Ba els, Nedo; Koch, Ma hias; Heß, Anne; Go dijn, Jaap
A icle — Published Ve sion
De elopmen and e alua ion o a axonomy o pla o m
e enue models
Elec onic Ma ke s
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Sugges ed Ci a ion: Ba els, Nedo; Koch, Ma hias; Heß, Anne; Go dijn, Jaap (2025) : De elopmen
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Elec onic Ma ke s (2025) 35:88
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RESEARCH PAPER
De elopmen ande alua ion o a axonomy o pla o m e enue
models
NedoBa els1,2· Ma hiasKoch1· AnneHeß1,3· JaapGo dijn2,4
Recei ed: 19 May 2025 / Accep ed: 15 Sep embe 2025
© The Au ho (s) 2025
Abs ac
A c i ical challenge in launching success ul pla o m business models is he design o iable e enue models. While exis ing
amewo ks and axonomies add ess pla o m business models mo e b oadly, concep ual cla i y ega ding pla o m e enue
models emains limi ed, pa icula ly in e ms o how alue is cap u ed among pla o m ac o s. The absence o a consis en
axonomy lea es dimensions and cha ac e is ics agmen ed ac oss s udies, he eby cons aining heo y-building and limi -
ing ac ionable guidance o manage ial decisions on alue-cap u e mechanisms and p icing s a egies. This s udy p oposes
a axonomy o pla o m e enue models comp ising 15 dimensions and 64 cha ac e is ics. Following a axonomy design
me hodology g ounded in design science esea ch, we apply i e a i e design cycles and p inciples. A con olled expe imen
is conduc ed o empi ically assess he use ulness o he axonomy. Ou esul s show ha he axonomy signi ican ly imp o es
he comple eness and accu acy o he designed pla o m e enue models. This esea ch ad ances pla o m business model
heo y by o e ing an e alua ed axonomy ha suppo s he concep ualiza ion o pla o m e enue models.
Keywo ds Taxonomy e alua ion· Taxonomy design· Design science esea ch· Business model· Re enue model· Digi al
pla o m
JEL classi ica ion L86· M15· O3· L10
In oduc ion
The pla o m economy has eme ged as a ans o ma i e
o ce (McA ee & B ynjol sson, 2017; Pa ke e al., 2016),
a ac ing schola ly in e es om di e se ields seeking
o examine he dis up i e impac o pla o m businesses,
such as Ai bnb in hospi ali y (Ze as e al., 2017), Ube in
mobili y (Cla ke, 2022; Ecke e al., 2024), o Spo i y in
he music indus y (Fleische , 2021; Vonde au, 2019). Pla -
o ms enhance ading e iciency by inc easing ansac ion
equency and educing sea ch, eplica ion, and e i ica ion
cos s (Xue e al., 2020), while hei apid g ow h, d i en by
ne wo k e ec s, o en leads o ma ke dominance h ough
winne - ake-i -all dynamics (A ms ong, 2006; Hagiu &
W igh , 2015; Roche & Ti ole, 2003). To gain a deepe
unde s anding o hei ole in shaping economic in e ac ions,
i is necessa y o look beyond hei echnical in as uc u e
and examine hei unde lying business logic (Guggenbe ge
e al., 2020; Täusche & Laudien, 2018), which is concep-
ualized h ough a business model ha desc ibes how alue
is c ea ed, deli e ed, and cap u ed (Teece, 2010). Unlike
Responsible Edi o : Juho Lindman
* Nedo Ba els
nedo.ba [email p o ec ed]e .de
Ma hias Koch
ma hias.koc[email p o ec ed]e .de
Anne Heß
[email p o ec ed]
Jaap Go dijn
j.go di[email p o ec ed]
1 F aunho e IESE, Kaise slau e n, Ge many
2 V ije Uni e si ei Ams e dam, Ams e dam, TheNe he lands
3 Technical Uni e si y o Applied Sciences
Wü zbu g-Schwein u , Wü zbu g, Ge many
4 The Value Enginee s, Soes , TheNe he lands
Elec onic Ma ke s (2025) 35:88 88 Page 2 o 23
alue chain o pipeline-o ien ed business models, which a e
cha ac e ized by adi ional i m-cus ome alue deli e y,
pla o m business models ely on pee - o-pee exchanges in
which alue is co-c ea ed wi hin ac o - o-ac o ne wo ks, as
exempli ied by pla o ms such as Ai bnb and Ube (Feh e
e al., 2018; Täusche & Laudien, 2018; Wi z e al., 2019).
Pla o m business models ans o m no only alue c ea ion
and deli e y bu also ede ine alue cap u e, as hey employ
e enue models, i.e., he mechanisms by which a i m gene -
a es e enue om i s alue c ea ion (Os e walde & Pigneu ,
2013), ha ex ac alue ac oss mul iple ma ke sides a he
han om a single cus ome segmen (Daxhamme e al.,
2019; Kenney & Zysman, 2016; Täusche & Laudien, 2018).
While p io esea ch has o e ed b oad insigh s in o he
alue c ea ion and deli e y dimensions o pla o m busi-
ness models (Fu e al., 2017; Rohn e al., 2021; Täusche
& Laudien, 2018), alue cap u e emains unde explo ed
(Feh e e al., 2018; Hein e al., 2020). In pla o m con-
ex s, designing e enue models is pa icula ly challenging,
as business model designe s mus make complex decisions
abou whom and wha o cha ge o ensu e scalabili y (Kim,
2016; Madanaguli e al., 2023; Pidun e al., 2020). Flawed
e enue model design is a cen al ac o in pla o m ailu e,
pa icula ly due o inadequa e su plus sha ing and insu i-
cien p o ec ion o mone iza ion oppo uni ies (Mancha &
Go don, 2022; Pa ke e al., 2016). Acco dingly, in o ma-
ion sys ems (IS) esea che s ha e emphasized he impo -
ance o gaining deepe insigh s in o he alue cap u e and
e enue model design o digi al pla o ms (c . Hein e al.,
2020; Madanaguli e al., 2023; Veile e al., 2022). This s udy
con ibu es o his call by p oposing a axonomy ha s uc-
u es pla o m e enue models and suppo s business model
designe s in de eloping iable e enue model s a egies. I
is guided by he ollowing wo ques ions.
• RQ1: Which dimensions and cha ac e is ics cons i u e a
axonomy o desc ibing pla o m e enue models?
• RQ2: How use ul is a de eloped axonomy in suppo ing
he design o pla o m e enue models?
This s udy ollows he axonomy design me hodology
o Kundisch e al. (2022) wi hin a design science esea ch
(He ne & Cha e jee, 2010) con ex . As a i s s ep owa d
add essing RQ1, we syn hesize exis ing esea ch on pla o m
e enue models and inco po a e insigh s om he analysis
o se en pla o m cases. To answe RQ2, we conduc a con-
olled expe imen wi h en p ac i ione s, equally di ided
in o es and con ol g oups, o e alua e he axonomy’s
use ulness in designing pla o m e enue models. Expe
e iewe s assessed he esul ing desc ip ions in e ms o
comple eness (i.e., co e age o ele an componen s) and
accu acy (i.e., cla i y, s uc u e, and logical cohe ence),
allowing o a measu able compa ison o ou comes.
The con ibu ion o his s udy is wo old. The i s con-
ibu ion is a axonomy o pla o m e enue models, com-
p ising 15 dimensions and 64 cha ac e is ics. The axonomy
dis inguishes be ween wo pe spec i es ha oge he o m a
comp ehensi e logic o a pla o m e enue model: he pla -
o m ope a o , wi h eigh dimensions, and he supply-side
ac o s, who o e p oduc s and se ices ia he pla o m,
wi h se en dimensions. By enabling a di e en ia ed pe -
spec i e on e enue model design ac oss pla o m ac o s,
his axonomy con ibu es o add essing a key un esol ed
issue in pla o m esea ch––namely, how alue is cap u ed
be ween pla o m owne s and supply-side ac o s (Hein e al.,
2020; Hel a & Raubi schek, 2018). The use ulness o he
p oposed axonomy is e alua ed in a con olled expe imen ,
which shows ha pa icipan s applying i designed mo e
comp ehensi e and accu a e models han hose in he con-
ol g oup.
The second con ibu ion esul s om applying he ax-
onomy o se en pla o m cases du ing i s de elopmen . This
applica ion iden i ied 26 dis inc e enue model ypes and
illus a es he simul aneous use o mul iple e enue s a -
egies wi hin he obse ed cases. Ou esul s ex end p io
esea ch on classi ied pla o m business models (S aub e al.,
2021; Täusche & Laudien, 2018) and unde sco e he need
o a deepe unde s anding o how di e en e enue model
ypes can be combined wi hin a single pla o m business
model.
Building on hese con ibu ions, we o e p ac i ione s a
s uc u ed amewo k o designing hei own pla o m e -
enue models. Fo esea che s, he axonomy p o ides a oun-
da ion o de eloping ex pos heo ies (Bapna e al.,2004)
and os e ing a deepe unde s anding o pla o m e enue
model design.
Theo e ical con ex
Pla o ms can be analyzed om a ma ke -o ien ed, socio-
echnical, echnical, o business-o ien ed pe spec i e (Hein
e al., 2020; Täusche & Laudien, 2018). This s udy akes
a business-o ien ed pe spec i e, ocusing on he business
model aspec s o digi al pla o ms.
Pla o m business models
In e es in business models is g owing in he ield o IS,
leading o a ich body o de ini ions and pe spec i es on he
concep (Massa e al., 2017; Mölle e al., 2022; Zo e al.,
2011). The concep ualiza ions p oposed by Teece (2010) and
Os e walde and Pigneu (2010) a e widely ecognized and
ha e become ounda ional e e ences (Ami & Zo , 2020;
Massa e al., 2017). In his s udy, we ollow he de ini ion o
Teece (2010), who desc ibes a business model as “ he design
Elec onic Ma ke s (2025) 35:88 Page 3 o 23 88
o a chi ec u e o he alue c ea ion, deli e y, and cap u e
mechanisms” employed. Simila o he b oade concep o
a business model, he e m pla o m business model lacks a
widely accep ed de ini ion and is o en used in e changeably
wi h ela ed e ms such as “mul i-sided pla o ms”, “mul i-
sided ma ke s”, “pla o m-based ma ke s”, and “pla o m
ecosys ems” (Feh e e al., 2018). A pla o m business
model educes ansac ion cos s by p o iding an in as uc-
u e h ough which mul iple ansac ions can ake place e i-
cien ly (Feh e e al., 2018; Rohn e al., 2021). T adi ional
business models ely on a cen alized exchange o alue by
managing a linea se ies o ac i i ies om inpu o ou pu ,
esembling a pipeline o alue chain (Wi z e al., 2019). In
con as , pla o m business models c ea e alue by acili-
a ing in e ac ions be ween di e en s akeholde s h ough a
digi al pla o m, c ea ing a alue ne wo k and o en esul ing
in co-c ea ed alue (Ceccagnoli e al.,2012; Smedlund e al.,
2018; R. Wie inga & Go dijn, 2023).
This s udy examines pla o m business models ha ely
on digi al in as uc u es o enable ansac ion-based alue
c ea ion, commonly e e ed o as “ ansac ion pla o ms”
(Cusumano e al., 2019; E ans & Gawe , 2016). In con-
as o inno a ion pla o ms ha p o ide a echnological
ounda ion o complemen a y inno a ions (Cusumano
e al., 2019), ansac ion pla o ms c ea e alue by acili-
a ing in e ac ions be ween dis inc pa icipan s engaged
in he exchange o asse s (Dushni sky e al., 2022; Koch
e al., 2022). In his con ex , an “asse ” can be de ined as any
good––ma e ial o imma e ial, such as p oduc s, se ices,
o da a–– ha is conside ed aluable by bo h p o ide s and
consume s (Koch e al., 2022). Such ansac ions ypically
in ol e collabo a ion be ween p o ide s and consume s
media ed by a pla o m ope a o who does no own hese
asse s hemsel es (Hein e al., 2020; Koch e al., 2022; Sub-
amaniam e al., 2019).
Acknowledging he a ying e minologies in he li e a u e
(Be e ungen e al., 2021; Hein e al., 2020; Pa ke e al.,
2016), we adop he amewo k p oposed by Koch e al.
(2022) o desc ibe he iadic ela ionship in which a digi-
al pla o m acili a es b oke ing ac i i ies pe o med by an
asse b oke (pla o m ope a o ) o ma ch asse p o ide s
(o ganiza ions o indi iduals o e ing in o ma ion, goods, o
se ices) wi h asse consume s (o ganiza ions o indi iduals
consuming hose o e ings).
Pla o m e enue models
We de ine a pla o m e enue model as an economic concep
wi hin he alue-cap u e dimension o a business model,
speci ying he mone iza ion mechanisms h ough which a
digi al pla o m gene a es e enue om i s in e media ion
ac i i ies be ween ma ke sides (Kim, 2016; Os e walde ,
2004; Täusche & Laudien, 2018). I highligh s he mech-
anisms by which alue is cap u ed by he pla o m, o en
h ough subsc ip ion ees, ansac ion ees, o ad e ising
e enue (Täusche & Laudien, 2018), using mone iza ion
mechanisms ha p ese e and enhance a he han unde -
mine ne wo k e ec s (Pa ke e al., 2016). Ne wo k e ec s
and hei c ea ion a e pa icula ly impo an o pla o ms
(Kim, 2016; T ischle & Meie , 2021) as illus a ed in Fig.1.
P io esea ch has ex ensi ely shown ha pla o m p ic-
ing and e enue model design play a key ole in os e ing
di ec and indi ec ne wo k e ec s (Caillaud & Jullien,
2003; Hagiu, 2006; Pa ke & an Als yne, 2005; Roche
& Ti ole, 2003). In he case o di ec ne wo k e ec s, he
alue o each use inc eases as he numbe o use s on he
Fig. 1 Illus a ed in e ac-
ion be ween pla o m ac o s,
b oke ed asse s, and ne wo k
e ec s
Digi al
pla o m
Asse b oke
(pla o m ope a o )
Asse p o ide s
(supply side)
Asse
consume s
(demand side)
Indi ec ne wo k e ec s
Di ec ne wo k e ec s
Legend
Asse
(e.g., p oduc o se ice)
Ac o
(e.g., indi idual o o ganiza ion)
Elec onic Ma ke s (2025) 35:88 88 Page 4 o 23
same side o he ma ke inc eases. Indi ec ne wo k e ec s
occu when he alue o use s on one side o he ma ke
inc eases as he size o he opposi e side o he ma ke
inc eases. These ex e nali ies in luence use beha io and
a e a d i e o pla o m scalabili y (Cusumano, 2015;
Ka z & Shapi o, 1994; So i e al., 2019; Tussyadiah &
Pesonen, 2016).
The li e a u e inc easingly emphasizes he impo ance
o choosing which side o he ma ke o se e as a e enue
sou ce wi hin a pla o m e enue model (Eisenmann e al.,
2006; Kim, 2016; Oma ini, 2017; Wi z e al., 2019).
Acco ding o Eisenmann e al. (2006), pla o m ope a o s
need o conside he p ice sensi i i y o each ma ke side
o de e mine he money and he subsidy side. Pa icipan s
on he money side, who pay o pla o m se ices, usu-
ally exhibi low p ice sensi i i y, whe eas he subsidy side
is mo e p ice sensi i e (Oma ini, 2017). Pla o ms (e.g.,
app s o es) subsidize he side wi h highe demand elas-
ici y (e.g., use s) o a ac pa icipa ion, while cha ging
he money side (e.g., de elope s) (Kim, 2016; Roche &
Ti ole, 2003). While some pla o m ope a o s subsidize
one o mo e ma ke sides, o he s may cha ge a single p ice
o all ma ke sides, di e en ia e be ween ma ke sides, o
e en a y ees wi hin a ma ke side (Daxhamme e al.,
2019).
Taxonomies o pla o m e enue models
A axonomy classi ies concep s o objec s, aiding s uc-
u ed insigh s and comp ehension o complex domains. I
p o ides esea che s wi h a means o analyze, s uc u e, and
unde s and complex domains (Nicke son e al., 2013). The
IS communi y has p oposed a ious axonomies o business
models and digi al pla o ms, as exempli ied in he wo ks
o Be gman e al. (2022), Dupa c e al. (2022), Lage e al.
(2022), Mölle e al. (2022), Tessmann and Elbe (2022),
and Weking e al. (2020a). The li e a u e on pla o m busi-
ness models has also iden i ied a a ie y o dimensions o
alue cap u e, e lec ing bo h gene al p inciples and con ex -
speci ic nuances, as exempli ied by selec ed s udies shown
in he concep map in Fig.2. The s udies included in he
concep map we e selec ed o hei ele ance o pla o m
e enue models and hei schola ly isibili y (each ci ed
mo e han en imes). The selec ion is no comp ehensi e
bu se es o highligh he agmen a ion o esea ch on pla -
o m e enue models and should be unde s ood as illus a-
i e a he han exhaus i e. Exis ing amewo ks emphasize
common dimensions such as key e enue s eams, p ice dis-
c imina ion (S aub e al., 2021; Täusche & Laudien, 2018),
p ice disco e y (S aub e al., 2021; Täusche & Laudien,
2018; an de Ven e al., 2021), and p icing models (Sp inge
& Pe ik, 2021; an de Ven e al., 2021). Sec o -speci ic
dimensions, such as pie-spli ing in indus ial pla o ms
(Sp inge & Pe ik, 2021) and sma con ac s in da a ma -
ke places ( an de Ven e al., 2021), unde sco e he adap -
abili y o alue-cap u e s a egies o di e en sec o s.
A axonomy inco po a ing he alue-cap u e pe spec i es
o asse b oke s and p o ide s could enable a mo e nuanced
and s uc u ed app oach o pla o m e enue model design
(Feh e e al., 2018; Hein e al., 2020; Hel a & Raubi schek,
2018).
Fig. 2 Concep map o selec ed
pla o m e enue model dimen-
sions
Elec onic Ma ke s (2025) 35:88 Page 5 o 23 88
Fig. 3 Applied ETDP esea ch design
Elec onic Ma ke s (2025) 35:88 88 Page 6 o 23
Ex ended axonomy design p ocess
Ou esea ch app oach ollows he ex ended axonomy
design p ocess (ETDP) wi h six phases1 as p oposed by
Kundisch e al. (2022) and is illus a ed in Fig.3. Kundisch
e al. (2022) ex end he o iginal axonomy de elopmen p o-
cess in oduced by Nicke son e al. (2013) by emphasizing
ex-pos e alua ion, which assesses he use ulness o a axon-
omy a e i s de elopmen . In addi ion o he applied ETDP
app oach, he 26 axonomy design ecommenda ions (TDRs)
ou lined by Kundisch e al. (2022) we e also applied. These
ecommenda ions p o ide esea che s wi h design guidelines
o each o he six ETDP phases and a e sys ema ically e -
e enced in he co esponding s eps o his s udy (see s eps
1–18 in Fig.3) and a e p esen ed in ESM1-Supplemen A.
Phase I: Iden i y p oblem andmo i a e
S ep 1: Obse ed phenomenon
The phenomenon unde conside a ion is he dimensions and
cha ac e is ics speci ic o e enue models o ansac ional
pla o ms ha a e c i ical o unde s anding he alue-cap u e
aspec o pla o m business models (TDR1). While exis -
ing axonomies and amewo ks b oadly add ess pla o m
business models (Täusche & Laudien, 2018), he e is s ill
limi ed concep ual cla i y ega ding e enue model design,
pa icula ly conce ning how alue is cap u ed be ween pla -
o m ac o s (Hein e al., 2020; Hel a & Raubi schek, 2018).
S eps 2 and3: Ta ge use g oups andin ended
pu poses
The p ima y pu pose o his axonomy is o suppo
esea che s and p ac i ione s, including manage s, business
analys s, and digi al inno a ion designe s (TDR 3), by p o-
iding a amewo k o desc ibing, designing and analyzing
pla o m e enue models (TDR 2).
Phase II: De ine objec i es o asolu ion
S ep 4: De e mine me a‑cha ac e is ics
The design aspec s o pla o m e enue models a e es ab-
lished as he me a-cha ac e is ic, p o iding a s uc u ed
basis o iden i ying and ca ego izing he key dimensions
and cha ac e is ics o pla o m e enue models (TDR 4). The
choice o his me a-cha ac e is ic is d i en by he need o a
mo e p ecise unde s anding o pla o m e enue models. Ou
me a-cha ac e is ic includes, o example, he con igu a ion
o e enue sou ces and s eams––speci ically, whe he an
asse b oke gene a es e enue om asse consume s h ough
ansac ion ees, subsc ip ions, o o he mechanisms––and
how asse p o ide s mone ize hei o e ings h ough he
pla o m. Du ing he de elopmen o he axonomy, no
changes we e made o his me a-cha ac e is ic (TDR 5).
S ep 5: De e mine ending condi ions ande alua ion
goal
The ending condi ions a e di ided in o objec i e c i e ia:
gene alizable, inclusi e, conclusi e, unique, and subjec i e
c i e ia: concise, obus , comp ehensi e, ex endible, explan-
a o y (Nicke son e al., 2013). All condi ions a e desc ibed
in Table5 o phase IV. Beyond hese condi ions, TDR 6
also emphasizes he impo ance o an icipa ing an e alua ion
objec i e, which is de ined in s ep 15.
Phase III: Design andde elopmen
The de elopmen o he axonomy ollowed an i e a i e
app oach, comp ising wo concep ual- o-empi ical (C2E)
and wo empi ical- o-concep ual (E2C) i e a ions, in line
wi h TDR 9, which equi es a leas one o each (Kundisch
e al., 2022). De ailed in o ma ion abou he wo C2E i e a-
ions and he li e a u e e iew is p o ided in Ba els e al.
(2023), while he wo E2C i e a ions a e de ailed in Ba els
e al. (2024).
S ep 6: Building app oach?
Mo i a ed by TDR 7, we began he p ocess wi h wo C2E
i e a ions o build a heo e ical ounda ion, as su icien
insigh s we e a ailable om he li e a u e. Once he con-
cep ual s uc u e was in place, he de elopmen con inued
wi h wo E2C i e a ions, in line wi h TDR 8, o inco po a e
empi ical insigh s om se en case s udies.
S eps 7c‑10: Concep ual‑ o‑empi ical i e a ions
(7c) concep ualize cha ac e is ics anddimensions
o objec s
To ensu e obus C2E i e a ions (TDR 10), a li e a u e
e iew, as de ailed in ESM4, is conduc ed ac oss he ields
o IS and Business Managemen . Inclusion was based on
whe he he pape con ibu es o he concep ualiza ion o
1 Whe eas he o iginal ETDP dis inguishes be ween objec i e end-
ing condi ions in phase IV and subjec i e ones in phase V (Kundisch
e al; 2022), his s udy p esen s all ending condi ions collec i ely in
phase IV.
Elec onic Ma ke s (2025) 35:88 Page 7 o 23 88
pla o m e enue models by add essing hei dimensions
o cha ac e is ics (e.g., e enue s a egies, p icing logic). A
o al o 930 pape s we e e ie ed using he sea ch e m:
(ecosys em OR pla o m) AND (business model OR alue-
cap u e OR e enue model OR p o i model). These pape s
we e sou ced om six da abases: Scopus (259), Web o Sci-
ence (149), IEEE Xplo e (23), ACM (11), Google Schola
(133), and Dimensions (355). As an addi ional s ep, i e
pape s we e manually included based on hei concep ual
ele ance o pla o m e enue models, which we e no ully
cap u ed by he ini ial sea ch: De a e e al. (2022), F eichel,
Fiege , and Winkelmann (2021), Sp inge and Pe ik (2021),
an de Ven e al. (2021), and Weking e al., (2020b). As
shown in Fig.4, om a o al o 935 pape s, 34 pape s a e
selec ed as ele an , wi h 68 dimensions and 258 cha ac e -
is ics ex ac ed.
The emaining 901 pape s we e excluded based on he
ollowing c i e ia: 204 we e duplica es (EC1), 30 we e no
w i en in English (EC2), six we e less han h ee pages
(EC3), ypically abs ac s o summa ies lacking su icien
dep h o analysis, 13 we e no esea ch pape s (EC4) as hey
lacked a clea me hodology, 41 we e no accessible (EC5)
e en a e con ac ing he au ho s, and 607 did no mee he
inclusion c i e ia (EC6) o ex ac ing dimensions and cha -
ac e is ics o pla o m e enue models.
The e iew o he 34 pape s e ealed 68 dimensions and
258 cha ac e is ics ele an o pla o m e enue models. To
syn hesize hese da a, a concep ma ix was de eloped ol-
lowing Webs e and Wa son (2002). The de ini ions p o ided
by he au ho s in he analyzed pape s we e ex ac ed and
documen ed in Excel. Nine dimensions we e unclassi iable
and he e o e labeled “n/a”.
The emaining 59 dimensions we e so ed and ca ego-
ized based on iden i ied commonali ies and hen dis-
cussed among h ee au ho s, esul ing in eigh sel -coded
dimensions as shown in Table1. The ull coding p ocedu e
is documen ed in ESM4.
Each s udy is ca ego ized based on whe he i p esen s a
classi ica ion (e.g., a axonomy) and i s alignmen wi h TDR
11, which emphasizes he impo ance o e e encing exis ing
Fig. 4 Summa y o he sea ch esul s
Elec onic Ma ke s (2025) 35:88 88 Page 8 o 23
Table 1 Concep ma ix o he 34 a icles iden i ied
*No e:S udies wi hou coding en ies could no be unambiguously assigned o any dimension
No Au ho s Classi ica ion p o ided
(e.g., axonomy)?
Re enue
model
Re enue
s eam
Re enue
sou ce
Paymen
equency
P icing model P ice mecha-
nism
P ice dis-
co e y
P ice dis-
c imina ion
1 Cu is and Mon (2020)Yes x x x x
2 De a e e al. (2022)Yes x x x x x
3 El Sawy and Pe ei a (2013)Yes x x
4 Ende s e al. (2008)No x
5 F eichel, Ho mann, e al. (2021)Yes x x
6 F eichel, Fiege , and Winkelmann (2021)Yes x x x x
7* Ghezzi (2012)No
8 Giessmann e al. (2014)Yes x
9* Hel a and Raubi schek (2018)No
10* Hoye and S anoe ska-Slabe a (2009)No
11 Hy ynsalmi e al. (2012)No x
12 Immonen e al. (2014)No x
13 Janssen and Zuide wijk (2014)No x
14 Kim (2016)No x
15 Kohle (2015)No x
16 Kübel and Za nekow (2014)Yes x x
17 Laczko e al. (2019)No x
18 Lin e al. (2020)No x
19 Mancha and Go don (2022)No x
20 Pa k e al. (2021)No x
21 Rohn e al. (2021)Yes x x x x
22 Ruggie i e al. (2018)No x
23* Sch eieck e al. (2017)No
24 Sp inge and Pe ik (2021)Yes x x
25 S aub e al. (2021)Yes x x x
26 S ill e al. (2017)Yes x
27 Täusche and Laudien (2017)Yes x x x x x
28 Täusche and Laudien (2018)Yes x x x x x
29* Teece and Linden (2017)No
30* Teece (2010)No
31 an de Ven e al. (2021)Yes x x x
32* Ve s egen and Doo newee (2017)No
33 Weking e al. (2020a)No x x
34 Weking e al., ( 2020b)Yes x x x x
Sum 10 12 11 4 8 5 5 5
Elec onic Ma ke s (2025) 35:88 Page 15 o 23 88
Pa icipan s ecei ed p esen a ions, a use case desc ip-
ion, and he p oposed axonomy ( es g oup only). The
con ol g oup ollowed he same p o ocol, bu wi hou he
axonomy p esen a ion and in e iew. Pa icipan s we e
gi en a use case desc ip ion. All pa icipan s used i ual
Mi o boa ds o design pla o m e enue models wi h ee-
dom o make assump ions.
Th ee expe s independen ly a ed all en neu al desc ip-
ions on Mi o boa ds using a s uc u ed eedback empla e,
blinded o axonomy applica ion and pa icipan iden i y.
The p o ocol (see ESM2 o mo e in o ma ion) included a
b ie ing on he use case and a ing ask, and a p esen a ion o
he pla o m e enue model heo y, wi h expe 1 in session
one and expe s 2 and 3 in session wo. Expe a ing c i e ia
included cla i y (ease o unde s anding o he desc ip ion),
comple eness (p esence o all essen ial in o ma ion), and
app op ia eness ( ele ance o he pla o m e enue model
concep desc ibed). Each c i e ion was a ed on a h ee-poin
scale: “ + ” (3 poin s) o posi i e, “0” (2 poin s) o neu-
al, and “ − ” (1 poin ) o nega i e, wi h op ional no es o
commen s.
Pa icipan composi ion andexpe ise
The e alua ion in ol ed en digi al inno a ion designe s
wi h so wa e and business skills om F aunho e IESE,
whose expe ise a ied om s uden s o senio s. In ec ui -
ing pa icipan s o he expe imen , we sough indi iduals
who possessed a blend o echnical and business acumen,
essen ial o g asping he in ica e connec ions be ween a
digi al pla o m’s echnology and i s business model. Fig-
u e6 illus a es he a e age compe encies o pa icipan s
h ough a spide cha , whe e a sco e o i e in each ca ego y
ep esen s an ideal p o ile, while a sco e o ze o indica es
unsui abili y o he expe imen . Since he sco es in each
ca ego y we e a ound ou and app oaching he maximum
o i e, i was concluded ha he p o iles o he en candi-
da es su icien ly me he equi emen s o pa icipa ion in
he e alua ion.
All en pa icipan s we e di ided in o wo g oups: a
es g oup equipped wi h he axonomy (WI) and a con ol
g oup wi hou i (WO), each consis ing o one s uden , h ee
designe s, and one senio designe , as seen in Table6. All
pa icipan s wo ked indi idually, wi hou collabo a ion
wi hin o ac oss g oups.
O e he cou se o en indi idual sessions, he pa ici-
pan s c ea ed unique e enue model desc ip ions. These
we e e alua ed by h ee expe s: one in e nal expe om
F aunho e IESE, iden i ied as expe 1, wi h speci ic domain
knowledge in ecosys ems om he MSP p ojec , and wo
ex e nal expe s (expe 2 and expe 3), who possess yea s
o esea ch expe ience and ha e en ep eneu ial insigh s
om unning hei own s a up in he pla o m business
sec o . We ec ui ed only pa icipan s who we e no in ol ed
in he axonomy de elopmen p ocess, in acco dance wi h
TDR 23, o ensu e an unbiased pe spec i e in he e alua ion.
S ep 17: E alua ion goal me ?
To assess whe he he e alua ion goal was me o hypo h-
eses 1 and 2, he e alua ion esul s a e p esen ed in Table7.
The Like sco es om es subjec s ega ding hei expe-
ience wi h he axonomy applica ion o hypo hesis 3 a e
shown in Table8. These esul s a e accompanied by a dis-
cussion o each hypo hesis. A de ailed explana ion o he
esul s o each me ic is p o ided in ESM1-Supplemen E,
wi h all calcula ions a ailable in ESM3 .
Hypo hesis 1 sugges s ha using he p oposed axon-
omy imp o es he comple eness o pla o m e enue model
desc ip ions. This hypo hesis was e alua ed using wo me -
ics: a e age co e age a e (M1.1) and a e age comple e-
ness g ade (M1.2). Me ic M1.1 shows signi ican ly highe
co e age a es o desc ip ions ha employed he axonomy
(WI) han hose ha did no (WO), wi h a es o 89% s.
39% o asse b oke s, and 82% s. 29% o asse p o ide s.
Fig. 6 A e age so wa e and business p o ile sco es o expe imen
pa icipan s (n = 10)
Table 6 Demog aphic p o ile (n = 10)
Demog aphic p o ile Numbe
Gende Female 7
Male 3
Age 21–25 1
26–30 8
31–35 1
Job s a us S uden 2
Digi al inno a ion designe 6
Senio digi al inno a ion
designe 2
Elec onic Ma ke s (2025) 35:88 88 Page 16 o 23
As seen in Table7, a Mann–Whi ney U- es con i med hese
di e ences as s a is ically signi ican wi h a p alue o 0.008
and a s ong e ec size o 0.83. Me ic M1.2 e eals ha WI
desc ip ions we e g aded highe by expe s o comple e-
ness compa ed o WO desc ip ions. S a is ical signi icance
is ound wi h a p alue o 0.008 and an e ec size o 0.8.
Gi en hese s a is ical esul s, hypo hesis 1 is suppo ed: he
p oposed axonomy demons ably enhances he comple e-
ness o designed pla o m e enue models.
Hypo hesis 2sugges s ha he employmen o he p o-
posed axonomy will yield mo e accu a e desc ip ions o
pla o m e enue models han hose gene a ed wi hou
i s guidance. To assess he alidi y o his hypo hesis,
wo me ics a e conside ed: a e age expe g ade (M2.1)
and expe eedback (M2.2). Fo me ic M2.1, he analy-
sis o he da a e lec s a mo e a o able ou come o he
g oup using he axonomy (WI), wi h an a e age g ade o
6 poin s ou o a possible 9, agains he 4-poin a e age
g ade o he g oup wi hou he axonomy (WO). Bo h
a e ages show a di e ence in he pe cei ed accu acy o
he pla o m e enue model desc ip ions. The epo ed p
alue o 0.008 om he Mann–Whi ney U- es and he
e ec size ( ) o 0.84, as seen in Table7, indica e ha
his di e ence is no only s a is ically signi ican bu also
ep esen s a obus e ec size, lending s ong suppo o
he hypo hesis. Me ic M2.2 p o ides quali a i e insigh s
h ough expe eedback, which unde sco es he cla i y
and p ecision achie ed by g oup WI in hei desc ip ions.
We a ibu e his enhancemen o he use o he p oposed
axonomy. Despi e he inhe en complexi ies, he desc ip-
ions om g oup WI appea o be mo e accu a e. In con-
as , g oup WO’s desc ip ions su e ed om p oblems
wi h cla i y and logical low, which nega i ely a ec ed
comple eness. C i icism di ec ed a bo h g oups ega d-
ing he lack o de ail on p icing mechanisms and money
low de ails highligh s an a ea o imp o emen bu does
no de ac om he o e all indings. The linea eg es-
sion analysis be ween “a e age expe g ade” (M2.1) and
“a e age co e age a e” (M1.1) unde lined his inding,
showing a s ong posi i e co ela ion (R = 0.85), hus
sugges ing ha he mo e comple e he desc ip ions, he
highe hei accu acy. In ligh o he indings om bo h
me ics, hypo hesis 2 is suppo ed: he p oposed ax-
onomy demons ably enhances he accu acy o designed
pla o m e enue models.
Hypo hesis 3 e alua es whe he use s ind he p oposed
axonomy a use ul ool in he design o pla o m e enue mod-
els. This e alua ion is in o med by analyzing he obse ed
esul s o he axonomy’s applica ion (M3.1) and he use
eedback ecei ed (M3.2). Rega ding me ic M3.1, pa ici-
pan s c ea ed se en e enue models o asse b oke s and
i e o asse p o ide s, adhe ing o he axonomy’s s uc u e.
Table 7 Desc ip i e s a is ics
and es esul s Dimensions Desc ip i e
s a is ic Mann–Whi ney U- es
Mean SD U p
M1.1: a e age co e age a e Wi hou (WO) 35 10 0 0.008 0.83
Wi h (WI) 89 11
M1.2: a e age comple eness g ade Wi hou (WO) 1 0.3 0.5 0.008 0.80
Wi h (WI) 2 0.4
M2.1: a e age expe g ade Wi hou (WO) 4 0.4 0 0.008 0.84
Wi h (WI) 6 1.3
Table 8 Desc ip i e s a is ics
o Like scale esponses ( i e
pa icipan s, 4-poin Like
scale: 1 = disag ee o 4 = ag ee)
*No e: One pa icipan did no p o ide a esponse o S10
S a emen s Mean SD
(S1) The axonomy co e s all aspec s o pla o m e enue models 3.6 0.5
(S2) The axonomy’s s uc u e is logical and in ui i e 3.2 1.3
(S3) The axonomy is clea and easy o unde s and 2.8 0.8
(S4) The axonomy is p esen ed in a s aigh o wa d manne , a oiding unnecessa y complexi y 3.2 0.8
(S5) The axonomy eels o e whelming 2.2 1.3
(S6) The axonomy is easy o apply 3.6 0.5
(S7) The axonomy allows o he ep esen a ion o a ious pla o m e enue model ypes 3.8 0.4
(S8) The axonomy is use ul o analyzing pla o m e enue models 3.4 0.9
(S9) The axonomy is bene icial o designing new pla o m e enue models 3.6 0.9
(S10)* The axonomy has he po en ial o ad ance pla o m business model esea ch 3.8 0.5
Elec onic Ma ke s (2025) 35:88 Page 17 o 23 88
Howe e , an analysis o he 12 models by he au ho s e ealed
11 issues, highligh ing con usion in model selec ion, di icul-
ies in swi ching pe spec i es be ween asse b oke s and asse
p o ide s, and missing aspec s (e.g., lack o speci ied p icing
mechanisms). Fu he de ails can be ound in ESM3.
Fo me ic M3.2, he axonomy ecei ed mixed eedback
ac oss s a emen s (Table8). While pa icipan s conside ed i
a use ul ool o designing pla o m e enue models (S9) and
o ep esen ing a ious model ypes (S7), some pe cei ed
i as o e whelming (S5). The axonomy was also iewed as
co e ing all ele an aspec s (S1), ha ing a logical s uc-
u e (S2), and being easy o apply (S6). Howe e , ewe
pa icipan s ag eed ha i is clea and easy o unde s and
(S3), indica ing po en ial issues wi h cla i y. Se e al pa ici-
pan s epo ed di icul ies e alua ing ce ain s a emen s (S1,
S7, and S10). Inconsis encies also eme ged, as he axonomy
was a ed only mode a ely o unde s andabili y (S3) bu
simul aneously as easy o apply (S6). This di e gence sug-
ges s ha pa icipan esponses may no ully e lec a cohe -
en assessmen . This is a known issue wi h use eedback,
which can be imp ecise o misleading ega ding an a e ac ’s
ac ual u ili y o e icacy (Venable e al., 2016).
Quali a i e eedback p aised he axonomy o p o id-
ing a s uc u ed checklis ha aids in co e ing all necessa y
Fig. 7 Finalized axonomy o pla o m e enue models
Elec onic Ma ke s (2025) 35:88 88 Page 18 o 23
aspec s and acili a ing idea gene a ion. Ye , he pa ici-
pan s iden i ied challenges wi h he axonomy’s o ma and
na iga ion, sugges ing ha enhancemen s such as sen ence
empla es and a mo e in ui i e s uc u e could make i mo e
use - iendly. The pa icipan s ad oca ed a design o e haul
o op imize he axonomy o p ac ical applica ion. In con-
clusion, hypo hesis 3 is pa ially suppo ed as pa icipan s
ound he axonomy use ul, bu also highligh ed challenges
and ecommended ha i be de eloped in o a mo e p ac ical
ool o designing pla o m e enue models.
Phase VI: Communica ion
The inal axonomy, de eloped h ough he ETDP app oach,
includes 15 dimensions and 64 cha ac e is ics (TDR 25),
isualized in Fig.7 and de ailed in ESM1-Supplemen F.
Clea desc ip ions o dimensions and cha ac e is ics ensu e
usabili y (TDR 26). Following TDR 24, he i e a i e de el-
opmen p ocess is anspa en ly documen ed in he ESM1-
Supplemen C, de ailing changes and ensu ing aceabili y.
S ep 18: Repo axonomy
As seen in Fig.7, he i s dimension o he asse b oke
(DB1) ou lines he asse b oke ’s e enue model ype.
The e enue s eam (DB2) de ails mone iza ion s a egies,
including access ees, lis ing ees, ad e ising ees, com-
mission ees, and dona ions and sponso ships. The e enue
sou ce (DB3) speci ies who is mone ized, whe he asse con-
sume s, asse p o ide s, o hi d pa ies. The paymen igge
(DB4) add esses he iming, e.g., pay pe access, while he
paymen equency (DB5) de ines he equency o cha ges,
i.e., one- ime o ecu ing. P ice disco e y (DB6) del es
in o he pla o m p ice se ing, po en ially by asse b oke s,
asse p o ide s, asse consume s, o h ough nego ia ions.
The p ice mechanism (DB7) examines how supply and
demand in luence pla o m p icing, be i ixed, a iable, o
nego iable, and p ice disc imina ion (DB8) explo es p icing
s a egies o he pla o m p ice, such as use ype, loca ion,
o a i op ions like basic o p emium.
The i s dimension o he asse p o ide s (DP1) desc ibes
he asse p o ide ’s e enue model ype. The e enue s eam
(DP2) ocuses on mone iza ion s a egies, such as sales o
asse s, en als, usage-based cha ges, and dona ions o spon-
so ships. The e enue sou ce (DP3) de ines who is mon-
e ized by he asse p o ide s, including asse consume s,
he asse b oke , o hi d pa ies. Paymen equency (DP4)
de ails paymen egula i y, i.e., one- ime, subsc ip ion,
usage, o en al-based. P ice disco e y (DP5) discusses asse
p ice de e mina ion, in ol ing b oke s, p o ide s, consum-
e s, o nego ia ions. The p ice mechanism (DP6) analyzes
he in luence o ma ke o ces on p ices, which can be ixed
o a iable. P ice disc imina ion (DP7) conside s p ice a i-
a ions based on ac o s like quan i y o use loca ion.
Limi a ions
This s udy has some limi a ions, which a e s uc u ed
acco ding o he amewo k p oposed by Wohlin e al.
(2024), co e ing cons uc , in e nal, ex e nal, and conclu-
sion h ea s o alidi y.
Cons uc alidi y conce ns whe he he axonomy accu-
a ely cap u es he concep o pla o m e enue models.
Du ing he 4 h i e a ion o de elopmen , wo p ojec use
cases (SLR and MSP) we e analyzed in a single empi ical-
o-concep ual (E2C) i e a ion. Combining bo h cases may
ha e a ec ed cons uc alidi y. Howe e , no u he changes
eme ged in he inal case (MSP), so we conside he axon-
omy o be s able. S ill, due o he e ol ing na u e o pla o m
business models, u u e i e a ions may unco e addi ional
ele an dimensions. Fu he mo e, he e is some po en ial
o e lap be ween dimensions, which may a ec obus ness.
“Re enue model ype” (DB1) and “ e enue s eam” (DB2)
bo h ela e o he e enue mechanism bu cap u e di e en
le els o abs ac ion and a e well suppo ed in he li e a u e
(c . Table4), so hey we e e ained sepa a ely. A simila case
applies o “paymen igge ” (DB4) and “paymen equency”
(DB5): in SLR’s lis ing model, asse p o ide s pay bo h a
one- ime and a ecu ing ee pe lis ed solu ion, making i
necessa y o dis inguish he igge “pay pe asse lis ing”
om he equency dimension (“pay once” o “ ecu ing”).
Finally, while he axonomy comp ises 15 dimensions, his
numbe exceeds he heu is ic o se en plus o minus (Nick-
e son e al., 2013).
In e nal alidi y add esses whe he he obse ed e ec s in
he expe imen can be a ibu ed o he use o he axonomy
a he han o he ac o s. The axonomy was used as a no -
ma i e model by he au ho s o assess he comple eness o
he desc ip ions c ea ed by he es and con ol g oups (see
M1.1). This may ha e con ibu ed o he highe comple e-
ness obse ed in he es g oup. Howe e , expe e alua ion
(M1.2) suppo s he use ulness o he axonomy and indica es
ha i co e s he essen ial componen s o pla o m e enue
models. In addi ion, po en ial bias in pa icipan s’ subjec i e
assessmen s o he axonomy’s use ulness (M3.2) mus be
acknowledged. Simila ly, au ho bias du ing quali a i e da a
analysis canno be uled ou , despi e mi iga ion e o s such
as de ailed documen a ion o suppo ex e nal e i ica ion.
Ex e nal alidi y conce ns he ex en o which he ind-
ings can be gene alized beyond he s udy con ex . The con-
olled expe imen in ol ed a small sample o en pa ici-
pan s wi h speci ic backg ounds. Al hough he esul s we e
s a is ically signi ican , he limi ed sample size and expe ise
cons ain gene alizabili y. Subjec p o iles we e documen ed
Elec onic Ma ke s (2025) 35:88 Page 19 o 23 88
o inc ease anspa ency. Mo eo e , he a i icial se ing o
he expe imen lacked eal-wo ld business p essu es, which
may ha e a ec ed pa icipan engagemen and educed he
p ac ical obus ness o he esul ing models. The absence
o e alua ion by indus y pla o m manage s cons ains he
p ac ical gene alizabili y o he indings. Finally, he ax-
onomy was applied o ansac ion pla o ms ope a ing in
Ge many, which may limi i s applicabili y o o he pla o m
ypes, such as inno a ion pla o ms, and di e en egional
o ins i u ional con ex s.
Conclusion alidi y e e s o he ex en o which he
obse ed e ec s can be a ibu ed o he ea men a he han
o chance. Despi e he small sample size, he expe imen
yielded s a is ically signi ican esul s ac oss all measu ed
a iables. Mann–Whi ney U- es s e ealed signi ican di e -
ences be ween he es and con ol g oups in e ms o co e age
a e (U = 0, p = 0.008, = 0.83), comple eness g ade (U = 0.5,
p = 0.008, = 0.80), and expe e alua ion (U = 0, p = 0.008,
= 0.84). These alues indica e la ge e ec sizes, suppo ing
he obus ness o he obse ed di e ences. In addi ion, expe
a ings subs an ia e he p ac ical alue o he axonomy.
Fu u e wo k
Building on he iden i ied h ea s o alidi y, u u e esea ch
should eplica e he expe imen wi h la ge and mo e di e se
samples o con i m he obus ness and gene alizabili y o he
esul s. Second, e alua ions in mo e ealis ic, business- el-
e an se ings could enhance ex e nal alidi y and p ac ical
applicabili y. Fu u e esea ch should inco po a e e alua ions
wi h pla o m manage s o enhance p ac ical applicabili y.
Thi d, o imp o e cons uc alidi y, u he s udies could
examine whe he he dis inc ion be ween closely ela ed
dimensions is meaning ul and consis en ac oss di e en
pla o m con ex s. Fou h, o educe po en ial esea che bias
and s eng hen in e nal alidi y, au oma ed classi ica ion ech-
niques could be in eg a ed o suppo quali a i e analysis. In
addi ion, u u e esea ch could also in es iga e business model
a che ypes (c . Be gman e al., 2022; Dupa c e al., 2022).
Conclusion
While exis ing esea ch p o ides aluable insigh s in o he
a chi ec u e and design o pla o m business models (Feh e
e al., 2018; Kim, 2016; Täusche & Laudien, 2018), a sys-
ema ic unde s anding o how pla o m e enue models can be
classi ied and designed emains unde de eloped. To add ess
his gap, we apply he enhanced axonomy de elopmen p ocess
(Kundisch e al., 2022) o de elop and e alua e a axonomy
o pla o m e enue models. The axonomy consis s o 15
dimensions and 64 cha ac e is ics, di ec ly add essing RQ1.
Fu he mo e, we demons a e he axonomy’s use ulness by
e alua ing i s applicabili y in a con olled expe imen , he eby
add essing RQ2.
This s udy makes wo main con ibu ions: i s , we p esen
he axonomy along wi h de ailed desc ip ions o all cha ac-
e is ics, and i s dimensions e lec he pe spec i es o bo h
asse b oke s and asse p o ide s. This dis inc ion esponds
o calls o a clea e sepa a ion in he alue-cap u e logic o
pla o ms (Hein e al., 2020; Hel a & Raubi schek, 2018).
F om a p ac ical s andpoin , he axonomy o e s a s uc u ed
amewo k o designing and analyzing pla o m e enue
models. I enables p ac i ione s o align e enue s a egies
wi h pla o m ope a ional oles and asse o e ings. F om a
heo e ical pe spec i e, ou axonomy ad ances esea ch by
concep ualizing e enue- ela ed design choices (e.g., e enue
s eams and p ice disco e y) o pla o m ope a o s and asse
p o ide s, he eby con ibu ing o a mo e nuanced unde s and-
ing o mul i-sided alue cap u e ac oss di e en pla o m oles
(Hein, 2020; Hel a & Raubi schek, 2018). This s udy also
ex ends p io esea ch on he in e play o mul iple e enue
model s a egies (Daxhamme e al., 2019; Li, 2023), as illus-
a ed by he analyzed cases–– o example, Ty e24’s combina-
ion o access and commission models wi h in e dependen
p icing s uc u es ha mu ually in luence each o he .
Second, he se en pla o m cases obse ed du ing he ax-
onomy de elopmen phase illus a e he complexi y o eal-
wo ld pla o m e enue models, esul ing in he iden i ica ion
o 26 dis inc e enue model ypes. In line wi h p io esea ch
by Täusche and Laudien (2018), who epo ha commission
models a e used by asse b oke s in 72% o obse ed pla o m
cases, ou analysis e eals a compa able pa e n, wi h com-
mission models p esen in 71% o he pla o ms examined.
Howe e , when analyzing all 26 e enue model ypes iden-
i ied ac oss he se en pla o ms, commission-based models
occu only 7 imes (27%), while access-based models appea 9
imes (35%). Al hough he numbe o cases in ou s udy is lim-
i ed, he indings e eal an impo an insigh : se e al pla o ms
employ mul iple e enue models simul aneously o cap u e
alue, such as Ty e24 ( i e e enue model ypes) and Vin ed
(six ypes), whe eas o he s ollow a mo e na owly ocused
app oach, such as emp o ( wo ypes) o MyHamme (one
ype). This disc epancy unde sco es he p e alence o mixed
mone iza ion s a egies in pla o m con ex s and opens up new
a enues o u u e esea ch on he in e play be ween comple-
men a y and mu ually exclusi e pla o m e enue model ypes.
In conclusion, by d awing on a heo e ical ounda ion
de eloped h ough a li e a u e e iew, oge he wi h empi i-
cal g ounding h ough he analysis o exis ing pla o m cases
and e alua ion in a con olled expe imen , his s udy in e-
g a es cu en insigh s om bo h esea ch and p ac ice on
pla o m e enue models. Acco dingly, his esea ch lays he
g oundwo k o u u e s udies and p omo es he de elop-
men o pla o m e enue models as a ocused line o inqui y
wi hin he b oade ield o business models.
Elec onic Ma ke s (2025) 35:88 88 Page 20 o 23
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- 025- 00841-4.
Acknowledgemen s We hank he Edi o and he anonymous e e ees
o hei help ul commen s and sugges ions, whichg ea ly imp o ed
he pape . We since ely hank Ch is ian Vo bohle o his cons uc i e
eedback and iendly p e e iew o ou manusc ip . Nedo Ba els and
Ma hias Koch acknowledge inancial suppo om he Digi al Eu ope
P og amme (DIGITAL) o he Eu opean Union unde G an Ag eemen
No.101123121 (EURIDICE).
Funding Open Access unding enabled and o ganized by P ojek
DEAL.
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