ion o deci-
sion-making (Rin a-Kahila e al., 2023). Howe e , he p es-
su e o au oma e knowledge-in ensi e asks g ows as he
wo k amoun in on and back o ices inc eases e e y yea
binding mo e and mo e capaci y (Willcocks, 2020).
To le e age back o ice and on o ice au oma ion po en-
ials in he pas , o ganiza ions ha e used symbolic p ocess
au oma ion enabled by business p ocess managemen
Responsible Edi o : Luba To lina
A p io e sion o his esea ch has been published he e: h ps://
aisel. aisne . o g/ icis2 023/ i ado p / i ado p /6/. We signi ican ly
ex ended he p io publica ion h oughou all sec ions, bu especially
in he me hodology, esul p esen a ion, and discussion. The au ho s
ha e he legal igh s o u he publica ions.
* Philip S ahmann
philip.s ahmann@ u-do mund.de
Alexande May
a.may @pax ay.com
Maximilian Nebel
maximilian.nebel@ u-do mund.de
Ch is ian Janiesch
ch is ian.janiesch@ u-do mund.de
1 Pax ay GmbH, Gmünde S . 14, 73557Mu langen,
Ge many
2 Chai o En e p ise Compu ing, TU Do mund Uni e si y,
O o-Hahn-S . 12, 44227Do mund, Ge many
Elec onic Ma ke s (2024) 34:5656 Page 2 o 22
(BPM) sys ems and obo ic p ocess au oma ion (RPA) so -
wa e (He m e al., 2021) o au oma e highly s anda dized
and ansac ion-in ensi e p ocesses (Asa iani & Pen inen,
2016; Fe sh & Slaby, 2012). As symbolic p ocess au oma-
ion necessi a es he explici o mula ion o sequence lows
and ules, i canno be used o a signi ican po ion o busi-
ness p ocesses ha equi e cogni i e e o s, such as com-
plex decision-making o judgmen (Chak abo i e al., 2020).
In elligen (p ocess) au oma ion (IPA) complemen s sym-
bolic p ocess au oma ion wi h a i icial in elligence (AI)
echnology, which mimics human cogni i e abili ies o
decision-making (Engel e al., 2023; Janiesch e al., 2021).
Enhanced by AI, he IPA oolbox spawns p omising oppo -
uni ies o au oma e complex p ocesses ha equi e cogni-
ion and had o be pe o med by human agen s un il ecen ly.
IPA may be use ul in ackling sophis ica ed p ocess s eps
such as e alua ion, easoning, decision-making, and p o-
cess ul illmen (Chak abo i e al., 2020; IEEE, 2017). IPA
can au oma e complex asks such as image and na u al lan-
guage p ocessing, op ical cha ac e ecogni ion, p edic ion,
o easoning and consequen ly inc eases e iciency and esul
quali y (He m e al., 2021). Al hough IPA can ep esen an
essen ial aspec o o ganiza ions o ensu e hei ele ance
and compe i i eness, many o ganiza ions a e no implemen -
ing hese solu ions on a la ge scale (Jyo i & Szu ley, 2021).
The low adop ion o echnologies in gene al can in ui i ely
be b oken down o inhibi ed success ul implemen a ions in
indi idual o ganiza ions, which in u n has been shown o
be highly dependen on indi idual employee adop ion o
echnologies (Venka esh & Bala, 2008). This aises he ques-
ion which ac o s de e mine success ul embedding o IPA
in o ganiza ions (Engel e al., 2022) and, hence, adop ion
by employees. To in es iga e he de e minan s and u he
iden i y implica ions ha a e likely o inc ease he adop ion
a e o IPA, we o mula e he ollowing esea ch ques ion:
RQ: Which de e minan s in luence he adop ion o in el-
ligen p ocess au oma ion by employees?
P o iding answe s o his esea ch ques ion, we espond
o he call o esea ch by Engel e al. (2022), who obse e
a low adop ion a e o IPA in business o ganiza ion despi e
hei awa eness o i s g ea po en ials. Speci ically, he call
add esses le e aging wo k sys em-o ien ed esea ch oppo -
uni ies ega ding a socio- echnical unde s anding o how o
embed IPA in o ganiza ions. Wi h ou esea ch, we iden i ied
de e minan s o IPA adop ion om li e a u e and p ac ice and
ex ended he es ablished Uni ied Theo y o Accep ance and
Use o Technology (UTAUT) model acco dingly. Ou con i-
bu ion ocuses an ex ension o he es ablished UTAUT model
speci ically o IPA adop ion. We e alua ed he ex ended
model in an i e a i e manne . Ou esul s show ha in addi-
ion o es ablished ac o s o echnology adop ion, us ,
anspa ency, and a i ude owa ds echnology a e p ima y
decision ac o s. The e o e, we a gue o he cul i a ion o a
posi i e a i ude owa ds IPA and he es ablishmen o acili-
a ing condi ions o i s use. In a simila ein, based on ou
s udy esul s, we emphasize he in luence o use expe ience
as well as us acili a ed by anspa ency on IPA adop ion.
The emainde o his pape is s uc u ed as ollows: “The-
o e ical backg ound” ou lines he heo e ical backg ound
on p ocess au oma ion and he adop ion o IPA. “Resea ch
design” co e s he esea ch design. Subsequen ly, “De i a-
ion o de e minan s and hypo heses” de ails he de i a ion
o po en ial de e minan s o adop ion. “Model e alua ion”
p esen s he e alua ion o he model, “Hypo hesis e alua ion”
he e alua ion o he hypo heses. “Discussion” includes a
discussion, implica ions o heo y and p ac ice, and limi a-
ions. Las ly, in “Conclusion and u u e wo k,” we d aw a
conclusion and p o ide s a ing poin s o u u e esea ch.
Theo e ical backg ound
Symbolic andin elligen au oma ion o p ocesses
(Knowledge) Wo k is usually o ganized in in e ela ed
p ocesses comp ising e en s, asks, and decision poin s.
In ol ed ac o s in e ac wi h physical o in angible objec s
o pu sue business goals ypically comp ising quan i iable
alue. Using adi ional p ocess au oma ion means such as
business p ocess managemen (BPM) sys ems o obo ic
p ocess au oma ion (RPA), he sequence o he asks is
de e mined by handc a ed p ocess models. Decision ga e-
ways enable a ian s in execu ion (Dumas e al., 2018).
P ocesses can be di e en ia ed by many means. One
example is equency and a iance o asks ( an de Aals
e al., 2018). T adi ionally, p ocesses ha a e o high e-
quency and only exhibi easonable a iance a e au oma ed
by hea yweigh BPM sys ems as wo k lows. These imple-
men a ions ely on handc a ed p ocess models, in e aces
o BPM so wa e and o en in ol e mul iple depa men s
wi hin o ac oss companies. P ocesses ha in ol e highly
epe i i e asks bu do no ha e a equency and easibili y
high enough o hea yweigh au oma ion a e—o ecen ly—
candida es o ligh weigh au oma ion wi h RPA. RPA is a
gene ic e m ha summa izes a la ge numbe o di e en
au oma ion app oaches. They ha e he common cha ac e -
is ic o pe o ming digi al, ye manual ac i i ies wi hou
changing exis ing so wa e by ins an ia ing so wa e obo s
as agen s ha imi a e human use s ins ead ( an de Aals
e al., 2018). These so wa e obo s ac on he use in e ace
(UI) and do no in e ene in o applica ion code (Agos inelli
e al., 2019) as sui able in e aces o he han he UI o en do
no exis . RPA use in ends o emo e labo -in ensi e, epe i-
i e asks om he wo kload o human wo ke s (Chak abo i
Elec onic Ma ke s (2024) 34:56 Page 3 o 22 56
e al., 2020). P ocesses ha a e ypically p one o au oma-
ion wi h RPA a e cha ac e ized by a high deg ee o s and-
a diza ion, no o ew excep ions, he di isibili y in o sim-
ple and unambiguous ules, a su icien ly la ge olume o
ansac ions, and low o no in e ac ion wi h human wo ke s
(Asa iani & Pen inen, 2016; Fe sh & Slaby, 2012). Mo -
ing, pas ing, copying, unpacking, and me ging da a be ween
sys ems a e ypical examples (Agui e & Rod iguez, 2017).
As wi h BPM sys ems, RPA equi es implemen a ion in a
symbolic manne by o mula ing explici sequence lows and
decision ules (Asa iani & Pen inen, 2016; Fe sh & Slaby,
2012). Bo h app oaches can be summa ized unde he e m
symbolic p ocess au oma ion (He m e al., 2021).
Howe e , a signi ican po ion o business p ocesses
canno be au oma ed in his manne as hey equi e cog-
ni i e capaci ies (Chak abo i e al., 2020). IPA subsumes
app oaches ha po en ially o e come he limi a ions o sym-
bolic p ocess au oma ion (Engel e al., 2022). IPA ep esen s
an app oach ha complemen s and augmen s he me hods o
symbolic p ocess au oma ion wi h he bene i s o AI.
Enhancing p ocess au oma ion wi h AI based on machine
lea ning en ails a signi ican shi om de e minis ic ule-
based o p obabilis ic lea ning-based logic (Engel e al.,
2023). As machine lea ning le e ages a ious kinds o
s a is ical me hods and is used o a a ie y o pu poses,
he e a e mul iple ace s o i s de ini ion (Russell & No ig,
2021). Rega ding p ocess au oma ion, AI based on machine
lea ning con ibu es wi h capabili ies o au onomous, sel -
adap ing decision-making beha io (Engel e al., 2022).
AI decision-making is inspi ed by biological cogni ion as
AI a emp s o emula e human in elligence (Janiesch e al.,
2021). Combined wi h ad ancemen s in compu ing powe ,
AI cons i u es a s ong accele a o o p ocess au oma ion
as i comp ises complex p obabilis ic models enabling
e lec ed, adap ing decision-making (Dalzochio e al., 2020).
IPA he e o e holds po en ial o au oma e complex p ocesses
and asks ha o he wise mus be comple ed by humans. P o-
cesses ha can po en ially bene i om hese abili ies ypi-
cally comp ise a la ge numbe o decision a iables, om
simple asks such as in oice e i ica ion o complex asks
such as enabling sha ing da a wi hin da a us models. Mo e
gene ally, IPA bea s po en ial o asks co e ing e alua ions,
easoning, decision-making, and p ocess ul illmen o de e -
minis ic and p obabilis ic na u e (Chak abo i e al., 2020;
IEEE, 2017). The e o e, IPA can signi ican ly con ibu e o
s a egic business ans o ma ion by le e aging ope a ional
e iciency (Laci y e al., 2021).
Adop ion o in elligen p ocess au oma ion
and heo ies o accep ance anduse
Despi e he po en ial o gain a compe i i e edge, compa-
nies a e hesi an when i comes o IPA adop ion. Repo s
on ealizing ad an ages due o IPA ha e p ognos ic cha -
ac e , bu do no e lec ope a ional p ac ice (Laci y e al.,
2018). Only abou one qua e o ea ly echnology adop e s
ha e implemen ed IPA (Laci y e al., 2021). Hesi a ion is
due o a a ie y o isks ha speci ically a ec knowledge
wo ke s on ope a ional le el. Exempla y isks a e disclosed
ad an ages, lack o communica ion, es ima ed complexi y o
IPA adop ion, insu icien change managemen , and ea o
being eplaced by echnology (Engel e al., 2023). Howe e ,
li e a u e on de e minan s o IPA adop ion is sca ce, while
calling o mo e ac ionable esea ch on IPA adop ion p e ail
(Engel e al., 2022; e.g., Engel e al., 2023).
When i comes o he adop ion o no el echnology,
In o ma ion Sys ems esea ch ypically d aws on es ab-
lished models o in es iga e adop ion de e minan s and
hei ela ionships. UTAUT models a e p ima ily e alua ed
in esea ch using s uc u al equa ion modeling (SEM) (Wil-
liams e al., 2015). S uc u al equa ion modeling (SEM)
aims o depic heo e ically o logically ounded ela ion-
ships be ween la en cons uc s in a sys em o equa ions.
The me hod can be used o es ima e dependencies and e o s
be ween he de ined cons uc s (Weibe & Mühlhaus, 2014).
The sui abili y o UTAUT was p o en in di e en con ex s
o echnology accep ance (Hsu e al., 2014). Fo example,
o e 70 pe cen o he a iance o he co esponding a ge
a iables could be explained in a la ge numbe o s udies
(Sohn & Kwon, 2020).
Besides UTAUT, u he heo ies exis ha ca e o simi-
la ye sligh ly di e en con ex s. The Technology Accep -
ance Model (TAM) aims a unde s anding accep ance and
adop ion o new echnologies (Venka esh & Bala, 2008).
Addi ional o beha io al in en ion and use beha io , pe -
cei ed use ulness and pe cei ed ease o use a e he con-
s uc s a i s co e, which a e al e ed by a ious de e minan s
such as social in luence. Fu he mo e, he Theo y o Planned
Beha io concep ualizes a use ’s in en ion o pe o ming a
beha io wi h an in o ma ion sys em as de e mined by hei
a i ude, subjec i e no ms as well as pe cei ed beha io al
con ol (Ajzen, 1991). The Social Cogni i e Theo y a ge s
os e ing an unde s anding o how obse a ion, imi a ion,
and ein o cemen in social en i onmen s in luence cogni-
i e p ocesses du ing echnology adop ion (D. Compeau
e al., 1999). Compa ably, he Theo y o Reasoned Ac ion
conside s use s’ beha io al in en ion he key de e minan o
hei ac ual beha io (Sheppa d e al., 1988). While he o ig-
inal heo y does no explici ly e e o echnology adop ion,
insigh s, o example on he ole o subjec i e no ms, ha e
been used in In o ma ion Sys ems esea ch in his ega d
(e.g., Albaya i e al., 2020; Jain e al., 2022). Mo eo e , he
Mo i a ional Model se s apa by sc u inizing indi idual
in insic and mo i a o s which lead o di e en le els o
echnology engagemen (Valle and, 1997). The indi idual
mo i a ional model o use s is concep ualized o be a majo
Elec onic Ma ke s (2024) 34:5656 Page 4 o 22
de e minan o echnology adop ion. In addi ion, he Inno-
a ion Di usion Theo y cha ac e izes a p ocess o he
adop ion o new echnologies by use s (Moo e & Benbasa ,
1991). In his ega d, echnology adop ion ollows a p edic -
able pa e n di e en ia ing, o example, inno a i e use s
adop ing new echnologies om mo e adi ional lagga ds.
Pu suing ou esea ch goal, we decided o use an es ab-
lished model, ei he UTAUT o TAM. This allows us o
s and on he shoulde s o hose who in oduced and e alu-
a ed he o iginal as well as u he a iables and i ems in
his con ex . This enables he compa ison o ou esul s wi h
p io and u u e esea ch and allows us o d aw b oade con-
clusions aking in o conside a ion he esul s o o he s as
well. De eloping ou own model would ha e inc eased he
complexi y o ou in es iga ion and would ha e made his
compa abili y o esul s mo e di icul . The isk o es ablish-
ing YAMA, ye ano he modeling app oach, is some hing we
s i ed o a oid (Oei e al., 1992). Aligning wi h Venka esh
(2022), who explici ly p oposed he use o UTAUT o in es-
iga e accep ance o AI- ela ed echnology, we decided o
UTAUT. Fu he mo e, UTAUT is o en ex ended in li e a-
u e o include speci ic cons uc s o cus omize o speci ic
adop ion con ex s (Cha e jee & Bha acha jee, 2020; Ven-
ka esh, 2022; Williams e al., 2015). As exempla y ex en-
sion, Venka esh e al. (2012) de eloped UTAUT2 con i ming
he s uc u e o UTAUT, bu addi ionally co e ing Hedonic
Mo i a ion and P ice Value.
In addi ion, we a gue ha business p ocesses may cons i-
u e a complex ield o au oma ion (e.g., Engel e al., 2022).
To g asp he complexi y om an accep ance pe spec i e, we
selec ed UTAUT due o he a ie y o conside ed cons uc s
o igina ing om a a ie y o es ablished echnology accep -
ance models (Ajzen, 1991; Bandu a, 1986; D. R. Compeau
& Higgins, 1995; Da is, 1985; c . Da is, 1989; Da is e al.,
1989, 1992; Moo e & Benbasa , 1991, 1996; Sheppa d
e al., 1988; Taylo & Todd, 1995; Thompson e al., 1991;
T iandis, 1977; Valle and, 1997; Venka esh e al., 2003;
Venka esh & Speie , 1999). This ounda ion is c i ical o
unde s anding echnology accep ance in he ocused o gani-
za ional se ing. As we a e among he i s ones wi h ou
ocus o in es iga ion, we wan ed o inco po a e a comp e-
hensi e spec um o meaning ul o ge a b oad unde s and-
ing o accep ance o IPA.
Resea ch design
The goal o ou esea ch is o iden i y de e minan s o IPA
adop ion. Using he de e minan s, we aim o an ex ension o
he UTAUT model ha con ibu es o he esea ch domains
o echnology accep ance and p ocess au oma ion. Figu e1
shows he me hodologies we used and how we ela ed hem
in co espondence o ela ed esea ch (c . Sumak e al.,
2010; Wanne e al., 2022).
The me hodological p ocedu e comp ises six s eps as
ou lined in he ollowing. The s eps ei he ocus on heo y
building o e alua ion.
(a) To unde s and he heo e ical basis and o he ini-
ial iden i ica ion o de e minan s o IPA adop ion, we
conduc ed a s uc u ed li e a u e e iew ( om B ocke
e al., 2009; om B ocke e al., 2015). We used he i e
da abases ACM Digi al Lib a y, AISeL, EBSCOhos
Business Sou ce P emie , IEEEXplo e, and Web o
Science. The choice o da abases was due o hei co -
e age o high-quali y ou le s o ela ed esea ch om
In o ma ion Sys ems. We used he sea ch e m ((“uni-
ied heo y o accep ance and use o echnology” OR
u au OR “ echnology accep ance model” OR “ heo y
o planned beha io ” OR “social cogni i e heo y” OR
“ heo y o easoned ac ion” OR “mo i a ional model”
OR “Inno a ion di usion heo y”) AND (“business
Fig. 1 Resea ch p ocedu e
Elec onic Ma ke s (2024) 34:56 Page 5 o 22 56
p ocess managemen ” OR “in elligen au oma ion” OR
“p ocess au oma ion” OR “a i icial in elligence”)). The
i s pa o he sea ch e m e e s o UTAUT as well
as ela ed heo ies comp ising po en ial de e minan s
ele an o ex ending UTAUT. The second pa o he
sea ch e m b oadly co e s e ms ela ing o he (in elli-
gen ) au oma ion o business p ocesses. We conside ed
scien i ic jou nals and con e ence p oceedings. Ini ially,
we iden i ied 2441 publica ions. A e emo ing dupli-
ca es and scanning abs ac s and keywo ds, we educed
he co pus o 152 publica ions. In a ull ex analysis,
we classi ied 67 pape s as ele an o ou esea ch goal.
Du ing scanning and ull ex analysis we excluded all
publica ions ha (1) did no e e o he in elligen
au oma ion o p ocesses o (2) did no con ibu e o he
iden i ica ion o adop ion de e minan s, o example,
as hey we e pu ely heo e ical. Publica ions co e ing
models on echnology accep ance we e omi ed om
he i s exclusion c i e ion o enable inco po a ing a
b oad pe spec i e on accep ance. Subsequen ly, we pe -
o med a o wa d and a backwa d sea ch and iden i ied
73 publica ions esul ing in 225 publica ions o e all.
O hese, 79 con ain speci ic esea ch models in he
con ex o echnology accep ance. The emaining pub-
lica ions include gene al as well as speci ic esea ch
di ec ly o indi ec ly ela ed o IPA. Fo he syn hesis o
he publica ions, we c ea ed a concep ma ix (Webs e
& Wa son, 2002).
(b) We assessed he iden i ied de e minan s wi h ou
in e iews wi h p ac i ione s ha engage wi h IPA. We
decided on a wo-pa in e iew s uc u e. In he i s pa ,
we asked he in e iewees o pe sonal a ibu es such as
hei o ganiza ional ole, ocus o expe ise, and yea s o
expe ience. A e ha , he in e iewees we e asked o
quan i y hei deg ee o amilia i y in he a eas o IPA,
(symbolic) p ocess au oma ion as well as AI on a 5-poin
Like scale. Subsequen ly, we p o ided he in e iew-
ees wi h he de i ed po en ial de e minan s o adop ion.
The in e iewees we e asked o quan i y he pe cei ed
ele ance o he cons uc s on a 5-poin Like scale o
inc easing ele ance o enhance compa abili y. In he
second pa , he e was an isola ed ee discussion o he
in e iewees’ pe cei ed ele ance o he iden i ied de e -
minan s. The in e iews we e eco ded and ansc ibed in
a dena u alized manne (Aze edo e al., 2017). The o al
du a ion o he in e iews was 172min. All dialogues
we e ansc ibed o 6,736 wo ds.
(c, d) Subsequen ly, we ela ed he e alua ed de e mi-
nan s o mula ing hypo heses (see Sec ion De i a ion
o De e minan s and Hypo heses). The hypo heses we e
o mula ed analogously o hose o UTAUT and ela -
able models (c . Appendix 4). Special conside a ion
was gi en o ensu ing ha each hypo hesis could be
e alua ed using quan i a i e measu emen s es ablished
in he li e a u e in he o m o ques ionnai e i ems.
(e) We e alua ed he hypo heses in a p elimina y online
su ey using p oli ic.com o pa icipan acquisi ion.
Pa icipan s we e p esen ed wi h a summa y o IPA
and he echnologies i inco po a es, as well as a hypo-
he ical use case. They illed in a s uc u ed ques ion-
nai e consis ing o he measu emen s ela ing o he
hypo heses. We e alua ed he answe s using pa ial
leas squa es s uc u al equa ion modeling (PLS-SEM).
The es s conduc ed on he measu emen model include
checking in e nal consis ency, con e gen p o i abili y,
disc iminan alidi y, and eliabili y o he indica o s
(Hai e al., 2011). The p elimina y s udy con ained 21
esponses.
( ) We c ea ed an ex ended UTAUT model o IPA
adop ion om he hypo heses alida ed in he p e-
limina y su ey (c . Venka esh, 2022). The ex ended
UTAUT model and hypo heses we e assessed in he
main s udy (see Model E alua ion and Hypo heses
E alua ion). To his end, we ec ui ed na i e English-
speaking employees wi h daily ouch poin s wi h p o-
cesses in ol ing digi al echnologies om di e en
o ganiza ions. Since IPA is a no el echnology and may
no be known o he pa icipan s in de ail, we p o ided
a comp ehensi e explana ion o he concep be o e he
su ey and illus a ed i wi h some eal-li e examples o
obse ed and unobse ed in elligen obo s. To coun e -
ac he p oblem o ca eless esponses and he associa ed
subop imal da a quali y, we used an a en ion check (Pei
e al., 2020). Su ey answe s we e again e alua ed wi h
PLS-SEM as i cons i u es a solu ion o small sample
sizes and complex models wi h many cons uc s and a
la ge numbe o i ems (Hai e al., 2019; Willaby e al.,
2015). I also causes low bias in e lec i e measu emen
models, which app oach ze o a sample sizes o n = 100
and abo e (Sa s ed e al., 2016). The assessmen o
he esul s ollows he guidelines by Hai e al. (2014)
and Hai e al. (2019). The ela ed calcula ions we e
pe o med ia Sma -PLS 3 (Ringle e al., 2015). We
used boo s apping wi h 500 esamples i e a i e model
op imiza ion (Kock & Hadaya, 2018). Fo he inal de i-
a ion o he model pa ame e s, we used boo s apping
wi h 5000 esamples (Hai e al., 2014).
To u he explo e he esul s, we conduc ed an impo -
ance-pe o mance map analysis (Hai e al., 2019). Impo -
ance-pe o mance map analysis was de eloped o p io i ize
managemen ac ions o e icien esou ce alloca ion (Ma -
illa & James, 1977). I enables he compa ison o he o al
e ec s on a de ined a ge cons uc . Compa ison is made in
he dimensions o pe o mance and impo ance in ela ion
o he a ge cons uc (Ringle & Sa s ed , 2016).
Elec onic Ma ke s (2024) 34:5656 Page 6 o 22
De i a ion o de e minan s andhypo heses
The s uc u ed li e a u e e iew and concep ma ix c ea ion
esul ed in 13 po en ial de e minan s o IPA adop ion. The
concep ma ix is shown in Appendix 1. In each con ibu ion,
a leas one cons uc o he UTAUT basic model acco ding
o Venka esh e al. (2003) was used, namely, Pe o mance
Expec ancy (n = 71), Beha io al In en ion (n = 69), E o
Expec ancy (n = 64), Social In luence (n = 48), Facili a ing
Condi ions (n = 36), o Use Beha io (n = 27). Fu he mo e,
a ious ex ensions o he model wi h he cons uc s T us
(n = 32), A i ude Towa ds Using IPA (n = 25), Pe cei ed
Risk (n = 25), P icing Value (n = 13), Hedonic Mo i a ion
(n = 11), T anspa ency (n = 8), and Anxie y (n = 5) we e
obse ed. Table1 shows he ope a ional de ini ions o he
cons uc s.
Table2 shows ole, occupa ional ocus, and expe ience
o he p ac i ione s ha we e consul ed o alida ion o he
iden i ied cons uc s. To his end, he p ac i ione s a ed
he pe cei ed ele ance o each iden i ied de e minan on a
5-poin Like scale du ing he in e iews. A a ing o 1 indi-
ca es low ele ance, a a ing o 5 indica es high ele ance.
Table3 shows hei a ings. All median alues a e abo e 2.0
(= a he no ele an o adop ion). Acco dingly, we con-
side ed all de e minan s as po en ially ele an o u he
model de elopmen .
The hypo heses we e de i ed analogously o UTAUT
and ela ed li e a u e. As consequence o ocusing on
UTAUT, we included Beha io al In en ion and Use
Beha io as dependen cons uc s as hey di ec ly ela e
o echnology adop ion. Beha io al In en ion ela es o an
employee’s subjec i e willingness o consis en ly use IPA
(Venka esh e al., 2012). Going beyond he in en ion, Use
Beha io e e s o conc e e ac ions o adop IPA in ope a-
ional p ac ice (Venka esh e al., 2012). Appendix 2 shows
he ela ionship be ween he hypo heses and he li e a u e
e e ences.
In addi ion o he iden i ied de e minan s, we conside
mode a o s ha a e di used in he UTAUT li e a u e, which
a e age, expe ience, and gende (Venka esh e al., 2012).
Job le el is conside ed a u he mode a o , as i s ele ance
is explici ly cla i ied in he expe in e iews. Table4
Table 1 Iden i ied cons uc s
Cons uc Sho Ope a ional De ini ion
Anxie y AN Sum o a ional and i a ional eelings o ea o anxie y expe ience when in e ac ing wi h IPA
A i ude AT Gene al a ec i e esponse o he use o IPA
E o Expec ancy EE Deg ee o pe cei ed ease o use o IPA
Facili a ing Condi ions FC Ex en o belie ha an o ganiza ional and echnical in as uc u e exis s o suppo he use o IPA
Hedonic Mo i a ion HM Joy o pleasu e ha comes om using IPA
Pe o mance Expec ancy PE Ex en o belie ha using IPA will help imp o e wo k pe o mance
Pe cei ed Risk PR Sum o all pe cei ed isks associa ed wi h he use o IPA
P icing Value PV Cogni i e ade-o be ween he pe cei ed bene i s and he mone a y cos s o IPA
Social In luence SI Ex en o pe cep ion ha o he s belie e ha he indi idual should use IPA
T us TT The deg ee o con idence in he speci ic echnology IPA
T anspa ency TY The ex en o comp ehension and unde s anding o he in e nal p ocesses and he ou pu o IPA
Table 2 Cha ac e is ics o consul ed p ac i ione s
# Role Focus Expe ience
(y s)
E1 Senio esea che Hype au oma ion,
explainable AI
3
E2 Senio esea che Hype au oma ion,
explainable AI
4
E3 Pa ne Cus ome ela ionship
managemen , cloud
compu ing
4
E4 Head o digi al p ocess
consul ing
BPM, RPA, IPA 10
Table 3 Ra ings o cons uc
ele ance by consul ed
p ac i ione s (1 = low ele ance,
5 = high ele ance)
# AN AT EE FC HM PE PR PV SI TT TY
E1 5 3 2 4 1 5 4 4 3 4 4
E2 5 4 2 4 3 5 4 4 2 5 2
E3 5 5 3 2 4 5 3 5 5 4 1
E4 4 4 5 4 3 5 3 5 5 5 3
Median 5 4 2.5 4 3 5 3.5 4.5 4 4.5 2.5
Elec onic Ma ke s (2024) 34:56 Page 7 o 22 56
summa izes he o mula ed hypo heses, which a e ou lined
in he ollowing.
We assume a posi i e in luence o Pe o mance Expec-
ancy on Beha io al In en ion o wo easons (H1a). Fi s ,
his in luence exis s in he UTAUT e e ence model and in
IPA- ela ed wo k, such as on RPA (Wewe ka e al., 2020)
and cha bo s (Danckwe s e al., 2020; Eiße e al., 2020;
Laume e al., 2019; Meye -Waa den e al., 2020). Second,
he p ac ical applica ion o IPA demons a es signi ican
ad an ages in e ms o e iciency and cos sa ings. Due o
he abili y o cos -e ec i ely au oma e epe i i e asks, a
connec ion o he in en ion o using IPA is assumed. The
deg ee o which IPA is expec ed o be use ul o powe ul
o le e age pe o mance may posi i ely ela e o he A i-
ude owa ds using IPA (Dwi edi e al., 2019) (H1b). This
applies in pa icula o he accep ance o so wa e solu ions
by employees (Amin e al., 2016). Se e al iden i ied con i-
bu ions show he in luence o Pe o mance Expec ancy on
A i ude o echnology in gene al as well as o AI-based
ools (Liu e al., 2019; e.g., Pan e al., 2019). We adop he
assump ion o mode a ing e ec s o Age and Gende on Pe -
o mance Expec ancy and A i ude in UTAUT (Venka esh
e al., 2003).
Fu he , we conside Job Le el as a mode a o o he
e ec s o Pe o mance Expec ancy (H1c, H1d). The in e -
iewees explained ha a c i ical poin would exis whe e
Pe o mance Expec ancy is so high ha he espec i e ech-
nology would be pe cei ed as a h ea o employmen , hin-
de ing he adop ion.
Analogous o Pe o mance Expec ancy, we assume
a posi i e in luence o E o Expec ancy on Beha io al
In en ion o wo easons (H2a). Fi s , we o ien owa ds
he UTAUT e e ence model. Fu he , we iden i ied con-
ibu ions inding his in luence speci ically o IPA (Eiße
e al., 2020; e.g., Wewe ka e al., 2020). Second, since IPA
o en ope a es a he use le el o so wa e ia obo s, he e
is no need o cos ly and ex ensi e modi ica ions ela ed o
he so wa e associa ed wi h he p ocess o be au oma ed
(Syed e al., 2020). In line wi h he idea o au oma ion and
mo e e icien esou ce u iliza ion, we also assume a posi-
i e in luence o E o Expec ancy on Pe o mance Expec-
ancy in he con ex o IPA (H2b). The in luence o E o
Expec ancy on Pe o mance Expec ancy was obse ed in he
iden i ied con ibu ions in es iga ing IPA (Eiße e al., 2020;
e.g., Wewe ka e al., 2020). The deg ee o which IPA use is
pe cei ed as complex compa ed o o he echnologies could
Table 4 Iden i ied hypo heses
_
→
signi ican nega i e in luence,
+
→
signi ican posi i e in luence, * mode a ing in luence
Age (AGE), Anxie y (AN), A i ude (AT), Beha io al In en ion (BI), E o Expec ancy (EE), Expe ience (EXP), Facili a ing Condi ions (FC),
Gende (GDR), Hedonic Mo i a ion (HM), Job Le el (JOL), Pe o mance Expec ancy (PE), Pe cei ed Risk (PR), P ice Value (PV), Social
In luence (SI), T us (TT), T anspa ency (TY), Use Beha io (UB)
# Hypo heses # Hypo heses # Hypo heses
1a PE
+
→
BI 4 FC * AGE, EXP, GDR
→
BI 7h TT * AGE, EXP, GDR
→
EE
1b PE
+
→
AT 4g FC * AGE, EXP, GDR
→
EE 7i TT * AGE, EXP, GDR
→
PE
1c PE * AGE, GDR, JOL
→
BI 4h FC * AGE, EXP, GDR
→
PE 7j TT * AGE, EXP, GDR
→
PR
1d PE * AGE, GDR, JOL
→
AT 5a AT
+
→
BI 8TY
+
→
TT
2a EE
+
→
BI 5b AT
+
→
UB 9a AN
_
→
BI
2b EE
+
→
PE 6a PR
_
→
BI 9b AN
+
→
PE
2c EE
+
→
AT 6b PR
_
→
PE 9c AN
_
→
EE
2d EE * AGE, GDR, EXP
→
BI 6c PR
_
→
AT 9d AN * AGE, EXP, GDR, JOL
→
BI
2e EE * EXP
→
AT 6d PR * AGE, GDR
→
BI 9e AN * AGE, EXP, GDR, JOL
→
PE
3a SI
+
→
BI 6e PR * AGE, GDR
→
PE 9 AN * AGE, EXP, GDR, JOL
→
EE
3b SI
+
→
AT 6 PR * AGE, GDR
→AT
10a HM
+
→
BI
3c SI * AGE, EXP, GDR, JOL
→
BI 7a TT
+
→
BI 10b HM * AGE, EXP, GDR, JOL
→
BI
3d SI * AGE, EXP, GDR, JOL
→
AT 7b TT
+
→
AT 11a PV
+
→
BI
4a FC
+
→
UB 7c TT
+
→
EE 11b PV * AGE, EXP, GDR, JOL
→
BI
4b FC
+
→
BI 7d TT
+
→
PE 12a BI
+
→
UB
4c FC
+
→
EE 7e TT
_
→
PR 12b BI * EXP
→
UB
4d FC
+
→
PE 7 TT * AGE, EXP, GDR
→
BI
4e FC * AGE, EXP
→
UB 7g TT * AGE, EXP, GDR
→
AT
Elec onic Ma ke s (2024) 34:5656 Page 8 o 22
also posi i ely in luence he A i ude owa ds he echnology
(Dwi edi e al., 2019) (H2c). The in luence be ween E o
Expec ancy and A i ude has been obse ed in adop ion-
ela ed li e a u e conce ning AI-based ools (Cao e al.,
2021; e.g., Pan e al., 2019). We inco po a e he assump ion
ha Age, Gende , and Expe ience mode a e he e ec om
E o Expec ancy on Beha io al In en ion om he UTAUT
e e ence model (Venka esh e al., 2003) (H2d). Fu he ,
he in e iewees men ioned ha E o Expec ancy and i s
e ec s s ongly depend on use s’ expe ience in luencing
hei A i ude (H2e). Fo ins ance, E o Expec ancy in he
use o IPA ools, ends o be lowe i he use has expe ience
wi h compa able echnologies.
We posi a posi i e in luence o Social In luence on
Beha io al In en ion (H3a). Consis en wi h he UTAUT e -
e ence model, iden i ied con ibu ions indica e his in luence
ega ding AI-based ools (Aboelmaged, 2010; Cox, 2012;
Gao e al., 2015; Handoko e al., 2018; Hsu e al., 2014; Lee
& Song, 2013; Lee, 2009; Li e al., 2020; Slade e al., 2015;
Wang e al., 2015). UTAUT me a-s udies co obo a e his
impac (Dwi edi e al., 2019; e.g., Williams e al., 2015).
Analogously, we assume an in luence o Social In luence
on A i ude (H3b). We jus i y he assump ion by he po en-
ial in luence o hi d pa ies who ha e adop ed o ejec ed
he espec i e echnology on he a i udes o po en ial use s
(Dwi edi e al., 2019). The in luence o Social In luence
on A i ude has been obse ed in li e a u e on RPA (e.g.,
Wewe ka e al., 2020) and AI (e.g., Pe e s e al., 2020). We
adop Age, Gende , and Expe ience as mode a o s on he
ela ions o Social In luence on Beha io al In en ion and
A i ude om he UTAUT e e ence model (Venka esh e al.,
2003). Addi ionally, he in e iewees emphasized ha Social
In luence owa ds IPA adop ion is exe ed less equen ly a
he same hie a chical le el bu p ima ily be ween Job Le els
(H3c, H3d). I can be in e ed ha he in luence o Social
In luence inc eases wi h he numbe o hie a chical le els
abo e.
Rega ding Facili a ing Condi ions, we assume a posi i e
in luence on Use Beha io (H4a). Facili a ing Condi ions
such as he managemen o high da a olume and consis -
en da a quali y comp ise majo challenges in he imple-
men a ion o AI-based au oma ion (Jyo i & Szu ley, 2021).
Also, Facili a ing Condi ion in luences Use Beha io in
he UTAUT e e ence model (Venka esh e al., 2003). The
in luence has addi ionally been p o en in a me a-analysis
(Williams e al., 2015). In he UTAUT e e ence model,
Facili a ing Condi ions do no di ec ly in luence Beha io al
In en ion. Venka esh e al. (2003) a gued ha he explana-
o y powe o Facili a ing Condi ions on Beha io al In en-
ion could only be demons a ed i Pe o mance Expec-
ancy and E o Expec ancy a e no included in he model.
Dwi edi e al. (2019) poin ou ha his limi a ion does no
hold ue in e e y con igu a ion, which is suppo ed by he
esul s o ou s uc u ed li e a u e e iew. In he con ex o
UTAUT2, Venka esh e al. (2012) a gued ha indi iduals
wi h access o an ad an ageous se o Facili a ing Condi-
ions exhibi a highe willingness o adop a echnology.
The e o e, he posi i e in luence o Facili a ing Condi ions
on Beha io al In en ion canno be excluded in he con ex o
IPA (H4b). Fu he mo e, we assume a posi i e in luence o
Facili a ing Condi ions on E o Expec ancy (H4c). This is
jus i ied by Facili a ing Condi ions being a di ec de e mi-
nan o E o Expec ancy in he accep ance o new so wa e
solu ions by employees (Amin e al., 2016) and echnology
accep ance in gene al (Venka esh & Bala, 2008). Mo eo e ,
Facili a ing Condi ions could exe a posi i e in luence on
Pe o mance Expec ancy (H4d). This is jus i ied by he p o-
ision o app op ia e aining and a su icien ly high-quali y
echnical and o ganiza ional in as uc u e, which assis
po en ial use s in gaining cla i y abou he ac ual sys em pe -
o mance (Cha e jee & Bha acha jee, 2020). The implied
e ec be ween Facili a ing Condi ions and Pe o mance
Expec ancy has been obse ed in AI-based ools (e.g., an
Hung e al., 2021), especially in he business con ex (e.g.,
Cao e al., 2021). We adop he mode a ing e ec s ega ding
Facili a ing Condi ions acco ding o he UTAUT e e ence
model (H4e-h).
In he UTAUT e e ence model, he e is no signi ican
e ec on Beha io al In en ion o Use Beha io due o
po en ial o e laps wi h Pe o mance Expec ancy and E o
Expec ancy (Venka esh e al., 2003). We do include posi i e
in luences as mo e ecen esea ch indica es ha A i ude
can be a ele an de e minan in he adop ion and usage o
inno a i e echnologies (Dwi edi e al., 2017) (H5a). Fu -
he mo e, i has been demons a ed ha A i ude can be a
di ec de e minan o Beha io al In en ion in he accep ance
o so wa e by employees (Amin e al., 2016; Mo is e al.,
2005; Pan e al., 2019). The posi i e in luence o A i ude
on Use Beha io is examined sepa a ely (H5b). A gene al
a e sion owa ds algo i hms inhe en o AI-based ools may
ha e a signi ican impac on IPA adop ion (Be ge e al.,
2021). Usage beha io is hus in luenced by A i ude (Ven-
ka esh, 2022).
We assume a nega i e in luence o Pe cei ed Risk on
Beha io al In en ion, Pe o mance Expec ancy, and A i-
ude in he con ex o IPA adop ion. Po en ially Pe cei ed
Risks a e mani old, such as inancial isks o Pe o mance
Risks in case IPA wo ks less e icien ly han assumed. The
nega i e in luence o Pe cei ed Risk on Beha io al In en-
ion has been obse ed in a la ge numbe o iden i ied
con ibu ions on IPA (e.g., Huang & Wang, 2009; Laume
e al., 2019) and gene ally in AI-based ools (H6a) (Gao
e al., 2015; Jianbin & Jiaojiao, 2013; M.-C. Lee, 2009; J.
Li e al., 2019; Slade e al., 2015). In addi ion o he abso-
lu e bene i (Da is, 1989), Pe o mance Expec ancy also
includes he ela i e ad an age (Moo e & Benbasa , 1991)
Elec onic Ma ke s (2024) 34:56 Page 15 o 22 56
(Willcocks e al., 2015). Fu he mo e, a posi i e A i ude can
be cul i a ed by s essing he impo ance o and coun e ac -
ing p e alen Pe cei ed Risk and Anxie y. Pe cei ed Risk’s
nega i e e ec could be coun e ed by he implemen a ion o
isk managemen (Powe , 2004, 2009), including A/B es -
ing (Deng e al., 2017), bandi se ices (Malekzadeh e al.,
2020), and cana y deploymen s (Ta o e al., 2015). Robo s
could also ha e he abili y o un wi hou isual ep esen-
a ion o ensu e p i acy (Syed e al., 2020). The nega i e
in luence o Anxie y should be emedia ed h ough con inu-
ous sensi iza ion. In pa icula , Anxie y abou losing one’s
job due o au oma ion should be add essed o os e IPA
adop ion.
Es ablish acili a ing condi ions
We ind ha o ganiza ions can in luence IPA adop ion es ab-
lishing Facili a ing Condi ions. Speci ically, ou esul s show
di ec e ec s o Facili a ing Condi ions on E o Expec-
ancy and Pe o mance Expec ancy and indi ec e ec s on
A i ude. These e ec s sugges ha o ganiza ions should p o-
ide app op ia e ools and suppo employees in he use o
IPA. The es ablishmen o hands-on aining o demons a e
IPA use and en ailed ad an ages o au oma ion cons i u es
an exempla y Facili a ing Condi ion (Alsha e & Lane, 2011;
Sabhe wal e al., 2006). Helpdesks can be es ablished o
ensu e con inuous suppo o bo h ini ial o ongoing IPA
use (Coeu de oy e al., 2014). To in luence Pe o mance
Expec ancy, E o Expec ancy, and A i ude posi i ely,
in as uc u es should acili a e IPA in eg a ion in o daily
ope a ional p ac ice. Addi ionally, designing use - iendly
in e aces o IPA ools suppo s i s adop ion (Zuide wijk
e al., 2015).
Mind expe ience o use s
Ou esul s show ha IPA adop ion depends on use cha -
ac e is ics, in pa icula Expe ience. The posi i e e ec o
Expe ience on Use Beha io implies ha po en ial adop e s
who ha e Expe ience a e mo e likely o use IPA han wo k-
e s wi hou p io Expe ience. The posi i e di ec e ec o
Expe ience on E o Expec ancy also implies ha po en-
ial adop e s who ha e Expe ience pe cei e he use o he
echnology o be easie han wo ke s wi hou ela ed p io
knowledge. Fu he mo e, he mode a ing e ec o Expe i-
ence be ween P ice Value and Beha io al In en ion sug-
ges s ha P ice Value is inc easingly nega i ely pe cei ed
by indi iduals wi h Expe ience. Addi ionally, he obse -
able posi i e mode a ing e ec s o Job Le el be ween P ic-
ing Value and Beha io al In en ion and P icing Value and
Use Beha io imply ha as Job Le el inc eases, he P icing
Table 8 Summa y o esul s and
implica ions
_
→
signi ican nega i e in luence,
+
→
signi ican posi i e in luence, * mode a ing in luence
Age (AGE), Anxie y (AN), A i ude (AT), Beha io al In en ion (BI), E o Expec ancy (EE), Expe ience
(EXP), Facili a ing Condi ions (FC), Gende (GDR), Hedonic Mo i a ion (HM), Job-Le el (JOL), Pe o -
mance Expec ancy (PE), Pe cei ed Risk (PR), P ice Value (PV), Social In luence (SI), T us (TT), T ans-
pa ency (TY), Use Beha io (UB)
Implica ions # Hypo heses
Cul i a e a posi i e a i ude owa ds IPA 1b PE
+
→
AT
2c EE
+
→
AT
5a AT
+
→
BI
6c PR
_
→
AT
7g TT * AGE, EXP, GDR
→
AT
12a BI
+
→
UB
Es ablish acili a ing condi ions 4c FC
+
→
EE
4d FC
+
→
PE
2b EE
+
→
PE
Mind expe ience o use s 11b PV * AGE, EXP, GDR, JOL
→
BI
7j TT * AGE, EXP, GDR
→
PR
9c AN
_
→
EE
10a HM
+
→
BI
T anspa ency is no end in i sel 8TY
+
→
TT
7d TT
+
→
PE
7e TT
_
→
PR
Elec onic Ma ke s (2024) 34:5656 Page 16 o 22
Value o he echnology is inc easingly pe cei ed posi i ely
o weigh ed mo e highly.
This en ails ha p o iding job-le el adequa e aining on
he capabili ies o IPA and in elligen sys ems o machine
lea ning in gene al could imp o e he o e all adop ion o
such sys ems. Ou inding is in line wi h esea ch ha inds
ha he le el o wo k expe ience in luences how in o ma ion
is pe cei ed and in o ma ion sys ems used. Fu u e esea ch
can expand on his o disen angle ela ions among expe i-
ence, job-le els, and equi ed aining o u he unde s and
de e minan s o IPA adop ion wi h ega d o use expe ience
(Kalyuga e al., 2003; Maye & Mo eno, 2003).
T anspa ency isnoend ini sel
In hei call o esea ch, Engel e al. (2022) explici ly e e
o a need o in es iga ions on making decisions o IPA ools
explainable o use s o os e IPA use. In his ein, we can
con i m he ele ance o T anspa ency and T us in he con-
ex o AI-based echnologies as highligh ed by Venka esh
(2022). Acco dingly, we p opose o in eg a e he cons uc s
T us and T anspa ency o accep ance esea ch a ound
IPA as well as ela ed echnologies as in eg al cons uc s in
u u e esea ch models.
We ag ee wi h esea ch on explainabili y o AI in ha
he c ea ion o T anspa ency o e AI- ela ed echnology
can acili a e T us and he e o e adop ion. This implica-
ion is consis en wi h he obse a ions and assump ions o
Kalime i and Tjos heim (2020), Lip on (2018), and Wan-
ne e al. (2022) ha he explainabili y o anspa ency o
models is a p e equisi e o he o ma ion o T us . Laci y
e al. (2016) we e able o de i e compa able indings when
in e iewing senio execu i es in an RPA con ex . Suppo -
ing he posi i e e ec s o T us , we iden i ied a s ong nega-
i e e ec on Pe cei ed Risk. A di ec mode a ing e ec o
Expe ience be ween T us and Pe cei ed Risk sugges s ha
he e ec may inc ease wi h Expe ience.
Rela ed esea ch on explainabili y and adop ion o AI-
ela ed echnology has e ealed ha T us can be imp o ed
h ough a ious measu es, including implemen ing and com-
munica ing amewo ks o us wo hy AI and de eloping
o ganiza ional us managemen (Thiebes e al., 2021).
In pa icula , T anspa ency can be acili a ed by he p o-
isioning o comp ehensi e global and local explana ions
o he inne wo kings as well as he ep esen a ion o cu -
en p ocess lows and by implemen ing eedback loops ha
e eal he s a es o so wa e obo s and including inpu s
and ou pu s (Holde e al., 2021). As ou esea ch shows,
T anspa ency wo ks h ough he T us and Pe cei ed Risk
as well as Pe o mance Expec ancy ela ion. Hence, we
posi i is insu icien o p o ide “explana ions” ha me ely
make hings anspa en by p o iding da a and in o ma ion
bu ocus on use -cen e ed explana ions ha p o ide cla i y
and unde s anding abou decisions o IPA ools, such as o
example p edic ions (He m e al., 2023). In he con ex o
IPA, his may be e en mo e impo an han o decisional
AI as he asks o he AI in ol e no only decision-making
bu also ask execu ion. Consequen ly, his wo k ela ion
be ween human and IPA esembles a delega ion si ua ion
a he han a so wa e selec ion decision which makes b idg-
ing he in o ma ion asymme y be ween he wo pa ies e e
mo e impo an .
Limi a ions
Ou esea ch has some me hodological and con en -wise
limi a ions o be conside ed when in e p e ing and using he
esul s. In e ms o me hodology, he li e a u e e iew and
concep ma ix c ea ion ha e subjec i e componen s, such as
he exclusion o publica ions. To mi iga e his po en ial limi-
a ion, we s ic ly adhe ed o guidelines di used in In o ma-
ion Sys ems ( om B ocke e al., 2009; i.e., om B ocke
e al., 2015; Webs e & Wa son, 2002). Mo eo e , he e a e
limi a ions inhe en o online s udies as hese do no enable
o moni o pa icipan s di ec ly. P oli ic.com includes a li e
cha o answe immedia e ques ions bu canno compensa e
o a lack o pe sonal in e ac ion. To ensu e he quali y o
answe s, we sc eened esul s o i egula execu ion imes
and also on he basis o a en ion checks.
Mo eo e , es ablished, gene ic models like UTAUT
p o ide only one s uc u ed app oach o s udying accep -
ance (Williams e al., 2009). We chose o use such a model
because we a e among he i s o in es iga e IPA adop-
ion on his scale. By doing so, we build on a la ge body
o exis ing knowledge, compa able o many ecen inno-
a i e con ibu ions in In o ma ion Sys ems (e.g., Hooda
e al., 2022; Mis a e al., 2022; Wanne e al., 2022; Xu
e al., 2024). A he same ime, we c ea e oppo uni ies o
u u e esea ch o explo e o he app oaches, such as pu ely
quali a i e, explo a o y s udies ha a e less es ic ed o
long-es ablished cons uc s. In his ein, we ind no el
app oaches o assessing echnology accep ance ha a e di -
using in In o ma ion Sys ems esea ch. Fo example, Bai d
and Ma uping (2021) emphasize he need o conside ing
agency ega ding he adop ion o AI a i ac s. Fu he , se -
e al esea che s p opose in eg a ing heo e ical no ions on
ask- echnology i wi h TAM and UTAUT o explain a i-
ance in use adop ion (e.g., Bouwman & an de Wijngae ,
2009; an Huy e al., 2024). While upcoming app oaches
like hese seem p omising also o he con ex o IPA, hei
ela i ely low le el o di usion educes compa abili y. In
e ms o con en -wise limi a ions, we ind ha he openness
o es ic i eness o an o ganiza ion may in luence he use ’s
a i ude owa ds adop ion and se e as an in e es ed playing
ield o analyze ela ed aspec s such as wo ka ounds o use
AI and quie qui ing. Fu he , esea ch has also shown ha
Elec onic Ma ke s (2024) 34:56 Page 17 o 22 56
cons uc s such as Social In luence and Pe cei ed Risk can
be dependen on he cul u al backg ound o he esponden s
(e.g., Bandyopadhyay & F accas o o, 2007; Ve hage e al.,
1990). The empi ical su ey was conduc ed in English only.
Due o he insepa able link be ween language and cul u e,
people om di e en cul u al backg ounds may ha e been
excluded (Jiang, 2000). This impe ec ion o he esea ch
(Williams e al., 2015) could lead o a bias in he esul s,
lea ing po en ial o u u e in es iga ions.
Conclusion and u u e wo k
IPA le e ages ad an ages o symbolic p ocess au oma ion
wi h AI o au oma e complex business p ocesses equi ing
decision-making capaci ies. Despi e he economic p essu e
o ake ad an age o IPA and i s po en ial compe i i e ad an-
ages, he adop ion a e o IPA is compa a i ely low. To
unde s and easons and iden i y a eas o ac ion owa ds IPA
adop ion as conside ed explici ly necessa y in IS esea ch
(Engel e al., 2022), we iden i ied 13 de e minan s and c e-
a ed an ex ended UTAUT model (c . Table1). P o iding
no ma i e knowledge wi h he UTAUT ex ension, we show
in luen ial ela ions be ween iden i ied de e minan s o
IPA adop ion. In pa icula , we ind ha i is impo an o
cul i a e a posi i e a i ude owa ds IPA, es ablish sui able
acili a ing condi ions, especially mind use expe ience, and
emb ace he ac ha anspa ency is no end in i sel and
does need o p o ide explainabili y o sys em beha io a he
han “explana ions” in e ms o me e da a and in o ma ion.
Ou esea ch en ails wo s a ing poin s o u u e esea ch.
Fi s , u he s udies wi h a la ge numbe o pa icipan s
o wi h a ocus on ce ain pa icipan cha ac e is ics, such
as cul u e, can u he es obus ness and con ingencies o
ou de eloped model. Second, esea ch can u he ex end
he model wi h mo e de e minan s o igina ing om p ac i-
cal applica ions, o example by conduc ing esea ch based
on case s udies. As a b idge owa ds o he design-o ien ed
esea ch, his esea ch can be used o in o m equi emen s
enginee ing and he design o complex IPA building blocks
such as da a us models whe e complex and lexible in e -
ac ions wi h mul iple pa ies exceed he bounda ies o sym-
bolic p ocess au oma ion.
Supplemen a y In o ma ion The online e sion con ains supplemen-
a y ma e ial a ailable a h ps:// doi. o g/ 10. 1007/ s12525- 024- 00737-9.
Acknowledgemen s This esea ch and de elopmen p ojec is unded
by he Ge man Fede al Minis y o Educa ion and Resea ch(BMBF)
wi hin he “Rich linie zu Fö de ung on P ojek en zu E o schung
ode En wicklung p axis ele an e Lösungsaspek e (“Baus eine”) ü
Da en euhandmodelle” (Funding No. 16DTM201B) and inanced by
heEu opean Union - Nex Gene a ionEU. The au ho s a e esponsible
o he con en s o his publica ion.
Funding Open Access unding enabled and o ganized by P ojek
DEAL.
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Publishe 's No e Sp inge Na u e emains neu al wi h ega d o
ju isdic ional claims in published maps and ins i u ional a ilia ions.
