Saeedikiya, Meh zad; Salunke, Sandeep; Kowalkiewicz, Ma ek
A icle
The nexus o digi al ans o ma ion and inno a ion: A
mul ile el amewo k and esea ch agenda
Jou nal o Inno a ion & Knowledge (JIK)
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The nexus o digi al ans o ma ion and inno a ion: A mul ile el
amewo k and esea ch agenda
Meh zad Saeedikiya
a,*
, Sandeep Salunke
b
, Ma ek Kowalkiewicz
a
a
Queensland Uni e si y o Technology, School o Managemen , Cen e o Fu u e En e p ise, 02 Geo ge S ee , B isbane Ci y, 4000, Queensland, Aus alia
b
Queensland Uni e si y o Technology, School o Managemen , 02 Geo ge S ee , B isbane Ci y, 4000, Queensland, Aus alia
ARTICLE INFO
JEL classi ica ion:
M10
M15
O31
O32
O33
L86
Keywo ds:
Digi al ans o ma ion
Digi al echnology
Inno a ion
Business model inno a ion
P oduc inno a ion
P ocess inno a ion
Pe o mance
DT
ABSTRACT
This s udy add esses he agmen ed unde s anding o he ela ionship be ween digi al ans o ma ion (DT) and
inno a ion by p oposing a mul i-le el amewo k ha in eg a es di e se disciplina y pe spec i es. This ame-
wo k p o ides deep insigh in o how DT in luences inno a ion. A sys ema ic li e a u e e iew was conduc ed,
and based on he indings, a esea ch agenda was ou lined as a oadmap o guide u u e s udies. This esea ch
con ibu es o he s a egic change and inno a ion li e a u e by p o iding a mul i-le el amewo k explaining
how DT-d i en s uc u al change a ec s inno a ion, conside ing a ious con ingencies such as ma ke dynamics,
echnological ad ancemen s, and o ganiza ional capaci ies. Addi ionally, his s udy con ibu es o he inno a ion
and s a egic in o ma ion sys ems domain by demons a ing ha DT’s ole as a s a egic asse o achie ing
inno a ion should align wi h i m capabili ies, s uc u al cha ac e is ics, and en i onmen al and ex e nal
dynamics.
In oduc ion
Digi al ans o ma ion (DT) has been de ined as a “ undamen al
change p ocess enabled by digi al echnologies ha aim o b ing adical
imp o emen and inno a ion o an en i y (o ganiza ion, business
ne wo k, indus y, o socie y) o c ea e alue o i s s akeholde s by
s a egically le e aging i s key esou ces and capabili ies” (Gong &
Ribie e, 2021, p. 10). Recen ly, DT ini ia i es ha e become a signi ican
ocus o companies’ in es men s (Appio e al., 2021; Calde on-Monge &
Ribei o-So iano, 2023) and ha e made a subs an ial impac on he
g ow h o na ional economies (Taylo , 2022). In 2018, digi ally ans-
o med companies con ibu ed app oxima ely 13.5 illion U.S. dolla s
o he global GDP (Calde on-Monge & Ribei o-So iano, 2023). In 2022,
he adop ion o digi al ans o ma ion in i ms g ew. In he same yea ,
he numbe o o ganiza ions in ending o implemen da a analysis o
analy ics p og ams inc eased signi ican ly compa ed wi h he p e ious
yea , and 30% o he o ganiza ions planned DT in es men s (Taylo ,
2022). Mo ing o wa d, DT speed con inued o inc ease. Expendi u es
on DT a e p ojec ed o each 2.15 illion U.S. dolla s by 2023 (She i
e al., 2024). By 2025, a new miles one will be achie ed in DT’s sha e o
digi aliza ion p ojec s. Pla o m-d i en in e ac ions accoun o
app oxima ely wo hi ds o he 100 illion U.S. dolla s ma ke . In he
same yea , app oxima ely 90% o he new en e p ise applica ions a e
p edic ed o in eg a e a i icial in elligence (AI) in o hei p ocesses and
p oduc o e ings (Appio e al., 2021). Looking ahead o 2027, global
spending on DT is expec ed o ise o 3.9 illion U.S. dolla s (Else sy
e al., 2021). This subs an ial in es men highligh s o ganiza ions’
inc easing awa eness o and eliance on DT o inno a e and g ow.
Di e en ac o s ha e a ec ed his accele a ed pace and inc eased
commi men o DT ini ia i es. The mos signi ican issues a e en i on-
men al conce ns (Minami e al., 2021; Wang & Su, 2021), e iciency and
p oduc i i y issues (Mülle e al., 2018,; 2019; Si a ajah e al., 2020),
and COVID and global heal h p oblems (Reuschl e al., 2022; Wade &
Shan, 2020; Li e al., 2022b). Whe he pushed by ex e nal shocks o
compe i i e p essu es o by en i onmen al o e iciency desi es, such
pe asi e DT has signi ican ly a ec ed o ganiza ional ou ines,
* Co esponding au ho .
E-mail add esses: [email p o ec ed] (M. Saeedikiya), [email p o ec ed] (S. Salunke), [email p o ec ed] (M. Kowalkiewicz).
Con en s lis s a ailable a ScienceDi ec
Jou nal o Inno a ion & Knowledge
jou nal homepage: www.else ie .com/loca e/jik
h ps://doi.o g/10.1016/j.jik.2024.100640
Recei ed 17 July 2024; Accep ed 5 Decembe 2024
Jou nal o Inno a ion & Knowledge 10 (2025) 100640
A ailable online 12 Decembe 2024
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 license (
h p://c ea i ecommons.o g/licenses/by/4.0/ ).
s uc u es, p ocesses, p ac ices, and ou comes. These changes ha e
conside ably a ec ed inno a ion and ela ed p ocesses, ede ining how
companies enew hei business models o in oduce p oduc and p o-
cess inno a ion (B esciani e al., 2021).
Along wi h hese dis up ions o he indus ial landscape, schola ly
esea ch has enhanced ou unde s anding o he ela ionship be ween
DT and inno a ion. Sca e ed ac oss a ious domains and disciplines,
li e a u e p o ides signi ican insigh s in o his in e play. Howe e , his
app oach is agmen ed and piecemeal. Fo ins ance, om a s a egic
iewpoin , ealizing inno a ion ou comes s emming om DT is impe -
a i e, conside ing o ganiza ional, echnological, and ne wo k capabil-
i ies (e.g., He e al., 2023; Yang & Du, 2023; Yao e al., 2023) and i s ole
in d i ing compe i i e ad an age o i ms (e.g., Fe ei a e al., 2019; Y.
Li e al., 2022; Y. Zhang e al., 2023). Using a se ice o ma ke ing lens,
DT’s ole in cus ome engagemen in inno a ion and i s con ibu ion o
alue co-c ea ion ha e gained schola ly a en ion (e.g., Hunke e al.,
2022; Kamalaldin e al., 2020; Rohn e al., 2021). O he s udies elying
on in o ma ion sys ems app oaches ha e ocused on how eme ging
digi al echnologies (such as AI) a ec companies’ inno a ion decisions
and ou comes (e.g., Sun e al., 2024) o how he pe asi e applica ion o
digi al echnology o i s ea u es can a ec inno a ion ne wo ks (e.g.,
Tang e al., 2023; Xing e al., 2023) o imp o e alue c ea ion and
cap u e in egional inno a ion ecosys ems (Wang & He, 2024; Yang &
Deng, 2023).
In addi ion o he agmen a ion o DT–inno a ion esea ch in
di e en domains (Appio e al., 2021), exis ing esea ch ends o adop
di e en le els o analysis (Nambisan e al., 2019) and a ies in e ms o
i s heo e ical ocus, concep ualiza ion o DT (Vial, 2019), and e alua-
ion o he bounda y condi ions a ec ing he bene i o DT o inno a-
ion. Mos s udies end o accep DT’s posi i e e ec on pe o mance as
gi en (X. C. Guo e al., 2023), discoun ing he s uc u al and ins i u-
ional cha ac e is ics and con ingencies ha a ec he scope and
magni ude o his e ec . The e o e, he implica ions o DT in inno a ion
a e ye o be es ablished (Nambisan e al., 2017).
To b idge his gap, his s udy in es iga es he inno a ion alue o DT
h ough a sys ema ic li e a u e e iew (SLR). This me hod is well-sui ed
o he agmen ed na u e o exis ing esea ch (T an ield e al., 2003). I
syn hesizes p e ious s udies and s eng hens he knowledge base, while
ensu ing anspa ency and educing bias (Williams J e al., 2021).
Ou s udy con ibu es o he s a egy domain by o e ing a mul i-
le el analy ical amewo k illus a ing how s uc u al changes h ough
DT in luence a i m’s inno a ion and pe o mance. I u he highligh s
inno a ion as a mul i ace ed, mul i-le el p ocess ha occu s as i ms
implemen digi al s uc u al changes alongside o he ac o s ope a ing
a he manage ial, i m, indus y, and b oade le els. Addi ionally, i
ex ends he discussion o in o ma ion sys ems (IS) on digi al a o dances
and hei ole in inno a ion (Nambisan e al., 2017, 2019) o s a egy
and inno a ion disciplines, examining DT as an inno a ion s a egy ha
d i es compe i i e ad an age (e.g., Appio e al., 2021).
Resea ch me hods
Design
Following Denye and T an ield (2009), Denye e al. (2008), and
T an ield e al. (2003), his s udy used an SLR app oach o s eng hen
me hodological igo (Tho pe e al., 2005). SLRs ha e gained popula i y
in managemen and business li e a u e owing o hei high p ocedu al
analy ical objec i i y and anspa ency (Hallinge , 2013). Unlike adi-
ional e iews, SLRs enhance igo , alidi y, and gene alizabili y
(Denye & T an ield, 2009). They syn hesized p io esea ch o ein o ce
he knowledge base o a speci ic subjec while main aining he p inciples
o openness and minimizing bias (Williams J e al., 2021).
Mo eo e , eliable knowledge o he esea ch opic can be gained
h ough well-de ined sys ema ic p ac ices and anspa en ly ep oduc-
ible p ocedu es (T an ield e al., 2003). The e o e, we conduc ed an SLR
o syn hesize agmen ed esea ch on he e ec s o DT on inno a ion
ac oss di e en disciplines. We adop ed he P e e ed Repo ing I ems
o Sys ema ic Re iews and Me a-Analyses (PRISMA) amewo k
(Mohe e al., 2016) o ensu e anspa ency and ep oducibili y, wi h
each s ep clea ly de ined.
P ocedu e
Following A i e al. (2021), T an ield e al. (2003), and Bilbao-U-
billos e al. (2024), a li e a u e e iew was conduc ed in h ee phases:
planning and implemen a ion, analysis and syn hesis, and epo ing.
Phase 1.Planning and implemen a ion
A planning p o ocol was designed o guide he o e all s udy. I
o mula ed esea ch ques ions, es ablished e iew bounda ies, selec ed
keywo ds and sea ch e ms, selec ed sea ch da abases, and de ined he
inclusion and exclusion c i e ia. Owing o i s ex ensi e co e age o pee -
e iewed jou nals in managemen and business, he Social Science
Ci a ion Index (SSCI) was selec ed as he p ima y da abase. A scoping
e iew was pe o med o iden i y he keywo ds. To ensu e he inclu-
si eness and comp ehensi eness o he e iew, he keywo ds we e
e ined h ough consul a ions wi h wo domain expe s.
Phase 2.Analysis and syn hesis
Two analyses we e pe o med on he selec ed a icles, including a
bibliog aphic analysis and a con en analysis. The bibliog aphic analysis
was used o iden i y key publica ions, ends, and me hodological and
heo e ical app oaches. Two independen e iewe s conduc ed da a
ex ac ion o ensu e eliabili y. Disc epancies we e esol ed by ho -
oughly discussing he ele ance o a icles o inclusion. The con en
analysis was ocused on iden i ying he main hemes in he li e a u e and
ca ego izing hem in o dis inc ca ego ies.
Phase 3.Repo ing
A his s age, he indings we e syn hesized o map he ole o DT in
inno a ion. A mul i-le el amewo k was de eloped o explain he
cu en li e a u e on he DT–inno a ion in e play.
Re iew ques ion
SLRs o e eliable answe s o well-de ined and speci ic esea ch
ques ions (Yuan & Hun , 2009). They p o ide a clea , unbiased, and
comp ehensi e summa y o he cu en unde s anding o a speci ic
esea ch ques ion (Tsa na e al., 2014). SLRs “bea on a pa icula
ques ion, using o ganized, anspa en , and eplicable p ocedu es a
each s ep in he p ocess.” (Li ell e al., 2008, pp. 1–2). The e o e, a
success ul SLR equi es clea e iew ques ions (K aus e al., 2021; Lame,
2019; Ro he , 2007). The p ima y esea ch ques ion was, “Wha ole
does DT play in inno a ion in i ms?” Following p e ious s udies
(Bilbao-Ubillos e al., 2024; Calde on-Monge & Ribei o-So iano, 2023;
Paschou e al., 2020), wo sub- esea ch ques ions we e p oposed.
-RQ1. Wha does he cu en li e a u e e eal abou he impac o DT
on inno a ion?
-RQ2. Based on he indings o ou s udy, wha a e he ecommended
di ec ions o u u e esea ch?
Re iew bounda ies
Following ecen SLR s udies (Bilbao-Ubillos e al., 2024), i e
c i e ia we e used o exclude/include and e ine he a icles: (1) publi-
ca ion ype, (2) concep ual bounda ies, (3) sea ch bounda ies, (4)
speci ied ime ames, and (5) keywo ds.
S ep 1: The concep ual bounda ies o DT we e de ined. De ini ions o
DT a y in ocus, anging om echnology-d i en changes o mo e
M. Saeedikiya e al.
Jou nal o Inno a ion & Knowledge 10 (2025) 100640
2
holis ic iews encompassing o ganiza ional and s a egic ans-
o ma ions. The lack o a pa simonious and uni e sally accep ed de i-
ni ion (Vial, 2021) highligh s he complexi y o DT and he challenges in
concep ualizing i as a mul i ace ed phenomenon. While some schola s
emphasize he echnological dimension, o he s ocus on DT’s b oade
o ganiza ional, s a egic, and socie al impac s. The ollowing sec ion
p o ides a c i ical e iew o exis ing concep ualiza ions o syn hesize a
de ini ion o DT ha is he bes i o in es iga ing i s in e play wi h
inno a ion.
The DT li e a u e shows ha , al hough he e m has been inc eas-
ingly discussed and adop ed, i has some concep ual cla i y issues (Vial,
2021). Mo eo e , i s scope a ies signi ican ly ac oss di e en con ex s.
Schola s ha e o e ed some basic and ounda ional de ini ions o DT.
(Vial, 2021) de ined DT as he imp o emen o an en i y by igge ing
signi ican changes h ough in o ma ion, compu ing, communica ion,
and connec i i y echnologies. This de ini ion es ablishes he in eg a i e
ole o digi al echnology in eshaping business s a egies and ope a-
ions. Gong and Ribie e (2021) concep ualized DT as “a undamen al
change p ocess enabled by digi al echnologies ha aim o b ing adical
imp o emen and inno a ion o an en i y, such as an o ganiza ion,
business ne wo k, indus y, o socie y, o c ea e alue o i s s ake-
holde s by s a egically le e aging i s key esou ces and capabili ies”
(p.12).
By con as , Li e al. (2018) ocused on DT as a ans o ma ion d i en
by in o ma ion echnology ha changes business p ocesses, ope a ional
ou ines, and o ganiza ional capabili ies. This pe spec i e emphasizes
DT’s dis up i e po en ial o business models. Simila ly, Chanias e al.
(2019) emphasized DT’s ex a-o ganiza ional in luence by desc ibing i
as a holis ic ans o ma ion associa ed wi h echnological and economic
changes a he o ganiza ional and indus y le els.
Wa ne and W¨
age (2019) ended o ely on echnological di-
mensions. They de ined DT as he use o digi al echnologies (social
media, mobile de ices, analy ics, and embedded de ices) o enable
signi ican business imp o emen s. Nambisan e al. (2019) added o his
by aming DT in e ms o i s ans o ma ional o dis up i e implica-
ions. Acco ding o hese au ho s, DT leads o new business models,
p oduc s, and cus ome expe iences.
Howe e , some de ini ions, such as hose p oposed by Schallmo e al.
(2017) and Nadka ni and P ügl (2021), ocus p ima ily on o ganiza-
ional changes igge ed by digi al echnologies. Schallmo e al. (2017)
desc ibed DT as in ol ing he ne wo king o ac o s (e.g., businesses and
cus ome s) and applying new echnologies o enhance company pe -
o mance. Nadka ni and P ügl (2021) iewed DT as an o ganiza ional
change shaped by he widesp ead di usion o digi al echnologies. Bo h
de ini ions emphasize s uc u al changes wi hin o ganiza ions ha a e
d i en by digi al adop ion. Hanel e al. (2021, p. 1160) buil on his by
de ining DT “as o ganiza ional change ha is igge ed and shaped by
he widesp ead di usion o digi al echnologies.”
Some de ini ions app oached DT mo e comp ehensi ely. They
concep ualized i as ans o ming business ac i i ies, p ocesses, com-
pe encies, and models o le e age digi al echnologies. This app oach
aligns wi h he pe spec i e o Bughin and Van Zeeb oeck (2017), who
highligh ed ha DT is no jus abou echnological in eg a ion. Ins ead,
i ede ines compe i i e ad an ages and alue c ea ion.
A ecu ing heme ac oss hese de ini ions was he dis inc ion be-
ween digi iza ion, digi aliza ion, and DT. Digi iza ion e e s o con-
e ing in o ma ion om analog o digi al, whe eas digi aliza ion
in ol es ans o ming digi al da a in o alue-c ea ion p ocesses. DT is
mo e subs an ial. I encompasses a comple e e hinking o how o gani-
za ions c ea e and deli e alue. This dis inc ion is c i ical. Some de i-
ni ions, such as hose o Wa ne and W¨
age (2019) and Fi zge ald e al.
(2014), may con la e digi aliza ion and DT.
While many esea che s ha e concen a ed on DT’s echnological
aspec s, o he s, such as Be ghaus and Back (2016), ocused on i s
co po a e dimensions. They a gued ha DT is a con inuous p ocess ha
equi es no only he adop ion o new echnologies bu also
o ganiza ional changes in s uc u e, p ocesses, and go e nance. This
iew was u he emphasized by Sacolick (2017), who highligh ed ha
DT in ol es agile o ganiza ional de elopmen al p ocesses.
The abo e e iew shows ha , while DT is widely discussed, i s
concep ual cla i y and scope a y signi ican ly ac oss con ex s. Syn-
hesizing he p e ious de ini ions, we de ine digi al ans o ma ion as
“an ongoing socio-s uc u al change ha le e ages digi al echnologies o
c ea e new alue owa d sus ained compe i i e ad an age.” This syn hesis
has di e en componen s ha align wi h he key aspec s o DT epo ed
in he li e a u e. I s “ongoing” componen e e s o he “con inuous in e-
g a ion and e olu ion o digi al echnologies” (Gup a, 2018; Mo akanyane
e al., 2017; Wa ne & W¨
age , 2019). The “socio-s uc u al change”
componen is oo ed in es uc u ing business models, p ocesses, and
o ganiza ional s uc u es (Hanel e al., 2021; Hess e al., 2016; Li e al.,
2018). I also emphasizes ha DT is no me ely a echnological o
s a egic phenomenon, bu also a social phenomenon ac oss he o ga-
niza ion dealing wi h human and human– echnology ela ionships. The
ph ase “s uc u al” emphasizes ha DT is no me ely associa ed wi h
changes in oles o asks concep ualized by digi aliza ion and digi iza-
ion. Ra he , i includes changes in o ganiza ional s uc u es, dis-
inguishing i om digi aliza ion and digi iza ion. The o he componen ,
ha is, “le e ages digi al echnologies,” highligh s he cen al ole o he
echnology desc ibed by Nambisan e al. (2017) and Schallmo e al.
(2017). The elemen s “c ea e new alue” and “sus ained compe i i e
ad an age” a e co e ideas in Gup a’s (2018) pe spec i e. In hei iew,
DT is a new alue p oposi ion ha conside s o ganiza ional s a egy.
The componen “sus ained” e e s o he emphasis on sus ained
compe i i e ad an age concep ualized by Mo akanyane e al. (2017)
and Fi zge ald e al. (2014).
This de ini ion u he helps o concep ualize i s ela ionship wi h
Schumpe e ’s (1934) de ini ion o inno a ion as a new p oduc ion
unc ion, including new p oduc s, me hods, ma ke s, and o ganiza ional
s uc u es. Schumpe e ’s ocus on enewing he o ganiza ional s uc u e
and explo ing un apped ma ke s esona es wi h he es uc u ing and
ma ke expansion componen s o DT, as discussed by Li e al. (2018) and
Hanel e al. (2021). By de ining inno a ion as c ea ing new p oduc ion
unc ions, Schumpe e ’s iew suppo s he idea ha DT is no me ely
abou digi izing exis ing p ocesses bu undamen ally eshaping busi-
ness models, compe i ion, and alue c ea ion. This holis ic and inclusi e
app oach is ob ious in ou de ini ion o DT as an ongoing socio-s uc u al
change ha le e ages digi al echnologies o c ea e new alue owa d sus-
ained compe i i e ad an age. This de ini ion e lec s Schumpe e ’s
concep ualiza ion o inno a ion as he in oduc ion o new p oduc ion
and compe i ion me hods. Mo eo e , Schumpe e ’s new p oduc s,
me hods, and o ganiza ional enewal as inno a ions e lec he idea ha
DT d i es long- e m inno a ion. As Wa ne and W¨
age (2019) and Vial
(2021) highligh , in eg a ing digi al echnologies in o o ganiza ional
p ocesses c ea es new p oduc and se ice oppo uni ies by imp o ing
p oduc ion me hods and accessing new ma ke s. These ideas a e cen al
o Schumpe e ’s de ini ion o inno a ion. Finally, he s uc u al change
componen in ou de ini ion u he allows us o unde s and he s uc-
u al and echnological changes b ough abou by DT as he c ea ion o
new p oduc ion unc ions. This syne gy jus i ies he use o ou de ini ion
o DT as a amewo k o in es iga ing i s impac on inno a ion. This is
pa icula ly ele an when s udying how DT boos s o ganiza ional
enewal, echnological ad ancemen , and ma ke expansion (Schum-
pe e ’s inno a ion ypes).
S ep 2. The sea ch bounda ies we e delimi ed o encompass jou nals
indexed in he SSCI because o he comp ehensi e indexing o high-
quali y, pee - e iewed jou nals in managemen , business, and social
sciences (Calde on-Monge & Ribei o-So iano, 2023). Mo eo e , o
quali y assu ance, and ollowing p e ious e iews (Ba al e al., 2023;
Pawa , 2023; Sha ma e al., 2023), only a icles ha appea ed in he
Aus alian Business Deans Council (ABDC) lis , based on he 2022 edi-
ion o he jou nal anking guide o he ABDC we e included.
M. Saeedikiya e al.
Jou nal o Inno a ion & Knowledge 10 (2025) 100640
3
Rega ding publica ion ype, he e iew ocused on jou nal a icles.
The language was se o be English. Book chap e s, indus y a icles,
edi o ials, con e ence p oceedings, and book e iews we e excluded
(Calde on-Monge & Ribei o-So iano, 2023; Paschou e al., 2020). A i-
cles discussing he in e play be ween DT and inno a ion we e also
included. S udies ocusing solely on ei he concep , wi hou linking
hem, we e excluded. Mo eo e , o ensu e a ocus on high-quali y and
igo ous academic esea ch, we excluded pape s ha we e no pee
e iewed. No ime ame was speci ied o ensu e maximum inclusion o
he publica ions.
S ep 3. Keywo d selec ion was pe o med as a c i ical ask (K aus
e al., 2021). We scoped he li e a u e and b ains o med wi h academics
o map ele an keywo ds (A ksey & O’malley, 2005). A e iden i ying
he ele an keywo ds (Table 1), a combina ion o keywo ds was used o
iden i y a icles on “Digi al T ans o ma ion” and “Inno a ion.” The
sea ch s ings/ o mulas we e de ined based on AND/OR ope a o s. Fo
example, “digi a* ans o *” was used o cap u e a ia ions such as
“digi al ans o ma ion” and “digi alized ans o ma ion.”
Selec ion o ele an s udies
A PRISMA app oach (Mohe e al., 2016) was used o iden i y,
sc een, check eligibili y, and selec ele an s udies based on ou
exclusion/inclusion c i e ia (see Fig. 1).
- Iden i ica ion: The iden i ica ion s age in ol ed se ing e iew
bounda ies o iden i y ele an con ibu ions ha would be
conside ed o u he sc eening. A li e a u e sea ch was conduc ed
using SSCI, based on he keywo ds lis ed in Table 1. Fi s , ollowing
K aus e al. (2022), we used he sea ch s ing “Digi a* ans o *” o
iden i y he pape s w i en on DT and dis inguish hem om he
con ibu ions abou associa ed e ms and concep s. We sea ched
o documen opics ( i les, abs ac s, and keywo ds). This
app oach iden i ied ele an pape s published on his opic. We
hen e ined he esul s using inno a ion keywo ds (Table 1) o
iden i y he con ibu ions o DT o inno a ion (2675 ele an
con ibu ions). Nex , we es ic ed he sea ch o pee - e iewed
a icles. This educed he numbe o con ibu ions o 2398 a i-
cles. Finally, he pape was w i en in English. This s ep u he
educed he numbe o a icles o 2347.
- Sc eening: To main ain he quali y o he e iews, he a icles we e
sc eened agains he ABDC jou nal guide ankings. Only a icles
published in ABDC-lis ed jou nals we e included in his s udy.
Following his p ocess, he numbe o a icles was u he educed
o 1183.
- Eligibili y: Nex , he eligibili y o a icle inclusion was assessed
using he i - o -pu pose c i e ion (Felice i e al., 2023; Kuma
e al., 2022). Two ac ions we e pe o med a his s age. Fi s , since
he ocus was o in es iga e he DT-inno a ion in e play, he a i-
cles exclusi ely ocused on inno a ion and DT was excluded. Sec-
ond, we ead he abs ac s and in oduc ions o he pape s o selec
he inal a icles. The wo au ho s ex ac ed each a icle’s heo-
e ical pe spec i es, me hodology, indings, and implica ions o
inno a ion. Disc epancies we e discussed and esol ed h ough a
consensus o minimize bias, which led o he inclusion o 118 a -
icles in he s udy.
- Inclusion: The da abase sea ch was ollowed by a c oss- e e ence
analysis o add ess he po en ial limi a ions o keywo d sea ches
and ensu e comple eness (Paschou e al., 2020). Eigh addi ional
a icles we e e ie ed and sc eened based on he exclusion/in-
clusion c i e ia. Following his s ep, 126 a icles we e chosen o be
included in ou s udy. We analyzed and e alua ed his se o a i-
cles o iden i y he bibliog aphic s uc u e and mechanisms
h ough which DT a ec s inno a ion. This analysis yielded a
mul i-le el amewo k ha mapped he esea ch on DT-d i en in-
no a ions and will guide u u e esea ch.
Analysis and syn hesis
Following p e ious s udies (B u on & Lau, 2008; Xu & Meye , 2013),
and o p o ide a comp ehensi e iew o how DT b ings inno a ion o
i ms, wo complemen a y analyses we e pe o med on he selec ed
a icles: bibliog aphic and quali a i e con en analysis. Such an
app oach enables a esea che o pe o m analysis and syn hesis, ha is,
o explo e he s uc u e and con en o exis ing esea ch. In he analysis
pa , he esea che examines he s uc u e o he exis ing esea ch by
p o iding desc ip i e and s a is ical analysis o he sample a icles. In
he con ex o he cu en esea ch, he bibliog aphic analysis iden i ied
s a is ical and desc ip i e esea ch pa e ns on he DT–inno a ion
in e play along he spa ial and empo al dimensions, while he quali-
a i e con en analysis yielded a comp ehensi e amewo k ha mapped
he esea ch domains o DT-based inno a ions and iden i ied how DT
may in luence inno a ion.
Fo he bibliome ic analysis, he au ho s ex ac ed da a om he
a icles, including publica ion ou le s, publica ion yea s, o e all ime
ends, heo e ical pe spec i es, me hodologies, and key indings.
Simila bibliome ic da a ha e been epo ed in high-impac SLRs
(Calde on-Monge & Ribei o-So iano, 2023; Chin alapa i & Pandey,
2022).
The quali a i e con en analysis aimed o iden i y gene al pa e ns in
he exis ing li e a u e. Following Ka e zopoulos (2022) and Shahbaz and
Pa ke (2022), we syn hesized ou indings using an AMO amewo k by
analyzing an eceden s (A), media o s/mode a o s (M), and ou comes
(O). The indings a e summa ized in he ollowing sec ions.
Pa A: Bibliome ic esul s
The dis ibu ion o he sample by publica ion ou le and discipline
Table 2 p esen s he dis ibu ion o he a icles pe publica ion
ou le . O e all, echnology and inno a ion managemen domina ed he
sample. The nex ie was business and managemen , inance, and
knowledge managemen . This dis ibu ion ac oss disciplines sugges ed
ha he impac o DT on inno a ion was mul i old and in e disciplina y.
The mos equen ou le s included Technological Fo ecas ing and Social
Change (10.32%), Technology Analysis & S a egic Managemen (8.73%),
and The Jou nal o Business Resea ch (8.73%).
Pape s published in Manage ial and Decision Economics and Finance
Resea ch Le e s had he highes equency (6.35% each) and e lec ed
DT’s economic and inancial implica ions (o in es men decisions
ega ding inno a ion ou comes). The second mos equen ca ego y
was inno a ion jou nals; he Eu opean Jou nal o Inno a ion Manage-
men (5.56%) and he Jou nal o Inno a ion & Knowledge (4.76%) we e
well ep esen ed, wi h a pa icula in e es in how DT in luenced inno-
a ion p ocesses and knowledge de elopmen wi hin i ms. IEEE
T ansac ions on Enginee ing Managemen (4.76%) and Technology in So-
cie y (4.76%) we e he hi d-mos ep esen ed ca ego ies, wi h a
Table 1
Keywo ds and sea ch s a egies used o iden i y sample a icles.
Keywo d Sea ch S ing
DT "Digi a* ans o *"
Inno a ion Inno a ion" OR "Inno a ion Pe o mance" OR "New Ma ke " OR
"Resea ch And De elopmen " OR "No el y" OR "Inno a i e" OR
"P oduc Inno a ion" OR "New Se ice" OR "Enhancemen " OR
"Radical" OR "Managemen Inno a ion" OR "Inno a ion E iciency" OR
"Technological Inno a ion" OR "P oduc Imp o emen " OR "Di usion"
OR "Resea ch & De elopmen " OR "P ocess" OR "Explo a o y
Inno a ion" OR "Imp o emen " OR "P ocess Inno a ion" OR "S uc u al
change" OR "compe i i e ad an age" OR "Technical Knowledge" OR
"Inc emen al Inno a ion" OR "Exploi a i e Inno a ion" OR "Inno a *"
M. Saeedikiya e al.
Jou nal o Inno a ion & Knowledge 10 (2025) 100640
4
pa icula ocus on he socio echnical aspec s o he DT-inno a ion
in e play. O he jou nals ha e in es iga ed DT’s impac on sus ainabil-
i y and g een ini ia i es, in addi ion o egula inno a ions. These
jou nals included Business S a egy and he En i onmen (2.38%) and he
Jou nal o En i onmen al Planning and Managemen (1.59%).
Jou nal o inno a ion & knowledge’s (JIK) con ibu ion o he
DT–inno a ion nexus
The JIK has signi ican ly con ibu ed o con e sa ions on DT and i s
inno a ion implica ions. JIK plays an e ol ing leade ship ole, and i s
con ibu ions a e summa ized and discussed below.
JIK co e ed a wide ange o opics, including he impac o DT on
inno a ion pe o mance (L. Li e al., 2022), digi al leade ship
(Cha e jee e al., 2023), and public policy (Peng & Tao, 2022). These
include a s a egic pe spec i e on isk- aking (M. Y. Liu e al., 2023), an
assessmen o o al ac o p oduc i i y (Yu e al., 2024; Liu e al.,
2023b), and an e alua ion o he ole o social capi al in inno a ion (Lyu
e al., 2022). Addi ionally, sec o -speci ic insigh s in o asse -in ensi e
o ganiza ions (Buck e al., 2023), highe educa ion (R. J. Li e al.,
2024), and ag ibusiness (Xue e al., 2024) we e p o ided, along wi h
en i onmen al inno a ion (Hung & Nham, 2023) in small and medium
(Bashi e al., 2023) and p i a e en e p ises (Chen & Yu, 2024).
Fo example, Yu e al. (2024) assessed he impac o DT on inno a-
ion in es men in Chinese manu ac u ing i ms. The au ho s epo ed a
signi ican posi i e ela ionship be ween DT and in es men in inno a-
ion. In hei esea ch, To al Fac o P oduc i i y (TFP) due o DT led o
in e nal esou ce compe i ion be ween he p oduc ion and inno a ion
depa men s. This s udy also p o ided policy ecommenda ions o
enhancing inno a ion in es men s in manu ac u ing i ms. These i ms
a e adi ionally known as less inno a i e sec o s. Xue e al. (2024)
ocused on Chinese ag ibusinesses. Thei indings emphasized DT’s ole
in acili a ing access o essen ial esou ces in adi ional sec o s, such as
echnology, alen , and capi al. Finally, Chen e al. (2024) in es iga ed
p i a e en e p ises and ound ha DT signi ican ly p omo ed inno a-
ion, pa icula ly in weal hy egions and la ge i ms (Zhang e al.,
2023b).
The sample’s dis ibu ion by yea
Fig. 2 summa izes he publica ions and ci a ions on he
DT–inno a ion in e play o e he yea s. An inc easing end can be
obse ed in publica ions and he unning sum o ci a ions. We compu ed
a end model o he numbe o publica ions and hei ci a ions o e
Fig. 1. Re iew bounda ies and selec ion o a icle sea ch p ocess (PRISMA app oach).
M. Saeedikiya e al.
Jou nal o Inno a ion & Knowledge 10 (2025) 100640
5
ime. The model was signi ican a p ≤0.05 and shows ha he ci a ions
pe published documen inc eased o e he yea s. The numbe o ci a-
ions has g own exponen ially o e he yea s. This inc ease o eshadows
in e es in his opic in he coming yea s.
Al hough a ime ame was no speci ied o he inclusion c i e ia, he
sample pape s ha me he selec ion c i e ia we e published be ween
2018 and 2024. This may be due o he global ise in DT and digi al
echnology spending by i ms in 2018–2023, which led o signi ican
in es men s in DT and inno a ion ac i i ies (Taylo , 2022). In line wi h
hese ends in he indus y, he academic landscape has expe ienced
exponen ial g ow h in publica ions on he ela ionship be ween DT and
inno a ion pos 2017 (Appio e al., 2021) and a sha p inc ease in
esea ch e idence on DT published om 2018 onwa d (Calde on-Monge
& Ribei o-So iano, 2023).
Dis ibu ion o me hodological app oaches
Table 3 summa izes he esea ch me hodologies used ac oss ou
sampled a icles. The e iewed s udies we e ca ego ized in o quan i a-
i e, quali a i e, mixed me hods, and concep ual pape s. Quan i a i e
me hods domina ed he samples, accoun ing o 73% o he s udies. The
mos common echniques included panel eg ession models, PLS-SEM,
Heckman wo-s age models, and GMM. Quali a i e me hods ep e-
sen ed 14.3% o he s udies. Mul iple case s udies, g ounded heo y, and
single case s udies explo ed how DT a ec ed pe o mance ou comes,
speci ically inno a ion. O he pape s, 8.7% used mixed quali a i e ap-
p oaches wi h quan i a i e echniques, such as PLS-SEM and sQCA.
Concep ual s udies, accoun ing o 4% o he o al, explained he
esou ce-based iew (RBV) and dynamic capabili y iew (DCV) iews in
he con ex o DT-enabled inno a ion.
Dis ibu ion o a icles pe coun y and unding s a us
Table 4 summa izes he signi ican geog aphical concen a ion o
s udies and p esen s an o e iew o he geog aphical dis ibu ion o
au ho s and unding sou ces o esea ch pape s on he e ec o DT on
inno a ion. Mos pape s (61.11%) we e a ilia ed wi h Chinese
ins i u ions.
This dominance can be a ibu ed o China’s subs an ial in es men
in digi al echnologies and i s s a egic ocus on becoming a global
leade in echnological ad ancemen . China’s commi men was u he
e idenced in ou sample, as 55.56% o he unded pape s ecei ed
suppo om Chinese ins i u ions (in con as o 22.22% o non-Chinese-
unded pape s). The a icles’ ocus and sha e o go e nmen - unded
pape s showed ha China speci ically a ge ed inno a ion h ough DT
o enew i s adi ional indus ies and manu ac u ing sec o s. Taiwan,
he Uni ed S a es, he Uni ed Kingdom, I aly, and o he Eu opean
coun ies con ibu ed signi ican ly (14.7%, 4.76%, 3.17%, and 3.97% o
he pape s, espec i ely).
Table 2
The dis ibu ion o he sample by publica ion ou le and discipline.
Publica ion Ou le Reco d
Coun
% o
126
Discipline
Technological Fo ecas ing and
Social Change
13 10.32% Technology and
Inno a ion
Managemen
Technology Analysis &
S a egic Managemen
11 8.73% Technology and
Inno a ion
Managemen
Jou nal o Business Resea ch 11 8.73% Business and
Managemen
Jou nal o he Knowledge
Economy
8 6.35% Knowledge
Managemen
Finance Resea ch Le e s 8 6.35% Finance
Manage ial and Decision
Economics
8 6.35% Economics
Eu opean Jou nal o
Inno a ion Managemen
7 5.56% Technology and
Inno a ion
Managemen
Jou nal o Inno a ion &
Knowledge
6 4.76% Technology and
Inno a ion
Managemen
IEEE T ansac ions on
Enginee ing Managemen
6 4.76% Enginee ing
Managemen
Technology in Socie y 6 4.76% Technology and Socie y
Techno a ion 3 2.38% Technology and
Inno a ion
Managemen
Business S a egy and he
En i onmen
3 2.38% Business and
Managemen
Jou nal o Knowledge
Managemen
3 2.38% Knowledge
Managemen
Business P ocess Managemen
Jou nal
3 2.38% Business and
Managemen
En i onmen De elopmen and
Sus ainabili y
2 1.59% En i onmen al
Sus ainabili y
Managemen Decision 2 1.59% Business and
Managemen
Re iew o Manage ial Science 2 1.59% Business and
Managemen
Jou nal o En i onmen al
Planning and Managemen
2 1.59% En i onmen al Planning
and Managemen
Ene gy Economics 2 1.59% Economics
Jou nal o In o ma ion &
Knowledge Managemen
1 0.79% Knowledge
Managemen
Jou nal o Managemen &
O ganiza ion
1 0.79% Business and
Managemen
In e na ional Re iew o
Economics & Finance
1 0.79% Finance
Jou nal o En i onmen al
Managemen
1 0.79% En i onmen al
Managemen
Business Ho izons 1 0.79% Business and
Managemen
F on ie s in En i onmen al
Science
1 0.79% En i onmen al Science
In e na ional Jou nal o
Inno a ion and Technology
Managemen
1 0.79% Technology and
Inno a ion
Managemen
Eu opean Jou nal o Finance 1 0.79% Finance
Asian Jou nal o Technology
Inno a ion
1 0.79% Technology and
Inno a ion
Managemen
Applied Economics Le e s 1 0.79% Economics
Co po a e Social Responsibili y
and En i onmen al
Managemen
1 0.79% En i onmen al
Managemen
In e na ional En ep eneu ship
and Managemen Jou nal
1 0.79% En ep eneu ship
Jou nal o En e p ise
In o ma ion Managemen
1 0.79% In o ma ion
Managemen and
Sys ems
Jou nal o Global In o ma ion
Managemen
1 0.79% In o ma ion
Managemen and
Sys ems
Long Range Planning 1 0.79% Business and
Managemen
Table 2 (con inued)
Publica ion Ou le Reco d
Coun
% o
126
Discipline
In e na ional Re iew o
Financial Analysis
1 0.79% Finance
Academy o Managemen
Disco e ies
1 0.79% Business and
Managemen
Jou nal o P oduc Inno a ion
Managemen
1 0.79% Technology and
Inno a ion
Managemen
R & D Managemen 1 0.79% Resea ch and
De elopmen
Managemen
In o ma ion and O ganiza ion 1 0.79% In o ma ion
Managemen and
Sys ems
M. Saeedikiya e al.
Jou nal o Inno a ion & Knowledge 10 (2025) 100640
6
Dis ibu ion o a icles by heo e ical app oach
The dis ibu ion o pape s based on heo e ical app oaches is shown
in Table 5. The DCV was he mos p e alen a 8.7%, closely ollowed by
he RBV a 7.1%, and he knowledge-based iew (KBV) a 6.3%.
Toge he , hese s udies highligh ha many ha e conside ed he
esou ce and knowledge in eg a ion dynamics o d i e DT-enabled
inno a ion. In addi ion, 5.6% o he s udies used open inno a ion he-
o y, which complemen ed he RBV, KBV, and DCV by ocusing on
ex e nal esou ces and knowledge.
These knowledge mechanisms we e u he highligh ed, conside ing
ha he abso p i e capaci y (AC) heo y and echnology-o ganiza ion-
en i onmen (TOE) amewo k appea ed in 4.8% o pape s. Inno a-
ion ambidex e i y, ep esen ed in 4% o he pape s by ocusing on in-
e nal and ex e nal knowledge, complemen ed he knowledge and
esou ce mechanisms associa ed wi h he DT–inno a ion ela ionship
and complemen ed he p e ious KBV, RBV, DCV, AC, and TOE
pe spec i es.
En i onmen al managemen /ci cula economy, ins i u ional heo y,
and o ganiza ional lea ning heo y con ibu ed equally (3.2%). In e -
es ingly, 20.6% o he s udies did no speci y a heo e ical amewo k.
This highligh s he need o g ea e heo e ical igo in u u e s udies.
Pa B: Con en analysis esul s
This sec ion syn hesizes he indings o he con en analysis on he
ela ionship be ween DT and inno a ion. Following Khos a i e al.
(2019), we employed a sys ema ic con en analysis me hod o ansla e
ex ual con en in o dis inc ca ego ies. Con en analysis was conduc ed
in i e s ages: open coding, coding shee s, g ouping, ca ego iza ion, and
abs ac ion (Elo & Kyng¨
as, 2008). An open code was assigned o each
a icle du ing his p ocess. These codes we e hen o ganized in o
b oade ca ego ies such as g ouping dynamic and echnological capa-
bili ies unde s a egic capabili y mode a o s. The ca ego ies o mod-
e a o s, en i onmen al ac o s, and i m cha ac e is ics we e in eg a ed
in o a gene al “mode a o s” ca ego y. To ensu e accu acy, he au ho s
conduc ed each coding and g ouping s age independen ly. We applied
he AMO amewo k o classi y he a iables in o an eceden s, media-
o s, mode a o s, and ou comes (Shahbaz & Pa ke , 2022). The an e-
ceden in ou amewo k was DT, which in luences i m inno a ion.
Media o s ep esen ed he mechanisms ha channel DT’s e ec s on
inno a ion, whe eas mode a o s we e bounda y condi ions ha ei he
s eng hened o weakened he ela ionships be ween DT, media o s, and
inno a ion ou comes. Finally, we syn hesized he esul s in o a
mul i-le el amewo k isually ep esen ing he linkages be ween hese
ca ego ies (Fig. 3). The ollowing sec ions discuss ou indings ega ding
hese linkages.
Ou comes
The esul s indica ed ha DT posi i ely a ec ed a i m’s inno a-
i eness (Chen & Yu, 2024; M. Y. Liu e al., 2023; Rome o & Mammado ,
2024; Yu e al., 2024) and o e all pe o mance (X. C. O e o-Bla e al.,
2024; Guo e al., 2023; Wang, 2023; Zhai & Liu, 2023; Guo e al.,
2023b). DT could d i e business models, p oduc s, and p ocess in-
no a ions (B esciani e al., 2021) and boos en e p ise i ali y by
Fig. 2. T end analysis o publica ions and ci a ions o e he yea s.
Table 3
Dis ibu ion o a icles by me hodological app oaches.
Ca ego y Numbe o
Pape s
Pe cen age Example Me hodologies Used
Quan i a i e 92 73.0% Panel eg ession, Fixed-e ec , and
andom-e ec models, PLS-SEM,
Fixed-e ec s Poisson model,
Heckman wo-s age model,
Hie a chical eg ession, Se ial
media ion, Spa ial Du bin model
Quali a i e 18 14.3% Mul iple case s udies, G ounded
heo y, Single case s udy
Mixed
Me hods
11 8.7% Mixing quali a i e me hods wi h
Ques ionnai e su ey, PLS-SEM, and
sQCA
Concep ual 5 4.0% Concep ual amewo ks based on
heo ies such as esou ce-based iew,
di usion o inno a ion heo y,
dynamic capabili ies iew,
o ganiza ion lea ning heo y
M. Saeedikiya e al.
Jou nal o Inno a ion & Knowledge 10 (2025) 100640
7
enhancing p oduc i i y by educing asymme ic in o ma ion and op i-
mizing esou ce alloca ion (Yang & Deng, 2023; Yu e al., 2024).
Sample a icles sugges ed ha DT’s e ec s on inno a ion could be
shaped wi hin and ac oss an o ganiza ion’s bo de s. In e nally, DT
in luenced labo inpu s and p omo ed in ap eneu ship (Cheng e al.,
2023). I s eng hens o ganiza ional inno a ion by es ablishing eam
cohesion, us , and in o ma ion exchange. These we e he enable s o
s uc u al, s a egic, and sys emic inno a ion wi hin he i m (Zhang &
Fan, 2024). Mo eo e , DT could es uc u e managemen con ol sys-
ems (Pizzi e al., 2021; Wang & He, 2024) o make inno a ion p ac ices
mo e e icien .
Ex e nally, DT imp o ed a i m’s capaci y o abso b and ans o m
knowledge and esou ces and enhance cus ome alue c ea ion h ough
dynamic capabili ies in small and medium en e p ises. This suppo ed
he c ea ion o new dis ibu ion channels, business models, and mech-
anisms o alue deli e y (Ma a azzo e al., 2021). DT also acili a ed
knowledge ecombina ion p ocesses (laye ing, g a ing, o in eg a ion)
owa d inno a ion (Lanzolla e al., 2021). Mo eo e , DT p omo ed open
inno a ion by boos ing collabo a ion and knowledge sha ing wi hin and
beyond he i m (Kim & Pa k, 2024; Luan e al., 2024; U bina i e al.,
2020). These collabo a ions imp o ed inno a ion quali y and encou -
aged he disclosu e o aluable in o ma ion (Be eczki & Fülle , 2024;
Che e al., 2023; Pang & Wang, 2023). Fu he mo e, DT sa ed i m e-
sou ces o suppo in es men in pa en applica ions and in en ion ac-
i i ies (Y. Zhang e al., 2023). DT also ex ended i s in luence ac oss
supply chains by imp o ing ne wo k layou s. In doing so, DT acili a ed
knowledge sha ing, enhanced esou ce in eg a ion, and enhanced
cus ome inno a ion capabili ies (Q. H. Liu e al., 2024). In addi ion, DT
educed inno a ion isk by alle ia ing inancing cons ain s and
educing p oduc ion cos s (Q. H. Liu e al., 2024).
Rega ding he ypes o inno a ion, DT d o e bo h comme cial and
g een/sus ainable inno a ions (R. J. Lian & Zhang, 2024; Li e al., 2024;
Ribei o-Na a e e e al., 2023). Fo ins ance, o comme cial in-
no a ions, cogni i e compu ing capabili ies enhanced i ms’ en ep e-
neu ial quali ies such as isk- aking and p oac i eness (Gup a e al.,
2023; Ko he e al., 2023). Simila ly, adop ing AR/VR solu ions in DT
enabled a ious inno a ions, including p oduc /se ice o e ings, busi-
ness p ocesses, and business model inno a ions (Pesso e al., 2023).
Addi ionally, sma echnologies os e ed digi al p ocess inno a ions by
acili a ing o ganiza ional unlea ning and econ igu ing es ablished
p ocesses (Wang e al., 2023).
DT also a ec ed g een and sus ainable inno a ion (Zhao and Fang,
2023). I posi i ely in luenced g een echnology inno a ion (Du e al.,
2023) by op imizing human capi al, easing inancial cons ain s, and
inc easing media a en ion (Lin & Xie, 2023; Lu e al., 2023; Qiong Xu
e al., 2023). Mo eo e , DT enhanced he quali y and quan i y o g een
echnological inno a ion by inc easing R&D in ensi y and educing isk
(Y. Xu e al., 2023). Th ough his p ocess, i ms could s eng hen hei
collabo a i e ne wo ks o inno a ion and access o inancing (Tang
e al., 2023).
DT’s impac on inno a ion could be channeled h ough inno a ion
ambidex e i y, ha is, simul aneous adical and inc emen al in-
no a ions (R. J. Li e al., 2024; Wang & He, 2024; Zhu & Li, 2023; Li
e al., 2024b). DT a ec ed a i m’s b ead h and dep h o knowledge. This
helped deepen he explo a ion and exploi a ion o new oppo uni ies
(Zhou, Yang e al., 2023). I was speci ically associa ed wi h inno a ion
adicali y o i ms wi h a high echnological o ien a ion in hei op
managemen eams (Pesso e al., 2023; Yang e al., 2023). Social media
pla o ms u ilized DT-enhanced ambidex e i y h ough knowledge
ans e p ac ices (Scuo o e al., 2020). DT shaped adical and inc e-
men al inno a ions di e en ly. The shape was an in e ed U o inc e-
men al inno a ion and i had a di ec linea e ec (Duan e al., 2023).
Finally, al hough many o he abo e s udies showed a posi i e link
be ween DT and inno a ion, in line wi h Usai e al. (2021), we a gue ha
Table 4
Dis ibu ion o a icles based on coun y and unding s a us.
Coun y Numbe o Au ho s Pe cen age (%) Funding Sou ce Numbe o Pape s Pe cen age (%)
China 77 61.11% Chinese-Funded 70 55.56%
Taiwan 9 7.14% Non-Chinese-Funded 28 22.22%
Sou h Ko ea 2 1.59% To al Funded Pape s 98 77.78%
No way 1 0.79% Un unded Pape s 28 22.22%
Spain 4 3.17% To al Pape s 126 100%
I aly 5 3.97%
B azil 2 1.59%
Saudi A abia 3 2.38%
Uni ed Kingdom 4 3.17%
Ge many 2 1.59%
Po ugal 3 2.38%
Uni ed S a es 6 4.76%
Sou h A ica 1 0.79%
India 2 1.59%
F ance 1 0.79%
Finland 1 0.79%
To al 126 100%
Table 5
The dis ibu ion o a icles based on hei heo e ical app oaches.
Theo e ical App oach Reco d
Coun
Pe cen age
Dynamic Capabili ies View 11 8.7%
Resou ce-Based View (RBV) 9 7.1%
Knowledge-Based View (KBV) 8 6.3%
Open Inno a ion Theo y 7 5.6%
Abso p i e Capaci y Theo y 6 4.8%
Technology-O ganiza ion-En i onmen (TOE)
F amewo k
6 4.8%
Inno a ion Ambidex e i y 5 4.0%
En i onmen al Managemen /Ci cula Economy 4 3.2%
Ins i u ional Theo y 4 3.2%
O ganiza ional Lea ning Theo y 4 3.2%
Co po a e Social Responsibili y (CSR) 3 2.4%
Human Capi al Theo y 3 2.4%
O ganiza ional Change Theo y 3 2.4%
Sea ch and Recombina ion Theo y 2 1.6%
O ganiza ional Ine ia Theo y 2 1.6%
To al Fac o P oduc i i y (TFP) 2 1.6%
O ganiza ional Unlea ning Theo y 2 1.6%
Inno a ion Di usion Theo y 2 1.6%
Pa h Dependency Theo y 1 0.8%
Low-Ca bon Knowledge Sea ch (LCKS) 1 0.8%
G een Knowledge Managemen (GKM) 1 0.8%
He d Beha io Theo y 1 0.8%
R&D S a egy and Flexibili y 1 0.8%
Compe i i e S a egy Theo y 1 0.8%
Unspeci ied Theo y 26 20.6%
To al 126 100%
M. Saeedikiya e al.
Jou nal o Inno a ion & Knowledge 10 (2025) 100640
8
Ano he poin ela ed o pa icipa ion dynamics is ha , as no ed
ea lie , hese in e ac ions a e dynamic, eedback-d i en, and eal- ime.
The e o e, a esou ce-based concep ualiza ion o hese dynamics canno
p o ide a sound unde s anding o he na u e o he phenomenon.
Ra he , we need o ocus on heo ies ha concep ualize DT-d i en
inno a ion as a co-c ea i e, collec i e, collabo a i e, and dynamic
phenomenon ha shapes he in e ac ions, commi men s, and in en ions
o i s s akeholde s. The e o e, i is necessa y o econcep ualize hese
mechanisms and ocus on dynamism. In his ega d, some sample a i-
cles elied on pe spec i es such as he dynamic capabili y pe spec i e (e.
g., Pang & Wang, 2023; Zhang e al., 2022) o open inno a ion (e.g., Wu
e al., 2022) heo ies. Howe e , he alue co-c ea ion pe spec i e and
he social exchange heo y ha e no ye been explo ed in his con ex .
Fu u e esea ch should de elop new explana ions o DT-d i en inno-
a ion and pe o mance using dynamic lenses. Fo example, he use
pa icipa ion phenomenon on DT pla o ms using a dynamic capabili y
lens should answe he ollowing ques ion: How can i ms s eamline
hei inno a ion p ocess conside ing ex ended ma ke each and
enhanced use pa icipa ion hanks o DT? Simila ly, u u e esea ch
should answe he ollowing ques ion h ough a social exchange lens:
How do social exchange con ac s shape and ac in DT-enabled collab-
o a i e ne wo ks o inno a ion?
Mode a o s: Deepe in es iga ion o bounda y condi ions
Resea ch on he e ec s o DT on inno a ion has included a a ie y o
bounda y condi ions ha may pu some con ingencies on his e ec .
Al hough hese bounda y condi ions a e di e se, u he in es iga ion o
he con ex ual condi ions ela ed o DT and inno a ion could gene a e
aluable insigh s in o his in e play. Fo example, a coun y’s digi al
economy o in as uc u e de elopmen le el can boos i s en ep e-
neu ial ou comes (O landi e al., 2021). Simul aneously, digi al in a-
s uc u e can signal he a ailabili y o echnological o echnical capi al
o he success o DT ini ia i es. The e o e, gaining c oss-coun y o
compa a i e insigh s in o exis ing esea ch based on he digi al in a-
s uc u e de elopmen le el is aluable.
In addi ion o he in ense need o c oss-coun y insigh s, indus y-
le el insigh s should be inco po a ed in o exis ing esea ch. While
he e a e some insigh s in o he echnical and echnological aspec s o
indus y compe i ion (e.g., Niu e al., 2023; Zhou, Xu e al., 2023),
ma ke iza ion (Wang & He, 2024), echnological en i onmen (Wang
e al., 2023), and echnological upg ading (Yang & Deng, 2023), insigh s
in o he sociological and cul u al aspec s o indus ies a e a e. Fo
example, DT adop ion a ies acco ding o indus y s anda ds and ech-
nological capabili ies. Thus, he bene i s o DT o inno a ion may di e
ac oss i ms wi h di e en capabili ies and in di e en indus ies. In-
dus ies expe iencing high cus ome expec a ions should es ablish
co-c ea ion mechanisms h ough DT o be e mee ma ke ing demands.
While he bounda y condi ions o he di ec e ec o DT on inno-
a ion a e di e se, he mode a ing ac o s be ween he an e-
ceden –media o s and hose be ween media o s–ou comes a e unde -
explo ed. The e o e, a mo e de ailed unde s anding o he mode a o s
ha in luence an eceden –media o s and media o –ou comes is needed.
In he i s ca ego y, i m capabili ies, including dynamic capabili ies,
inno a ion capabili ies, decision-making s yles, and change manage-
men p ac ices, we e s udied. Howe e , many o he ac o s enable i ms
o ac i a e o ansla e DT in o hese media ing mechanisms. Su p is-
ingly, he e is a lack o s udies in es iga ing he en i onmen al and
con ex ual ac o s. Fo example, while DT enables i ms o access
inno a ion inancing o educe he inancial isks o inno a ion (Liu
e al., 2024; Q. H. Liu e al., 2024; Yong Xu e al., 2023), he ease o
access o inancing and he s eng h o inancial ins i u ions a he
egional o na ional le el can modi y his ela ionship. DT also p o ides
a knowledge-accele a ing mechanism (Gong e al., 2023; Lanzolla e al.,
2021; Scuo o e al., 2020; U bina i e al., 2020; an Mee e en e al.,
2022). Howe e , his accele a ion o knowledge sha ing and accumu-
la ion can depend on indus y no ms, he le el o echnological so-
phis ica ion, and he pe o mance o inno a ion ecosys ems and
ne wo ks in egions o indus ies. The e o e, indus ial, egional, and
na ional ins i u ions can ac i a e o enhance DT-enabled inno a ion
mechanisms.
The same lines o easoning can be applied o u he in es iga e he
mode a o s be ween he media o s and ou comes. Fo example, once
inno a ion mechanisms a e ac i a ed, i m cha ac e is ics should enable
hem o maximize hei use o hese mechanisms owa d inno a ion. In
ou sample a icles, a i m’s abso p i e capaci y, agili y, en i onmen al
dynamism, echnological capabili y, and ambidex ous inno a ion
s a egies we e ound o accele a e i s bene i s om hese mechanisms
owa d inno a ion. Fo example, he ma ke s uc u e and size can
de ine a i m’s incen i e o inno a e. In e ms o ma ke s uc u e, small
agmen ed ma ke s in which playe s ha e limi ed powe can incen i ize
i ms o inno a e as a means o di e en ia ion. In he p esence o la ge
playe s, inno a ion can ocus mo e on quali y o help small playe s
main ain hei ela i e powe . Ma ke s wi h highe le els o echnology
in as uc u e o hose wi h a high p esence o incuba o s can p o ide
i ms wi h ools o maximize hei bene i s om he inno a ion ha DT
enables. They can also help i ms imp o e hei ime- o-ma ke o mee
eme ging use demands. The egula o y en i onmen can also a ec a
i m’s inno a ion s anda ds and quali y (Q. Xu e al., 2023).
Table 7
Resea ch agenda based on bibliog aphic esul s.
Resea ch Agenda Gaps Fu u e Resea ch
Recommenda ions
Add essing
F agmen ed
Resea ch and
In e disciplina y
In eg a ion
F agmen ed esea ch
landscape ac oss disciplines,
unde ep esen a ion in
s a egy and
en ep eneu ship
In eg a e s a egic and
en ep eneu ial
pe spec i es o explo e
how DT s a egies impac
en ep eneu ial ac i i ies
and business model
inno a ions.
Inc easing
Geog aphical and
Funding Di e si y
Geog aphical concen a ion
in China, hea y eliance on
Chinese unding sou ces
Include pe spec i es om
unde ep esen ed egions
o cap u e a
comp ehensi e pic u e o
DT’s in luence on
inno a ion ac oss
di e en cul u al and
economic con ex s.
Di e si ying
Me hodological
App oaches
Limi ed adop ion o
longi udinal app oaches,
lack o me hodological
di e si y, and need o
mul i-le el s udies.
Inc ease he adop ion o
longi udinal app oaches
o unde s and how DT’s
pe o mance ou comes,
such as inno a ion,
eme ge o e ime.
Di e si y me hodological
app oaches, e.g., using
con igu a ional
app oaches (Lin e al.,
2022; Co ese e al.,
2024). Include mo e
mul i-le el s udies.
In es iga e eam-le el
dynamics, no ms, and
p ac ices enabling
inno a ion in digi ally
ans o med i ms.
In eg a ion o
Theo e ical
Pe spec i es
Lack o in eg a ion o
heo e ical pe spec i es a
di e en le els, limi ed
conside a ion o DT’s
dynamic na u e
In eg a e heo e ical
pe spec i es a mac o,
meso, and mic o le els o
be e concep ualize and
explain i ms’ inno a ion
ou comes. Conside he
dynamic na u e o he
exchanges be ween i ms
and hei wide
en i onmen beyond
iewing DT as me ely an
inno a ion op imiza ion
unc ion.
M. Saeedikiya e al.
Jou nal o Inno a ion & Knowledge 10 (2025) 100640
15
Finally, while many s udies in es iga e how DT b ings abou suc-
cess ul ou comes o a i m, he e is a pauci y o esea ch on he o ga-
niza ional oles ha ampli y DT’s pe o mance e ec s (Zoppelle o
e al., 2023). Fo example, he sample a icles show ha DT can inc ease
a i m’s knowledge dep h, b ead h, and esou ce sha ing. Howe e , i is
impo an o know how di e en oles and he s uc u e o hei e-
la ionships can acili a e he exchange o knowledge, esou ces, and
suppo o e ilize DT’s inno a ion ou comes.
Resea ch agenda based on bibliome ic esul s
Add essing agmen ed esea ch and in e disciplina y in eg a ion
Bibliome ic analysis e ealed a agmen ed esea ch landscape
ac oss a ious disciplines, including business and managemen , eco-
nomics, echnology and inno a ion, knowledge managemen , en i on-
men al managemen , in o ma ion sys ems, and inance. Jou nals such as
he Jou nal o Business Resea ch, Technological Fo ecas ing, and Social
Change publish he maximum numbe o a icles on DT and inno a ion.
Howe e , en ep eneu ship and s a egy jou nals we e unde ep e-
sen ed. Fu u e esea ch should b idge his gap by in eg a ing s a egic
and en ep eneu ial pe spec i es in he s udy o DT and inno a ion.
Fu he me hodological di e si ica ion and heo e ical in eg a ion
Mos a icles in his e iew we e based on a single heo y (e.g.,
Holopainen e al., 2022), and 26 did no ha e dis inc i e heo e ical
pe spec i es. Su p isingly, many s udies ha e used single heo e ical
lenses o s udy a mul i ace ed, in e disciplina y phenomenon and i s
pe o mance e ec s, and mos s udies ha e app oached DT’s pe o -
mance e ec s in c oss-sec ional designs (e.g., Ba agan e al., 2024;
Lozada e al., 2023) o sho pe iods a e implemen a ion (e.g., Yong Y.
Zhao e al., 2023; Xu e al., 2023). Combining hese wo endencies in
he sample a icles, we a gue ha u u e esea ch could ely on adop ing
mo e longi udinal app oaches and in eg a ing mo e heo e ical lenses
(mul i-le el designs). Speci ically, longi udinal s udies p o ide an op-
po uni y o unde s and how DT pe o mance ou comes, such as inno-
a ion, eme ge o e ime. In e ms o he need o mul i-le el s udies o
mul i-pe spec i e ones (U bina i e al., 2020), he sample a icles sug-
ges ha u u e esea ch can be e concep ualize and explica e he
inno a ion ou comes o i ms using a combina ion o mac o-, meso‑,
mic o le els, and heo e ical pe spec i es.
Conside ing heo e ical in eg a ion, mul i-le el me hodological ap-
p oaches o unde s anding DT and i s ela ionship wi h inno a ion can
be a ui ul s udy a ea. Fo example, while ansac ion cos economics
can p o ide some insigh s in o in o ma ion asymme y and ansac ion
cos s, i can be in eg a ed wi h inno a ion di usion heo ies o o he
i m-le el heo ies, such as a knowledge-based iew o he i m, o be e
concep ualize he isk pe cep ion o incen i e s uc u es o knowledge
sha ing o DT-enabled inno a ion. These insigh s can be u he
enhanced using game- heo y app oaches o unde s and use s’
knowledge-sha ing beha io s. Simila ly, he social exchange heo y can
be coupled wi h he knowledge in eg a ion pe spec i e o be e explain
he na u e o ela ional con ac s ha a ec use s’ knowledge-sha ing
beha io s and mechanisms o DT-enabled in e ac ions and alue co-
c ea ion e o s. The same easoning can be applied o sha ed e-
sou ces and go e nance mechanisms beyond he i m le el (e.g.,
ecosys em) and how hese exchanges can elimina e oppo unis ic be-
ha io s when using sha ed esou ces and enhance he iabili y o he
ecosys em o digi al pla o ms on which i ms play a ole (Zoppelle o
e al., 2023).
P ac ical and policy implica ions
This sys ema ic e iew e ealed ha DT’s po en ial o boos inno-
a ion was mos e ec i e when aligned wi h an o ganiza ion’s s a egic
o ien a ions, en i onmen al con ingencies, i m cha ac e is ics, and
con ex . DT mus be iewed no me ely as a echnological upg ade o
enewal bu also as a s a egic asse o boos ing inno a ion in p oduc s,
se ices, and p ocesses. An in e ac i e and syne ge ic ela ionship
among echnology, c ea i i y, and he ex e nal en i onmen is equi ed
o bene i om DT owa d inno a ion and i m pe o mance.
Ano he key insigh om his e iew is ha DT deals wi h he
knowledge and esou ce dynamics a ound i ms and acili a es inno a-
ion mechanisms by connec ing a ious s akeholde s such as cus ome s,
supplie s, and pa ne s h ough digi al ecosys ems. Howe e , he
assump ion ha DT au oma ically enhances inno a ion pe o mance
may be misleading. To le e age he ull po en ial o DT owa d inno-
a ion, i ms mus communica e a cul u e o openness, knowledge
sha ing, suppo , and us wi hin hemsel es and he b oade ecosys em
and ensu e ha DT is aligned wi h he in e nal s a egies and he
esou ce and knowledge dynamics in he b oade ecosys em and
en i onmen .
Conclusion
The cu en s udy aimed o in es iga e he s a e o esea ch on DT’s
e ec on inno a ion and p o ide a esea ch agenda ha ac s as a
oadmap o u u e esea ch. Based on a bibliog aphic and con en
analysis o 126 a icles, his s udy p esen s a mul i-le el amewo k o
unde s anding he e ec s o DT on inno a ion, media ing mechanisms,
and bounda y condi ions. Gi en he con ingencies shaped by he
s uc u al, en i onmen al, and i m cha ac e is ics ha a ec he
magni ude and scope o his e ec , he amewo k p o ides a unda-
men al unde s anding o how he e ec s o DT on i m inno a ion and
pe o mance a e ac ualized.
CRediT au ho ship con ibu ion s a emen
Meh zad Saeedikiya: W i ing – e iew & edi ing, W i ing – o iginal
d a , Me hodology, In es iga ion, Fo mal analysis, Da a cu a ion,
Concep ualiza ion. Sandeep Salunke: W i ing – e iew & edi ing,
W i ing – o iginal d a , Valida ion, Supe ision, Me hodology, In es-
iga ion, Da a cu a ion, Concep ualiza ion. Ma ek Kowalkiewicz:
W i ing – e iew & edi ing, W i ing – o iginal d a , Visualiza ion, Su-
pe ision, Me hodology, Concep ualiza ion.
Decla a ion o compe ing in e es
The au ho s decla e ha hey ha e no known compe ing inancial
in e es s o pe sonal ela ionships ha could ha e appea ed o in luence
he wo k epo ed in his pape .
Acknowledgmen s
We would like o hank he edi o ial eam o JIK, and he e iewe s
o hei in aluable eedback h oughou he e iew p ocess. The i s
au ho would also like o hank P o esso Michael Rosemann, D . Zeynab
Aeeni, he QUT Cen e o Fu u e En e p ise membe s, and he QUT
Aus alian Cen e o En ep eneu ship Resea ch membe s o hei
suppo du ing his esea ch.
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Au ho Biog aphies: Meh zad Saeedikiya is a Ph.D. Schola a Queensland Uni e si y o
Technology Business School, whe e he is he ecipien o wo p es igious schola ships om
QUT and he Cen e o Fu u e En e p ise. Meh zad has con ibu ed his expe ise h ough
eaching and esea ch oles a Tsinghua Uni e si y, Bologna Uni e si y, UAB Ba celona,
and he Uni e si y o Milan. His cu en esea ch a QUT is cen e ed on explo ing he
in e sec ion o digi al ans o ma ion and inno a ion. Guided by QUT’s leade ship in he
in o ma ion sys em and en ep eneu ship domains, Meh zad is in e es ed in how dynamic
capabili ies eme ge and in e ac wi h o he i m capabili ies, ul ima ely in luencing
companies’ inno a i e pe o mance. Meh zad’s wo k on he in e play o digi al echnol-
ogy, digi al ans o ma ion, and inno a ion has appea ed in he Jou nal o Cleane P o-
duc ion, In e na ional Jou nal o En ep eneu ship Beha iou and Resea ch and Small Business
Economics.
Sandeep Salunke: Associa e P o esso Sandeep Salunke ea ned his PhD a he Uni e si y
o Queensland. His doc o al hesis in es iga es he compe i i e s a egies o en ep e-
neu ial p ojec -o ien ed i ms h ough he lens o dynamic capabili ies. Sandeep’s esea ch
is being de eloped in o pape s o leading academic jou nals, including Indus ial Ma -
ke ing Managemen and Jou nal o Business Resea ch, and Jou nal o P oduc Inno a ion
Managemen , o name a ew. He is in ol ed in h ee la ge ARC Disco e y p ojec s a QUT
Business School and UQ Business School. Sandeep’s esea ch is cen e ed a ound dynamic
capabili ies, se ice inno a ion, compe i i e s a egy, and echnology en ep eneu ship.
Ma ek Kowalkiewicz: Ma ek Kowalkiewicz is a P o esso and Chai o Digi al Economy a
QUT Business School. Recognized as one o he Top 100 Global Though Leade s in A i-
icial In elligence by hinke s360, he has ex ensi e expe ience leading global inno a ion
eams in Silicon Valley and holding esea ch posi ions a SAP and Mic oso Resea ch Asia.
His upcoming book, i led "The Economy o Algo i hms: AI and he Rise o he Digi al
Minions," del es in o he impac o AI on he digi al economy.
M. Saeedikiya e al.
Jou nal o Inno a ion & Knowledge 10 (2025) 100640
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