University maturity model – a bibliometric analysis

ECONOMICS AND ENVIRONMENT 1
4(91) • 2024 eISSN 2957-0395
UNIVERSITY MATURITY MODEL –
A BIBLIOMETRIC ANALYSIS
Joanna Szydło (ORCID: 0000-0002-2114-4770) – Bialys ok Uni e si y o Technology
Agnieszka Sakowicz (ORCID: 0009-0007-9076-984X) – Bialys ok Uni e si y o Technology
Filippo di Pie o (ORCID: 0000-0003-1573-8553) – Uni e si y o Se ille
Co espondence add ess:
Wiejska S ee 45A, 15-312 Bialys ok, Poland
e-mail: [email p o ec ed]
Joanna SZYDŁO
•
Agnieszka SAKOWICZ
•
Filippo DI PIETRO
Economics and En i onmen • No. 4(91) 2024 • pages: 1-19 DOI: 10.34659/eis.2024.91.4.938
ABSTRACT: In oday's dynamic and compe i i e en i onmen , uni e si ies play a key ole in gene a ing, ansmi ing, and apply-
ing knowledge and inno a ion. The g owing in e es in e alua ing uni e si y pe o mance a na ional and in e na ional le els has
led o de eloping and applying uni e si y ma u i y models as e ec i e assessmen ools. This a icle aims o p esen a ious
app oaches o modelling uni e si y ma u i y. A bibliome ic analysis was based on publica ions in he Web o Science and
Scopus da abases. The esea ch que y included TITLE-ABS-KEY ("ma u i y model" and uni e si *) o Scopus and TS = ("ma u-
i y model" and uni e si *) o he Web o Sciences da abase. A o al o 123 publica ion eco ds we e analysed. Ma e ials pub-
lished be ween 1994 and 2024 in English we e examined. A o al o 123 publica ions we e selec ed o he inal analysis. Based
on he li e a u e e iew, key ac o s ha may in luence uni e si y ma u i y ac oss nine a eas we e iden i ied. A heo e ical Uni-
e si y Ma u i y Model (UMM) is also p esen ed, which should unde go expe e alua ion in subsequen s ages. Findings sug-
ges ha he applica ion o ma u i y models can signi ican ly enhance uni e si ies' managemen and ope a ional e iciency,
o e ing aluable insigh s o policymake s in o mula ing educa ional policies.
KEYWORDS: ma u i y model, uni e si y
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In oduc ion
In oday’s dynamic and compe i i e wo ld, highe educa ion plays a key ole in c ea ing, ansmi -
ing and applying knowledge and inno a ion. As a esul , mo e and mo e a en ion is paid o unc ion-
ing and e alua ing uni e si ies a bo h na ional and in e na ional le els. Ma u i y models in uni e si-
ies a e one o he ools ha ha e become ex emely use ul in assessing uni e si ies.
Highe Educa ion Ins i u ions a e complex o ganisa ions. Al hough au onomous, hey ha e o
execu e a numbe o unc ions and de elop a a ie y o p ocedu es o ensu e he ul ilmen o hei
du ies, which ine i ably aise cons an challenges. The numbe o unc ions hey pe o m and he
a ie y o p ocedu es de eloped unde hei au onomy o ensu e he accomplishmen o all hei
du ies aise cons an managemen and adminis a ion challenges. Di icul ies in p ocedu e sys ema-
isa ion and in wo k low analysis, e alua ion, and op imisa ion ca y p oblems no only o manage-
men i sel bu also o in o ma ion sys ems design (Zaca ias & Ma ins, 2011).
Uni e si y ma u i y models a e a comp ehensi e analy ical ool ha enables he assessmen o
a ious aspec s o he unc ioning o highe educa ion (uni e si y). These models allow he iden i i-
ca ion o s eng hs, a eas o imp o emen and elabo a ion o de elopmen s a egies by de ining he
le el o ma u i y in a eas such as managemen , eaching and lea ning, scien i ic esea ch, echnology
ans e , in e na ional coope a ion and social in ol emen . Since uni e si ies a e o ganisa ions,
ma u i y models ha e p o en o be aluable in e alua ing hei p ocess and de e mining by le els he
pa h o academic excellence (Toc o-Cano e al., 2020). Acco ding o Min zbe g (1979), based on he
in e ac ions o people and he di e en ia ion o hei oles, he uni e si y is an o ganisa ion o “p o-
essional bu eauc acies”. A bu eauc acy, o M. Webe , is an e icien o ganisa ion ha de ines e en in
he smalles de ails how hings should be done. Also, Webe belie ed ha bu eauc acies a e he mos
e icien way o o ganise la ge o ganisa ions and we e a esul o he ine i able a ionalisa ion and
pe sonalisa ion o socie y (Chia ena o, 2019). In esponse o he need o measu e he p og ess
achie ed by an o ganisa ion, which is also a uni e si y, ma u i y models ha e been c ea ed.
This a icle p o ides an o e iew o se e al uni e si y ma u i y models, highligh ing hei a i-
e y, applica ions, bene i s, and d awbacks. I s goal is o showcase di e en me hods o modeling
uni e si y ma u i y.
Li e a u e e iew
The de ini ion o o ganisa ional ma u i y, al hough i may a y depending on con ex and sou ce,
gene ally e e s o he deg ee o which an o ganisa ion is able o manage i s p ocesses esou ces, and
achie e i s s a egic goals e ec i ely and e icien ly. I can be de ined as he le el o de elopmen o
p ocesses, s uc u es, and echnologies ha allow he o ganisa ion o ope a e s able and p edic ably.
Ma u i y commonly means eaching he inal s age o de elopmen o p ocess shaping, o he deg ee
o in ellec ual, emo ional o biological de elopmen o any indi idual o ganisa ion, pe son o uni
(Głuszek & Ma usewicz, 2015). P. C osby, who, in 1979, in his book en i led Quali y is F ee, published
he i s ma u i y model, is belie ed o be a p ecu so o his e m. I included a desc ip ion o i e
le els o o ganisa ional skills in using quali y managemen me hods and ools. This model showed
he de elopmen pa h o hese skills, speci ying wha ac i i ies mus be aken o each he nex le el
o ma u i y. Acco ding o Kalinowski (2011), p ocess ma u i y is he abili y o an o ganisa ion, includ-
ing i s p ocesses, o sys ema ically imp o e he deli e ed esul s in i s ope a ions. A a highe le el o
de ail, he ma u i y o he p ocess is iewed as he ield o which p ocesses a e d i en, well-de ined,
managed, lexible, measu ed and e ec i e (G ajewski, 2012). In ano he app oach, i is indica ed ha
p ocess ma u i y is he deg ee o op imal alloca ion o o ganisa ional esou ces in s able and meas-
u ed p ocesses (G ela, 2013). The ma u i y p ocess is he awa eness ha he p ocesses occu ing
ho izon ally wi hin an o ganisa ion c ea e ha o ganisa ion (B aje -Ma czak, 2012a). Those p o-
cesses need o be managed in an app op ia e way. In addi ion, p ocess ma u i y also indica es how
he pe cep ion o p ocesses i s in o he company’s s a egy. On he one hand, he le el o p ocess
ma u i y in o ms abou he awa eness o employees in e ms o pa icipa ion in business p ocesses,
and on he o he hand, how he manage s use he knowledge abou p ocesses in o ganisa ional
de elopmen decisions.
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In ano he publica ion, B aje -Ma czak (2012b) de ines he p ocess ma u i y o an o ganisa ion
as a s a e in which i is possible o epea edly achie e he same esul o p ocesses (o cha ac e ised
by a small, accep able il ) in ela ion o p e iously de ined key ac o s. Acco ding o he same au ho ,
ano he de ini ion o he abo e-men ioned p ocess ma u i y is he abili y o o ganise e ec i e man-
agemen p ocesses suppo ing he achie emen o s a egic goals. B aje -Ma czak poin s ou a wo-
old ela ionship he e: i s ly, he goals o he p ocesses mus be a esul o he s a egic goals, and
secondly – he achie emen s o planned p ocess goals enable he implemen a ion o he s a egy.
Ma u i y models de ine he cu en s a e o he o ganisa ion ha esul s om he way he o gan-
isa ion exis s and i s possibili ies o he use o exis ing esou ces o p e ious expe ience, as well as
wha i is no possible o achie e in he u u e by applying depa men p io i ies and inancial
esou ces and me hods o hei implemen a ion (Kosie adzka & Smagowicz, 2016). The ma u i y
model is a se o di e se ools and p ac ices ha enable he assessmen o he compe encies o a gi en
o ganisa ion in he ield o managemen (OGC, 2007), as well as he imp o emen o key ac o s lead-
ing o achie ing he assumed goals (Van Looy, 2014). In he li e a u e on he subjec o ma u i y
models in o ganisa ions, you can ind se e al dozen p ocess ma u i y models. Szewczyk (2018) com-
pa ed h ee ma u i y models: he P ocess and En e p ise Ma u i y Model (PEMM), he Business P o-
cess Ma u i y Model (BPMM) and Fishe ’s model. The i s model o PEMM was de eloped by M.
Hamme , a specialis in eenginee ing heo y, in 2000-2006. Acco ding o his model, o de e mine
p ocess ma u i y, you ha e o analyse wo a eas: p ocess enable s and en e p ise capabili ies (Ham-
me , 2007; c . Powe , 2007). The second model is Business P ocess Ma u i y Model (BPMM) which
he owne is he Objec Managemen G oup. BPMM model poin s ou i e le els o p ocess ma u i y:
ini ial, managing, s anda dised, p edic able and inno a i e (OMG, 2008). The hi d model is w i en
by D.M. Fishe . The au ho o he model clea ly emphasises he nonlinea i y and complexi y o he
p ocess o inc easing he ma u i y o he o ganisa ion, in which he dis inguishes and desc ibes
5 le els o change (Fishe , 2014). Those le els a e s a egy, con ol, people, echnology, and p ocesses.
Fo each o he le els men ioned be o e, Fishe ’s model de ines i e le els o ma u i y (silo o ganisa-
ion, ac ically in eg a ed o ganisa ion, p ocess-d i en, op imised o ganisa ion, and in elligen ope -
a ional ne wo k).
Kosie adzka and Smagowicz (2016) compa ed wen y ma u i y models om se en managemen
a eas. Those se en a eas a e: p ocess managemen , p oduc ion managemen , p ojec managemen ,
so wa e de elopmen managemen , adminis a ion managemen , quali y managemen , isk and
con inui y speed o ac ion managemen . Below he au ho s sys emised hose models acco ding o
di ision (c . Kosie adzka, 2016).
1. In p ocess managemen :
1.1. Business P ocess Ma u i y Model de eloped by OMG.
1.2. Business P ocess Ma u i y Model de eloped by Ga ne .
1.3. P ocess and En e p ise Ma u i y Model de eloped by Hamme .
2. In p oduc ion managemen :
2.1. P oduc i i y Managemen Model de eloped by Kosie adzka.
3. In p ojec managemen :
3.1. P ojec Managemen Ma u i y Model de eloped by Ke zne .
3.2. PRINCE 2 (P2M) Ma u i y Model de eloped by O ice o Go e nmen Comme ce.
3.3. OPM3 de eloped by P ojec Managemen Ins i u e.
3.4. P3M3 de eloped by Cabine O ice.
4. In so wa e de elopmen managemen :
4.1. Capabili y Ma u i y Model In eg a ion de eloped by So wa e Enginee ing Ins i u e.
4.2. P ocess Ma u i y F amewo k.
4.3. IT Se ice Managemen Ma u i y Model.
4.4. Model Con ol Objec i es o In o ma ion and ela ed Technology de eloped by ISACA and IT
Go e nance Ins i u e.
5. In quali y managemen :
5.1. Quali y Managemen Ma u i y G id de eloped by C osby.
5.2. ISO 9004.
5.3. EFQM de eloped by Eu opean Founda ion o Quali y Managemen .
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6. In isk and con inui y speed o ac ion managemen :
6.1. Business Con inui y Ma u i y Model de eloped by Vi ual Co po a ion.
6.2. En e p ise Risk Managemen Ma u i y Model.
6.3. Risk and Insu ance Managemen Socie y Ma u i y Model.
7. In adminis a ion managemen :
7.1. Planning o ins i u ional de elopmen .
7.2. Common Assesmen F amewo k.
As i is seen he e a e many ele an ma u i y models in li e a u e. In one s udy, a lis o h ee
ma u i y models was poin ed ou . In o he s udy, wen y ma u i y models we e ound. One mo e
s udy shows nine ca ego ies o selec ed Ma u i y Models connec ed wi h uni e si ies (Toc o-Cano e
al., 2020). Those a e he ca ego ies:
1. Ma u i y models o ien ed owa ds eaching.
2. Ma u i y models o ien ed owa ds In o ma ion and Communica ion Technology (ICT).
3. Ma u i y models o ien ed owa ds s uden moni o ing.
4. Ma u i y models o in ellec ual capi al.
5. Ma u i y models o E-Lea ning.
6. Ma u i y models aimed a e alua ing uni e si y en ep eneu ship.
7. Ma u i y model o ien ed o he employabili y o g adua es.
8. Ma u i y model o ien ed o he s a egic planning o uni e si ies.
9. Ma u i y model o IT go e nance in uni e si y ins i u ions.
In one mo e s udy (Dua e & Ma ins, 2013), i is shown compa ison be ween nine educa ional
ma u i y models. Mos models ound a e based on CMM o on he s aged ep esen a ion o CMMI. The
p esen ed models by Dua e and Ma ins (2013) ha e he same i e le els o ma u i y. They all sug-
ges a ibu es ha he o ganisa ion should possess o be posi ioned a each s age. Howe e , unlike
he model in which hey we e based, mos eaching ma u i y models do no explici ly iden i y any key
p ocess a eas. Only he models de eloped by Dounos and Boho is (2010) and by Ma shall and Mi ch-
ell (2002, 2004, 2005, 2006a, 2006b, 2008, 2009) p o ide hese a eas as well as he me hodologies
and e alua ion echniques o assess he ul ilmen o equi emen s, o e ec i ely place an o ganisa-
ion in a ce ain le el o ma u i y.
Also s eng hs and weaknesses o he educa ional ma u i y models a e shown in his a icle. Those
ma u i y models which ocused on Highe educa ion ins i u ion a e:
1. eMM (Ma shall & Mi chell, 2002),
2. MRAIES (Pe ie e al., 2009),
3. ICTMMEI-DV (Bass, 2010),
4. CMMI-ISC (Whi e e al., 2003),
5. OCDMM (Neuhause , 2004),
6. LPMM (Thompson, 2004),
7. ITIL-ITSMM (Wang & Zhang, 2007),
8. CEMM (Lu e o h e al., 2007),
9. CMMI – TQM (Dounos & Boho is, 2010).
Selec ion o models o p esen in his a icle is based on subjec i e assessmen o he au ho s and
is connec ed wi h uni e si y as an o ganisa ion.
Resea ch me hods
Resea che s equen ly use bibliome ic analysis, pa icula ly when explo ing a speci ic esea ch
opic. Gi en he as numbe o a ailable publica ions, his me hod aids in he iden i ica ion, syn he-
sis, analysis, and c i ical e alua ion o hei con en (Kea hley-He ing e al., 2016; Gudanowska,
2017; Bo nmann & Haunschild, 2017; Cichowicz & Rollnik-Sadowska, 2018; Glińska & Siemieniako,
2018; Side ska & Jadaa, 2018; Cze niawska & Szydło, 2020; Lene -Gansiniec, 2021; Szpilko e al.,
2023). The bibliome ic analysis aims o p o ide knowledge abou he main esea ch di ec ions in a
ield, esea ch ends, changes in he numbe o publica ions o e he yea s, he mos p oduc i e
au ho s, jou nals, coun ies, o esea ch uni s (Niñe ola e al., 2019; Szum, 2021).
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The esea ch p ocess was conduc ed ollowing a me hodology comp ising se en dis inc phases
(Szpilko e al., 2023). These phases encompassed he (1) selec ion o bibliog aphic da abases, (2) he
choice o keywo ds, and (3) he c i e ia o na ow down he sea ch o publica ions. (4) Subsequen ly,
da a ex ac ion and selec ion was pe o med, (5) ollowed by he analysis o he selec ed publica-
ions. The las wo phases in ol ed (6) iden i ying esea ch a eas and (7) de ining hema ic clus e s
(Figu e 1).
Figu e 1. Me hodology o bibliome ic analysis
The bibliome ic analysis was based on publica ions a ailable in Web o Science and Scopus da a-
bases. I co e s publica ions con aining he ph ases (“ma u i y model” and uni e si *) in he i le,
abs ac and keywo ds. The sea ch was conduc ed o ma e ials published be ween 1994 and 2024
in English. A icles, p oceedings pape s, con e ence pape s, books, book chap e s and e iews we e
conside ed. O he publica ion ypes (ea ly access, edi o ial ma e ials, e ac ed publica ions, no es)
we e ejec ed. The esul s o he i s sea ch a e p esen ed in Table 1.
An ini ial sea ch o he e m “ma u i y model” and uni e si * ac oss he en i e se o a icles in
he i s sample yielded 25 154 eco ds in Scopus and 15 494 eco ds in Web o Science. Howe e ,
a e ini ial analysis, i became appa en ha many o hese publica ions we e no di ec ly ela ed o
he s udy a ea. Only a e na owing he sea ch c i e ia did he numbe o publica ions dec ease.
Ul ima ely, he e we e 151 eco ds om he Scopus da abase and 132 om he Web o Science da a-
base.

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Table 1. Sea ch esul s
S age Web o Science Scopus
Fi s sea ch
Resea ch que y ALL: “ma u i y model” and uni e si * ALL: “ma u i y model” and uni e si *
Numbe o a icles be o e inclusion c i e ia 15 494 25 154
Numbe o a icles a e inclusion c i e ia 2 390 4 766
Second sea ch
Resea ch que y TOPIC: (“ma u i y model”
and uni e si *)
TITLE-ABS-KEY: (“ma u i y model”
and uni e si *)
Numbe o a icles be o e inclusion c i e ia 146 258
Numbe o a icles a e inclusion c i e ia 132 151
Con en e alua ion and inal selec ion o a icles 123
Sou ce: au ho s’ wo k based on he Scopus and Web o Science da abases.
In he nex s age, he iles we e downloaded in CSV o ma . Subsequen ly, da a om he wo
da abases we e me ged, and duplica es we e emo ed. Ul ima ely, a e e iewing all he eco ds,
123 publica ions we e selec ed o assessmen . F om he au ho s’ pe spec i e, i was impo an o
explo e he in e es in he opic o e he yea s as well as he mos equen ly ci ed a icles. The nex
s ep in ol ed using he VOS iewe p og am o gene a e a map ha e lec s he co-occu ence o key-
wo ds in he analysed se o publica ions. The nex s ep in ol ed using he VOS iewe p og am o
gene a e a map ha e lec s he co-occu ence o keywo ds in he analysed se o publica ions.
Resul s o he esea ch
Ini ially, he au ho s obse ed a g owing in e es in he subjec o e he yea s (Figu e 2). I is
impo an o highligh ha he explo a ion o issues ela ed o uni e si y ma u i y models inc eased
no ably a e 2008. Mo eo e , a subs an ially highe numbe o publica ions was iden i ied in he
Scopus da abase compa ed o he Web o Science.
Figu e 2. Numbe o publica ions in he ield o uni e si y ma u i y model in Scopus and Web o Science (indexed
om Janua y 1994 o Sep embe 2024)
Sou ce: au ho s’ wo k based on he Web o Science and Scopus.
Figu e 2. Numbe o publica ions in he ield o uni e si y ma u i y model in Scopus and Web
0
2
4
6
8
10
12
14
16
18
20
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
Scopus WoS
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The nex ques ion conce ned he mos equen ly ci ed publica ions. I can be seen ha an a e -
age numbe o ci a ions was eco ded in bo h da abases. No ewo hy a e he a icles published in he
Jou nal o In ellec ual Capi al (Table 2).
Table 2. The mos ci ed a icles ela ed o he opic o uni o med se ices
No. Au ho s A icle i le Jou nal
Numbe o ci a ions
Scopus Web o
Science
1. Ganza ain and
E as i (2016)
Th ee s age ma u i y model in SME’s owa ds
indus y 4.0
Jou nal o Indus ial
Enginee ing and Manage-
men
262 202
2. Secundo e al.
(2016)
Managing in ellec ual capi al h ough a collec-
i e in elligence app oach: An in eg a ed
amewo k o uni e si ies
Jou nal o In ellec ual
Capi al
145 115
3. Secundo e al.
(2010)
In angible asse s in highe educa ion and
esea ch: Mission, pe o mance o bo h?
Jou nal o In ellec ual
Capi al
120 110
4. Secundo e al.
(2015)
An in ellec ual capi al ma u i y model (ICMM)
o imp o e s a egic managemen in Eu o-
pean uni e si ies: A dynamic app oach
Jou nal o In ellec ual
Capi al
110 112
5. Pee and Kankan-
halli (2009)
A model o o ganisa ional knowledge man-
agemen ma u i y based on people, p ocess,
and echnology
Jou nal o In o ma ion
and Knowledge Manage-
men
91 54
6. Secundo e al.
(2018)
In ellec ual capi al managemen in he ou h
s age o IC esea ch: A c i ical case s udy in
uni e si y se ings
Jou nal o In ellec ual
Capi al
84 91
7. Secundo e al.
(2017)
Mobilising in ellec ual capi al o imp o e
Eu opean uni e si ies’ compe i i eness: The
echnology ans e o ices’ ole
Jou nal o In ellec ual
Capi al
63 49
8. F ondizi e al.
(2019)
The e alua ion o uni e si ies’ hi d mission
and in ellec ual capi al: Theo e ical analysis
and applica ion o I aly
Sus ainabili y 61 45
9. Dzimińska e al.
(2018)
T us -based quali y cul u e concep ual model
o highe educa ion ins i u ions
Sus ainabili y 52 169
10. Dayan and E ans
(2006)
KM you way o CMMI Jou nal o Knowledge
Managemen
51 -
11. Heinemann and
Usko (2018)
Sma uni e si y: Li e a u e e iew and c e-
a i e analysis
Sma Inno a ion, Sys-
ems and Technologies
39 -
12. Secundo e al.
(2016)
Measu ing uni e si y echnology ans e
e iciency: a ma u i y le el app oach
Measu ing Business
Excellence
37 30
No e: N/A – no applicable.
Sou ce: au ho s’ wo k based on he Scopus and Web o Science da abases.
In he con ex o bibliome ic analysis, keywo ds equen ly associa ed wi h he opic o uni e -
si y ma u i y modelling we e iden i ied. The analy ical p ocess u ilised VOS iewe so wa e. The
esul ing da ase consis ed o 145 wo ds o ph ases ha appea ed a leas h ee imes in he key-
wo ds o 123 analysed a icles. The da ase also included e ms un ela ed o he main opic o he
analysis (e.g., ‘a icle,’ ‘analysis,’ ‘su ey,’ ‘li e a u e e iew’). To sys ema ise he keywo d se , unnec-
essa y e ms ( ela ed o he opic o analysis) we e in en ionally excluded. Te ms and abb e ia ions
wi h simila meanings we e s anda dised. The e ined da ase con ained 98 keywo ds. The mos
equen e ms and hei in e ela ions a e illus a ed in Figu e 3. The names o he indi idual clus-
e s a e p esen ed in Figu e 4.
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Figu e 3. Keywo d co-occu ence map o uni e si y ma u i y model
Sou ce: au ho s’ wo k using VOS iewe so wa e.
Figu e. 4. Thema ic clus e s o a uni e si y ma u i y model
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The analysis gene a ed nine clus e s con aining keywo ds (Table 3):
• yellow clus e : knowledge and de elopmen (e.g. Ha in e al., 2024; Ko zeb e al., 2024; Alghail
e al., 2017, 2022, 2023; Ilke Mu a e al., 2023; Peck, 2023; Ca doso & Su, 2022; Su & Ca doso,
2021; Edi isinghe e al., 2021; Rico-Bau is a e al., 2022; Secundo e al., 2010, 2015, 2016, 2017,
2018; F ondizi e al., 2019; Ka iliu e & Dauno iene, 2015; Ki agawa & Ligh owle , 2013; Pee &
Kankanhalli, 2009; Dayan & E ans, 2006; Teah e al., 2006),
• g een clus e : HRM (e.g. Beh oozi & Khodadadi, 2017; Llamosa-Villalba e al., 2014; K opsu-Veh-
kape ä & Kess, 2013; Pee & Kankanhalli, 2009),
• ed clus e : educa ion (e.g. Naim & Malik, 2023; San ally e al., 2020; Annan-Diab & Molina i,
2017),
• ligh blue clus e : digi aliza ion (e.g. Kalende & Žilka, 2024; Paños-Cas o e al., 2024; Acuna e
al., 2024; Teiche , 2019; Jaico e al., 2019; Uhl & Gollenia, 2016; Bianchi & de Sousa, 2015),
• da k blue clus e : o ganisa ional cul u e (e.g. Abo amadan, 2021; Mo ei a e al., 2021;
Dzimińska e al., 2020; Ma shall, 2010),
• g ey clus e : s a egy (e.g. Szpilko & Ejdys, 2022; Kobylińska e al., 2024; Fowle , 2019; Heine-
mann & Usko , 2018; Ganza ain & E as i, 2016),
• pu ple clus e : collabo a ion (e.g. Sil a e al., 2021; F ondizi e al., 2019; Awas hy e al., 2018;
O hman & Oma , 2012),
• da k g een clus e : secu i y and in es men s (e.g. Moczydłowska e al., 2023; Mille e al., 2014;
Sheen & Chung, 2011; Ca ca y, 2012),
• blue clus e : quali y (e.g. An hony & An ony, 2016, 2022; Painén-Paillale e al., 2022; Maciąg,
2019; Dzimińska e al., 2018; Llamosa-Villalba & Méndez Ace os, 2010).
Table 3. Clus e names, keywo ds and gene a ed ac o s in he uni e si y ma u i y modelling a ea
No. Clus e name Selec ed keywo ds Fac o s ha can in luence he ma u i y o a uni e si y
1. Knowledge and
de elopmen
knowledge managemen , inno a ion,
e alua ion, enginee ing esea ch, echno-
logy ans e , in ellec ual capi al, capabili y
ma u i y model, knowledge managemen
ma u i y, sus ainable de elopmen ,
inno a ions
• Oppo uni ies o aining and de elopmen
• Le el o knowledge ans e
• Le el o achie emen o sus ainable de elopmen
goals
• In ol emen in echnology ans e
• Measu es o conduc inno a i e esea ch
• Le el o comme cializa ion o esea ch esul s
2. Human
Resou ce
Managemen
human esou ce managemen , socie ies
and ins i u ions, manage s, pe sonal
so wa e p ocess, leade ship
• Leade ship s yle
• Abili y o ec ui , de elop, and e ain highly quali ied
academic and adminis a i e s a
• Dis ibu ion o oles and esponsibili ies
• T anspa ency o egula ions
• Oppo uni ies o ad ancemen
• Access o psychological suppo
• Heal h p o ec ion
3. Educa ion highe educa ion, eaching, lea ning ma u-
i y model, in e ac i e me hod, lea ning
sys ems, agile me hods, lea ning objec s,
pe sonal aining, quali y o eaching,
decision making, e-lea ning ma u i y model,
s uden s
• Quali y o educa ional p og ams
• Di e si y o eaching me hods
• Oppo uni ies o aining and de elopmen
• Le el o inno a ion in eaching
• Le el o ma ching o p og ams o s udy o labou
ma ke needs
• S uden engagemen
4. Digi aliza ion digi al ans o ma ion, in o ma ion use,
in o ma ion sys ems, e-lea ning, ma u i y
le els, compu e -aided ins uc ion,
in o ma ion echnology
• Le el o implemen a ion o mode n IT echnologies
• Le el o implemen a ion in o ma ion managemen
sys ems
5. O ganisa ional
cul u e
o ganisa ional cul u e, beha iou s,
o ganisa ional change, managemen
• A mosphe e a he uni e si y
• Le el o alignmen o pe sonal and o ganisa ional
alues
• Le el o accep ance o he o ganisa ional s uc u e
• Le el o unde s anding o he mission
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o _Business_P ocess_Disco e y
Joanna SZYDŁO
•
Agnieszka SAKOWICZ
•
Filippo DI PIETRO
MODEL DOJRZAŁOŚCI UCZELNI – ANALIZA BIBLIOMETRYCZNA
STRESZCZENIE: We współczesnym dynamicznym i konku encyjnym ś odowisku uniwe sy e y odg ywają kluczową olę
wgene owaniu, p zekazywaniu i zas osowaniu wiedzy o az innowacji. Rosnące zain e esowanie oceną unkcjonowania uniwe -
sy e ów na poziomie k ajowym i międzyna odowym dop owadziło do ozwoju i zas osowania modeli doj załości uczelni jako
sku ecznych na zędzi oceny. Celem a ykułu jes p zeds awienie óżnych podejść do modelowania doj załości uczelni. Zas oso-
wano analizę bibliome yczną. Wyko zys ano publikacje dos ępne w bazach Web o Science i Scopus. Obję o nią eks y zawie-
ające azy („ma u i y model” i uni e si *) w emacie o az w y ule, abs akcie i słowach kluczowych. Zapy anie badawcze
obejmowało TITLE-ABS-KEY („ma u i y model” and uni e si *) dla bazy Scopus o az TS = („ma u i y model” and uni e si *) dla
bazy Web o Sciences. P zeszukano ma e iały opublikowane w la ach 1994-2024 w języku angielskim. Do os a ecznej analizy
wyb ano 123 publikacje. Na pods awie p zeglądu li e a u y ziden y ikowano kluczowe czynniki, k ó e mogą wpływać na doj za-
łość uniwe sy e ów w dziewięciu obsza ach. P zeds awiono ównież eo e yczny model doj załości uczelni, k ó y w kolejnych
e apach powinien zos ać poddany ocenie ekspe ów. Wyniki suge ują, że zas osowanie modeli doj załości może znacznie pop a-
wić za ządzanie i e ek ywność ope acyjną uniwe sy e ów, o e ując cenne in o macje dla decyden ów w o mułowaniu poli yki
edukacyjnej.
SŁOWA KLUCZOWE: model doj załości, uczelnie