Vodă, Adina-Ma ia; Ciobo ea, Mihai; Badea, Doina; Roman, Monica; S an, Ma ian
A icle
The in e ac ions be ween digi aliza ion, inno a ion and
employmen in Eu opean companies: Insigh s om a
La en Class Analysis
Economies
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MDPI – Mul idisciplina y Digi al Publishing Ins i u e, Basel
Sugges ed Ci a ion: Vodă, Adina-Ma ia; Ciobo ea, Mihai; Badea, Doina; Roman, Monica; S an,
Ma ian (2025) : The in e ac ions be ween digi aliza ion, inno a ion and employmen in Eu opean
companies: Insigh s om a La en Class Analysis, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13,
Iss. 4, pp. 1-19,
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Ci a ion: Vodă, A.-M., Ciobo ea, M.,
Badea, D., Roman, M., & S an, M.
(2025). The In e ac ions Be ween
Digi aliza ion, Inno a ion and
Employmen in Eu opean Companies:
Insigh s om a La en Class Analysis.
Economies,13(4), 104. h ps://doi.o g/
10.3390/economies13040104
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A icle
The In e ac ions Be ween Digi aliza ion, Inno a ion and
Employmen in Eu opean Companies: Insigh s om a La en
Class Analysis
Adina-Ma ia Vodă, Mihai Ciobo ea , Doina Badea * , Monica Roman and Ma ian S an
Depa men o S a is ics and Econome ics, Bucha es Uni e si y o Economic S udies,
010371 Bucha es , Romania; [email p o ec ed] (A.-M.V.); [email p o ec ed] (M.C.);
[email p o ec ed]o (M.R.); ma ian.s an. [email p o ec ed] (M.S.)
*Co espondence: [email p o ec ed]
Abs ac : The e is inc easing conce n ega ding he associa ion be ween echnological
change and jobs. This s udy explo es how di e en pa e ns o digi aliza ion and inno a ion
ela e o job c ea ion in Eu opean companies. We use da a om he Eu opean Company
Su ey 2019 collec ed by Eu o ound and Cede op. We apply La en Class Analysis (LCA)
o iden i y he ypologies o companies, mainly based on hei le el o echnology adop ion,
inno a ion p ac ices and employmen pa e ns. We showcase ou dis inc classes o
companies: mode a e adop ion o digi al echnology and s ong in e na ional o ien a ion,
adi ional and local, medium digi aliza ion, p ocess inno a i e wi h local ocus and digi al
leade s and inno a o s, wi h speci ic pa e ns ega ding digi aliza ion, inno a ion and job
c ea ion. The digi al leade s and inno a o s class e ealed a high le el o digi aliza ion
and inno a ion and main ained s able employmen le els, wi h inc eased in es men s in
s a aining and endency owa ds au oma ion. Con e sely, less-digi alized adi ional
companies a e mo e suscep ible o s agna ion o employmen decline. In gene al, he
employmen ou look is s able, wi hou signi ican employmen g ow h, signaling he
need o balanced in es men s in inno a ion and digi aliza ion ha s imula e mo e and
be e jobs. This is he i s s udy o apply LCA o explo e complex ela ionships be ween
digi aliza ion, inno a ion, o eign ade, aining in es men s and employmen ends and
o e s esh insigh s in o company iews owa ds employmen in he digi al e a.
Keywo ds: digi aliza ion; employmen ; human capi al; inno a ion; la en class analysis;
job c ea ion
1. In oduc ion
O e he las 20 yea s, he apid pace o echnological change has ans o med labo
ma ke s, eshaping skill equi emen s and wo k o ce dynamics in an unp eceden way.
None heless, he impac o echnology adop ion on wo k o ce demand a he company
le el can esul in job loss due o au oma ion. Howe e , i can also c ea e new jobs h ough
he up ake o new echnologies, de elopmen o new p oduc s and se ices and inc eased
demand o p oduc s and se ices, o due o he eme gence o hea y indus ies (Acemoglu
& Res epo,2020).
As new echnologies become much mo e a o dable, o ganiza ions emained compe -
i i e, ye hei abili y o align wo k o ce capabili ies wi h hese echnological demands
a ies signi ican ly om coun y o coun y, om one sec o o ac i i y o ano he , and om
company o company. Al hough exis ing esea ch has add essed he e ec s o digi aliza ion
Economies 2025,13, 104 h ps://doi.o g/10.3390/economies13040104
Economies 2025,13, 104 2 o 19
on labo ma ke s, he e is a gap in unde s anding he associa ion be ween company-le el
echnology adop ion and employmen ou comes.
This s udy aims o add ess his gap in esea ch by using la en class analysis (LCA) o
iden i y he dis inc ypologies o companies based on hei le el o echnology adop ion,
inno a ion p ac ices, employmen pa e ns and o he key cha ac e is ics. LCA is a powe ul
me hodological ins umen o e ealing hidden subg oups wi hin a popula ion, hus
allowing us o explo e simple co ela ions o iden i y complex, mul idimensional pa e ns
o beha io wi hin Eu opean companies.
D awing on da a om he Eu opean Company Su ey (2019), conduc ed by he
Eu opean Founda ion o he Imp o emen o Li ing and Wo king Condi ions (Eu o ound)
and Eu opean Cen e o he De elopmen o Voca ional T aining (Cede op), ou analysis
is based on 21,869 manage s’ esponses o a s anda dized su ey om subsidia y si es o
mul i-si e companies ac oss he EU-27 and he Uni ed Kingdom. This ich da ase p o ides
aluable unde s anding o employmen pa e ns, inno a ion s a egies and app oaches o
skill de elopmen and digi aliza ion, o e ing a g ea ou look in o Eu opean companies’
beha io s in he digi al e a.
Ou esea ch ques ions a e he ollowing:
Q1.
Wha a e he key ypologies o companies ha can be iden i ied based on hei ap-
p oach o digi aliza ion, inno a ion and in e na ional ma ke exposu e and combined
cha ac e is ics such as size, sec o and coun y o esidence?
Q2.
How do he iden i ied ypologies in e ac wi h he companies’ app oaches o p esen
and u u e employmen changes and s a aining?
Since he beginning o he Fi s Indus ial Re olu ion in 1750, jobs and people’s
li es yles ha e been h ea ened by machines, which ha e he po en ial o eplace hem. Job
loss and he lack o he capaci y o adap o echnological ad ancemen s a e among he
g ea es ea s indi iduals ace (Kozak e al.,2020).
E e y indus ial e olu ion has b ough high unce ain y and ea s ela ed o jobs
becoming obsole e. John Mayna d Keynes e en p edic ed ha jobs au oma ion would
lead o he amous ‘ echnical unemploymen ’ (Keynes,1931). Ne e heless, hese ea s
ha e been apidly abandoned, as e idence shows ha echnology and inno a ion ha e
g ea po en ial o c ea e new indus ies and jobs while making olde sec o s obsole e. The
scien i ic li e a u e was domina ed by hese concep s, buil a ound he well-known heo ies
o ‘c ea i e des uc ion’ (Schumpe e ,1942) and ‘compensa ion heo y’ (Ma x,1867).
Wi hin he con ex o he Fou h Indus ial Re olu ion, which b ough abou an
exponen ial pace o echnological de elopmen , a ious ques ions a ose: Is his ime any
di e en ? Will obo s s eal jobs (Acemoglu & Res epo,2020)? Will ad ances in a i icial
in elligence and au oma ion lead o impo an job losses (e.g., B ynjol sson & McA ee,2014;
Fo d,2015)?
The eme ging scien i ic li e a u e highligh s ha o ganiza ions wi h highe le els o
echnology adop ion o en demons a e a g ea e p opensi y o inc eased p oduc i i y, in-
no a ion and wo k o ce skilling (Ba el e al.,2007). Con e sely, low le els o echnological
in eg a ion ha e been associa ed wi h job pola iza ion, a o ing he alloca ion o low-paid
ou ine asks o he se ice sec o (Au o & Do n,2013;Obadic,2020).
While he u u e o labo is unce ain (Wo ld Bank,2019), esea ch highligh s ha he
inc eased use o digi al echnologies in companies is associa ed wi h a g ow h in he numbe
o employees o posi i e u u e employmen p ospec s (Ga cía-Romanos & Ma ínez-
Ros,2024). Va ious scien i ic opinions ha e eme ged in his ega d. On he one hand,
acco ding o c ea i e des uc ion heo y, inno a ion is conside ed o elimina e old jobs bu
c ea es new, highe - alue oles, esul ing in a ne posi i e impac (Mas os e ano & Pian a,
2009). Likewise, inno a ion leads o job c ea ion h ough inc eased i m p oduc i i y and
Economies 2025,13, 104 3 o 19
expansion (Blanch lowe & Bu gess,1999;Van Reenen,1997). In addi ion, he ise o digi al
indus ies c ea es jobs in new sec o s; hus, digi aliza ion has con ibu ed o he eme gence
o en i ely new indus ies, c ea ing millions o jobs in ields such as p og amming and
da a analysis (Acemoglu & Res epo,2020). Finally, digi aliza ion is lexible, acili a ing
job c ea ion ac oss di e se indus ies, including se ices, by enabling e iciency and new
asks (B ynjol sson & McA ee,2014). Fo ins ance, digi aliza ion has a small bu posi i e
ne e ec on employmen when i is e alua ed ac oss he Ge man economy using s uc u al
models (A n z e al.,2016). Mo e speci ically, non-machine-based digi al echnologies such
as En e p ise Resou ce Planning (ERP) and e-comme ce posi i ely a ec employmen by
enabling ask e iciency and c ea ing complemen a y human oles (Aube -Ta by e al.,
2018). Howe e , digi aliza ion has he po en ial o in luence he wo k o ce s uc u e
di e en ly a he company le el. Tha is, inc eased in es men in digi aliza ion is associa ed
wi h inc eased employmen o highly skilled wo ke s and educes employmen o low-
skilled wo ke s (Au o & Do n,2013;Balsmeie & Woe e ,2019). Thus, au oma ion
and digi aliza ion a e unlikely o esul in subs an ial job losses. Wo ke s wi h lowe
quali ica ions may need o adap mo e because o hei highe isk o au oma ion. The main
challenge emains ega ding he capaci y o add ess ising inequali y and ensu e wo ke
e aining (A n z e al.,2016).
Fu he esea ch e eals ha inno a ion a he i m le el, including digi aliza ion,
gene ally leads o employmen g ow h by c ea ing new job oppo uni ies (Blanch lowe
& Bu gess,1999). Inno a ion, pa icula ly h ough esea ch and de elopmen (R&D)
in es men s, os e s job c ea ion wi hin i ms and ac oss indus ies, con ibu ing o posi i e
employmen e ec s (Ga cía-Romanos & Ma ínez-Ros,2024;Van Reenen,1997). This belie
con inues o domina e he concep o employmen g ow h o e ime. Ten yea s la e ,
based on Schumpe e ’s heo y o “c ea i e des uc ion”, i was a gued ha inno a ion,
including digi aliza ion, con inues o ha e a ne posi i e impac on jobs a he indus y
le el (Mas os e ano & Pian a,2009). Howe e , i m s a egies con inue o be essen ial, as
p oduc inno a ions associa ed wi h digi aliza ion o en ha e mo e posi i e employmen
e ec s han p ocess inno a ions (Mas os e ano & Pian a,2009).
The posi i e syne gies be ween p oduc and p ocess inno a ions, digi al echnology
adop ion and use and he lea ning capaci y o he o ganiza ion ha e been la gely explo ed
by esea che s o e he las hal o he cen u y. Adop ing inno a ion boos s bo h p oduc
and p ocess imp o emen s, enhancing i m compe i i eness h ough digi al echnologies
ha s eamline p oduc ion and imp o e cus ome engagemen (Roge s,1983;To na zky
& Klein,1982). Digi al echnology can undoub edly p omo e impo an p ocesses and
p oduc inno a ion a he company le el (Ayoko,2021).
Digi al echnologies also acili a e o ganiza ional lea ning, which is impo an o
he implemen a ion o new p ocesses. Technologies such as ERP sys ems and elec onic
da a in e change assis o ganiza ions in de eloping dynamic capabili ies, allowing hem
o c ea e new wo k lows and p oduc s (Cohen & Le in hal,1989,1990). Bibliome ic
analysis concludes ha digi al inno a ion adop ion is p e alen in indus ies ocusing on
p oduc and p ocess ad ancemen s, while high R&D indus ies, such as echnology and
manu ac u ing, hea ily adop digi al ools, leading o inno a i e p oduc s and p ocess
edesign (Van Oo scho e al.,2018).
Mo e ecen ly, analyses conduc ed a he EU le el ha e shown ha in es men s in
digi al echnology and lea ning capaci ies can posi i ely in luence p oduc and p ocess
inno a ion and inno a ion o e all (Cu zi & Fe a ini,2024;G eenan & Napoli ano,2024).
Ne e heless, i is widely accep ed ha inc eased p oduc i i y a he company le el is
achie ed h ough in es men s in digi al echnology and employee aining (Chen e al.,
2016;Siegen hale & S ucki,2015).
Economies 2025,13, 104 4 o 19
Digi aliza ion c ea es a landscape in which la ge i ms and ech-sa y indus ies lead
he cha ge. La ge companies and hose in echnology-in ensi e sec o s (e.g., in o ma ion
and communica ions) a e mo e likely o adop digi aliza ion (e.g., e-comme ce, obo s, o
da a analy ics).
Undoub edly, company size ma e s o la ge i ms, and expo e s demons a e highe
le els o digi aliza ion, sugges ing a co ela ion be ween i m size and echnology adop ion.
La ge i ms a e mo e likely o adop digi al echnologies. Fo ins ance, in Swi ze land,
adop ion a es a e no ably highe among i ms wi h mo e han 20 employees (Balsmeie &
Woe e ,2019). The size o an o ganiza ion, as well as i s eadiness, a ec s he adop ion o
digi al echnology and i s impac on inno a ion. The p esence o ba ie s o inno a ion and
digi aliza ion in small and medium en e p ises nega i ely a ec s e iciency (Mi opoulos
e al.,2024). Howe e , la ge o ganiza ions and hose wi h highe echnological eadiness
a e mo e inclined o adop digi al ools ha con ibu e o inno a i e p ocesses and p oduc s
(Damanpou & Schneide ,2006).
La ge i ms in ad anced indus ies p e e machine-based digi al echnologies ha com-
bine da a access, compu a ion and ha dwa e. Smalle i ms and less ad anced sec o s ocus
mo e on non-machine-based echnologies (Aube -Ta by e al.,2018;A n z e al.,2016).
Mo eo e , indus y ype is equally impo an : manu ac u ing sec o s, which a e mo e
echnologically ad anced, adop complex digi al echnologies mo e equen ly han he
se ice sec o s. Fo example, complex digi al echnologies a e associa ed wi h sec o s ha
equi e high echnical sophis ica ion. Technologies such as obo ics a e p ima ily adop ed
in sec o s wi h signi ican manu ac u ing o echnological componen s, highligh ing he
ole o indus y ype in digi aliza ion le els (Balsmeie & Woe e ,2019). Ne e heless,
echnological ad ancemen s ha e an impo an in luence on knowledge-in ensi e and
highly echnologized sec o s, and con e sely, a nega i e in luence on employmen g ow h
in less echnologically in ensi e manu ac u ing sec o s (Obadic,2020).
Addi ionally, compe i i eness s imula es digi aliza ion, as exposu e o in e na ional
compe i ion s imula es i ms in expo -d i en indus ies o emb ace digi al echnologies.
The le el o digi aliza ion wi hin a i m’s indus y is closely linked o i s compe i i eness.
Swi ze land’s signi ican exposu e o in e na ional compe i ion s imula es i ms, especially
hose in he manu ac u ing and expo -dominan sec o s, o emb ace digi al echnologies
mo e ex ensi ely. This indica es ha an indus y’s compe i i eness plays a c ucial ole in
de e mining i s deg ee o digi aliza ion (B is & Cabolis,2017). When ocusing on in e -
egional ade in Eu ope, esea che s ound ha ou ine- eplacing echnological change
had an impo an displacemen e ec on jobs du ing 1999 and 2010 and also led o ne
g ow h in job c ea ion due o inc eased p oduc demand (G ego y e al.,2022).
Human capi al plays a signi ican ole, as high sala ies and skills in ad anced indus-
ies suppo he adop ion o digi al echnology. Swi ze land’s skilled wo k o ce and high
sala ies ha e led i ms in ad anced indus ies o in es in digi al echnologies o enhance
e iciency and educe cos s. This end is pa icula ly no iceable in la ge companies and
hose in he manu ac u ing sec o (Siegen hale & S ucki,2015). F om an employmen
pe spec i e, he skill le el o an employee is pa icula ly impo an , as IT can be a subs i u e
o , bu can also complemen , human labo in Eu ope (Peng & Zhang,2020).
The economic heo ies p esen ed abo e sugges ha he ela ionship be ween digi-
aliza ion, inno a ion and employmen ou comes ope a es h ough a laye ed mechanism.
Technological inpu s—such as he adop ion o obo s, e-comme ce pla o ms and spe-
cialized so wa e and IT sys ems, along wi h p oduc and p ocess inno a ion—a e i s
co ela ed as i ms’ in e nal capaci ies and s a egies. These inpu s a e linked o how com-
panies o ganize p oduc ion, in oduce new p oduc s and se ices o he ma ke and adap
hei wo k o ce. In ou esea ch, we use LCA o cap u e his mul idimensional complexi y
Economies 2025,13, 104 5 o 19
by iden i ying la en company, based on obse able pa e ns in i ms’ echnology adop ion
and inno a ion beha io . These p o iles— anging om digi ally ad anced companies o
adi ional, low-inno a ion i ms—a e used as an analy ical lens h ough which we explo e
how Eu opean o ganiza ions espond s a egically o digi al ans o ma ion. Al hough
he LCA me hod does no allow us o es ablish causal ela ionships, we use i as p oxy o
unde s and he economic adjus men mechanisms unde lying company-le el esponses
such as ecen employmen changes, u u e employmen expec a ions and aining in es -
men . We use his as an o ganiza ional media o o link inno a ion and digi aliza ion o
employmen ou comes such as s abili y, job g ow h o decline. This app oach in eg a es
he heo e ical pe spec i es on he c ea i e des uc ion economic model in o an empi ical
amewo k ha e lec s he a ia ions in i m beha io .
Fueled by enewed Eu opean Union (EU) in e es o ebuild he EU companies’ com-
pe i i eness globally by unding in es men s ha should close he inno a ion gap wi h
China and Uni ed S a es o Ame ica, especially by elaunching ad anced echnologies
and closing he skills gap, he p esen analysis could con ibu e o be e unde s anding o
whe e EU inno a i e and digi alized companies s and in e ms o employmen p ospec s
and wha can be done u he (D aghi,2024).
2. Ma e ials and Me hods
The da a sou ce o esponding o he esea ch ques ions was he Eu opean Company
Su ey (ECS) o he yea 2019, conduc ed by Eu o ound in collabo a ion wi h Cede op
(Eu o ound & Cede op,2020). ECS 2019 ocuses on wo kplace o ganiza ion, inno a ion and
companies’ app oaches o skills and digi aliza ion. Da a we e collec ed om 21,869 sub-
sidia y si es o mul i-si es companies ac oss Eu ope, co e ing all 27 EU membe s a es and
he UK. The a ge popula ion o he su ey was companies wi h mo e han en employees.
The da a e e o manage s’ esponses o he su ey. ECS 2019 employs a push- o-web
me hodology combined wi h ieldwo k. Companies we e ini ially con ac ed by phone o
designa e a senio manage o he su ey. The designa ed indi iduals we e in i ed o
comple e an online ques ionnai e. The pu pose o he ECS 2019 edi ion was o analyze
wo kplace o ganiza ion and inno a ion a he company le el, as well as companies’ ap-
p oaches o skills and digi aliza ion (Eu o ound & Cede op,2020). A mul is age s a i ied
andom sampling me hod was used, and p ocedu es a ied ac oss coun ies. The sample
was s a i ied by company size (numbe o employees) and sec o o ac i i y (manu ac u -
ing, cons uc ion and se ices). This app oach aimed o ensu e high-quali y da a and o
collec ep esen a i e da a a bo h he na ional and EU le els. A o al o 21,869 in e iews
we e conduc ed wi h managemen pe sonnel.
To espond o he esea ch ques ions, his s udy uses a comp ehensi e la en class
analysis aimed a iden i ying la en employmen beha io s in EU companies g ouped
mainly based on hei le el o echnology adop ion and inno a ion pa e ns.
To explo e how di e en pa e ns o digi al echnology adop ion a he company le el
a e associa ed wi h employmen ends in he Eu opean Union (EU), we applied a La en
Class Analysis (LCA), conside ing i s b oad use in scien i ic esea ch in mul iple domains
pe aining o social sciences esea ch (San os e al.,2015;Zhu,2024), psychology (Pe e sen
e al.,2019) and heal h (Zhou e al.,2018;Kongs ed & Nielsen,2017). The essence o he LCA
me hod in ol es inding unde lying g oups in a popula ion using ca ego ical da a based on
p obabilis ic modeling, esul ing in in a-classes homogenei y and in e -class he e ogenei y.
S a ing om he wo esea ch ques ions (company ypologies ela ed o hei app oach
o digi aliza ion, inno a ion and in e na ional ma ke exposu e and companies’ app oach
o p esen and u u e changes in employmen and s a aining), his esea ch aimed o
iden i y g oups in i m popula ions wi h simila ea u es.
Economies 2025,13, 104 6 o 19
The da ase is peculia , as he da a a e ca ego ical. This disquali ies o he me hods
designed o use on nume ical da ase s, such as clus e analysis (de e minis ic me hod
used o con inuous nume ical da a) o neu al ne wo k analysis o clus e ing (which
is adequa e o analyzing la ge da ase s wi h many nume ical a iables and complex
nonlinea ela ionships).
LCA was u ilized, since i is a mode n, powe ul modeling echnique ha esponds
o he ypology o he a iables (quali a i e da a esul ing om a la ge su ey) and he
scope o he analysis. Mo e speci ically, o iden i y hidden company p o iles, LCA p o ed
help ul in e ealing la en subg oups o companies ha sha e simila pa e ns in echnology
adop ion, inno a ion ac i i ies and employmen beha io s, conside ing he ype o ECS
2019 a iables (mainly ca ego ical). T adi ional me hods (e.g., linea eg ession) o en mask
such he e ogenei y, whe eas LCA helps unco e nuanced p o iles (Welle e al.,2020).
Simila ly, LCA b ings a mul idimensional pe spec i e o he analysis. As he da ase
includes a ious measu es ( echnology adop ion, aining, inno a ion and wo k o ce e o-
lu ion), LCA allows us o pe o m a simul aneous analysis o hese a iables. I g oups
companies wi h s a is ically simila esponse pa e ns in o classes and p o ides a comp e-
hensi e iew o how echnology sp eads ac oss di e en company ypes.
F om a p ac ical iewpoin , LCA esul s e eal new pe spec i es on employmen
ends in he EU ha a e use ul o a ious s akeholde s, such as policy make s o company
managemen . By unde s anding he speci ic classes o companies, decision make s ha e
be e ools o adjus policies and managemen s a egies o he speci ic needs o each
p o ile, especially in he con ex o he o e all beha io s unco e ed by he LCA.
Finally, we used LCA o inno a ion pu poses, conside ing he uniqueness o he
app oach. While many s udies on he in e sec ion be ween digi aliza ion and employmen
use desc ip i e s a is ics o econome ic models, LCA is ela i ely unde u ilized o explo e
how echnology shapes wo k o ce dynamics a he company le el ac oss Eu ope. The
me hod’s abili y o segmen companies based on mul iple dimensions simul aneously
p o ides no el insigh s and complemen s exis ing mac oeconomic and case-based esea ch.
The a iables used o he scope o his analysis (see Appendix A) we e selec ed based
on he ou comes o he li e a u e e iew.
Employmen ends a e measu ed using wo o dinal a iables ha ocus on how he
o al numbe o employees in he company e ol ed since he beginning o 2016 (CHEMP)
and he e olu ion o he o al numbe o employees in he company o e he nex h ee
yea s (CHEMPFUT) (Acemoglu & Res epo,2020). P o essional aining (o ganized inside
o ou side he company) o e ed by he company o employees du ing paid wo king hou s
was cap u ed by an o dinal a iable (PAIDTRAIN).
Digi aliza ion in companies was measu ed using speci ic ECS 2019 o dinal a iables,
showcasing he deg ee o digi al adop ion in he company. These a iables a e e-comme ce
(in es iga ing whe he he company buys o sells goods o se ices online (e.g., h ough
business- o-business po als, e-comme ce, e c.); obo usage (ICTROB, explo ing whe he
he company uses obo s, which in his con ex a e de ined as p og ammable machines
ha can pe o m a se ies o complex ac ions au oma ically, including in e ac ions wi h
humans); and usage o so wa e designed o cus omized o i s needs o e he las h ee
yea s (ICTAPP) (Acemoglu & Res epo,2020;Aube -Ta by e al.,2018;B ynjol sson &
McA ee,2014).
Inno a ion a he company le el was measu ed using wo dicho omous a iables,
in line wi h o he s udies ha ha e used he Eu opean Company Su ey 2019 (Della
To e e al.,2021;Cu zi & Fe a ini,2024), by ocusing bo h on inno a ion in p oduc s
(INNOPROD) and p ocesses (INNOPROC) o e a pe iod o h ee yea s be o e he su ey
(s a ing in 2016) (Mas os e ano & Pian a,2009).
Economies 2025,13, 104 7 o 19
Exposu e o in e na ional ma ke s was cap u ed by an o dinal a iable (SALESINT),
which measu es he pe cen age o sales o in e na ional clien s o e a h ee-yea pe iod
s a ing in 2016 (B is & Cabolis,2017).
A se o a iables ega ding he company p o ile and coun y o esidence was also
used (see Appendix A), such as he size o he company (WPSIZE_MM_N), he main sec o
o ac i i y ollowing he NACE 2-Digi codes le el (MAINACT) and coun y o esidence,
he la es co e ing he EU 27 Membe S a es and Uni ed Kingdom (Aube -Ta by e al.,
2018;A n z e al.,2016;Balsmeie & Woe e ,2019).
The La en Class Analysis (LCA) was pe o med using he R p og amming language
wi h a code designed by he au ho s. Subsequen s eps we e ollowed, s a ing wi h he
p epa a ion o he en i onmen , including by loading he necessa y lib a ies. Fu he mo e,
he da a we e cleaned, and missing alues we e handled (by illing gaps in ca ego ical
a iables using he mode and nume ical a iables using he mean). As LCA can only be
pe o med on ca ego ical da a, he nume ical a iables we e disc e ized. The a iables
we e coded as ac o s o make hem i he s a is ical model. Fo he LCA modeling, we
iden i ied ou unde lying la en g oups o he da a conside ed.
The LCA me hod is p obabilis ic, and i models he p obabili y ha an obse a ion is
in a ce ain la en class Ckon he basis o he o mula below:
P(X=x|Ck)=
J
∏
j=1
PXj=xj
Ck
whe e
P(X=x|Ck)
ep esen s he p obabili y ha a se o X answe s will be included in
a la en class C
k
; in ou case, i ep esen s he p obabili y ha a company will be pa
o a C
k
class wi h he answe s X = x
·∏J
j=1
ep esen s he p oduc o he p obabili ies
o each obse ed a iable; we assume ha he a iables a e independen inside a la-
en class.
Xj=xj
Ck
ep esen s he p obabili y ha a iable X
j
will ake he alue x
j
,
conside ing class C
k
. This shows how each a iable con ibu es o a la en class de ini ion.
Addi ionally, we se up he da a ames used in he model, as well as he h ee
co a ia es pe aining o employmen and aining, and we encoded hem as ac o s.
Fu he mo e, we conduc ed an LCA analysis.
Wi hin he analysis, he Bayesian In o ma ion C i e ion (BIC) o se e al scena ios
conside ing wo, h ee, ou , i e, six, o se en classes was also calcula ed. The numbe o
classes was de e mined based on he BIC.
The BIC can be calcula ed as ollows:
BIC =−2·ln(L)+k·ln(N)
whe e:
•ln(L): model log-likelihood;
•k: numbe o pa ame e s;
•N: sample size.
The BIC was chosen as he indica o because i s ikes a balance be ween model i and
complexi y (Wagenmake s,2007). The model wi h he lowes BIC was selec ed as he mos
sui able o in e p e a ion (see Table 1).
Economies 2025,13, 104 8 o 19
Table 1. BIC alues.
No. o Classes BIC Value
2 classes 443,551.8
3 classes 439,827.7
4 classes 438,033.4
5 classes 437,955.7
6 classes 452,663.7
7 classes 452,876.0
Sou ce: Au ho ’s own calcula ions.
The quali y o he clus e ing was ensu ed by selec ing he numbe o classes based on
he lowes BIC. The BIC was un o se e al possible numbe s o clus e s, esul ing in he
alues gi en below (see Table 1):
Al hough he BIC showed ha he 5-class solu ion would be op imum om he me hod
poin o iew, he eam op ed o a 4-class model o clea e in e p e abili y and p ac ical
ele ance o his esea ch. The sligh s a is ical imp o emen o he 5-class o e he 4-class
did no jus i y adding mo e complexi y. Based on a compa ison o he esul ing BIC alues,
i was de e mined ha he mos app op ia e numbe o classes was ou . The esul s we e
hen isualized h ough se e al g aphical ep esen a ions, such as he p obabili ies o each
class, he hea map, he coun ies’ hea map and he co a ia e e ec on class membe ship.
In o de o es ima e he quali y o he class cla i y, he eam calcula ed he en opy
o his LCA model. The en opy was e alua ed using he ollowing o mula (Celeux &
So omenho,1996):
En opy =1+∑N
i=1∑K
k=1Pik·lnPik +e−12
N·ln(K)
whe e:
•N: o al numbe o obse a ions.
•K: o al numbe o la en classes.
The alue o he en opy is be ween 0 and 1, wi h alues close o 1 indica ing be e
classi ica ion. The calcula ed en opy was 0.6837. This alue shows mode a e cla i y o he
la en class model. The e a e se e al easons o why his alue is no highe , and pe haps
he mos impo an is ha he BIC alue o a 5-class model is e y close. The calcula ed
en opy i we conside 5 classes is 0.7275; close o he i s 4-class si ua ion (chosen by he
eam o analysis easons).
3. Resul s
The esul s o he LCA, conside ing he op imal BIC o choosing he numbe o classes,
show ha ECS 2019 companies a e classi ied in o ou classes based on hei la en p o iles
o digi aliza ion and inno a ion de e minan s. When in es iga ing he class membe ship
dis ibu ion o he pa icipan s in he su ey (See Figu e 1), we obse ed ha Class 3 was
he mos nume ous one (7791 companies), ollowed by Class 2 (5038 companies), Class 4
(4934 companies) and Class 1 (3609 companies).
Economies 2025,13, 104 15 o 19
shows a complex in e ac ion linked o he le el o echnology and inno a ion adop ion and
key company cha ac e is ics.
Class 1 and Class 3 companies demons a ed mode a e le els o digi aliza ion and he
endency o main ain s able employmen ends. Con e sely, Class 2 companies, cha ac e -
ized by low digi aliza ion, displayed declining employmen ends. The highly digi alized
Class 4 companies p esen ed signs o au oma ion ha could hampe employmen g ow h in
he u u e. No ably, none o hese ca ego ies demons a ed a signi ican inc ease in job c e-
a ion, he eby ques ioning he applicabili y o he p ima y heo y o ‘c ea i e des uc ion’,
which has been highligh ed by a ious au ho s h oughou all echnological e olu ions.
This s udy empi ically aces he link be ween i ms’ echnological p o iles, s a egic
beha io and employmen ou comes. While LCA does no allow o causal in e ence, he
consis en pa e ns obse ed ac oss la en classes p o ide aluable insigh in o how i ms
adap o ail o adap o digi al ans o ma ion. Ou esul s indica e ha digi aliza ion and
inno a ion, e en when accompanied by aining in es men , do no au oma ically ansla e
in o job c ea ion. The expec ed pa e n o compensa ion, whe e job losses due o au oma ion
a e compensa ed by new oles in digi ally ma u e i ms, is no clea ly p esen . This has
impo an implica ions o policy: i echnological ans o ma ion emains con ined o a
na ow segmen o i ms and does no ansla e in o b oade employmen gains, mo e
a ge ed in e en ions may be equi ed o ensu e inclusi e labo ma ke ou comes in he
digi al e a.
In conclusion, he indings o he ou la en class analyses highligh ha public poli-
cies need o p omo e he bene i s o digi aliza ion while mi iga ing po en ial nega i e
employmen ou comes. Public policies should aim o suppo he e o s o Class 2 less
digi alized, adi ional companies, and o adop digi aliza ion and inno a ion s a egies
obse ed in mo e ad anced i ms, such as hose in Classes 1 and 4, by o e ing inancial
incen i es o encou age digi al adop ion and inno a ion (C isan e al.,2023). Likewise,
p oac i e measu es o c ea e di e se employmen oppo uni ies o jobseeke s should be
incen i ized. Ne e heless, p o essional aining should become a p io i y bo h o compa-
nies and go e nmen s, as in o med by he success o Class 4 i ms. The e o e, go e nmen al
unds, including Eu opean in es men s, should be di ec ed owa ds upskilling, eskilling
and imp o ing he digi al skills o Eu opean employees o inc ease hei adap abili y o
labo ma ke s (Ca a ella e al.,2023). While ad anced digi aliza ion does no necessa ily
educe jobs, he obse ed beha io o Class 4 companies, which showed a endency owa ds
au oma ion and aining o e wo k o ce expansion, sugges he need o s a egies ha
balance digi aliza ion and au oma ion wi h employmen sus ainabili y.
This s udy has impo an limi a ions. These include he lack o longi udinal da a and
a ious mac oeconomic a iables. The da a conce ning u u e employmen endencies a e
based on HR ep esen a i es’ subjec i e esponses and expec a ions, in oducing a ce ain
deg ee o subjec i i y, while he po en ial impac o he COVID-19 pandemic was also
missed. Ne e heless, o he limi a ions s em om missing da a on in es men s in R&D
ac oss Eu ope, p oduc i i y, echnological in es men s and skill misma ch.
Using LCA o iden i y speci ic ea u es and ypologies o digi alized companies in
conjunc ion wi h employmen is an inno a i e app oach ha should be u he explo ed
and eplica ed. Fu he esea ch is essen ial o de elop a deepe unde s anding o he un-
de lying condi ions unde which digi aliza ion leads o inclusi e and sus ainable economic
g ow h and sus ainable job c ea ion. Conduc ing a simila analysis on he la es ECS da a
could help shape EU policies and in es men s in digi aliza ion, inno a ion, aining and
employmen p og ams, pa icula ly in ligh o he D aghi epo (D aghi,2024).
Economies 2025,13, 104 16 o 19
Au ho Con ibu ions: Concep ualiza ion, A.-M.V. and M.C.; me hodology, A.-M.V., M.C. and M.R.;
so wa e, M.C.; alida ion, M.R., A.-M.V. and M.C.; o mal analysis, A.-M.V. and M.C.; in es iga ion,
A.-M.V., M.S. and D.B.; esou ces, A.-M.V., M.S. and D.B.; da a cu a ion, A.-M.V. and M.C.; w i ing—
o iginal d a p epa a ion, A.-M.V.; w i ing— e iew and edi ing, A.-M.V., M.R., M.S., M.C. and
D.B.; isualiza ion, M.C. and A.-M.V.; supe ision, A.-M.V.; p ojec adminis a ion, A.-M.V.; unding
acquisi ion, all au ho s. All au ho s ha e ead and ag eed o he published e sion o he manusc ip .
Funding: This esea ch ecei ed no ex e nal unding.
Ins i u ional Re iew Boa d S a emen : No applicable.
In o med Consen S a emen : No applicable.
Da a A ailabili y S a emen : The da a used in his s udy can be accessed a UK Da a Se ice:
h p://doi.o g/10.5255/UKDA-SN-8691-1 (accessed on 24 May 2023).
Con lic s o In e es : The au ho s decla e no con lic o in e es .
Abb e ia ions
The ollowing abb e ia ions a e used in his manusc ip :
ECS Eu opean Company Su ey (2019)
EU Eu opean Union
UK Uni ed Kingdom
LCA La en Class Analysis
R&D Resea ch and De elopmen
Appendix A
Table A1. ECS 2019 su ey ques ions. Au ho s’ own selec ion.
Desc ip ion I em Scale and De ails Va iable Name
Coun y o he es ablishmen Coun y
EU 27, Uni ed Kingdom coun ycode
Es ablishmen ’s main ac i i y ca ego y
Nace-1 digi codes
The ollowing sec o s we e excluded:
- [1]—Ag icul u e, o es y
and ishing
- [15]—Public adminis a ion and
de ence; compulso y social
secu i y
- [16]—Educa ion
- [17]—Human heal h and social
wo k ac i i ies
- [20]—Ac i i ies o households as
employe s;
- [21]—Ac i i ies o ex a e i o ial
o ganisa ions and bodies
mainac _d
How many people wo k in
his es ablishmen ?
1–3
- Small (10–49 employees)
- Medium (50–249 employees)
- La ge (250+ employees)
mm_size_g p
Economies 2025,13, 104 17 o 19
Table A1. Con .
Desc ip ion I em Scale and De ails Va iable Name
In e na ional sales
Since he beginning o 2016, wha pe cen age
o his es ablishmen ’s sales we e o cus ome s
in o he coun ies?
1–4
- We do no engage in expo (0%)
- 1% o 24%
- 25% o 49%
- 50% o mo e
salesin _ca
Digi aliza ion
Does his es ablishmen buy o sell goods o
se ices on he in e ne ? Fo ins ance, by using
business- o-business po als, e-comme ce e c.
Yes/No ecomme ce
Since he beginning o 2016, did his
es ablishmen pu chase any so wa e ha was
speci ically de eloped o cus omized o mee
he needs o he es ablishmen ?
Yes/No ic app_ca
Robo s a e p og ammable machines ha a e
capable o ca ying ou a complex se ies o
ac ions au oma ically, which may include he
in e ac ion wi h people. Does his
es ablishmen use obo s?
Yes/No ic ob_ca
Inno a ion
Since he beginning o 2016, has his
es ablishmen in oduced any new o
signi ican ly changed p ocesses ei he o
p oducing goods o supplying se ices?
1–3
- Yes, new o he ma ke
-
Yes, new o he es ablishmen , bu no
new o he ma ke
- No
innop oc
Since he beginning o 2016, has his
es ablishmen in oduced any new o
signi ican ly changed p oduc s o se ices?
1–3
- Yes, new o he ma ke
-
Yes, new o he es ablishmen , bu no
new o he ma ke
- No
innop od
T aining in es men s
In 2018, how many employees in his
es ablishmen pa icipa ed in aining sessions
on he es ablishmen p emises o a o he
loca ions du ing paid wo king ime? (%)
1–7
- None a all
- Less han 20%
- 20% o 39%
- 40% o 59%
- 60% o 79%
- 80% o 99%
- All
paid_ ain_d.1
Employmen ends
How has he o al numbe o employees in his
es ablishmen changed since he beginning o
2016?
1–5
- Inc eased by mo e han 10%
- Inc eased by up o 10%
- S ayed abou he same
- Dec eased by up o 10%
- Dec eased by mo e han 10%
chemp_change_employees
In he nex h ee yea s, how do you expec he
o al numbe o employees in his
es ablishmen o change?
1–3
- I will inc ease
- I will s ay abou he same
- I will dec ease
chenmp u _change_empl_3_yea s
Economies 2025,13, 104 18 o 19
Re e ences
Acemoglu, D., & Res epo, P. (2020). Robo s and jobs: E idence om US labo ma ke s. Jou nal o Poli ical Economy,128(6), 2188–2244.
A n z, M., G ego y, T., & Zie ahn, U. (2016). The isk o au oma ion o jobs in OECD coun ies: A compa a i e analysis. OECD Social,
Employmen and Mig a ion Wo king Pape s, No. 189. O ganisa ion o Economic Co-Ope a ion and De elopmen .
Aube -Ta by, C., Escoba , O. R., & Rayna, T. (2018). The impac o echnological change on employmen : The case o p ess digi iza ion.
Technological Fo ecas ing and Social Change,128, 36–45. [C ossRe ]
Au o , D. H., & Do n, D. (2013). The g ow h o low-skill se ice jobs and he pola iza ion o he US labo ma ke . Ame ican Economic
Re iew,103(5), 1553–1597. [C ossRe ]
Ayoko, O. B. (2021). Digi al ans o ma ion, obo ics, a i icial in elligence, and inno a ion. Jou nal o Managemen and O ganiza ion,
27(5), 831–835. [C ossRe ]
Balsmeie , B., & Woe e , M. (2019). Is his ime di e en ? How digi aliza ion in luences job c ea ion and des uc ion. Resea ch Policy,
48(8), 103765. [C ossRe ]
Ba el, A., Ichniowski, C., & Shaw, K. (2007). How does in o ma ion echnology a ec p oduc i i y? Plan -le el compa isons o p oduc
inno a ion, p ocess imp o emen , and wo ke skills. The Qua e ly Jou nal o Economics,122(4), 1721–1758. [C ossRe ]
Blanch lowe , D. G., & Bu gess, S. M. (1999). New echnology and jobs: Compa a i e e idence om a wo coun y s udy. Economics o
Inno a ion and New Technology,5(2–4), 109–138.
B is, A., & Cabolis, C. (2017). IMD wo ld digi al compe i i eness anking 2017; IMD Business School. A ailable online: h ps://in es c oa ia
.go .h /wp-con en /uploads/2018/01/wo ld_digi al_compe i i eness_yea book_2017.pd (accessed on 10 Feb ua y 2025).
B ynjol sson, E., & McA ee, A. (2014). The second machine age: Wo k, p og ess, and p ospe i y in a ime o b illian echnologies.
WW No on & Company.
Ca a ella, S., Ci illo, V., C espi, F., Gua ascio, D., & Menghini, M. (2023). The di usion o digi al skills ac oss EU egions: S uc u al
d i e s and pola isa ion dynamics. Regional S udies, Regional Science,10(1), 820–844.
Celeux, G., & So omenho, G. (1996). An en opy c i e ion o assessing he numbe o clus e s in a mix u e model. Jou nal o Classi ica ion,
13(2), 195–212. [C ossRe ]
Chen, W., Niebel, T., & Saam, M. (2016). A e in angibles mo e p oduc i e in ICT-in ensi e indus ies? E idence om EU coun ies.
Telecommunica ions Policy,40(5), 471–484. [C ossRe ]
Ci illo, V., Fan i, L., Mina, A., & Ricci, A. (2023). The adop ion o digi al echnologies: In es men , skills, wo k o ganisa ion. S uc u al
Change and Economic Dynamics,67, 101–118. [C ossRe ]
Cohen, W. M., & Le in hal, D. A. (1989). Inno a ion and lea ning: The wo aces o R&D. The Economic Jou nal,99(397), 569–596.
Cohen, W. M., & Le in hal, D. A. (1990). Abso p i e capaci y: A new pe spec i e on lea ning and inno a ion. Adminis a i e Science
Qua e ly,35(1), 128–152. [C ossRe ]
C isan, G.-A., Popescu, M. E., Mili a u, E., & C is escu, A. (2023). EU Di e si y in Te ms o Digi aliza ion on he Labo Ma ke in he
Pos -COVID-19 Con ex . Economies,11(12), 293. [C ossRe ]
Cu zi, Y., & Fe a ini, F. (2024). High-pe o mance wo k sys ems and i m inno a ion: The mode a ing ole o digi al echnology and
employee pa icipa ion. E idence om Eu ope. Managemen Resea ch Re iew,47(13), 51–68.
Damanpou , F., & Schneide , M. (2006). Phases o he adop ion o inno a ion in o ganiza ions: E ec s o en i onmen , o ganiza ion
and op manage s. B i ish Jou nal o Managemen ,17(3), 215–236.
Della To e, E., G i i, A., & Salimi, M. (2021). Di ec and indi ec employee oice and i m inno a ion in small and medium i ms.
B i ish Jou nal o Managemen ,32(3), 760–778. [C ossRe ]
D aghi, M. (2024). The u u e o Eu opean compe i i eness: A compe i i eness s a egy o Eu ope. Eu opean Commission. A ailable
online: h ps://commission.eu opa.eu/documen /download/97e481 d-2dc3-412d-be4c- 152a8232961
_
en? ilename=The+ u u e+
o +Eu opean+compe i i eness+_+A+compe i i eness+s a egy+ o +Eu ope.pd (accessed on 10 Feb ua y 2025).
Eu o ound & Cede op. (2020). Eu opean Company Su ey 2019: Wo kplace p ac ices unlocking employee po en ial. Eu opean Company
Su ey 2019 Se ies. Publica ions O ice o he Eu opean Union.
Fo d, M. (2015). The ise o he obo s. Basic Books.
Ga cía-Romanos, J., & Ma ínez-Ros, E. (2024). Digi aliza ion and employmen in Eu ope: The ole o i m’s size and he complemen-
a i y o R&D. R&D Managemen ,54, 695–712.
G eenan, N., & Napoli ano, S. (2024). Digi al echnologies, lea ning capaci y o he o ganiza ion and inno a ion: EU-wide empi ical
e idence om a combined da ase . Indus ial and Co po a e Change,33(3), 634–669. [C ossRe ]
G ego y, T., Salomons, A., & Zie ahn, U. (2022). Racing wi h o agains he machine? E idence on he ole o ade in Eu ope. Jou nal o
he Eu opean Economic Associa ion,20(2), 869–906. [C ossRe ]
Keynes, J. M. (1931). Economic possibili ies o ou g andchild en. In Essays in pe suasion (pp. 358–374). Macmillan.
Kongs ed, A., & Nielsen, A. M. (2017). La en class analysis in heal h esea ch. Jou nal o Physio he apy,63(1), 55–58. [C ossRe ]
Kozak, M., Kozak, S., Kozako a, A., & Ma inak, D. (2020). Is ea o obo s s ealing jobs haun ing Eu opean wo ke s? A mul ile el
s udy o au oma ion insecu i y in he EU. IFAC Pape sonline,53(2), 17493–17498. [C ossRe ]
Economies 2025,13, 104 19 o 19
Ma x, K. (1867). Capi al: A c i ique o poli ical economy. Volume I: The p ocess o capi alis p oduc ion. O o Meissne .
Mas os e ano, V., & Pian a, M. (2009). Technology and jobs. Economics o Inno a ion and New Technology,18(8), 729–741. [C ossRe ]
Mi opoulos, P., Mi opoulos, A., & Vlami, A. (2024). Fac o s a ec ing high-quali y en ep eneu ial pe o mance in small- and
medium-sized amily i ms. Jou nal o Family Business Managemen ,14(5), 974–996.
Obadic, A. (2020). In luence o echnological change and digi al echnology on job pola iza ion and occupa ional change. In G. D uzic,
& T. Gelo (Eds.), Con e ence p oceedings o he in e na ional con e ence on he economics o decoupling, ICED 2020 (pp. 433–450).
Uni e si y o Zag eb, The Facul y o Economics & Business.
Peng, G., & Zhang, D. (2020). Does in o ma ion echnology subs i u e o o complemen human labo ? A dynamic s a i ied analysis
on Eu opean coun ies. Decision Sciences,51(3), 720–754. [C ossRe ]
Pe e sen, K. J., Qual e , P., & Humph ey, N. (2019). The applica ion o la en class analysis o in es iga ing popula ion child men al
heal h: A sys ema ic e iew. F on ie s in Psychology,10, 1214. [C ossRe ]
Roge s, E. M. (1983). Di usion o inno a ions (3 d ed.). F ee P ess.
San os, L. M., Amo im, L. D., San os, D. N., & Ba e o, M. L. (2015). Measu ing he le el o social suppo using la en class analysis.
Social Science Resea ch,50, 139–146. [C ossRe ]
Schumpe e , J. A. (1942). Capi alism, socialism, and democ acy. Ha pe and B o he s.
Siegen hale , M., & S ucki, T. (2015). Di iding he pie: Fi m-le el de e minan s o he labo sha e. ILR Re iew,68(5), 1157–1194.
To na zky, L. G., & Klein, K. J. (1982). Inno a ion cha ac e is ics and inno a ion adop ion-implemen a ion: A me a-analysis o indings.
IEEE T ansac ions on Enginee ing Managemen ,EM-29(1), 28–45.
Van Oo scho , P., Ho man, E., Halman, J. I. M., & Bosch-Rek eld , M. (2018). A bibliome ic e iew o he inno a ion adop ion li e a u e.
Technological Fo ecas ing and Social Change,134, 1–21.
Van Reenen, J. (1997). Employmen and echnological inno a ion: E idence om U.K. manu ac u ing i ms. Jou nal o Labo Economics,
15(2), 255–284.
Wagenmake s, E.-J. (2007). A p ac ical solu ion o he pe asi e p oblems o p alues. Psychonomic Bulle in and Re iew,14, 779–804.
[C ossRe ] [PubMed]
Welle , B. E., Bowen, N. K., & Faube , S. J. (2020). La en class analysis: A guide o bes p ac ice. Jou nal o Black Psychology,46(4),
287–311. [C ossRe ]
Wo ld Bank. (2019). Wo ld de elopmen epo 2019: The changing na u e o wo k. Wo ld Bank. [C ossRe ]
Zhou, M., Thaye , W. M., & B idges, J. F. P. (2018). Using la en class analysis o model p e e ence he e ogenei y in heal h: A sys ema ic
e iew. Pha maco Economics,36, 175–187. [C ossRe ]
Zhu, L. (2024). Me i oc a ic belie s in he Uni ed S a es, Finland, and China: A mul idimensional app oach using la en class analysis.
The B i ish Jou nal o Sociology,76(1), 153–172. [C ossRe ]
Disclaime /Publishe ’s No e: The s a emen s, opinions and da a con ained in all publica ions a e solely hose o he indi idual
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