Lee, Kyoung-Joo; Choi, Sun-Yong
A icle
Con igu a ions o esou ce ul and demanding a ibu es
o o ganiza ional cul u e in US ho els: An inno a i e
app oach using opic modeling and sQCA
Jou nal o Inno a ion & Knowledge (JIK)
P o ided in Coope a ion wi h:
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Sugges ed Ci a ion: Lee, Kyoung-Joo; Choi, Sun-Yong (2024) : Con igu a ions o esou ce ul and
demanding a ibu es o o ganiza ional cul u e in US ho els: An inno a i e app oach using
opic modeling and sQCA, Jou nal o Inno a ion & Knowledge (JIK), ISSN 2444-569X, Else ie ,
Ams e dam, Vol. 9, Iss. 4, pp. 1-13,
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Configu a ions o esou ce ul and demanding a ibu es o o ganiza ional
cul u e in US ho els: An inno a i e app oach using opic modeling and
sQCA
Kyoung-Joo Lee
a
, Sun-Yong Choi
b,
*
a
Depa men o Tou ism Managemen , Gachon Uni e si y, 1342 Seongnamdae o, Sujeong-gu, Seongnam, Gyeonggi-do, Republic o Ko ea
b
Depa men o Finance and Big Da a, Gachon Uni e si y, 1342 Seongnamdae o, Sujeong-gu, Seongnam, Gyeonggi-do, Republic o Ko ea
ARTICLE INFO
A icle His o y:
Recei ed 3 Ap il 2023
Accep ed 24 Sep embe 2024
A ailable online 3 Oc obe 2024
ABSTRACT
This s udy dis inguishes i sel om exis ing o ganiza ional cul u e s udies by in es iga ing he ela i ely
unde -s udied heo e ical ela ionship be ween o ganiza ional cul u e and employe a ac i eness. To cap-
u e he holis ic na u e o o ganiza ional cul u e heo y, his s udy adop s configu a ional analysis, which
con as s esou ce ul cul u al a ibu es (collabo a i e, employee de elopmen , and ai -compensa ion cul-
u es) wi h demanding a ibu es ( esul -o ien ed, o e wo ked, and job-insecu i y cul u es). This s udy p o-
poses h ee configu a ional p oposi ions o employe a ac i eness and employs e iew-based opic
modeling and uzzy-se quali y compa a i e analysis ( sQCA) o o e come he limi a ions o adi ional su -
ey-based measu emen and eg ession analysis. Fo he empi ical analysis, his s udy cons uc s an indus-
y-wide da ase comp ising 2209 qua e ly samples om 157 ho els o e six yea s, u ilizing 54,889
employee e iews pos ed on Glassdoo in he Uni ed S a es. Topic modeling analysis adop ing La en Di ich-
le Alloca ion ex ac s he p obabili ies o six cul u al a ibu es. Finally, he sQCA gene a es h ee g oups o
13 configu a ions, leading o employe a ac i eness: a ully esou ced cul u e, a esou ced and low-
demanding cul u e, and a ai ly compensa ed o e wo k cul u e. The findings confi m he co e concep s o
configu a ional analysis in con as o eg ession analysis and p esen no el heo e ical, me hodological, and
p ac ical implica ions.
© 2024 The Au ho s. Published by Else ie España, S.L.U. on behal o Jou nal o Inno a ion & Knowledge. This
is an open access a icle unde he CC BY-NC-ND license
(h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/)
Keywo ds:
O ganiza ional cul u e
Configu a ion
Employe a ac i eness
La en Di ichle Alloca ion
Fuzzy-se quali y compa a i e analysis
JEL classifica ion:
C55
C63
C81
D23
J28
In oduc ion
The success o o ganiza ions is hea ily dependen no only on p od-
uc and se ice ma ke pe o mance o win cus ome s bu also on he
compe i i eness o he labo ma ke o a ac compe en employees
(Geh els & de Looij, 2011;Theu e e al., 2018). Thus, enhancing
employe a ac i eness and imp o ing employe b anding a e essen ial
s a egic app oaches o enhance he loyal y o cu en employees and
a ac p ospec i e employees (Geh els & de Looij, 2011;Lee & Choi,
2022;S i as a a & Bha naga , 2010;Theu e e al., 2018). Employe
a ac i eness e e s o he benefi s and alues ha bo h cu en and
p ospec i e employees pe cei e in a specific o ganiza ion (Geh els & de
Looij, 2011;Lee & Choi, 2022;Theu e e al., 2018).
This s udy ocuses on o ganiza ional cul u e as a c i ical p edic o o
employe a ac i eness. O ganiza ional cul u e s udies ha e long in es-
iga ed he impac o cul u al a ibu es on in e nal o ganiza ional
membe s o explain o ganiza ional e ec i eness, pe o mance, and
compe i i eness (Hobe e al., 2021;Jiang e al., 2023;Na eed e al.,
2022;Quinn & Roh baugh, 1983;Schein, 1990;Schul e e al., 2009;
Velasco Vizcaíno e al., 2021). By explo ing a ela i ely unea hed heo-
e ical ela ionship, we p opose ha o ganiza ional cul u e can play a
c i ical ole in de eloping and main aining an o ganiza ion’sa ac i e-
ness as an employe in he ex e nal labo ma ke (Ca anza o e al.,
2010;Lee & Choi, 2022;Somme e al., 2017).
The ho el indus y aces emendous challenges owing o a sho -
age o skilled wo ke s and a high u no e a e. The e o e, acqui ing
alen and e aining ully mo i a ed employees a e impo an success
ac o s o ho els (Geh els & de Looij, 2011;Lee & Choi, 2022).
Acco ding o a su ey by he Bu eau o Labo S a is ics in he Uni ed
S a es (US), he u no e a e in he hospi ali y sec o inc eased sig-
nifican ly om 66.7 % in 2014 o 78.9 % in 2019, al hough he na ional
a e age u no e a e in 2019 was 36.4 % (Lee & Choi, 2022). A p ob-
lema ic aspec o labo ma ke compe i ion o ho el fi ms is ha i is
a duous o di e en ia e hemsel es om compe i o s as a ac i e
employe s because jobs wi hin he same ho el indus y a e la gely
* Co esponding au ho .
E-mail add esses: [email p o ec ed] (K.-J. Lee), [email p o ec ed]
(S.-Y. Choi).
h ps://doi.o g/10.1016/j.jik.2024.100582
2444-569X/© 2024 The Au ho s. Published by Else ie España, S.L.U. on behal o Jou nal o Inno a ion & Knowledge. This is an open access a icle unde he CC BY-NC-ND license
(h p://c ea i ecommons.o g/licenses/by-nc-nd/4.0/)
Jou nal o Inno a ion & Knowledge 9 (2024) 100582
Jou nal o Inno a ion
&Knowledge
h ps://www.jou nals.else ie .com/jou nal-o -inno a ion-and-knowledge
simila (Lee & Choi, 2022;Lie ens & Highhouse, 2003). Gi en hese
issues, unde s anding ho els’a ac i eness as employe s is c i ical
o building he mos qualified applican pool.
To elucida e ho els’employe a ac i eness, we in es iga ed
o ganiza ional cul u e’s ole in de e mining ec ui men pe o mance
and applican s’job accep ance. Beyond exis ing pe spec i es ha
highligh he impac o o ganiza ional cul u e on o ganiza ional
e ec i eness and pe o mance, ou s udy adds o he heo e ical
posi ion ha an a ac i e o ganiza ional cul u e can appeal o al-
en ed employees, encou age applican s o accep a job, and posi i ely
influence hei enu e once hi ed (Ca anza o e al., 2010;Lee & Choi,
2022;Somme e al., 2017). Fu he mo e, o in es iga e o ganiza-
ional cul u e, we adop ed a configu a ional analysis o cul u e o
ma e ialize he heo e ical po en ial o he holis ic cha ac e is ics o
o ganiza ional cul u e. The configu a ional analysis in his s udy
in ensifies ou unde s anding o he complex combina ional impac s
o di e ging cul u al a ibu es, o e coming he limi a ions o a a i-
able-cen e ed app oach using eg ession analysis o specific cul u al
a ibu es (Jung e al., 2009;Ma ino a e al., 2019;Misangyi e al.,
2017;Os o & Schul e, 2014;Schul e e al., 2009). This s udy
assumes complex combina ions o esou ce ul (collabo a i e,
employee de elopmen , and ai -compensa ion cul u e) and
demanding ( esul -o ien ed, o e wo k, and job-insecu i y cul u e)
a ibu es o o ganiza ional cul u e and p esen s h ee configu-
a ional p oposi ions o ho el employe a ac i eness (Bakke &
Deme ou i, 2007;Lee & Choi, 2023,2024).
In addi ion o he dis inc i e heo e ical pe spec i es, we adop ed a
no el combina ion o me hodological app oaches. We pe o med
e iew-based opic modeling and uzzy-se quali y compa a i e analysis
( sQCA) o o ganiza ional cul u e, o e coming he limi a ions o adi-
ional su ey-based measu emen and eg ession analyses in he exis -
ing li e a u e. In p e ious s udies on o ganiza ional cul u e, a a iable-
cen e ed app oach pe o med a eg ession analysis o specificcul u al
a ibu es using sel - epo ed employee su eys in a limi ed numbe o
o ganiza ions (Lee & Choi, 2022;Ma ino a e al., 2019;Os o &
Schul e, 2014). Howe e , he exis ing me hodological app oach has
nume ous es ic ions in ully ma e ializing he heo e ical po en ial o
holis ic o ganiza ional cul u es. Wi h i s an h opological oo s, he he-
o y o o ganiza ional cul u e has adop ed a holis ic pe spec i e om i s
ea ly s udies, sugges ing ha he combined pa e ns o cul u al a ib-
u es cause di e en ou comes depending on how hey a e a anged
(Fiss, 2007;Os o & Schul e, 2014).
To add ess hese limi a ions, we employed sQCA o he configu-
a ion analysis o o ganiza ional cul u e. sQCA, u ilizing se - heo e ic
analysis, enables esea che s o iden i y su ficien o necessa y condi-
ions o explain ou comes and de e mine he ela i e impo ance o
di e en combina ions o condi ions (Chen e al., 2023;Fiss, 2007;
Os o & Schul e, 2014;Pappas & Woodside, 2021). I p o ides a
mo e fine-g ained analysis o e alua ing he impo ance o cul u al
dimensions in a configu a ion by dis inguishing be ween co e and
pe iphe al dimensions (Fiss, 2007;Os o & Schul e, 2014;Pappas &
Woodside, 2021).
Fu he mo e, ou s udy used an indus y-wide employee e iew
o ho els pos ed on Glassdoo , one o he la ges job sea ch pla o ms
in he US (Co i o e e al., 2020;Lee & Choi, 2022;Sull, Tu coni, &
Sull, 2020), which o e came he limi a ions o indi idual sel - epo -
ing su eys in a small numbe o o ganiza ions (Denison e al., 2014).
We p epa ed a sample da ase consis ing o 54,889 employee e iews
om 157 ho el fi ms and agg ega ed pe sonal e iews o e alua e
co po a e-le el o ganiza ional cul u e wi h minimum pe sonal bias.
To measu e he di e se dimensions o o ganiza ional cul u e in
indus y-wide big da a, we adop ed La en Di ichle Alloca ion (LDA),
a opic modeling so wa e (Blei, 2012;Co i o e e al., 2020;Lee &
Choi, 2022;Maie e al., 2018), which calcula es he p obabili y o
opics o o ganiza ional cul u e. The heo e ical ele ance and alid-
i y o each opic we e es ima ed using manual coding.
Explo ing he ela i ely unea hed heo e ical ela ionship be ween
o ganiza ional cul u e and employe a ac i eness, his s udy d aws on
aconfigu a ional analysis o ac ualize he heo e ical po en ial o he
holis ic na u e o o ganiza ional cul u e. To achie e his goal, we
adop ed a no el esea ch me hodology o e iew-based opic modeling
and sQCA o he US ho el indus y. The analysis esul s no only
explo e new heo e ical po en ials o o ganiza ional cul u e bu also
p esen a compa a i e analysis be ween sQCA’sconfigu a ional analy-
sis o holis ic o ganiza ional cul u e and he eg ession analysis o indi-
idual a ibu es, gene a ing no el me hodological implica ions and
p ac ical insigh s o ho el employe s.
Li e a u e e iew
O ganiza ional cul u e e e s o he se o sha ed alues, belie s,
no ms, basic assump ions, and beha io s among o ganiza ional
membe s ha significan ly influence how employees in e ac wi h
each o he and ex e nal s akeholde s (Lee & Choi, 2022;2023,2024;
Na eed e al., 2022;Quinn & Roh baugh, 1983;Schein, 1990;Schul e
e al., 2009). Focusing on di e en influences o di e se cul u al
a ibu es, esea che s ha e long in es iga ed how o ganiza ional
cul u e a ec s c i ical o ganiza ional ou comes, such as o ganiza-
ional e ec i eness, pe o mance, and compe i i eness (Hobe e al.,
2021;Jiang e al., 2023;Lee & Choi, 2023,2024;Na eed e al., 2022;
Quinn & Roh baugh, 1983;Schein, 1990;Schul e e al., 2009;Velasco
Vizcaíno e al., 2021). Al hough exis ing li e a u e p ima ily ocuses
on he cul u al impac on in e nal membe s o explain c i ical o gani-
za ional ou comes, i is c ucial o ecognize ha dis inc i e cul u al
a ibu es can also send impo an signals o he ex e nal labo ma -
ke , a ec ing an o ganiza ion’s a ac i eness as an employe (Ca a-
nza o e al., 2010;Lee & Choi, 2022;Somme e al., 2017).
Recognizing his heo e ical po en ial o ex end o employe b and-
ing s a egy, Somme e al. (2017) conduc ed a scena io-based expe i-
men in Ge many. Thei findings e ealed ha an inno a i e cul u e
esona es mo e s ongly wi h inno a i e indi iduals. Ca anza o e al.
(2010) in es iga ed gende di e ences in he con ex o compe i i e
and suppo i e o ganiza ional cul u es ha a ec job applican s. Based
on he hypo he ical o ganiza ions depic ed in ec ui men b ochu es in
he US, mos esponden s we e ound o p e e wo king o a suppo -
i e o ganiza ion. Howe e , men we e mo e inclined o pu sue jobs in
compe i i e cul u es. Finally, Lee and Choi (2022) collec ed employee
e iew da a om ho el fi ms in he US, applied opic modeling analysis,
and conduc ed a eg ession analysis. They epo ed ha a se o posi i e
o ganiza ional cul u es, such as collabo a ion, employee de elopmen ,
ai -compensa ion, and cus ome ocus, posi i ely a ec employe
a ac i eness. Howe e , inno a ion cul u e has no significan e ec .
Despi e he esea ch e o s discussed abo e, s udies on he impac o
o ganiza ional cul u e on employe a ac i eness emain heo e ically
and empi ically limi ed.
O ganiza ional cul u e possesses holis ic and sys ema ic cha ac-
e is ics as one o he mos significan aspec s. O ganiza ional cul u e
combines di e en and some imes conflic ing a ibu es wi hin an
o ganiza ion, and each indi idual a ibu e should be ega ded as
pa o he en i e cul u e (Lee & Choi, 2022;Ma ino a e al., 2019;
Os o & Schul e, 2014). The holis ic pe spec i e emphasizes ha
each dimension o o ganiza ional cul u e should no be ea ed inde-
penden ly as hey in e ac in a complex manne o gene a e essen ial
ou comes (Os o & Schul e, 2014). Despi e he significan heo e ical
po en ial o he holis ic na u e o o ganiza ional cul u e, eg ession
analysis, which domina es exis ing s udies, has nume ous limi a ions
in cap u ing causal complexi y o o ganiza ional cul u e (Ma ino a e
al., 2019;Misangyi e al., 2017;Os o & Schul e, 2014;Schul e e al.,
2009). Gi en he holis ic and sys ema ic na u e o o ganiza ional cul-
u e, he configu a ional app oach used in ou s udy can be e fi he
analysis o o ganiza ional cul u e han eg ession models, e ec i ely
in ensi ying ou unde s anding o he ela ionship be ween he
K.-J. Lee and S.-Y. Choi Jou nal o Inno a ion & Knowledge 9 (2024) 100582
2
complex configu a ions o o ganiza ional cul u e and di e en ial
o ganiza ional ou comes (Ma ino a e al., 2019;Misangyi e al.,
2017;Os o & Schul e, 2014;Schul e e al., 2009).
Configu a ional p oposi ions o ho els’employe a ac i eness
Focusing on analyzing he complex combina ions and in e ac ions
o di e en cul u al a ibu es in gene a ing ou comes, configu-
a ional analysis emphasizes he equifinali y o a ying configu a-
ions, he syne gis ic e ec s o di e en a ibu es, and he
nonlinea i y o causa ion (Fiss, 2007;Misangyi e al., 2017;Os o &
Schul e, 2014). The equifinali y concep sugges s ha di e en config-
u a ions can gene a e he same final ou comes om di e en ini ial
condi ions and pa hs (Fiss, 2007;Misangyi e al., 2017;Os o &
Schul e, 2014). The equifinali y o o ganiza ional cul u e p esumes
ha o ganiza ions can de elop a ying configu a ions o o ganiza-
ional cul u e wi h di e en se s o in e nal a ibu es o ob ain he
same ou comes. Syne gis ic in e ac ions p esume complex in e ac-
ions be ween di e en a ibu es, sugges ing ha causally ele an
ou comes esul om he in e sec ions be ween di e en o ces and
e en s (Misangyi e al., 2017). The nonlinea i y o causa ion p esumes
ha he cul u al a ibu es ha a e causally associa ed in a configu a-
ion a e no ela ed o a e e en in e sely ela ed in di e en configu-
a ions (Fiss, 2007;Misangyi e al., 2017;Os o & Schul e, 2014).
Fu he mo e, his sugges s ha he absence o any a ibu e, as much
as i s p esence, can lead o an impo an ou come when combined
wi h o he a ibu es (Misangyi e al., 2017).
O ganiza ional cul u e encompasses a ious cul u al a ibu es
simul aneously, al hough hey o en conflic wi h one ano he o
mee complex en i onmen al needs. Wi hin a cul u al configu a ion,
di e en a ibu es in e ac in a complex manne o gene a e dis inc-
i e configu a ions ha a ec employe a ac i eness. An e ec i e
configu a ional analysis o o ganiza ional cul u e begins by ca ego iz-
ing dis inc i e configu a ions and iden i ying di e en cul u al a ib-
u es ha a e commonly ele an o he c i ical ou comes in ques ion
(Ma ino a e al., 2019;Os o & Schul e, 2014;Schul e e al., 2009).
As many ho els su e om pe sis en p oblems, such as sho age
o a skilled wo k o ce and high u no e a e, employe a ac i e-
ness is a c i ical success ac o o winning agains compe i o s
(Lie ens & Highhouse, 2003). Employe a ac i eness e e s o he
en isioned benefi s and alues ha cu en and p ospec i e employ-
ees gain when hey wo k o a specific o ganiza ion (Lie ens & High-
house, 2003). S ong employe a ac i eness enhances he
o ganiza ional commi men o cu en employees and e ec i ely
appeals o p ospec i e employees (Geh els & de Looij, 2011;Theu e
e al., 2018). As o ganiza ional cul u e a ec s he de elopmen o
employees’dedica ion and commi men o an o ganiza ion, a ying
configu a ions o o ganiza ional cul u e a e s ongly associa ed wi h
he ex en o which ho els a e a ac i e o employees and applican s
(S i as a a & Bha naga , 2010).
Resou ce ul and demanding a ibu e o o ganiza ional cul u e
O ganiza ional cul u e heo y posi s ha di e en and conflic ing
cul u al a ibu es coexis wi hin an o ganiza ion and ha hei com-
plex combina ions gi e ise o c i ical o ganiza ional ou comes (Ma i-
no a e al., 2019;Os o & Schul e, 2014). Conside ing he di e si y
o o ganiza ional cul u es and he causal complexi y o hei in e ac-
ions, a axonomy ha classifies hese a ibu es in o ca ego ies
ela ed o employe a ac i eness can significan ly educe he in e -
p e a i e complexi y o cul u al in e ac ions, he eby p omo ing a
mo e in ui i e unde s anding o hei dis inc i e impac s. Fu he -
mo e, a classifica ion ha includes bo h posi i e and nega i e cul-
u al a ibu es enables esea che s o o e come po en ial bias om
iewing only one side and adop a mo e holis ic and balanced
pe spec i e when analyzing he complex in e ac ions o con as ing
o ganiza ional cul u es.
Rega ding he axonomy o o ganiza ional cul u e, Lee and Choi
(2023,2024) p oposed a use ul amewo k ha dis inguishes
be ween he esou ce ul and demanding a ibu es o o ganiza ional
cul u e based on he job demands- esou ces (JD-R) model. The JD-R
model emphasizes he in e ac ions be ween he esou ce ul and
demanding aspec s o he job o p edic no only employees’well-
being and high pe o mance bu also hei job s ess and bu nou
(Bakke & Deme ou i, 2007;Jang e al., 2017;Lee & Choi, 2023,2024).
D awing on Lee and Choi’s (2023,2024) axonomy, his s udy classi-
fies cul u al a ibu es in o wo ca ego ies: esou ce ul and demand-
ing a ibu es o o ganiza ional cul u e. This s udy p oposes ha
esou ce ul cul u al a ibu es enhance employe a ac i eness,
whe eas demanding a ibu es nega i ely a ec i .
Resou ce ul a ibu es such as collabo a i e, employee de elop-
men , and ai -compensa ion cul u e a e s ongly associa ed wi h
employee sa is ac ion, well-being, and high pe o mance, signifi-
can ly imp o ing ho el employe a ac i eness (Lee & Choi, 2022,
2023,2024). Collabo a i e cul u e in o ganiza ions highligh s he
sha ing o a common ision and no ms among employees. I consid-
e s employees as people (Tjos old & Tsao, 1989), and p omo es
knowledge sha ing and lea ning (Yang, 2007). In an employee de el-
opmen cul u e, employees a e empowe ed o acqui e new knowl-
edge and skills o mee new job equi emen s (Hassan e al., 2006;
Ku aas & Dys ik, 2009). Fai -compensa ion cul u e in o ganiza ions
upholds he no ms o ai ness, no only in he p ocess o making deci-
sions, bu also in he esul s o dis ibu ion decisions, hus p omo ing
employees’con inuous e o s o imp o e pe o mances (Choi &
Chen, 2007;Edga e al., 2014;Namasi ayam e al., 2007).
In con as , demanding a ibu es such as esul -o ien ed, o e -
wo k, and job-insecu i y cul u es c ea e s ess ul and high-p essu e
wo king condi ions, he eby nega i ely a ec ing employe a ac i e-
ness. By ca e ully moni o ing employee e o s (Coge , 2010;Kohli e
al., 1998) and c ea ing s ong inwa d p essu e (Mi chell e al., 2019),
he esul -o ien ed cul u e s esses he achie emen o end esul s
h ough goal se ing. The o e wo k cul u e equi es employees o
dedica e ex a ime o hei wo k beyond he s anda d, ag eed-upon
hou s, which may escala e a ious wo k isks (Ishaq e al., 2021;Maz-
ze i e al., 2016). Unde he job-insecu i y cul u e, employees pe -
cei e powe lessness o main ain jobs and ha e widesp ead conce ns
abou job loss and ca ee de elopmen isks (La
s ad e al., 2015;
Nikolo a e al., 2018).
Configu a ional p oposi ion
The equifinali y concep o configu a ional analysis (Fiss, 2007;
Misangyi e al., 2017;Os o & Schul e, 2014) sugges s ha mul iple
cul u al configu a ions combining bo h esou ce ul and demanding
a ibu es may coexis o achie e he same o ganiza ional esul s o
high employe a ac i eness. As di e en se s o cul u al a ibu es
ha e syne ge ic in e ac ions (Fiss, 2007;Misangyi e al., 2017;Os o
& Schul e, 2014), di e en configu a ions o cul u al a ibu es may
lead o iden ical esul s in enhancing employe a ac i eness. Thus,
ho els may de elop di e en cul u al a ibu e configu a ions and
achie e he same ou comes o ensu e employe a ac i eness.
P oposi ion 1. The a ac i e configu a ion o ho els’o ganiza ional
cul u e has mul iple se s o esou ce ul and demanding a ibu es as di -
e en combina ions o he cul u al a ibu es can lead o employe
a ac i eness.
The JD-R model sugges s con as ing oles be ween esou ce ul
and demanding a ibu es in employee commi men and o ganiza-
ional sa is ac ion (Bakke & Deme ou i, 2007;Jang e al., 2017),
di ec ly a ec ing employe a ac i eness (Jang e al., 2017).
Resou ce ul a ibu es p o ide employees wi h social suppo ,
g ow h oppo uni ies, and ai ewa ds o ob ain high job sa is ac ion
K.-J. Lee and S.-Y. Choi Jou nal o Inno a ion & Knowledge 9 (2024) 100582
3
and pe o mance. Howe e , demanding a ibu es lead o bu nou
and u no e in en ion by inc easing pe o mance p essu e, wo k
o e load, and job s abili y conce ns. Thus, gi en he opposing e ec s,
a ac i e configu a ions o o ganiza ional cul u e a e likely o
de elop mo e esou ce ul han demanding a ibu es.
P oposi ion 2. The a ac i e configu a ion o ho els’o ganiza ional
cul u e encompasses mo e esou ce ul a ibu es han demanding a ib-
u es as he esou ce ul a ibu es posi i ely con ibu e o employe
a ac i eness, whe eas he demanding a ibu es nega i ely con ibu e
o he a ac i eness.
Acco ding o he compe ing alue amewo k (Ma ino a e al.,
2019;Quinn & Roh baugh, 1983), o ganiza ional cul u e configu a-
ions may encompass con as ing a ibu es ha wo k oge he o
gene a e meaning ul ou comes. Fu he mo e, configu a ional analy-
sis in ol es he syne gis ic e ec s o di e en a ibu es as well as
he nonlinea i y o causa ion (Fiss, 2007;Misangyi e al., 2017;Os -
o & Schul e, 2014). To be e ec i e, o ganiza ions mus be simul a-
neously conce ned wi h con as ing alues and no ms. Fu he mo e,
a g owing body o pa adox heo y sugges s ha esol ing pa adox
and wo king wi h con adic ions a e c i ical o o ganiza ional su -
i al and high pe o mance (Schad e al., 2016). To be compe i i e,
o ganiza ions should achie e mul iple goals while main aining high
cong uence be ween conflic ing cul u al no ms and alues. Thus, an
a ac i e configu a ion o ho els’o ganiza ional cul u e can ha e
bo h esou ce ul and demanding a ibu es ha gene a e employe
a ac i eness.
P oposi ion 3. The a ac i e configu a ions o ho els’o ganiza-
ional cul u e encompass conflic ing a ibu es simul aneously as he
cong uence be ween he esou ce ul a ibu es and demanding a ib-
u es can gene a e employe a ac i eness.
Me hodology
Dis inguishing i sel om exis ing su ey-based measu emen s and
eg ession analyses, his s udy p oposes a no el me hodological s a -
egy ha collec s employee e iew da a om Glassdoo and pe o ms
opic modeling o measu e o ganiza ional cul u e in e iew ex s
(Fig. 1). Topic modeling encompasses h ee phases: okeniza ion o ex ,
gene a ion o 100 LDA opics, and manual coding o opics. Based on
he measu emen da a, his s udy conduc ed bo h co ela ion and
eg ession analyses and configu a ion analysis using sQCA, leading o a
compa a i e discussion o he analysis esul s. The eg ession analysis
assumes ha he ela ionships a e symme ical and linea (Pappas &
Woodside, 2021). I ocuses on assessing he unique con ibu ion o a
cul u al a ibu e while main aining he influence o o he cul u al
dimensions (Pappas & Woodside, 2021). Wi h a symme ical assump-
ion, eg ession analysis aims o iden i y de e minan s ha explain high
le els o he ou come a iable, assuming ha he exac opposi e will
esul in low le els o he same ou come (Pappas & Woodside, 2021).
Consequen ly, eg ession analysis encoun e s di ficul ies in accoun ing
o po en ially complex in e ac ions among he di e en cul u al a ib-
u es ha ope a e as a sys em (Os o & Schul e, 2014;Pappas & Wood-
side, 2021). Conside ing he di e ences in me hodologies, we ini ially
pe o med a eg ession analysis and hen employed a configu a ional
analysis using sQCA, p o iding a compa a i e analysis be ween he
wo me hodological app oaches. The sQCA p ocess in ou s udy con-
sis s o h ee phases: calib a ion, necessi y and su ficiency analysis, and
es ing o he configu a ional p oposi ion. A compa a i e analysis o he
findings p o ided a c ucial unde s anding o he me hodological di e -
ences and hidden insigh s e ealed by he configu a ional analysis.
Collec ing employee e iew da a
To pe o m an empi ical analysis, we apped in o he Glassdoo
websi e in he US and collec ed e iews o ho el fi ms w i en by cu -
en and o me employees. Glassdoo , one o he la ges job sea ch
pla o ms in he US, has accumula ed 55 million e iews o 900,000
o ganiza ions since 2008 and eco ded 67 million unique mon hly
isi o s in 2020. A e au hen ica ing iden i y, Glassdoo enables
use s o e iew anonymously and sea ch o job pos e iews o gain
employmen de ails (Co i o e e al., 2020;Lee & Choi, 2022,2023,
2024). This s udy combines all ex s (bo h p os and cons) o iden i y
employees’di e se cul u al a ibu es. Many exis ing s udies o o ga-
niza ional cul u e ely on su eys o a ew indi iduals in a fi m, ais-
ing conce ns o pe sonal bias and limi ed alidi y (Denison e al.,
2014;Lee & Choi, 2022;2023;2024). Re iews on he Glassdoo
Fig. 1. Resea ch analysis map.
K.-J. Lee and S.-Y. Choi Jou nal o Inno a ion & Knowledge 9 (2024) 100582
4
pla o m enable employees o p o ide ee ex esponses abou a
fi m’s o ganiza ional cul u e and help esea che s gain a mo e com-
p ehensi e pic u e o a fi m’s o ganiza ional cul u e han su eys
(Sull, Tu coni, & Sull, 2020).
To sea ch o ho el fi ms on Glassdoo , we adop ed a lis o ho el
b ands and chains in he US om he Looking o Booking websi e
and used no only he di ec e ms such as “ho el(s),”“mo el(s),”
“lodge(ing),”bu also indi ec e ms such as “ eso (s),”“ aca ion(s),”
“casino(s),”“pa k(s),”and “hospi ali y” o he sea ch p ocess. Ini-
ially, 78, 563 employee e iews we e collec ed om 189 ho el fi ms,
wi h a minimum o 100 e iews published in No embe 2020. Con-
side ing ha his s udy assessed o ganiza ional cul u e a he fi m-
le el, i should ha e a su ficien numbe o employee e iews o e al-
ua e cul u al a ibu es app op ia ely (Co i o e e al., 2020;Lee &
Choi, 2022;2023;2024). The e o e, his s udy se s fi e e iews pe
qua e as he minimum equi emen o he analysis. Fu he mo e,
i es ic s e iews w i en be ween Janua y 2014 and Decembe
2019 o a oid unusual business dis u bances caused by COVID-19.
A e fil e ing and o ma ing he da a, we cons uc ed an indus y-
wide da ase encompassing 2209 qua e ly samples om 157 ho els
o e six yea s, including 54,889 employee e iews.
Measu emen by opic modeling analysis
To measu e o ganiza ional cul u e om e iew ex s, his s udy
adop ed he LDA opic modeling echnology and assessed he p oba-
bili y dis ibu ion o cul u al opics in qua e ly e iews. As an unsu-
pe ised machine-lea ning ool, LDA opic modeling enables use s o
iden i y he hidden s uc u e o documen s by gene a ing in e p e -
able opic dis ibu ions in he documen s (Blei, 2012;Co i o e e al.,
2020;Lee & Choi, 2022,2023,2024;Maie e al., 2018). In he opic
modeling p ocess, LDA assumes a “bag o wo ds” ha igno es wo d
o de s in ex ual documen s. LDA is designed o ha e a mixed-mem-
be ship model o g ouped da a, whe e each g oup p esen s mul iple
opics in di e en p opo ions. The opic modeling iden ifies dis inc-
i e opics ac oss he co pus in such a way ha i collec s wo ds ha
equen ly co-occu wi hin each employee e iew. Subsequen ly, i
gene a es a opic ma ix ha p esen s a p obabilis ic mix u e o
opics in each e iew by calcula ing he pe cen ages ac oss all he
opics (Co i o e e al., 2020;Lee & Choi, 2022;Maie e al., 2018).
To measu e ho el fi ms’o ganiza ional cul u e, we in eg a ed he
qua e ly Glassdoo e iews o a fi m and pe o med an LDA analysis
ha gene a ed he p obabili y o 100 cul u al opics by adop ing an
unsupe ised machine-lea ning p ocess. Specifically, we conduc ed a
h ee-s ep analysis (Lee & Choi, 2022;Maie e al., 2018). Fi s , we
pe o med basic and s anda d p ocedu es o da a cleaning and p e-
p ocessing o uns uc u ed ex . The cleaning p ocess conduc ed ex
okeniza ion, such as disca ding punc ua ions and wo d capi aliza-
ions, elimina ing s op wo ds, emo ing highly equen and in e-
quen wo ds, and pe o ming s emming and/o lemma izing (Lee &
Choi, 2022;Maie e al., 2018).
Second, 100 LDA opics we e gene a ed. Choosing he numbe o
LDA opics gene a ed by machine-lea ning ools is a highly compli-
ca ed ask, because he e is no s anda d p ocedu e o calcula ing he
app op ia e numbe o opics (Lee & Choi, 2022;Maie e al., 2018).
Use s can eely se he numbe o opics in he LDA ope a ion (e.g.,
30, 50, 100, and 500). A dilemma ha esea che s may ace is ha
gene a ing a small numbe o b oad opics c ea es he p oblem o
ob aining gene al opics con aining di e en hemes, whe eas gene -
a ing many opics leads o o e ly specific and na ow opics (Lee &
Choi, 2022;Maie e al., 2018). Ra he han unning he isk o miss-
ing c i ical cul u al opics, we gene a ed a ela i ely la ge numbe o
100 opics, which helped gain na owly defined opics o manual
iden ifica ion o di e se cul u al a ibu es.
Thi d, o iden i y cul u al a ibu es in he LDA opics, we pe -
o med a manual coding p ocess o es ima e he heo e ical ele ance
and alidi y o each opic (Table 1). Al hough unsupe ised machine
lea ning o LDA can gene a e mul iple opics, i canno o e he
meaning o he opics and, hus, necessi a es human in e p e a ion o
es ima e he cul u al ele ance o he opics. The meanings o opics
a e no de e minis ic and mus be e alua ed based on subs an i e
heo e ical cons uc s (Lee & Choi, 2022;Maie e al., 2018). Thus, we
adop ed he mos s aigh o wa d app oach in which esea che s
ead he e ms in he model esul s, in e p e ed he meaning o
opics, and ma ched hem o cul u al a ibu es (Co i o e e al.,
2020;Lee & Choi, 2022;2023;2024;Maie e al., 2018). Du ing he
ma ching p ocess, he h ee esea che s independen ly ead, in e -
p e ed, and selec ed opics ele an o he heo e ical cons uc s o
cul u al a ibu es. A e examining 100 opics wi h he 15 mos e-
quen e ms, one esea che p oposed 35 opics, ano he 39 opics,
and a hi d 44 opics.
To selec independen ly coded opics, we adop ed he decision
ule sugges ed by Co i o e e al. (2020) and selec ed opics o which
a leas wo esea che s independen ly ma ched he cul u al a ib-
u es. The selec ion p ocess pe o med by he h ee esea che s
esul ed in 39 opics. As Table 1 shows, he conflic ing a ibu es o
o ganiza ional cul u e include mul iple opics and key e ms. Rega d-
ing esou ce ul a ibu es, collabo a i e cul u e has 12 opics,
employee de elopmen has se en opics, and ai -compensa ion has
10 opics. Fo demanding a ibu es, esul s-o ien a ion, o e wo k,
and job insecu i y encompassed wo, fi e, and h ee opics, espec-
i ely. Finally, we added he qua e ly p obabili ies o opics o mea-
su e he six cul u al a ibu es a he fi m le el and analyzed he
a ac i e configu a ions o o ganiza ional cul u e.
This s udy’s ou come is he a ac i eness o ho els as employe s,
and i adop ed wo indica o s a ailable in Glassdoo e iews as meas-
u emen s (Fig. 1). Fi s , Glassdoo allows use s o a e “gene al
employe sa is ac ion”on a fi e-poin scale, which we adop ed as an
employe a ac i eness ou come. Second, he esponse o he ques-
ion, i he use “ ecommends he fi m o iends”was also used as an
ou come. We assigned 1 o a ecommenda ion, 0 o neu al o no
answe , and 1 o no ecommenda ion. Fo a fi m-le el e alua ion
o employe a ac i eness, we used he means o all qua e ly a ings
p o ided by he e iewe s.
Analysis esul s
Co ela ion and mul iple eg ession analysis
To es he p oposi ions, we adop ed a wo-s ep app oach, pe -
o ming a adi ional co ela ion and eg ession analysis be o e con-
figu a ional analysis using sQCA. Table 2 p esen s he desc ip i e
s a is ics and co ela ions. As p edic ed in he heo e ical discussion,
esou ce ul cul u al a ibu es, such as collabo a ion, employee
de elopmen , and ai -compensa ion cul u e, a e posi i ely and sig-
nifican ly co ela ed wi h employe a ac i eness ou comes, such as
employe sa is ac ion and ecommenda ion. The esul -o ien ed and
job-insecu i y cul u es wi h demanding cul u al a ibu es ha e a
nega i e and significan co ela ion wi h employe a ac i eness
ou comes. Howe e , con a y o heo e ical p edic ion, o e wo k cul-
u e had no significan co ela ion wi h a ac i eness ou comes, sug-
ges ing i s complex ela ionship wi h o he a ibu es in gene a ing
a ac i eness ou comes.
The eg ession model helps e alua e he influence o indi idual
cul u al a ibu es on employe a ac i eness. As some cul u al
a ibu es can be mo e essen ial o de e mining he o e all o ganiza-
ional cul u e and employe a ac i eness ou comes, he eg ession
model can help esea che s assess he ela i e impo ance o indi id-
ual a ibu es (Os o & Schul e, 2014). Fu he mo e, he p elimina y
eg ession analysis enables us o compa e be ween he adi ional
a iable-cen e ed analysis and configu a ional app oach, in ensi ying
K.-J. Lee and S.-Y. Choi Jou nal o Inno a ion & Knowledge 9 (2024) 100582
5
ou unde s anding abou he s eng hs in configu a ional analysis o
o ganiza ional cul u e.
The eg ession analysis esul in Table 3 demons a es ha all he
esou ce ul a ibu es, such as collabo a i e (A: b= 0.263, p<.001; B:
b= 0.200, p<.001), employee de elopmen (A: b= 0.217, p<.001;
B: b= 0.224, p<.001), and ai -compensa ion (A: b= 0.180, p<.001;
B: b= 0.158, p<.001), ha e posi i e and significan e ec on bo h
employe sa is ac ion and ecommenda ion, espec i ely, he eby
confi ming he heo e ical p edic ions. Addi ionally, he demanding
a ibu es, such as esul -o ien ed (A: b=.128, p<.001; B:
Table 1
Cons uc measu emen by opics gene a ed by LDA.
Cons uc Topic F equen e m
Collabo a i e cul u e 3 iendly, en i onmen , g ea , wo k, a mosphe e, help ul, e e yone, cowo ke s, eam, wo ke , un, some imes, pleasan , elaxed,
welcoming
5 wo k, lo e, g ea , amazing, people, eally, in e n, awesome, place, wish, enjoy, ca ing, ha d, eam, en i onmen
8 make, e e yone, help, eel, like, hing, way, e e y, willing, know, same, hink, some hing, jus , else
9 like, amily, eel, s ay, ea , pa , iend, always jus , well, numbe , come, defini ely, enjoy, wo k
14 wo k, en i onmen , place, g ea , eally, posi i e, un, eam, enjoy, ha d, p o essional, ough, exci ing, suppo i e, chance
16 good, wo k, nice, people, a , cowo ke , bi , place, building, e e y hing, load, a ea, defini ely, hea y, gene ally
22 wo k, g ea , place, good, home, excellen , colleague, well, sma , hink, social, clea , condi ion, igh , add
33 wo k, people, eally, nice, ge , ha d, lo , ime, supe , cool, easy, un, e e yone, ex emely, awesome
47 s a , iendly, managemen , sho , always, wo k, some imes, busy, good, shi , colleague, hou , en i onmen , finish, ai mon
51 eam, membe , alue, sha e, amily, oge he , need, suppo i e, op, co e, uly, well, pa , wonde ul, posi i e
53 en i onmen , wo k, un, g ea , good, as , iendly, paced, challenge, suppo i e, colleague, o e all. beau i ul, p o essional, win
64 wo k, eally, lo e, people, back, enjoy, ace, help, e e yone, laid, e e yday, in e ac , able, communi y, app ecia e
Employee de elopmen cul u e 13 oppo uni y, oom, ad ancemen , g ow h, benefi , plen y, small, li le, o e , compensa ion, p o, compe i i e, posi ion, dis-
coun ed, ull ime
23 company, oppo uni y, mo e, g ow, g ow h, lo , willing, ad ance, ci y, ca ee , o e , g ea , quickly, limi , limi ed
24 oppo uni y, ca ee , g ow h, g ow, de elopmen , g ea , cul u e, lea n, challenge, company, excellen , p og ession, leade ship,
pa h, compensa ion
41 aining, p og am, oppo uni y, de elopmen , excellen , p ope y, g ea , g ow h, p omo ion, company, p ocess, p omo e, ca ee ,
whi e, engage
45 mo e, many, oppo uni y, posi ion, depa men , di e en , ans e , a ound, loca ion, lo , di ficul , ha d, ad ance, a ailable, en y
61 expe ience, lea n, lo , skill, di e en , gain, knowledge, di ficul , g ea , in e ac ion, aining, exposu e, a ie y, new, ype
76 expec a ion, managemen , suppo , aining, high, well, se , goal, expec , due, li le, need, p essu e, issue, un ealis ic
Fai -compensa ion cul u e 11 g ea , benefi , sala y, a el, compe i i e, cul u e, amazing, package, company, ule, s ic , awesome, minimal, co po a e,
ad ancemen
18 ho el, discoun , employee, oom, a e, benefi , a el, pe k, ca e e ia, meal, ad ance, eno a e, loyal y, paid, educe
20 g ea , benefi , pe k, awesome, loca ion, a mosphe e, some imes, a y, s ic , empowe , di e se, s a ed, h oughou , s a ing,
spli
32 hou , good, pay, flexible, wo k, schedule, scheduling, flexibili y, o e ime, managemen , li le, enough, manage , boss,
inconsis en
34 company, g ea , benefi , ewa d, well, an as ic, amazing, o e , pe k, excellen , ecognize, ision, especially, ecogni ion, ho els
42 good, benefi , pay, union, ai , sala y, plus, employee, app ecia ion, p o ide, non, eamwo k, communi y, lowe , quali y
44 good, wo k, sala y, en i onmen , p o essional, aining, s anda d, expe ience, benefi , pe sonal, acili y, g ea , less, wo ldwide,
lea ning
52 g ea , benefi , lo , good, a mosphe e, en i onmen , un, beau i ul, cul u e, upwa d, mobili y, mo e, mo emen , po en ial,
consis en
67 pay, job, good, people, easy, decen , enough, suck, ge , nice, some imes, low, slow, amoun , wo k
96 benefi , heal h, insu ance, 401k, discoun , medical, plan, aca ion, o e , den al, pay, ma ch, include, a el, e c
Resul -o ien ed cul u e 80 sale, bonus, sell, make, base, p oduc , incen i e, pe o mance, high, e iew, po en ial, ma ke , money, goal, lea e
87 money, make, cu , business, ip, s a , spend, need, se e , sa e, es au an , enough, cos , keep, budge
O e wo k cul u e 12 hou , long, wo k, g ea , pay, flexible, oo , demand, simple, unp edic able, hai , emo ional, anspo , excessi e, log
17 shi , nigh , hou , la e, o , wo k, ea ly, ime, schedule, ee, holiday, day, eques , weekend, co e
21 day, week, e e y, yea , pe , mon h, hou , wo, o , ge , wo k, e en, h ee, ew, aise
55 holiday, day, o , hou , weekend, season, ime, long, schedule, expec , week, e e y, open, busy, ou
79 pay, wage, low, minimum, hou ly, hou , o e ime, inc ease, less, ip, li ing, ai , ba ely, a e, uni o m
Job-insecu i y cul u e 31 people, ge , bad, jus , know, e e , hi e, lea e, fi e, igh , keep, job, way, happen, look
35 job, wo k, di ficul , secu i y, keep, ha d, esponsibili y, si ua ion, able, deal, always, someone, help, find, come
71 high, u no e , a e, pay, employee, low, managemen , ex emely, s ess, posi ion, p essu e, co-wo ke , amoun , decen ,
ch is mas
No e:(Lee & Choi, 2022).
Table 2
Desc ip i e s a is ics and co ela ions (N= 2209).
Means S.D. 1 2 3 4 5 6 7
Collabo a i e .117 .029
Employee de elopmen .061 .021 0.056
**
Fai -compensa ion .078 .023 .003 .174
**
Resul -o ien ed .017 .010 0.129
**
0.052* 0.069
**
O e wo k .052 .019 0.027 0.236
**
0.078
**
0.069
**
Job-insecu i y .031 .014 0.142
**
0.130
**
0.131
**
.068
**
0.050*
Employe sa is ac ion 3.371 .584 .276
**
.242
**
.234
**
0.192
**
0.032 0.165
**
Employe ecommenda ion .166 .348 .213
**
.249
**
.213
**
0.177
**
0.030 0.158
**
.829
**
No e. * and ** deno e significance a he 5 % and 1 % le els, espec i ely.
K.-J. Lee and S.-Y. Choi Jou nal o Inno a ion & Knowledge 9 (2024) 100582
6
b=.122, p<.001) and job-insecu i y cul u e (A: b=.066, p<.05;
B: b=.070, p<.05) ha e a nega i e and significan e ec on
employe sa is ac ion and ecommenda ion, espec i ely. Howe e ,
con a y o ou heo e ical p edic ion, an o e wo k cul u e has no
nega i e impac on a ac i eness ou comes.
Configu a ional analysis by sQCA
Fo a configu a ional analysis o ho els’o ganiza ional cul u e, we
adop ed sQCA using he sQCA 3.0 so wa e. The sQCA app oach
examines he ela ionship be ween all possible combina ions o p e-
dic o s and he ou come o in e es , gene a ing a ious combina ions
o condi ions ha lead o he absence o ou comes, posi i e ou -
comes, o nega i e ou comes (Fiss, 2007;Pappas & Woodside, 2021;
Ragin, 2009). Gi en ou esea ch goal o unde s anding he cul u al
configu a ion ha gene a es employe a ac i eness, we ocused on
iden i ying configu a ions leading o he p esence o ou comes, spe-
cifically employe sa is ac ion and ecommenda ion.
Fi s , we ini ially pe o med calib a ion, which was aimed a allo-
ca ing he se membe ships o each case unde causal condi ions,
such as six cul u al a ibu es and wo employe a ac i eness a ia-
bles. In sQCA, he calib a ion p ocess assigns collec i e membe ship
o cases using ei he di ec o indi ec me hods. In di ec calib a ion,
esea che s se exac ly h ee quali a i e b eakpoin s indica ing ull-
se membe ship, ull-se non-membe ship, and in e media e-se
membe ship in he uzzy se o each case. By con as , in indi ec cal-
ib a ion, he measu emen is escaled based on quali a i e e alua-
ions, and esea che s may choose o calib a e a measu e di e en ly
based on hei in es iga ion (Fiss, 2007;Ragin, 2009). The selec ion
o ei he me hod depends on esea che s’subs an i e knowledge o
he da a and he unde lying heo y (Fiss, 2007;Ragin, 2009). Consid-
e ing ha he p ocedu e is clea o e ifica ion and eplica ion by
o he s, esea che s ha e disc e ion in de e mining he p ocedu e o
assigning uzzy alues o cases and adop ing h eshold alues.
Because he da a in his s udy do no comple ely ollow a no mal
dis ibu ion and exhibi skewness, we se he ull membe ship poin s
o he six an eceden s and wo ou come a iables as he uppe qua -
ile (75 %) o he case da a, he in e sec ion poin s as he median
(50 %) o he case da a, and he comple e non-membe ship poin s as
he lowe qua ile (10 %). Table 4 lis s he ancho poin calib a ions.
Mo eo e , in he sQCA, cases wi h an in e media e se membe ship
o exac ly 0.5 a e ypically d opped. To add ess his issue, i is ecom-
mended o add a cons an o 0.001 o he causal and ou come condi-
ions below a ull membe ship sco e o 1 (Fiss, 2007;Ragin, 2009).
Thus, we added 0.001 o all p edic o and ou come condi ions a e
calib a ion was conduc ed.
Second, based on he calib a ion, we pe o med bo h necessi y
and su ficiency analysis. On he one hand, be o e building a u h
able, we examined whe he any o he causal condi ions could be
conside ed necessa y. The sQCA ool enables esea che s o de ec
necessa y condi ions ha help acili a e he occu ence o an e en .
The consis ency h eshold o he necessi y analysis is se a 0.9.
Table 5 displays he esul s o he necessi y analysis. No necessa y
consis ency o he an eceden cul u al condi ions on employe sa is-
ac ion and ecommenda ion is abo e 0.9, indica ing ha no single
cul u al a ibu e is necessa y o a ac i eness ou comes. In o he
wo ds, a single cul u al a ibu e has insu ficien explana o y powe
o employe a ac i eness. The e o e, his esul led us o u he
analyze he combined e ec o an eceden condi ions.
On he o he hand, a e confi ming ha no single cul u al a i-
bu e cons i u es a necessa y condi ion o employe a ac i eness,
we analyzed he condi ional combina ion o he six cul u al dimen-
sions o ob ain employe a ac i eness. To de e mine he app op i-
a e causal combina ions gene a ing he ou come, he numbe o ows
in he u h able should be educed. We adop ed wo educ ion c i-
e ia. Fi s , conside ing he la ge sample size o ou s udy, he mini-
mum numbe o cases o conside a combina ion ele an was se a
12. Second, consis ency c i e ia indica ed he ex en o which a com-
bina ion o causal condi ions is consis en in ela ion o he ou come.
Table 3
Resul s o eg ession analysis (N= 2209).
Model A Employe sa is ac ion Model B Employe ecommenda ion
Pa h coe ficien T-s a is ic (P- alue) Pa h coe ficien T-s a is ic (P- alue)
Collabo a i e .263*** 13.478 (0.000) .200*** 10.042 (0.000)
Employee de elopmen .217*** 10.755 (0.000) .224*** 10.891 (0.000)
Fai -compensa ion .180*** 9.240 (0.000) .158*** 7.924 (0.000)
Resul -o ien ed 0.128*** 6.598 (0.000) 0.122*** 6.133 (0.000)
O e wo k .029 1.446 (0.148) .029 1.438 (0.150)
Job-insecu i y 0.066* 3.347 (0.001) 0.070* 3.480 (0.001)
F- alue 92.621*** 71.979***
R
2
.199 .162
No e. *, **, and *** deno e significance a he 5 %, 1 % and 0.1 % le els, espec i ely.
Table 4
Calib a ion pe cen ile and s a is ics.
Pe cen ile
.75 .5 .1
Employe sa is ac ion 3.7857 3.4286 2.5714
Employe ecommenda ion .4 .2001 - 0.3333
Collabo a i e .1338 .1151 .1338
Employee de elopmen .0739 .06 .0739
Fai -compensa ion .0922 .0776 .0922
Resul -o ien ed .0224 .0153 .0224
O e wo k .0638 .0502 .0638
Job-insecu i y .0395 .0303 .0395
Table 5
Necessi y analysis.
Employe sa is ac ion Employe ecommenda ion
Consis ency Co e age Consis ency Co e age
Collabo a i e .6711 .6817 .6544 .6743
»Collabo a i e .4656 .5514 .4801 .5768
Employee de elopmen .6871 .6940 .6889 .7059
»Employee
de elopmen
.4605 .5489 .4572 .5529
Fai -compensa ion .6807 .6937 .6675 .6900
»Fai -compensa ion .4576 .5398 .4674 .5595
Resul -o ien a ion .5605 .5742 .5647 .5868
»Resul -o ien a ion .5719 .6707 .5677 .6754
O e wo k .6031 .6142 .5969 .6167
»O e wo k .5303 .6261 .5355 .6414
Job-insecu i y .5735 .5785 .5767 .5901
»Job-insecu i y .5597 .6682 .5548 .6719
K.-J. Lee and S.-Y. Choi Jou nal o Inno a ion & Knowledge 9 (2024) 100582
7
As he consis ency h eshold should no be less han 0.75 (Pappas &
Woodside, 2021;Ragin, 2009), we se he consis ency h eshold a
0.89. A e elimina ing he combina ions ha do no mee he abo e
wo condi ions om he u h able, Figs. 2 and 3p esen he esul s
o sQCA o employe sa is ac ion and ecommenda ion, espec-
i ely.
The ou pu o he u h able ypically p esen s h ee ypes o sol-
u ions: complex, in e media e, and pa simonious. To gene a e a alid
u h able, esea che s ypically ocus on bo h in e media e and pa -
simonious solu ions and iden i y bo h co e and pe iphe al condi ions
(Fiss, 2007;Pappas & Woodside, 2021;Ragin, 2009). A complex solu-
ion gene a es all possible combina ions o a ibu es, making in e -
p e ing he solu ions di ficul . The pa simonious solu ion only
p esen s he co e condi ions, which a e essen ial condi ions ha can-
no be excluded om any solu ion. The in e media e solu ion
p esen s bo h he co e and pe iphe al condi ions by pe o ming a
coun e ac ual analysis o he complex and pa simonious solu ions.
Many esea che s ha e p esen ed final solu ions by compa ing and
elabo a ing on pa simonious and in e media e solu ions (Pappas &
Woodside, 2021).
Howe e , in ou analysis, all h ee solu ions (complex, in e medi-
a e, and pa simonious) yielded iden ical solu ions wi hou dis in-
guishing be ween he co e and pe iphe al condi ions. These iden ical
solu ions may esul om he la ge da ase used in his s udy (Reich-
e e al., 2016). P e ious sQCA s udies using la ge sample sizes ha e
demons a ed his endency. Fo example, Yang’s (2018) esea ch
wi h a sample o 2163 adul s, Tho and T ang’s (2015) wo k wi h a
sample o 843 s uden s, and Reiche e al.’s (2016) analysis o 614
low- ech fi ms yielded iden ical solu ions o all h ee ypes. Thus,
we in e p e ed he solu ion esul s di ec ly, wi hou elabo a ing on
possible scena ios, by compa ing he co e and pe iphe al condi ions.
Each column in Figs. 2 and 3demons a es a combina ion o con-
di ions o configu a ion o cul u al a ibu es associa ed wi h
employe a ac i eness, such as employe sa is ac ion and ecom-
menda ion. The black ci cle in he figu e indica es he p esence o a
condi ion ha leads o an ou come o in e es ; he c ossed-ou ci cle
deno es he absence o a condi ion ha causes an ou come; and he
blank spaces ep esen a “don’ ca e”si ua ion, sugges ing ha nei-
he he p esence no absence o he condi ion is ela ed o he ou -
come. A he bo om, he figu es show he consis ency, unique
Fig. 2. sQCA analysis o employe sa is ac ion (N= 2209).
K.-J. Lee and S.-Y. Choi Jou nal o Inno a ion & Knowledge 9 (2024) 100582
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