Lange , Benedic
A icle
Unde s anding da a & analy ics ma u i y: Asys ema ic
e iew o ma u i y model composi ion
Schmalenbach Jou nal o Business Resea ch (SBUR)
P o ided in Coope a ion wi h:
Schmalenbach-Gesellscha ü Be iebswi scha e.V.
Sugges ed Ci a ion: Lange , Benedic (2025) : Unde s anding da a & analy ics ma u i y: Asys ema ic
e iew o ma u i y model composi ion, Schmalenbach Jou nal o Business Resea ch (SBUR), ISSN
2366-6153, Sp inge , Heidelbe g, Vol. 77, Iss. 2, pp. 205-227,
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Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227
Unde s anding Da a & Analy ics Ma u i y:
A Sys ema ic Re iew o Ma u i y Model Composi ion
Benedic Lange
Recei ed: 21 Augus 2023 / Accep ed: 20 Decembe 2024 / Published online: 29 Janua y 2025
© The Au ho (s) 2025
Abs ac Le e aging da a is becoming inc easingly impo an o businesses. How-
e e , his ans o ma ion can be complex, as i equi es a as a ay o social and
echnical capabili ies. To gene a e consensus in his domain, his s udy examines da a
& analy ics ma u i y models by analyzing hei a chi ec u es, ma u i y le els, and
ma u i y domains. A sys ema ic e iew based on he PRISMA amewo k iden i ies
38 ma u i y models and induc i ely de i es insigh s in o hei composi ion. Th ee
di e en con en ypes a e di e en ia ed, namely o ganiza ion-o ien ed, echnology-
o ien ed and da a-o ien ed models. The ini ial indings p o ide a comp ehensi e
o e iew o he s a us quo in da a & analy ics ma u i y models and p o ide a oun-
da ion o u he esea ch in his ield. The s udy hus con ibu es owa ds enabling
businesses o conduc mo e sophis ica ed da a & analy ics ma u i y assessmen s and
suppo mo e e ec i e use o da a.
Keywo ds Da a · Analy ics · Ma u i y · Ma u i y models · Li e a u e e iew
1 In oduc ion
Digi al echnologies a e indispensable in oday’s socie y ha ing a signi ican impac
on o ganiza ions and hei businesses (Reis e al 2018; Tho dsen e al 2020). The
p oli e a ion o digi al echnologies has led o a signi ican inc ease in he amoun
o da a gene a ed and collec ed by o ganiza ions (Bianchini and Michalko a 2019;
Mca ee and B ynjol sson 2012; Buhl e al 2013). Da a is conside ed a aluable asse ,
wi h subsequen analy ics p o iding insigh s ha can in o m s a egic decision-
Consen o publica ion This manusc ip does no con ain pe sonal da a.
Benedic Lange
TUM School o Managemen , Technical Uni e si y o Munich, Munich, Ge many
E-Mail: benedic .lange @ um.de
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206 Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227
making and d i e business g ow h (Pohl e al 2022; G ossman 2018;Wange al
2015). O ganiza ions a e acing inc easing p essu e o acqui e and e ec i ely use
da a o emain e icien and compe i i e (Ba on and Cou 2012; Da enpo 2006).
Da a & analy ics ma u i y e e o an o ganiza ion’s capabili y o e ec i ely
manage and u ilize da a (G ossman 2018; Comuzzi and Pa el 2016). This includes,
among o he s, he abili y o collec , p ocess, and in e p e da a o suppo decision
making, achie e s a egic goals, and suppo business objec i es (Ga ne 2022).
Howe e , da a & analy ics ma u i y is no a s a ic concep and can a y widely among
indi iduals and o ganiza ions (K ól and Zdonek 2020). And, while da a & analy ics
p omise inc eased anspa ency and op imiza ion a mul iple le els o managemen
and ope a ions (Pohl e al 2022), he ansi ion o ealizing his po en ial equi es
ex ensi e acquisi ion o new compe encies and skills (Mca ee and B ynjol sson
2012; Comuzzi and Pa el 2016). In many cases, new s anda d p ocesses mus be
implemen ed and new skills as well as high quali y da a mus be acqui ed. Many
o ganiza ions, especially SMEs, a e wa y o he challenges associa ed wi h ad anced
analy ics. They ask o a de ined s uc u e, clea s eps and a oadmap o mas e his
ans o ma ion (Bianchini and Michalko a 2019; Comuzzi and Pa el 2016).
To guide ans o ma i e endea o s and s uc u e capabili y de elopmen , ma u i y
models a e aluable a i ac s o e ing guidance o esea ch and p ac ice (Me le
2011; Becke e al 2009). F om a esea ch pe spec i e, ma u i y models ep esen
heo ies o how o ganiza ional capabili ies de elop along a ma u a ion pa h (Pöp-
pelbuß and Röglinge 2011). The amewo ks ypically de ine a se ies o ma u i y
le els h ough which an o ganiza ion can p og ess (Poeppelbuss e al 2011; Wendle
2012). In p ac ice, ma u i y models a e use ul o assessing an o ganiza ion’s s a us
quo, de e mining a desi ed a ge s a e, and iden i ying ields o ac ion (Pöppelbuß
and Röglinge 2011). Ma u i y models can se e as a s a ing poin and p o ide
me hods o assessing an o ganiza ion’s capabili ies in a pa icula a ea (Hüne e al
2009; De B uin e al 2005).
Indeed, exis ing ma u i y models o e aluable con ibu ions o le e age da a &
analy ics. These da a & analy ics ma u i y models co e a wide ange o applica-
ion-speci ic echnical domains (Comuzzi and Pa el 2016) and business capabili ies
(Wo och and S obel 2021). The models ypically p o ide a p ocedu e o assessing
an o ganiza ion’s cu en le el o compe ence in a eas such as da a go e nance, da a
quali y, and da a managemen , while also helping o iden i y a eas o imp o emen
(K ól and Zdonek 2020; Comuzzi and Pa el 2016; Helmy e al 2022).
Despi e he exis ence o a ious ma u i y models examining di e en a eas o
analy ics, such as da a wa ehousing (Sp ui and Sacu 2015) da a acquisi ion (Mu -
phy and Chang 2009), o a i icial in elligence (Alsheiabni e al 2019), as well as
app oaches o de elop gene al ma u i y models o da a & analy ics capabili ies o
an o ganiza ion o indus y (e. g. Pe ales-Man ique e al 2019; Cosic e al 2012),
he e is no consensus on wha such a model should look like. The e is a lack o
cla i y abou he con en , s uc u e and applica ion o exis ing models (Reis e al
2018; Gökalp e al 2021b; Fa o e o e al 2021).
While he e a e li e a u e e iews a ailable (P oença and Bo binha 2018;K ól
and Zdonek 2020), mos a e limi ed o a desc ip i e analysis o da a & analy ics
ma u i y models. The e is a lack o dep h, pa icula ly in he analysis o ma u i y
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Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227 207
le els and domains ha need o be conside ed o a comp ehensi e analysis o he
models. This gap is signi ican since i limi s a holis ic iew o an o ganiza ion’s
da a & analy ics capabili ies. To he bes o cu en knowledge, he e is cu en ly
no s udy ha comp ehensi ely compa es and analyzes da a & analy ics ma u i y
models.
Wi hou a deepe unde s anding o he composi ion o ma u i y models, o gani-
za ions may no be able o iden i y he a eas whe e hey need o imp o e and ealize
he ull po en ial o hei da a & analy ics capabili ies. The de iciencies in knowl-
edge o da a & analy ics and i s ma u i y models, and he gene al lack o s uc u ed
app oaches o ma u i y dimensions, may lead o unde -exploi ed and unde -demon-
s a ed po en ial in he ield (Ki on e al 2015; LaValle e al 2011;Kanee al2017).
Academics and p ac i ione s alike can bene i om a be e unde s anding o da a &
analy ics ma u i y and he associa ed ma u i y models o be e use o da a. A sys-
ema ic li e a u e e iew is needed o syn hesize exis ing knowledge, analyze and
abs ac he a ious ex an da a & analy ics ma u i y models, and iden i y esea ch
gaps in he a ea o da a & analy ics ma u i y. The ollowing o e a ching esea ch
ques ion is de i ed:
RQ: Wha a e s uc u es and con en s o da a & analy ics ma u i y models?
By e iewing he body o exis ing ma u i y models, he s udy aims o u he he
unde s anding o how da a & analy ics ma u i y is cu en ly de ined and measu ed,
and wha domains a e conside ed impo an o o ganiza ions. In addi ion, by com-
pa ing and con as ing di e en ma u i y models, common composi ions and a eas
o di e gence can be iden i ied ha help in o m he de elopmen o new models
o he e inemen o exis ing ones. The analysis hus ocuses on he s uc u e and
con en o he models, speci ically in e ms o hei a chi ec u e, ma u i y le els,
and ma u i y domains. The ini ial indings may p o ide a s a ing poin o busi-
nesses looking o assess and imp o e hei da a & analy ics ma u i y, as well as o
esea che s s udying da a & analy ics ma u i y and he ela ed ans o ma ion.
To achie e he objec i e, add ess he esea ch gap and answe he esea ch ques-
ion, he emainde o he pape is s uc u ed as ollows: Fi s , he heo e ical back-
g ound o da a & analy ics as well as ma u i y model de elopmen and da a & an-
aly ics ma u i y models is gi en. Sec . 3p esen s he me hodology o he li e a u e
e iew. The s uc u es and con en s o he iden i ied ma u i y models a e p esen ed
in Sec . 4. Model app oaches ound a e b oken down o hei basic concep s h ough
o maliza ion in o de o ca ego ize and abs ac hem. Main indings, implica ions,
u he esea ch di ec ions as well as limi a ions a e p esen ed in Sec . 5. The pape
concludes wi h a summa y o he indings.
2 Backg ound
This sec ion p o ides he heo e ical backg ound o he s udy by discussing he
ounda ion unde lying da a & analy ics ma u i y models. This includes de ining he
espec i e concep s and gaining an unde s anding o he mo i a ion o and bene i s
o using hese models.
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208 Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227
2.1 Da a & Analy ics
The abili y o e ec i ely manage and le e age da a has become a co e compe-
ency o o ganiza ions (Pohl e al 2022;Wange al2015). Howe e , he inc easing
complexi y and olume o da a gene a ed in many o ganiza ions oday, can p esen
challenges in unde s anding and de i ing insigh s om i (Bianchini and Michalko a
2019). Da a gene a ed by ERP sys em ansac ions alone can be o e whelming o
many businesses. Da a analy ics, business analy ics, business in elligence, and o he
ela ed disciplines ha e eme ged as c ucial a eas o ocus o o ganiza ions seek-
ing o maximize he po en ial o hei da a. They aim o employ echnologies om
simple desc ip i e analyses o mo e ad anced machine lea ning o a i icial in el-
ligence. Di e en e ms in he gene al ield o echnology ha e been used mo e o
less synonymously, wi h di e ences in he ime ame examined and how he esul s
a e used o gene a e alue (Schniede jans e al 2014). Fo simplici y and o he
pu poses o he analyses in his s udy, all o hese disciplines a e e e ed o when
alking abou da a & analy ics.
Da a can be desc ibed as aw ac s and igu es ha can be collec ed and p ocessed
(Pa a e al 2017). In a business con ex , analy ics e e s o he me hods used o
analyze and in e p e da a o suppo decision making (G ossman 2018; O’Dono an
e al 2016). The common goal o da a & analy ics is o de i e insigh s om da a
o make decisions ha d i e business alue (Paczkowski 2021; LaValle e al 2011).
O ganiza ions seek o imp o e hei business p ocesses, inc ease e iciency, and gain
a compe i i e ad an age h ough he e ec i e use o da a (Pohl e al 2022).
Despi e he g owing impo ance o da a & analy ics, companies con inue o s ug-
gle wi h building he necessa y capabili ies and compe encies o e ec i ely collec ,
manage, and use da a (Ki on e al 2015; LaValle e al 2011; Da enpo 2006). Ex an
esea ch in da a & analy ics ocuses mo e on echnological and p ocedu al ad ances
in a eas such as da a go e nance, da a quali y, da a in eg a ion, da a secu i y, and
da a isualiza ion (Al-Sai e al 2022;Wange al2022;Shi2022). Many o ganiza-
ions s ill lack he necessa y knowledge abou he skills and esou ces equi ed o
e ec i ely le e age da a o d i e business pe o mance (Hashem e al 2015; Pohl
e al 2022). An o e iew and de ini ion o he capabili ies and compe encies equi ed
o collec , manage, and use da a is a c i ical p e equisi e o o ganiza ions seeking
o build he skills and in as uc u e o emain compe i i e in he cu en da a age.
2.2 Ma u i y Models
Ma u i y models ha e become es ablished manage ial ools o o ganiza ions o
assess and imp o e hei pe o mance in a pa icula a ea o wi h a pa icula ech-
nology (Me le 2011;Hüne e al2009). The models p o ide a amewo k o
iden i ying and add essing gaps in p ocesses and capabili ies (Poeppelbuss e al
2011). The concep o ma u i y models o igina ed in he ield o o ganiza ional de-
elopmen and was ini ially p oposed as a ool o assessing he ma u i y o so wa e
de elopmen p ocesses in he Capabili y Ma u i y Model (CMM) (Humph ey 1988;
Paulk e al 1993). Since hen, ma u i y models ha e been widely used in a a i-
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Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227 209
e y o con ex s, including manu ac u ing, supply chain, and IT (Becke e al 2009;
Schumache e al 2019; Hellweg e al 2021).
Ma u i y models ypically consis o a se o le els o s ages, ha ep esen
di e en deg ees o o maliza ion and op imiza ion (Wendle 2012). The models
map ma u i y pa hs by desc ibing he dis inc i e capabili ies, p ac ices, o ou comes
ha o ganiza ions should exhibi a each s age (Pöppelbuß and Röglinge 2011;
De B uin e al 2005). As o ganiza ions p og ess h ough he s ages, hey a e expec ed
o demons a e inc easing le els o ma u i y, esul ing in imp o ed pe o mance.
Ma u i y models can be used as a diagnos ic ool o iden i y a eas o imp o emen ,
as a p esc ip i e oadmap o he de ini ion o a ge s a es and as a compa ison
ins umen (Wendle 2012; Becke e al 2009). By p o iding a s uc u ed way o
assess an o ganiza ion’s ma u i y, ma u i y models can help o ganiza ions selec
imp o emen e o s and ack p og ess o e ime.
Se e al amewo ks ha e been p oposed o he de elopmen o ma u i y models.
One o he mos widely used amewo ks is Capabili y Ma u i y Model In eg a ion
(CMMI), which p o ides a se o guidelines o de eloping and alida ing ma u-
i y models (Ch issis e al 2007,2011). The ISO/IEC 3100x amily o s anda ds –
he successo o he ISO/IEC 15504 amily o s anda ds, also known as he So -
wa e P ocess Imp o emen and Capabili y De e mina ion (SPICE) model – p o ides
ano he commonly used amewo k o ma u i y model de elopmen (In e na ional
O ganiza ion o S anda diza ion 2015a,b). App oaches de eloped by Becke e al
(2009) and De B uin e al (2005) a e well es ablished in ma u i y model de elop-
men . The key idea behind he de elopmen amewo ks is o p o ide a s uc u ed
way o build ma u i y models and subsequen ly assess an o ganiza ion’s cu en
le el o ma u i y in a gi en a ea. Each amewo k p o ides a se o guidelines o
de eloping and alida ing ma u i y models ha can be adap ed o mee he speci ic
needs o di e en o ganiza ions and indus ies.
O e all, while he e a e many di e en ma u i y models and se e al ma u i y
model de elopmen amewo ks in use oday, hey sha e he common goal o helping
o ganiza ions imp o e hei pe o mance. The li e a u e sugges s ha while ma u i y
models can be a aluable ool o assessing and imp o ing o ganiza ional p ocesses
and capabili ies, hei e ec i eness depends on he quali y o hei de elopmen and
alida ion (Becke e al 2009; Poeppelbuss e al 2011). Ma u i y model de elope s
should ensu e ha hei models a e well de ined, alida ed h ough empi ical es ing,
and ocused on con inuous imp o emen (Becke e al 2009).
2.3 Da a & Analy ics Ma u i y Models
As da a has become inc easingly aluable, mo e and mo e o ganiza ions ha e been
seeking o assess and imp o e hei capabili ies in da a- ela ed a eas (Buhl e al
2013;Wange al2015). The g owing impo ance o da a-d i en decision making
has led o he de elopmen o da a & analy ics ma u i y models speci ically designed
o assess an o ganiza ion’s da a compe ency and i s gene al abili y o use da a e ec-
i ely (Ilin e al 2022; Helmy e al 2022; Comuzzi and Pa el 2016; Hausladen and
Schosse 2020). In he con ex o his s udy, he e m da a & analy ics ma u i y is
expanded o include commonly used e ms such as analy ics ma u i y, da a analy ics
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210 Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227
ma u i y, da a science ma u i y, e c., as hei meaning is simila , hey a e o en used
in e changeably and hey pu sue he same goal.
The e a e many ypes o da a & analy ics ma u i y models. Some g ay li e a u e
models, like hose om IBM (2007) and Accen u e (2018), a e p oposed by con-
sul ing i ms, p i a e esea ch ins i u es, and e en companies hemsel es. O he da a
& analy ics ma u i y models ollow a mo e academic and igo ous de elopmen ,
alida ion and e iew p ocess o emo e bias and inc ease gene alizabili y. The e
a e mul iple app oaches o assessing da a & analy ics ma u i y, leading many ma-
u i y models o ocus on a speci ic a ea o o ganiza ional da a compe ence. Some
models a e managemen -o ien ed, such as hose p oposed by Comuzzi and Pa el
(2016) and Pa a e al (2017), ocusing on o ganiza ional issues such as ‘cul u e’,
‘s a egic alignmen ’, o ‘p ocesses’. O he ma u i y models a e echnical in na u e,
like he models p oposed by Sp ui and Pie zka (2015) and Mu phy and Chang
(2009) co e ing ‘da a acquisi ion’ and ‘da a managemen ’. The de elopmen ap-
p oach, e alua ion app oach, and e alua ion con en o da a & analy ics ma u i y
models can di e widely. This di e si y can make i challenging o o ganiza ions
o selec an app op ia e model o hei needs, and can also hinde esea che s and
p ac i ione s seeking o e alua e and compa e di e en models (Gökalp e al 2021b;
K ól and Zdonek 2020).
To add ess he he e ogenei y o da a & analy ics ma u i y models and o ana-
lyze he exis ing e alua ion app oaches, li e a u e e iews such as (K ól and Zdonek
2020) and (P oença and Bo binha 2018) we e conduc ed. Howe e , despi e he
g owing popula i y o da a & analy ics ma u i y models, he e is no consensus on
how hese models should be s uc u ed and wha hei con en should be (Reis e al
2018; Gökalp e al 2021b). Cons an change and p og ess in he ield o da a &
analy ics make s a ic ma u i y models quickly ou da ed (K ól and Zdonek 2020).
An in eg a ed pe spec i e on equi ed business and echnology capabili ies h ough
a comp ehensi e e iew o hese models is s ill lacking. As a esul , i is desi able o
c ea e anspa ency o esea che s and p ac i ione s by e alua ing exis ing models
and eaching consensus on da a & analy ics ma u i y model composi ion. This can
help businesses ocus on app op ia e capabili ies, ensu e ha exis ing ma u i y mod-
els a e igo ously e ined and alida ed, and ul ima ely imp o e he e ec i eness o
u u e da a & analy ics ma u i y models as a ool o o ganiza ional imp o emen .
3Me hod
To u he he unde s anding o da a & analy ics ma u i y and add ess he iden i ied
de ici s in esea ch on he espec i e ma u i y models, a li e a u e e iew was cho-
sen as he unde lying esea ch me hod. The aim o he e iew was o sys ema ically
agg ega e and analyze s uc u es and con en s o da a & analy ics ma u i y models,
hus answe ing he esea ch ques ion. The P e e ed Repo ing I ems o Sys ema ic
Re iews and Me a-Analyses (PRISMA) amewo k was selec ed o assess he ma-
u i y models as i p o ides a igo ous and widely accep ed ounda ion o li e a u e
based esea ch. PRISMA se s ou he p ocess o inding and e alua ing he li e a u e
o a esea ch ques ion (Page e al 2021).
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Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227 211
3.1 Da a Collec ion
In his s udy, he da abases Web o Science, Scopus, ScienceDi ec , and EBSCOhos
we e selec ed as sou ces o eco ds. Using mul iple scien i ic da abases o di e en
publishe s co e ing bo h in o ma ion sys ems and business esea ch ensu ed a mo e
holis ic pic u e o ex an li e a u e in di e en domains. The sea ch was pe o med
in each o hese da abases using a p ede ined and conca ena ed que y consis ing
o h ee pa s. The o e a ching goal o he sea ch s ing was o iden i y dis inc
ma u i y models in he ele an da a & analy ics domains. The i s pa o he que y
co e s hese domains, add essed by se e al keywo ds such as da a,a i icial
in elligence,o machine lea ning as iden i ied in Sec . 2. The second pa de-
sc ibes he concep o ma u i y, ep esen ed by he wo d ma u i y i sel . Since each
eco d mus ela e o a speci ic ma u i y model, each i le mus con ain he keywo ds
model o amewo k. The comple e sea ch que y can be w i en as:
(da a OR analy ic*OR a i icial in elligence OR ai OR machine
lea ning OR ml OR digi al OR business in elligence) AND ma u i y
AND (model OR amewo k).
The sea ch was pe o med in Augus 2024. The i les o eco ds we e sea ched,
as he sea ch e ms induced oo gene ic abs ac o ull ex sea ches, leading o oo
many un ela ed esul s. Apa om his egula ion, a b oad sea ch was pe o med,
wi h no es ic ions on publica ion yea , publishe , ca ego y, o documen ype. A lis
o all eco ds was gene a ed and all a ailable eco ds we e downloaded. A sea ch
o all da abases using his p ocedu e esul ed in a o al o 916 eco ds, o which 547
emained a e emo ing duplica es.
In he i s sc eening phase, eco ds we e assessed based on hei i le, abs ac ,
and me ada a. Reco ds we e sc eened o assess whe he hey i wi hin he b oade
domains o da a & analy ics ma u i y models. As his i s sc eening emo ed clea ly
unsui able eco ds, his ask was pe o med by a single esea che and emo ed
eco ds we e c oss-checked by a second esea che . The jou nal o publica ion o he
eco ds was also conside ed as pa o he me ada a in o ma ion. Only pee - e iewed
pape s we e conside ed, i.e. ma u i y models de i ed om news a icles o consul ing
epo s we e excluded. Al hough g ay li e a u e p o ides some da a & analy ics
ma u i y models, hese s udies o en do no de ail he de elopmen o alida ion
p ocesses. This hinde s hei wide adop ion in p ac ice and does no gua an ee an
unbiased academic iew. Ma u i y models ha co e he da a & analy ics ac i i ies
o companies, as de ined in he in oduc ion, we e il e ed o . To limi he scope and
educe in luence om egional egula o y and cul u al di e ences he ocus pu was
on s anda d p o i -o ien ed companies, excluding NGOs, go e nmen al ins i u ion,
and he heal hca e sec o . The i s sc eening phase excluded 258 eco ds. This
esul ed in a o al o 289 eco ds sough o e ie al. Wi h 12 eco ds inaccessible,
277 eco ds we e assessed o eligibili y.
The second sc eening included a ull- ex eligibili y assessmen using he same
guidelines as o he i le/abs ac sc eening. As his assessmen is ambiguous, i was
pe o med independen ly by wo esea che s o he ull eco d se . Reco ds ha did
no yield he same assessmen we e discussed in de ail. The mos common easons
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212 Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227
Duplica es emo ed
(n = 369)
Reco ds
excluded
(n = 258)
Reco ds
no e ie ed
(n = 12)
Repo s ex cluded
(n = 242)
(W ong subjec : n = 121)
(No pee e iew : n = 51)
(No model: n = 30)
Reco ds iden i ied
(n = 916)
Reco ds
sc eened
(n = 547)
Reco ds sough
o e ie al
(n = 289)
Repo s assessed
o eligibili y
(n = 277)
Reco ds included
(n = 38)
Assessmen Iden i ica ion Resul s
Backwa d sea ch
(n=3)
Fig. 1 Applied PRISMA sea ch low diag am (based on Page e al 2021)
o exclusion we e ha he ma u i y models did no add ess da a & analy ics o o -
p o i companies (n= 121) and ha he pape s we e no pee - e iewed (n= 51). The
hi d mos common eason o exclusion was ha he publica ion did no de elop
i s own ma u i y model (n= 30). A o al o 242 eco ds we e excluded a his s age,
lea ing 35 eco ds emaining.
Las , a backwa d sea ch was conduc ed by examining all p ima y sou ces o he
iden i ied eco ds as well as he excluded li e a u e e iews and me a s udies. Th ee
addi ional eco ds we e iden i ied. This small numbe o eco ds no iden i ied by
he sys ema ic sea ch s a egy suppo s he b oad li e a u e sea ch app oach. Wi h
he addi ion o hese wo eco ds, a o al o 38 eco ds we e ob ained using he
PRISMA sea ch s a egy, as shown in he low diag am in Fig. 1.
3.2 Da a Analysis
Subsequen o da a collec ion and il a ion o ele an ma u i y models, he iden i ied
eco ds we e analyzed. Thei composi ion was inspec ed in de ail. The mos no able
di e ence be ween he conside ed ma u i y models is he way hey a e s uc u ed.
In his s udy his is e e ed o as he a chi ec u e o he ma u i y models. The
a chi ec u e desc ibes he le el and domain dimensions ha cons i u e he ma u i y
model. A le el dimension e e s o he di e en le els used in he ma u i y model.
Mos o en he ma u i y le el is measu ed on a disc e e scale, such as 1 o 5. In
con as , a domain dimension e e s o he con en o he ma u i y model and i s
assessmen subjec s, such as he ma u i y o he IT in as uc u e.
To ge a be e unde s anding o he employed domain dimensions o he iden i ied
models, a concep map was de eloped ha g aphically illus a es he domains o
da a & analy ics ma u i y. A concep map is a isual ool used o o ganize and
ep esen knowledge o in o ma ion (Gu li 2012;No ak1990). I s pu pose is o
help lea ne s o esea che s unde s and complex ela ionships be ween concep s o
ideas and o acili a e he de elopmen o new ideas o insigh s (Gu li 2012).
The de elopmen o he da a & analy ics ma u i y domain concep map ollowed
an i e a i e collabo a i e p ocess adap ed om T ochim (1989a,b). S a ing poin
was an agg ega ion o all ma u i y dimensions and domains o he 38 iden i ied
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Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227 219
and complexi y, as well as domains ega ding di e en business unc ions such as
supply chain managemen , esea ch and de elopmen , p oduc ion, ma ke ing, and
sales. The go e nance domains con ain policies, e hics, compliance, and o mal-
iza ion, while he human esou ces subclus e consis s o leade ship, cul u e, and
people. Leade ship includes sponso ship, empowe men and e alua ion, along wi h
clea communica ion and well-de ined objec i es o he o ganiza ion. The people
domain includes a ange o skills o en ep eneu ship, HR, managemen , business,
echnology/analy ics, and domain expe ise. Sus ainable lea ning and educa ion a e
u he ma u i y domains in his a ea. The p oduc s & se ices domain ela es o
p oduc da a, da a-d i en se ices, and p oduc connec i i y. Abs ac ed domains
in he p ocesses subclus e include p ocess digi aliza ion, ho izon al and e ical
p ocess in eg a ion, p ocess au oma ion, and p ocess quali y assu ance. Decision
p ocesses should be e idence-based and sel -op imizing, and de elopmen should
be agile wi h clea equi emen de ini ions. Change managemen and se ice p o-
cesses a e addi ional domains in he concep map.
These o ganiza ion-o ien ed ma u i y domains help businesses unde s and hei
cu en o ganiza ional s uc u e and cul u e, and iden i y op imiza ion po en ial in
e ms o da a & analy ics p ocesses, go e nance, alignmen , and suppo . This in-
cludes ensu ing ha da a in o ms s a egic decision-making, enabling he o ganiza-
ion o make mo e a ional decisions (G ossman 2018). I also encompasses da a
li e acy among employees, meaning ha he o ganiza ion is able o co e he skills
and knowledge necessa y o e ec i ely use and in e p e da a (Cosic e al 2012).
C ea ing a cul u e whe e da a-d i en decision-making is alued and encou aged and
whe e da a is seen as a s a egic asse is a cen al d i e o da a & analy ics (Comuzzi
and Pa el 2016; Hausladen and Schosse 2020).
5 Discussion
By u he examining he esul s and implica ions o his e iew, he aim is o
conc e ize he e ec s o he s udy, and o iden i y oppo uni ies o u u e esea ch
and p ac ical implemen a ion.
5.1 Theo e ical Implica ions
The pape con ibu es o he demanded clea and consis en unde s anding o da a
& analy ics ma u i y models (Reis e al 2018; Gökalp e al 2021b) by de ining and
di e en ia ing he a ious models’ a chi ec u es, le els and domains. By e iewing
he exis ing li e a u e and iden i ying key concep s and amewo ks, he pape p o-
ides consensus and a common language o esea che s. The indings can se e
as an ‘analy ical lens’ ha allows an in es iga ion o he wid h o he analy ics
ans o ma ion (Pöppelbuß and Röglinge 2011). Wi h he gained insigh s, esea ch
can iden i y mo e de ailed ma u a ion pa hs and pa e ns. This may un a el success
ac o s o impedimen s associa ed wi h dis inc pa hs o ma u a ion, as demons a ed
in he s udy o Mugge e al (2020) o digi al ans o ma ion endea o s.
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220 Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227
The consolida ed composi ions o da a & analy ics ma u i y models p o ide a new
dep h in he analysis o he ma u i y domains compa ed o p e ious e iews (P oença
and Bo binha 2018; K ól and Zdonek 2020). In pa icula , he esul s e eal a ension
be ween s anda diza ion and indi idualiza ion o da a & analy ics ma u i y models.
This ension esul s in he lack o a gene alizable app oach o assessing o gani-
za ional da a & analy ics capabili ies. The indings suppo exis ing heo ies ha
ans o ma ions owa ds da a-d i en businesses a e no accomplished in a uni o m
and aligned manne . Fo ins ance, Vanaue e al (2015) assume ha hese p ojec s
can, on he one hand, be d i en om a business pe spec i e (‘business i s ’), based
on a business ision and equi emen s. On he o he hand, hese p ojec s can be ini-
ia ed by a esou ce pe spec i e ha builds on exis ing da a and asse s (‘da a i s ’).
This was suppo ed by indings om S ahl e al (2023) and can be a i med by he
gene a ed clus e s o da a & analy ics ma u i y domains.
While exis ing models ocus on ei he o ganiza ional o echnical capabili ies,
none o he models analyzed has a high deg ee o de ail in he e alua ion dimen-
sions while s ill co e ing a p edominan pa o he domains o da a & analy ics
ma u i y. To ill his iden i ied esea ch gap and o add ess he known challenge o
he as changing da a & analy ics domain (K ól and Zdonek 2020), i is necessa y
o de elop a be e unde s anding o he dependencies be ween di e en da a &
analy ics ma u i y concep s, allowing u u e ma u i y models o be mo e sui ed o
add ess he complex eal-wo ld phenomenon o digi al ans o ma ions (Baske ille
e al 2018). Fu u e models should be b oade in scope and adap able in bo h scope
and dep h. The ma u i y domains agg ega ed by his e iew can be he ounda ion
o a mo e gene al ma u i y e alua ion based on a dynamic amewo k.
5.2 P ac ical Implica ions
Aside om i s me i s o esea ch, he li e a u e e iew p o ides p ac i ione s a alu-
able o e iew o he domain o da a & analy ics ma u i y. By applying he insigh s
om he concep map and i s ma u i y domains, e.g. h ough adap a ion in s a egy
de elopmen o ma u i y assessmen , o ganiza ions can in o m mo e ma u e da a
& analy ics p ac ices ha suppo be e decision making and imp o ed ou comes.
P ac ical bene i s can be achie ed in a mo e a ge ed way by add essing he a eas
d i ing ma u i y di ec ly, consequen ly enabling alue gene a ion in he unde -ex-
ploi ed ield o da a & analy ics (Ki on e al 2015;Kanee al2017; Lange and
Pü e ich 2024).
The implica ion can be d awn om he di e gence in ocus o he analyzed ma u-
i y domains, ha o ganiza ions should y o inco po a e bo h a social and a echnical
pe spec i e when assessing da a & analy ics ma u i y (Appelbaum 1997). On he one
hand, managemen may le e age a social pe spec i e - including o ganiza ional and
human-o ien ed domains - o iden i y aluable, cus ome -o ien ed use cases (Mülle
and Buliga 2019; Bal u is e al 2022). Leade ship is called upon o de elop a clea
ision o mobilize he o ganiza ion and c ea e a da a-d i en cul u e (Da enpo and
Bean 2018). Hence, managemen mus es ablish da a-d i en ’p ocesses’ ea ly on
and de ine equi ed ‘skills’ (Fö s e e al 2022; Mal a and Sousa 2016). On he o he
hand, managemen may use he echnical pe spec i e - associa ed wi h echnological
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Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227 221
and da a capabili ies (Leh e e al 2018) - o add ess he in as uc u e equi emen s
wi h dedica ed in es men s in ha dwa e and so wa e (Pa hak e al 2021). A dedi-
ca ed s a egy and oadmap will help iden i y le e s ha op imize da a quali y and
a ailabili y in he long e m (Keh e e al 2016) as well as suppo he c ea ion o
scalable analy ics p ac ices (G ossman 2018).
Fu he added alue can be c ea ed h ough adap a ion o he esul s o his s udy,
e.g. by indi idualizing he ma u i y assessmen p ocedu es and de i ing a dynamic
ma u i y model om he iden i ied ma u i y domains.
5.3 Limi a ions
Some limi a ions o his esea ch need o be highligh ed o poin owa d he po en ial
o u u e wo k in his a ea.
Fi s , his esea ch is limi ed by i s ocus on gene aliza ion, which is inhe en ly
challenging due o he di e se con igu a ions and associa ed capabili ies o o ga-
niza ions. Speci ic a en ion is equi ed o pa icula con ex s, such as small and
medium-sized en e p ises (SMEs) o manu ac u ing sec o s, as hese may di e sig-
ni ican ly in hei echnical capabili ies and cus ome equi emen s. Fo ins ance,
he ocus a eas o SMEs may di e ge om hose o la ge o ganiza ions, as high-
ligh ed by p io esea ch (Lange and Pü e ich 2024; Bianchini and Michalko a
2019). Simila ly, manu ac u ing companies may p io i ize di e en aspec s com-
pa ed o o he indus ies, as sugges ed by Hein-Pensel e al (2023). Addi ionally,
his s udy exclusi ely conside ed models published in pee - e iewed jou nals, ex-
cluding g ay li e a u e. While his ensu es a scien i ic basis and empi ical alida ion,
i also limi s he scope, as g ay li e a u e and o he da a sou ces—such as in e iews
o su eys—could p o ide aluable insigh s in o da a and analy ics app oaches and
ela ed ma u i y models.
Second, while e o s we e made o p o ide a holis ic ep esen a ion o he iden-
i ied models, he domains o da a and analy ics ma u i y p esen ed a e nei he ex-
haus i e no mu ually exclusi e. The analysis p io i ized he con en o he models
a he han he amewo ks unde pinning hei de elopmen , lea ing aspec s such
as s uc u al in e dependencies and hie a chies o he assessmen dimensions un-
de explo ed. This ocus aligns wi h he s udy’s objec i es bu es ic s he dep h o
ce ain a eas. Fu he mo e, as business, en i onmen al, and echnological condi ions
e ol e (Becke e al 2009), he con en mus be pe iodically e iewed o main ain i s
ele ance and ideli y. Fu u e empi ical esea ch is necessa y o alida e he indings
and ensu e hei comp ehensi eness, as he eliance on exis ing models inhe en ly
limi s he amewo k’s cu ency and applicabili y in apidly changing con ex s.
Thi d, he p ac ical applica ion o he indings is cons ained by he explo a o y
na u e o he s udy. While he iden i ied domains p o ide a ounda ional amewo k,
u he alida ion and e inemen a e needed o ensu e a comple e and ac ionable lis
o ma u i y domains. The insigh s ep esen ing an ini ial s ep a he han a comp e-
hensi e solu ion. Fu u e wo k could adop an induc i e app oach, de i ing ac ionable
p ac ices di ec ly om empi ical da a, as sugges ed by S elzl e al (2020) and Becke
e al (2009). This could lead o ools ha mo e beyond diagnos ics o ope a ionalize
ans o ma ions, o e ing ac ionable guidance and lessons lea ned o p ac i ione s.
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222 Schmalenbach Jou nal o Business Resea ch (2025) 77:205–227
The cu en s udy, he e o e, ep esen s a s a ing poin o de eloping mo e p ac ical
and p esc ip i e ools ha can deli e immedia e alue.
6 Summa y and Conclusion
This s udy in es iga es da a & analy ics ma u i y models and assesses hei s uc-
u e and con en . The e alua ion domains o da a & analy ics ma u i y a e analyzed
in de ail o be e unde s and he echnologies equi emen s. A mo e accu a e de-
pic ion o he o ganiza ional exigencies con ibu es owa ds enabling o ganiza ions
o mo e e icien implemen a ion and mo e e ec i e use o da a. Applying a wo-
s ep esea ch app oach, i s , a sys ema ic li e a u e e iew is conduc ed using he
PRISMA amewo k. The e iew iden i ies 38 ma u i y models in he con ex o da a
and digi aliza ion as objec s o analysis. F om he li e a u e, gene al a chi ec u es
o da a & analy ics ma u i y models a e disco e ed induc i ely. The a chi ec u es
consis o le els and domains - wi h each conside ed ma u i y model using di -
e en e alua ion dimensions. A de ailed unde s anding o di e en ypes o da a
& analy ics ma u i y models is c ea ed by dis inguishing be ween o ganiza ion-o i-
en ed, echnology-o ien ed, and da a-o ien ed models. By de ailing he unde lying
e alua ion domains, he esea ch ad ances he unde s anding o da a & analy ics
ma u i y.
Manage s may bene i om o malizing ac i i ies o de elop e ec i e s a egies
o implemen ing da a as a echnology and as a s a egic asse . A s anda diza ion-
indi idualiza ion ension in da a & analy ics ma u i y models is unco e ed and he
idea o a dynamic app oach based on a gene al adap able amewo k is b ough
o wa d. The ini ial indings desc ibe he s a us quo o da a & analy ics ma u i y,
deepen he unde s anding o he ma u i y models and unde lying ma u i y domains,
and o m a basis o u he esea ch and applica ion in he ields o da a & analy ics.
Acknowledgemen s Recogni ion and hanks a e ex ended o e e yone in ol ed in conduc ing his s udy
as well as o he anonymous e iewe s whose insigh ul and cons uc i e eedback g ea ly con ibu ed o
he imp o emen o his manusc ip .
Funding This esea ch ecei ed no speci ic g an om any unding agency in he public, comme cial, o
no - o -p o i sec o s.
A ailabili y o da a and ma e ial The da ase s gene a ed and analyzed du ing he s udy a e a ailable
om he co esponding au ho upon eques .
Con lic o in e es The au ho decla es ha hey ha e no con lic o in e es s.
E hical s anda ds Fo his a icle no s udies wi h human pa icipan s o animals we e pe o med. All
s udies men ioned we e in acco dance wi h he e hical s anda ds indica ed in each case.
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