Tomczyk, P zemyslaw; B üggemann, Philipp; Paul, Jus in
A icle — Published Ve sion
Va iable science mapping as li e a u e e iew me hod
Jou nal o Ma ke ing Analy ics
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Sugges ed Ci a ion: Tomczyk, P zemyslaw; B üggemann, Philipp; Paul, Jus in (2024) : Va iable science
mapping as li e a u e e iew me hod, Jou nal o Ma ke ing Analy ics, ISSN 2050-3326, Palg a e
Macmillan, London, Vol. 12, Iss. 4, pp. 829-841,
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ORIGINAL ARTICLE
Va iable science mapping asli e a u e e iew me hod
P zemyslawTomczyk1· PhilippB üggemann2 · Jus inPaul3
Re ised: 9 Ap il 2024 / Accep ed: 19 June 2024 / Published online: 2 July 2024
© The Au ho (s) 2024
Abs ac
This s udy in es iga es a no el mapping app oach o he sys ema ic analysis o empi ical esea ch, e med Va iable Science
Mapping (VSM). This app oach enhances he cu en capabili ies o Sys ema ic Li e a u e Re iews (SLRs) by inco po a ing
a iables and hei in e ela ionships, su passing adi ional me hods, such as Science Mapping (SM), which p ima ily analyze
keywo ds, ci a ions, and au ho ship. We p esen a s ep-by-s ep concep ual p o ocol o implemen ing he VSM app oach.
Subsequen ly, he s eng hs and limi a ions o VSM compa ed o SM a e examined ac oss 12 SLR s ages. To his end, we assess
he ac ual usage o SM o each s age based on an analysis o 63 pape s employing he SM app oach. Addi ionally, expe
in e iews a e conduc ed o e alua e he u ili y o bo h SM and VSM ac oss iden ical analy ical s ages. No ably, a dis inc
alignmen eme ged be ween he ou comes o he SLR and expe assessmen s pe aining o SM. The indings e eal VSM’s
a o able a ings in eigh ou o 12 s ages. Equi alence in expe a ings be ween SM and VSM su aced in one s age, while
SM was deemed mo e bene icial in h ee s ages. This nuanced e alua ion unde sco es he con ex ual s eng hs and limi a-
ions o bo h app oaches. The implica ions ex end o bo h scien i ic and manage ial domains, o e ing aluable insigh s in o
he p ospec i e ad ancemen s in SLRs. In conclusion, his analysis no only sheds ligh on he po en ial ad an ages o VSM
bu also se es as a ounda ion o guiding u u e esea ch me hodologies o widen capabili ies among di e en SLR s ages.
Keywo ds Li e a u e analysis· Empi ical mapping· Sys ema ic li e a u e e iew· Science mapping· Va iable science
mapping
In oduc ion
Sys ema ic li e a u e e iew (SLR) is a esea ch me hod ha
enables he iden i ica ion, selec ion, c i ical e alua ion, and
syn hesis o exis ing li e a u e in a igo ous, anspa en , and
epea able manne , leading o obus conclusions abou wha
is known and wha is no known in pee - e iewed esea ch
a eas (Ch is o i e al. 2021). Acco ding o Scopus, in 2022,
667 scien i ic a icles in he ield o managemen , accoun -
ing, and inance we e published, in which a SLR was he
basic one. Compa ed o 487 in 2021 and 379 in 2021, an
upwa d end close o exponen ial can be seen.
The p ocedu e o SLRs can be complex. The use o his
me hod equi es a e iew ques ion (Ch is o i e al. 2021;
Leonidou e al. 2018; Mcquade e al. 2021; V on is and
Ch is o i 2019), da a collec ion (Ch is o i e al. 2021;
Mcquade e al. 2021), inclusion o exclusion c i e ia, selec-
ion o ele an s udies, inal da abase p epa a ion (Leonidou
e al. 2018; V on is and Ch is o i 2019), bibliome ic analy-
sis (Mcquade e al. 2021; Siemieniako e al. 2022; V on is
and Ch is o i 2019), esea ch esul s p esen a ion in he o m
o hema ic analysis (Leonidou e al. 2018; Mcquade e al.
2021; Siemieniako e al. 2022; V on is and Ch is o i 2019)
o syn hesis (Ch is o i e al. 2021; Leonidou e al. 2018;
V on is and Ch is o i 2019), con ibu ion p esen a ion
(Ch is o i e al. 2021), and de eloping a sec ion on u u e
esea ch agenda (Paul and Menzies 2023; Ch is o i e al.
2021). One equen ly employed echnique in SLRs is Sci-
ence Mapping (SM).
SM is used in bibliome ic analysis, unde s ood as a pa
o a SLR o a sepa a e analysis o achie e isual da a p es-
en a ion (Chen 2017; Gho bani e al. 2021; ElKa an e al.
2023). The cu en app oach ha domina es oday consis s o
mapping a eas, keywo ds, e ms, au ho s, o ci a ions (León-
Cas o e al. 2021). Nume ous di e en so wa e solu ions
* Philipp B üggemann
philipp.b ueggemann@ e nuni-hagen.de
1 Depa men o Ma ke ing, Kozminski Uni e si y,
Jagiellonska S . 59, Wa saw, Poland
2 Uni e si y o Hagen, Hagen, Ge many
3 Plaza Uni e si a ia, Río Pied as, SanJuan, Pue oRico, USA
830 P.Tomczyk e al.
exis o he gene a ion o Science Maps (e.g., VOS iewe ,
Ci eSpace, o SciMAT). In ecen publica ions, SM has been
used o pe o m ex ensi e li e a u e e iews, especially in
a eas wi h a ela i ely high numbe o publica ions, such as
in eg a ed ma ke ing communica ion (Ch is ian e al. 2021;
Wu e al. 2022) o digi al ma ke ing (Aksoy e al. 2021;
León-Cas o e al. 2021; Gao e al. 2021). Despi e he dis-
inc ad an ages o e ed by SM, such as he comp ehensi e
p esen a ion o he esea ch ield om mul iple pe spec i es
and i s ease o use, his app oach is no wi hou limi a ions.
While his app oach has s eng hs in analyzing pape s on a
desc ip i e basis, e.g., by conside ing keywo ds and ci a-
ions, i migh be impossible o in es iga e key a iables,
key heo ies, o an eceden s and consequences o he s udied
li e a u e. While his app oach is limi ed o summa izing
me ada a, he deg ee o in o ma ion ex ac ion and he close-
ness o an app op ia e pic u e o he unde lying li e a u e a e
se e ely limi ed (Tomczyk 2022). Fu he mo e, he e has
been li le esea ch on he bounda ies o he SM app oach,
especially as limi a ions in esea ch pape s (e.g., Ma ic
e al. 2021; Zupic and Ča e 2015).
While he e is me i in using he app oach o SM, i is
cons ained o he desc ip i e con en analysis phase (Naja
e al. 2022). As such, his app oach can ha dly p o ide sup-
po a he i s s age o he li e a u e e iew, i.e., o mula ing
a esea ch p oblem. The e o e, esea che s ha e c i icized
bibliome ic e iews based on SM (Paul e al. 2021; Paul
e al. 2023) because he heo e ical con ibu ions can be
limi ed.
In gene al, he de elopmen o a domain a ea is based on
he de elopmen o knowledge abou a iables as an exp es-
sion o social phenomena. P og ess is made by c ea ing new
a iables, in es iga ing new ela ionships, and analyzing
hese connec ions in di e en con ex s. Su p isingly, he e
is no app oach o sys ema ically in es iga e such e ol ing
a iables and ela ionships.
Ano he widely ecognized and commonly employed
me hod in SLRs is Me a-analysis (B üggemann and Rajgu u
2022). This echnique is pa icula ly e ec i e o examining
selec ed ela ionships ac oss mul iple s udies (Glass 1976)
using specialized so wa e. Despi e i s me i and ele ance,
Me a-analysis is cons ained o o e ing an agg ega ed o e -
iew o selec ed ela ionships.
Di e ences wi hin a gi en body o esea ch, which
encompass posi i e, nega i e, and non-signi ican esul s
ac oss se e al pape s, canno be ully add essed by SM o
Me a-analysis. To add ess his gap, we p opose a no el
app oach e med Va iable Science Mapping (VSM). This
echnique aims o conduc a comp ehensi e syn hesis o
ela ionships be ween a iables in empi ical esea ch. Addi-
ionally, i p o ides a g aphical isualiza ion encompassing
all a iables and hei in e ela ionships. Consequen ly, his
new app oach acili a es he p esen a ion o he cu en s a e
o knowledge o any selec ed a iable wi hin any domain
o he social sciences. This mapping echnique is expec ed
o be aluable bo h in o mula ing esea ch p oblems and in
analyzing con en o add ess hese p oblems.
In his s udy, ou objec i e is no o de e mine whe he
VSM is supe io o exis ing me hods bu o explo e po en ial
enhancemen s in SLRs h ough he applica ion o VSM. To
asce ain he app op ia e con ex s o using SM and VSM in
SLRs, we i s iden i y 12 ele an s ages in SLRs based on
he exis ing li e a u e. We hen conduc a SLR o 63 op- ie
a icles, examining he u iliza ion o SM ac oss hese p ede-
ined 12 s ages. Subsequen ly, h ough a se ies o in-dep h
in e iews wi h expe ienced esea che s in managemen , we
in es iga e whe e and how VSM and SM can be bene icial
in SLRs.
Syn hesizing knowledge inSLRs
SM andMe a‑analysis
To syn hesize knowledge om scien i ic a icles, esea che s
can u ilize SM and Me a-analysis me hodologies. SM usually
employs ci a ion, co-ci a ion, and co-occu ence analyses
o depic he knowledge bases wi hin a ield (Sanguankaew
and Rac ham 2019). Using his app oach, esea che s can
unde s and he ela ionships be ween scien i ic wo ks and
he e olu ion o knowledge wi hin a domain. SM ools, such
as bibliome ic so wa e, help isualize knowledge domains,
li e a u e, and ci a ion ne wo ks (Chen 2017). These ools
acili a e he iden i ica ion o ends, in luen ial wo ks, and
eme ging opics wi hin a ield, aiding in he in e p e a ion o
ex ensi e schola ly in o ma ion, as well as mapping esea ch
landscapes and isualizing connec ions be ween s udies
(Cobo e al. 2011).
Me a-analysis, on he o he hand, p o ides a quan i a-
i e syn hesis o coe icien s om mul iple s udies, allow-
ing esea che s o d aw conclusions by analyzing empi ical
indings om a ious sou ces (Ha los e al. 2016). This
s a is ical me hod is widely used ac oss disciplines o
agg ega e and analyze esul s epo ed in di e en s udies
(Huang and Hu 2017). Th ough Me a-analysis, esea ch-
e s can calcula e e ec sizes and assess ends o pa e ns
p esen in he li e a u e (Suciana and Sausan 2023). Me a-
analysis o e s a sys ema ic app oach o agg ega ing and
analyzing da a om mul iple s udies o d aw o e a ch-
ing conclusions o iden i y pa e ns ac oss esea ch ind-
ings (Huang and Hu 2017). By applying me a-analy ical
echniques, esea che s can quan i a i ely assess he con-
sis ency and magni ude o e ec s obse ed in di e en
s udies, p o iding a comp ehensi e unde s anding o a
esea ch ques ion (Dolapçıoğlu and Subaşi 2022). Me a-
analysis is aluable in syn hesizing e idence om di e se
831Va iable science mapping asli e a u e e iew me hod
sou ces and esol ing disc epancies p esen in indi idual
s udies (Anjani 2023). By inco po a ing Me a-analysis,
esea che s can quan i a i ely analyze and syn hesize da a
om iden i ied s udies, o e ing a igo ous and e idence-
based app oach o knowledge syn hesis (Huang and Hu
2017).
The in eg a ion o SM ools and Me a-analysis ech-
niques enhances he unde s anding o complex esea ch
landscapes, p o iding a obus amewo k o syn hesiz-
ing knowledge om scien i ic a icles. Howe e , hese
app oaches also ha e ce ain limi a ions. SM canno iden-
i y a iables, ela ionships, o esea ch models ac oss
di e en pape s; i can only compa e elemen s such as
keywo ds and au ho ship be ween pape s in a map. While
Me a-analysis ocuses on empi ical s udies and can ana-
lyze ela ionships, i does so only om an agg ega ed pe -
spec i e ac oss all examined pape s, p o iding insigh in o
speci ic ela ionships. Me a-analysis canno c ea e a depic-
ion o he esea ch implemen ed in he pape s, no can i
analyze con adic o y esul s, such as hose wi h posi i e
o nega i e coe icien s, o u he esea ch. To add ess
hese limi a ions o cu en app oaches, we in oduce VSM
in he nex sec ion.
The concep o VSM
This sec ion in oduces he VSM echnique o ex end he
capabili y o SLRs. The a ionale behind his app oach is
no o supplan SM o Me a-analysis bu o enhance SLRs
o empi ical pape s. We con end ha his app oach is pa -
icula ly aluable in esea ch a eas wi h nume ous empi ical
s udies on simila opics. While SM can iden i y keywo ds,
co-au ho ship, and o he ele an elemen s o a speci ic
a iable, VSM aims o map all a iables ac oss he pape s
unde in es iga ion. This comp ehensi e mapping can o e
esea che s a mo e holis ic o e iew o a p ede ined esea ch
ield.
Tailo ed o sys ema ic analysis o empi ical esea ch,
VSM also complemen s he well-es ablished me hod o
Me a-analysis by cap u ing a iables and ela ionships,
examining commonali ies and dispa i ies ac oss di e se
pape s. This app oach, e.g., un eils po en ial esea ch gaps
o inconsis en indings. Figu e1 delinea es he p ocedu al
s eps in ol ed in employing VSM. We de ail hese s eps
below ollowing om a VSM p o ocol we sugges o ollow
when using VSM. Figu e1 p esen s a concep ual p o ocol
o applying he VSM app oach. This p o ocol can be u ilized
Fig. 1 Concep ual p o ocol o
applying he VSM app oach
832 P.Tomczyk e al.
by esea che s in u u e o implemen he VSM app oach
s ep by s ep, he eby ensu ing an adequa e execu ion o his
me hodology.
Be o e inc emen ally implemen ing VSM, i is impe a i e
o p ecisely delinea e he esea ch domain unde in es iga-
ion in s ep 1. This ini ial s ep holds signi ican impo ance
as i p o oundly in luences all subsequen p ocedu es. Op ing
o a b oad esea ch scope (e.g., echnology accep ance in
elec onic comme ce) will esul in a co espondingly ex en-
si e and in ica e Va iable Science Map. While his b ead h
can o e a comp ehensi e o e iew o en i e esea ch
s eams, i also en ails inc eased e o and complexi y in
analysis. Fo eme ging and na owe esea ch domains, VSM
can se e o gene a e an o e iew o pas esea ch endea o s
and ex ac po en ial u u e esea ch di ec ions.
Once he esea ch ield unde sc u iny has been iden i ied,
he subsequen s ep 2 in ol es seeking pe inen li e a u e.
This phase should adhe e o a s uc u ed app oach akin o a
SLR me hod, as ad oca ed by V on is and Ch is o i (2019)
and ou lined by Paul and C iado (2020).
Upon comple ion o he p epa a o y s ages, he appli-
ca ion o VSM ensues in s ep 3. This in ol es he ini ial
iden i ica ion o a iables wi hin each o he a icles unde
in es iga ion. These a iables migh inhe en ly ep esen
an eceden s, consequences, media o s, o mode a o s. I is
impe a i e o no e ha a a iable could se e di e se oles
ac oss mul iple a icles.
Following he iden i ica ion o a iables wi hin each o
he examined pape s, s ep 4 in ol es ex ac ing he neces-
sa y in o ma ion o he Va iable Science Map. This encom-
passes he iden i ied a iables (an eceden s, consequences,
media ion, o mode a ion), he analyzed di ec ions o ela-
ionships and de ails ega ding he signi icance o empi ical
indings. To acili a e in o ma ion ex ac ion, i migh be
bene icial o conside he igu es o o mulas po aying he
empi ical analyses wi hin he pape s, p o ided such in o ma-
ion is a ailable.
In s ep 5, all a iables a e inco po a ed in o a model o
depic he disco e ed ela ionships. This p ocess esul s in
a comp ehensi e esea ch model based on he p e iously
examined a icles. Figu e1 illus a es, on he igh side,
an exempla y analysis o i e a icles. These i e a icles
encompass i e dis inc esea ch models, which a e sub-
sequen ly in eg a ed in o an agg ega ed esea ch model.
Following he c ea ion o his agg ega ed model, de ails
ega ding signi icance and ela ionship di ec ion (± o non-
signi ican ) can be documen ed.
In he subsequen s ep 6, he augmen a ion o e alu-
a ion me ics becomes pi o al o a mo e comp ehensi e
con ex ualiza ion o he analysis ou comes. These me ics
encompass di e se dimensions, including he numbe o
included pape s, a iables, iden i ied ela ionships, he heo-
e ical maximum o po en ial ela ionships, and ins ances o
con lic ing esul s. Le e aging hese desc ip i e speci ics,
a ious me ics can be calcula ed, such as he a io o ela-
ionships de i ed om a icles o he heo e ical maximum,
he p opo ion o con lic ing esul s, o he p e alence o
non-signi ican indings. Figu e3 se es as an illus a i e
ins ance, p o iding a po ayal o a Va iable Science Map
coupled wi h e alua ion me ics.
In s ep 7, p esen ing he esea ch esul s is ecommended,
in ol ing he display o bo h he Va iable Science Map and
accompanying me ics. This inclusi e app oach o e s a
comp ehensi e iew o empi ical esea ch wi hin a speci ic
ield o s udy. Simul aneous conside a ion o he Va iable
Science Map and me ics aids in con eying and assessing
he analysis’s quali y and ex en .
Mo ing o s ep 8, he in e p e a ion o indings elies on
he Va iable Science Map and e alua ion me ics. By con-
solida ing mul iple empi ical wo ks, his app oach enables
esul e i ica ion, iden i ica ion o con adic o y indings,
and he e ela ion o u he esea ch needs. While de i ing
in ica e empi ical models is plausible, i is i al o s ess
cau ion in u ilizing he esul ing esea ch model as a em-
pla e o subsequen empi ical esea ch. The agg ega ion o
mul iple models may in oduce s a is ical conce ns, such as
mul icollinea i y o endogenei y. In he nex sec ion, we will
in es iga e he use ulness o SM and VSM ela ed o 12 SLR
s ages. We ha e delibe a ely chosen SM as he compa a i e
me hod because i is also a mapping app oach, making i
pa icula ly sui able o compa ison wi h VSM. I is impo -
an o no e ha VSM is no in ended o eplace SM, bu
a he o complemen his es ablished mapping app oach.
Analyzing SM andVSM alongSLR s ages
De i a ion o SLR s ages
In his in es iga ion, ou ocus cen e s on a SLR ha inco -
po a es he SM echnique. Ou p ima y aim is o con as he
app oach ou lined in his a icle wi h he es ablished me hod-
ologies p e alen in he scien i ic communi y. Employing he
SLR me hod ecommended by expe s (V on is and Ch is -
o i 2019; Paul and C iado 2020), we ini ia ed ou explo a-
ion using Scopus, he la ges eposi o y o pee - e iewed
academic publica ions, employing widely accep ed sea ch
algo i hms (Glińska and Siemieniako 2018). Ou sea ch c i-
e ia comp ised a speci ic s ing o h ee keywo ds ela ed o
SM along wi h he names o he wo mos widely so wa e:
VOS iewe and Ci espace. Wi hin he domains o manage-
men , inance, and accoun ing, ou sea ch yielded 590 occu -
ences in VOS iewe and 163 in Ci espace om Scopus
using i le–abs ac –keywo ds c i e ia. Subsequen ly, ano he
SM applica ion, Biblioshiny p esen ed only 66 occu ences.
The sea ch s ing u ilized is p o ided below o e e ence.
833Va iable science mapping asli e a u e e iew me hod
TITLE-ABS-KEY(”science mapping” OR “bibliome -
ic mapping” OR “ a iable mapping” OR “ os iewe ”
OR “ci espace”) AND (LIMIT-TO(SRCTYPE, “j”))
AND (LIMIT-TO(DOCTYPE, “a ”)) AND (LIMIT-
TO(LANGUAGE, “English”)) AND (LIMIT-TO(SUB-
JAREA, “BUSI”)).
On Decembe 27, 2022, we execu ed he s ing sea ch,
ini ially cas ing a wide ne ac oss he en i e da abase.
Gi en he ex ensi e yield o se e al housand esul s,
we e ined ou sea ch pa ame e s o encompass only he
ca ego ies o managemen , inance, and accoun ing. This
ocused app oach yielded 532 a icles. Subsequen analysis
o he abs ac s led o he e en ion o 443 a icles. Among
hese, 12 lacked accessible PDFs, esul ing in a inal coun
o 431 a icles o quali a i e analysis. Applying he Aca-
demic Jou nal Guide (AJG) c i e ion, we sie ed h ough
hese a icles, ul ima ely selec ing hose alling wi hin he
3, 4, and 4* ca ego ies. This me iculous selec ion p ocess
culmina ed in he inal sample comp ising 63 a icles o
high quali y.
We analyze each o he 63 a icles, ga he ing gene al
in o ma ion, such as so wa e ype, indus y, mapping
ype, and esea ch ield. Fu he mo e, each a icle unde -
wen an examina ion based on a SLR app oach consis ing
o 12 dis inc s ages de i ed om V on is and Ch is o i
(Ch is o i e al. 2021; V on is and Ch is o i 2019). Wi hin
hese s ages, spanning om e iew ques ion iden i ica-
ion o ecommenda ions iden i ica ion, we sys ema ically
checked whe he he ou comes om SM we e u ilized. The
12 s ages in ol ed in his assessmen encompass:
1. Re iew ques ion iden i ica ion,
2. Da a collec ion analysis,
3. Bibliome ic analysis,
4. Key a iable analysis,
5. Key heo ies analysis,
6. Thema ic clus e ing,
7. An eceden s’ iden i ica ion,
8. Consequences iden i ica ion,
9. Resea ch gap iden i ica ion,
10. T end iden i ica ion,
11. Conclusions,
12. Recommenda ions iden i ica ion.
In he subsequen sec ion, in addi ion o p esen ing he
ou comes o he SLR, we in oduce indings de i ed om
in-dep h in e iews. These in e iews aimed o asce ain
expe s’ pe spec i es on he use ulness o bo h SM and
VSM conce ning he 12 s ages in eg al o SLRs, as p e i-
ously delinea ed. Following his, we consolida e he SLR
esul s ega ding he use o SM in scien i ic esea ch wi h
he expe s’ e alua ions and discuss he indings.
In‑dep h expe in e iews
In he subsequen phase o ou esea ch, we conduc ed a
se ies o nine in-dep h in e iews, adhe ing o he K ale
and B inkmann (2018) me hodology. Ou pa icipan s
comp ised p o essionally ac i e esea che s in manage-
men , holding a PhD deg ee, and hailing om Poland and
Ge many. The expe s we e selec ed om ou p o essional
ne wo k. We delibe a ely chose expe s who a e no amil-
ia wi h he opic being analyzed h ough SM and VSM
me hodologies o a oid po en ial bias a ising om p io
engagemen wi h he subjec ma e .
Fo he in e iews, we cu a ed a se o i e a icles
wi hin he domain o cus ome ideas: Ba asa e al., (2021),
Bu nham e al. (2020), Casaló and Rome o (2019), Chan
e al. (2015), Chan e al. (2021). This selec ion was delib-
e a e, as hese a icles we e deemed su icien o e eal dis-
ce nible di e ences be ween he me hods unde sc u iny.
We easoned ha i dispa i ies we e clea ly disce nible
wi hin his limi ed se , hei p esence would be e en mo e
p onounced in a b oade selec ion. Mo eo e , he ield o
cus ome ideas wi hin managemen was chosen o i s el-
e ance and un amilia i y o he pa icipan s, mi iga ing
any o eknowledge bias. We gene a ed wo dis inc maps:
a con en ional map using VOS iewe (aligned wi h he
SM app oach) and a a iable map manually c a ed (by
he VSM app oach). To c ea e he Va iable Science Map,
we ollowed s eps 3 o 6 as ou lined in Fig.1. Figu es2
and 3 wi hin his con ex depic he isual ep esen a ions
p esen ed o he in e iewed expe s.
The expe in e iews ollowed a consis en p o ocol o
a oid bias a ising om di e en p ocedu es. Ini ially, he
expe s we e shown he con en ional Science Map, which
was b ie ly explained. Subsequen ly, o each o he 12 SLR
s ages, he expe s we e asked o assess whe he SM could
be e ec i ely u ilized a ha s age. A e wa d, he Va iable
Science Map was p esen ed and b ie ly explained, and he
same ques ions we e posed. To acili a e comp ehension du -
ing he in e iews, succinc explana ions we e p o ided o
bo h maps.
Figu e2 ep esen s a isualiza ion o keywo d occu -
ences occu ing a leas wice, gene a ed h ough he
VOS iewe so wa e. The isualiza ion displays wo dis inc
clus e s, indica ed by ed and g een colo s. Speci ically, he
h ee keywo ds highligh ed in ed (i.e., idea gene a ion, pe -
cep ions, wo d o mou h) we e equen ly in e linked wi hin
he analysis o he i e a icles. Simila ly, he h ee keywo ds
highligh ed in g een (i.e., pa icipa ion, cus ome eedback,
inno a ion) exhibi ed ecu en co-occu ences wi hin his
se o a icles.
Mo eo e , Fig.2 demons a es ins ances whe e a icles
u ilized keywo ds om bo h clus e s (e.g., pa icipa ion
and idea gene a ion). No ably, wi hin he i e a icles unde
834 P.Tomczyk e al.
analysis, he e was no ins ance o an a icle using bo h inno-
a ion and pe cep ion as keywo ds.
Figu e3 p esen s a manually c ea ed Va iable Science
Map de eloped by he au ho s in acco dance wi h he con-
cep ual p o ocol o applying he VSM app oach, as ou lined
in Fig.1. This map in eg a es in o ma ion de i ed om he
same se o i e a icles u ilized in he ea lie SM app oach
u ilizing VOS iewe .
Figu e3 includes a iable names, illus a ing all in es-
iga ed ela ionships, and enume a es he numbe o posi-
i e, nega i e, and non-signi ican empi ical indings o
each ela ionship wi hin he subse o i e pape s u ilized in
ou SLR. Fo ins ance, examining he ela ionship be ween
Bene i s (SYNT) and Cus ome idea ion, ou analysis
e ealed one posi i e, one nega i e, and wo no signi ican
ou comes among he i e a icles sc u inized. This inding
unde sco es he ambigui y inhe en in he cu en empi ical
esul s unco e ed by he VSM app oach. Fu he mo e, wi hin
his limi ed subse , only one o he i e a icles explo ed
he associa ion be ween Cus ome idea ion and Inno a ion,
de ec ing a signi ican posi i e ela ionship. Howe e , he
VSM app oach iden i ies po en ial a enues o u he in es-
iga ion o alida e hese ou comes. I is impo an o no e
ha hese conclusions can only be en a i ely d awn when
comp ehensi e li e a u e pe inen o he esea ch ques ion
is conside ed. In ou ins ance, he analysis was limi ed o a
Fig. 2 Con en ional Science
Map made wi h VOS iewe
(acco ding o he SM app oach)
idea gene a ion
pe cep ions
wo d-o -mou h
pa icipa ion
c
c
u
u
s
s
o
o
m
m
e
e
e
e
e
e
d
d
b
b
a
a
c
c
k
k
inno a ion
Fig. 3 Va iable Science Map made manually by he au ho s (acco ding o he VSM app oach)
835Va iable science mapping asli e a u e e iew me hod
small se o i e a icles. None heless, his illus a i e exam-
ple unde sco es he signi ican u ili y o he VSM app oach
in acili a ing mul i ace ed insigh s.
Figu e3 is accompanied by desc ip i e in o ma ion and
p ecision me ics. I is impo an o no e ha he esea ch
models unde sc u iny ha e no ye unde gone e alua ion
by expe judges. Howe e , hei p esen a ion he e se es
as an exempla o he six h poin wi hin he VSM p o ocol,
as ou lined in Fig.1. Exac ly as wi h he p esen a ion o he
con en ional Science Map, e alua ion o hese consolida ed
esea ch models was conduc ed by expe s. This e alua ion
elied on a succinc desc ip ion o he Va iable Science Map
p o ided by he in e iewe s.
D awing inspi a ion om he applica ion o goodness-o -
i me ics in s a is ical me hodologies, such as eg ession
o s uc u al equa ion models, we p opose me hodologies
aimed a o e ing a comp ehensi e e alua ion o in o ma-
ion gene a ion and he p ecision inhe en in VSM ou comes.
Ini ially, we p esen he coun o a icles (5) and a iables
(5) iden i ied in ou analysis. Following his, we enume -
a e he ela ionships e ealed h ough he VSM app oach
(7). Subsequen ly, u ilizing his da a, we calcula e he heo-
e ical maximum numbe o ela ionships a ainable wi hin
his analy ical amewo k (5 × 7 = 35). Howe e , i is c ucial
o no e ha his heo e ical maximum is seldom achie ed
in VSM analyses due o con inual e inemen s in esea ch
models and ela ionships, o en aimed a gene a ing no el
insigh s in esponse o e ol ing ci cums ances. Despi e his,
using he Theo e ical Maximum o Rela ionships (35), we
de i e he Model Accu acy (MA) as a a io o obse ed
ela ionships o he heo e ical maximum (7/35 = 20%). This
me ic unc ions as an indica o o he comp ehensi eness
conce ning he analysis o ela ionships wi hin he pape s
unde conside a ion.
An addi ional i al me ic pe ains o con lic ing esul s,
whe e he occu ence o bo h posi i e and nega i e signi i-
can ou comes in a ela ionship deno es a con lic ing esul .
F om hese ins ances, we compu e he Con lic Ra e (CR) o
gauge he p e alence o con lic ing esul s ela i e o all ela-
ionships iden i ied using he VSM app oach. Fo ins ance,
wi hin ou analysis (Fig.3), wi h 1 con lic ing esul among
7 ela ionships, he indica i e CR s ands a 28.57% (2/7).
This me ic aids in iden i ying ambiguous o con adic o y
indings, po en ially unco e ing lacunae in exis ing esea ch
and illumina ing unexplo ed esea ch a enues.
Fu he aiding ou e alua ion is he Non-Signi icance
Ra e (NSR), calcula ed by de e mining he a io o non-sig-
ni ican esul s (3) o all iden i ied ela ionships (7), esul ing
in an NSR o 42.86% (3/7). This illus a es ha 42.86% o
he ela ionships s udied in he i e a icles lacked s a is ical
signi icance.
These me ics o e aluable insigh s o esea che s
and p ac i ione s, enabling he assessmen o esul alue,
iden i ica ion o dispa i ies among a icles, and he iden-
i ica ion o esea ch gaps. While long- e m u iliza ion
necessi a es de ining h esholds akin o quali y measu es in
eg essions o s uc u al equa ion models, immedia e es ab-
lishmen o subs an ial h esholds o alua ion emains elu-
si e due o limi ed p ac ical expe ience. Rigo ous es ing
and compa ison o nume ous VSM esul s a e impe a i e o
es ablishing c edible h esholds.
Resul s
In Fig.4, he empo al dis ibu ion o he 63 a icles u i-
lized o he SLR is depic ed. No ably, all hese a icles u i-
lize he SM app oach, as VSM does no cu en ly appea in
he p e ailing li e a u e. Un il 2017, he u iliza ion o he
SM app oach was spa se, e iden only in isola ed a icles.
Howe e , a no ewo hy su ge in publica ions commenced in
2018, wi h a subs an ial spike obse ed in 2021. Rema k-
ably, he dynamics illus a e a signi ican and consis en
inc ease in he numbe o high-quali y a icles employing
he SM app oach, ex ending un il he conclusion o 2022.
Despi e ela i ely modes absolu e igu es, he disce nible
end in Fig.4 unde sco es he g owing popula i y o he
SM app oach as a p e e ed esea ch me hodology wi hin
op-quali y a icles.
Following he acknowledgmen o he escala ing impo -
ance o he VSM app oach, ou ocus now shi s o p esen -
ing, compa ing, and in e p e ing he ou comes de i ed om
dis inc in es iga ions—speci ically, he indings ob ained
om ou SLR and he in-dep h expe in e iews pe aining
o SM and VSM. Summa izing hese esul s, Fig.5 p esen s
alues ans o med in o pe cen ages. Fo ins ance, he uppe -
mos black ba depic ed in Fig.5 indica es an 83% alue,
signi ying ha wi hin he 63 a icles, he u iliza ion o SM
o hema ic clus e ing amoun ed o 52 ins ances.
Obse ing he black ba s wi hin Fig.5 e eals ha in
six ou o he 12 s ages analyzed, he u iliza ion o he SM
app oach exceeded 20% in ou li e a u e e iew. No ably,
esea che s p edominan ly employed SM (83% occu -
ence) o hema ic clus e ing. Fu he mo e, we iden i ied
wo s ages whe e a me e 3% o he a icles (i.e., 2 ou o
63 a icles) inco po a ed SM, while SM was absen in ou
s ages. The absence o SM usage in iden i ying key a i-
ables, an eceden s, and consequences can be a ibu ed o a
undamen al limi a ion: Science Maps do no acili a e hei
iden i ica ion. Ope a ing p ima ily on keywo ds ha do no
always ep esen a iables, hese maps lack he capaci y o
deciphe and epo he s eng hs o ela ionships among
a iables. This limi a ion s ands as one subs an ial cons ain
wi hin he SM echnique.
In e es ingly, ou in es iga ion indica e a dispa i y
be ween he pe cep ions o expe s we in e iewed and he
836 P.Tomczyk e al.
Fig. 4 SM usage end (quali y c i e ia: minimum AJG 3; n = 63)
Fig. 5 P esen a ion o he esul s om h ee di e en in es iga ions on SM and VSM
