The nexus of big data analytics, knowledge sharing, and product innovation in manufacturing

Yildiz, Bülen ; Çiğdem, Şemse in; Meidu ė-Ka aliauskienė, Ie a; Činčikai ė, Rena a
A icle
The nexus o big da a analy ics, knowledge sha ing, and
p oduc inno a ion in manu ac u ing
Jou nal o Business Economics and Managemen (JBEM)
P o ided in Coope a ion wi h:
Vilnius Gediminas Technical Uni e si y (VILNIUS TECH)
Sugges ed Ci a ion: Yildiz, Bülen ; Çiğdem, Şemse in; Meidu ė-Ka aliauskienė, Ie a; Činčikai ė,
Rena a (2024) : The nexus o big da a analy ics, knowledge sha ing, and p oduc inno a ion in
manu ac u ing, Jou nal o Business Economics and Managemen (JBEM), ISSN 2029-4433, Vilnius
Gediminas Technical Uni e si y, Vilnius, Vol. 25, Iss. 1, pp. 66-84,
h ps://doi.o g/10.3846/jbem.2024.20713
This Ve sion is a ailable a :
h ps://hdl.handle.ne /10419/317669
S anda d-Nu zungsbedingungen:
Die Dokumen e au EconS o dü en zu eigenen wissenscha lichen
Zwecken und zum P i a geb auch gespeiche und kopie we den.
Sie dü en die Dokumen e nich ü ö en liche ode komme zielle
Zwecke e iel äl igen, ö en lich auss ellen, ö en lich zugänglich
machen, e eiben ode ande wei ig nu zen.
So e n die Ve asse die Dokumen e un e Open-Con en -Lizenzen
(insbesonde e CC-Lizenzen) zu Ve ügung ges ell haben soll en,
gel en abweichend on diesen Nu zungsbedingungen die in de do
genann en Lizenz gewäh en Nu zungs ech e.
Te ms o use:
Documen s in EconS o may be sa ed and copied o you pe sonal
and schola ly pu poses.
You a e no o copy documen s o public o comme cial pu poses, o
exhibi he documen s publicly, o make hem publicly a ailable on he
in e ne , o o dis ibu e o o he wise use he documen s in public.
I he documen s ha e been made a ailable unde an Open Con en
Licence (especially C ea i e Commons Licences), you may exe cise
u he usage igh s as speci ied in he indica ed licence.
h ps://c ea i ecommons.o g/licenses/by/4.0/
This is an Open Access a icle dis ibu ed unde he e ms o he C ea i e Commons A ibu ion License (h p://c ea i ecommons.o g/
licenses/by/4.0/), which pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in any medium, p o ided he o iginal au ho and sou ce
a e c edi ed.
Copy igh © 2024 The Au ho (s). Published by Vilnius Gediminas Technical Uni e si y
ISSN 1611-1699 / eISSN 2029-4433
2024
Volume 25
Issue 1
Pages 66–84
h ps://doi.o g/10.3846/jbem.2024.20713
THE NEXUS OF BIG DATA ANALYTICS, KNOWLEDGE SHARING, AND
PRODUCT INNOVATION IN MANUFACTURING
Bülen YILDIZ 1, Şemse in ÇIĞDEM 2, Ie a MEIDUTĖ-KAVALIAUSKIENĖ 3, Rena a ČINČIKAITĖ 4
1Facul y o Economics and Adminis a i e Sciences, Kas amonu Uni e si y, Kas amonu, Tu key
2Facul y o Economics, Managemen and Law, Khoja Akhme Yassawi In e na ional Kazakh-Tu kish Uni e si y,
Tu kes an, Kazakhs an
3Ins i u e o Dynamic Managemen , Business Managemen Facul y, Vilnius Gediminas Technical Uni e si y,
Vilnius, Li huania
4Business Managemen Facul y, Vilnius Gediminas Technical Uni e si y, Vilnius, Li huania
A icle His o y: Abs ac . In oday’s highly compe i i e business en i onmen s, manu ac u e s ace s i com-
pe i ion. As digi al echnologies ha e become mo e pe asi e, many businesses in he man-
u ac u ing sec o ha e begun o ap in o he po en ial o big da a analy ics o gain an edge
in hei ma ke s. Companies in he manu ac u ing sec o can gain a signi ican compe i i e
ad an age by s a egically u ilizing big da a analy ics o unco e p o ound insigh s ha ha e
he po en ial o signi ican ly enhance hei capabili ies in p oduc inno a ion.
This esea ch del es in o communica ion’s ole as a go-be ween o big da a analy ics and
p oduc inno a ions’ success a manu ac u ing i ms. The alidi y and eliabili y o he meas-
u emen scales we e i s ho oughly examined in his s udy. The esea ch model was hen
es ed using s uc u al equa ion modeling and p ocess mac o analysis.
The analy ical indings un eil hose big da a analy ics exe a p onounced, posi i e, and s a is-
ically signi ican impac on p oduc inno a ion pe o mance and in o ma ion-sha ing dynam-
ics. Fu he mo e, i is disce ned ha in o ma ion-sha ing exe s a subs an ial and a i ma i e
in luence on he capaci y o p oduc inno a ion. Addi ionally, i is es ablished ha he impac
o big da a analy ics on p oduc inno a ion pe o mance unde goes mode a ion by he in o -
ma ion-sha ing mechanism.
■ ecei ed 08 Sep embe 2023
■ accep ed 18 Decembe 2023
Keywo ds: big da a analy ics, p oduc inno a ion, in o ma ion sha ing, analy ics-d i en inno a ion, da a analy ics in manu ac u ing,
inno a ion pe o mance.
JEL Classi ica ion: M00, O31, D83.
Co esponding au ho . E-mail: ie a.meidu e-k[email p o ec ed]
JOURNAL o BUSINESS
ECONOMICS & MANAGEMENT
1. In oduc ion
The de elopmen o compu e and in e ne echnologies has elimina ed he p oblem o
accessing da a, a p ima y issue 20 yea s ago. Howe e , he widesp ead use o in o ma ion
echnologies, pa icula ly mobile echnologies and social media, has led o he accumula ion
o as amoun s o da a, which is con inuing o accele a e (M. Chen e al., 2014). Digi al ech-
nology has allowed o excessi e da a s o age, making i easy o access la ge amoun s o da a
(Elgendy & El agal, 2014). As a esul , he amoun o da a p oduced, s o ed, and manipula ed
has signi ican ly inc eased, leading o he de elopmen o big da a and da a science (Gü -
sakal, 2017). This de elopmen has made da a and i s analysis he essen ial opics in mode n
science and business (Kaly as & Albe son, 2015) as da a is ob ained om a ious sou ces.
The de elopmen o in e ne echnology has esul ed in almos all da a being p oduced and
Jou nal o Business Economics and Managemen , 2024, 25(1), 66–84 67
p ocessed by in e ne companies (Sagi oglu & Sinanc, 2013), such as Google, Facebook,
Baidu, Taobao, and Alibaba, which p ocess pe aby es o da a.
Big da a’s ole in mode n socie y is pi o al, as i unde pins inno a ion and compe i i e
p owess in business and science. The ad en o echnologies like social media and sma
de ices has led o an unp eceden ed da a deluge, which, when ha nessed, can o e com-
panies a compe i i e edge (Hu e al., 2021). Since he 1990s, knowledge managemen has
been c ucial o le e aging expe ise o os e inno a ion and main ain ma ke leade ship. In
oday‘s global economy, he abili y o ans o m da a in o ac ionable knowledge is essen ial
o success in all indus ies (Tian, 2017). The shi om he “IT Age” o he “Da a Age” is
ma ked by a su ge in knowledge and echnological p og ess, eshaping human ci iliza ion.
Big da a’s in luence is p o ound and wide- eaching, se ing as a key s a egic asse ha d i es
co po a e inno a ion, compe i i eness, and p oduc i i y (Su e al., 2022).
Big da a analy ics (BDA), he managemen , analysis, and p ocessing o la ge amoun s
o da a, is becoming a popula opic o p ac i ione s and esea che s as i helps o gan-
iza ions imp o e ope a ional e iciency, s a egic di ec ion, cus ome se ice, p oduc and
se ice de elopmen , and mo e. Companies mus e alua e he e ec s o BDA capabili ies
on pe o mance o s ay compe i i e (Bah ami & Shokouhya , 2021). New echnologies like
AI, he In e ne o Things, and cloud compu ing ha e c ea ed unp eceden ed da a c i ical o
compe i i e ad an age, business pe o mance, and inno a ion (Muni e al., 2023). Resea ch-
e s and p ac i ione s a e in e es ed in BDA and managemen ools o imp o e e iciency and
decision-making. Business manage s mus adop new echnology o s ay compe i i e and
unde s and cus ome needs (Saleem e al., 2021). New p oduc inno a ion elies hea ily on
mobile de ices, social media pla o ms, and he in e ne o es ablish be e cus ome con-
nec ions and ecei e eedback as e and cheape han o icial su eys (Zhan e al., 2017).
P oduc Inno a ion Capaci y (PIC) helps o manage o ganiza ional knowledge o imp o e
cus ome se ice and success. Companies mus inno a e cons an ly and in ol e supplie s o
enhance inno a ion, lexibili y, quali y, de elopmen ime, and cos , bu i can educe con ol
o e he p ojec i no managed p ope ly (Ak oush & Awwad, 2018; Kulanga a e al., 2016;
Zhan e al., 2017).
In o ma ion sha ing be ween companies is c ucial in new p oduc de elopmen because
i allows o be e coo dina ion and collabo a ion among pa ne s, imp o es communica ion,
and educes he isk o delays o e o s. By sha ing in o ma ion such as p oduc designs,
p oduc ion schedules, and in en o y le els, pa ne s can iden i y and esol e po en ial issues
ea ly on, which can help o speed up he p oduc de elopmen p ocess and ensu e ha he
inal p oduc mee s he needs and expec a ions o cus ome s (Chen e al., 2021; Wang e al.,
2020). In o ma ion sha ing can lead o mo e e icien and cos -e ec i e p oduc ion p ocesses
and lexibili y and adap abili y o changing ma ke condi ions (Huo e al., 2021).
Companies wi h comp ehensi e ma ke knowledge can in eg a e a ious ma ke insigh s
o enhance p oduc inno a ion. This dep h o unde s anding, encompassing cus ome and
compe i o insigh s, is pi o al o p ac ical inno a ion and p oblem-sol ing. I allows i ms o
disce n complex ela ionships be ween cus ome needs and compe i o o e ings, os e ing
he c ea ion o supe io p oduc s. Companies can os e inno a ion and de elop solu ions
ha esona e wi h cus ome needs by empowe ing employees and cus ome s wi h he neces-
sa y esou ces and a suppo i e en i onmen (Wan & Liu, 2021). Regula cus ome da a anal-
ysis, including dynamic ma ke segmen a ion, is c ucial o an icipa ing cus ome demands,
which equi es signi ican esou ce in es men (Fe nando e al., 2018). A company‘s inno a i e
capabili ies, de ined as he abili y o gene a e and implemen new ideas and solu ions, a e
68 B. Yildiz e al. The nexus o big da a analy ics, knowledge sha ing, and p oduc inno a ion in manu ac u ing
c i ical o esponsi eness o ma ke demands and can signi ican ly in luence i s compe i i e
s ance and g ow h (Bah ami & Shokouhya , 2021).
S udies examine he e ec s o BDA and in o ma ion sha ing on new p oduc de elopmen
by imp o ing inno a ion capabili y. Companies can use BDA o analyze in o ma ion supplie s
sha e on aw ma e ials and componen s o iden i y ends and pa e ns ha can in o m new
p oduc de elopmen o he op imiza ion o exis ing ones (Sun & Liu, 2021; Tsang e al.,
2022). In addi ion, in o ma ion sha ing be ween companies can posi i ely impac inno a ion
capabili y (Jiaxi, 2009). Companies can also use BDA o analyze in o ma ion sha ed by cus-
ome s and o he s akeholde s o iden i y new p oduc ea u es o se ices ha mee hei
needs o p e e ences.
Ad anced BDA capabili ies allow a company o ga he and analyze di e se da a, yielding
mo e p ecise insigh s and imp o ing in o ma ion sha ing wi hin he o ganiza ion and wi h
pa ne s, hus enhancing e iciency and decision-making (Mo imu a & Sakagawa, 2023; Jans-
sen e al., 2017).
BDA capabili y can au oma e da a p ocessing asks and ensu e da a secu i y. Mo e ac-
cu a e and comp ehensi e da a inpu s can posi i ely impac in o ma ion sha ing and PIC.
The e o e, his esea ch aimed o examine he ole o in o ma ion exchange as a media o
be ween BDA capabili y and PIC.
The s udy p esen s he heo e ical amewo k in Sec ion 2, whe e we in oduce BDA, in-
o ma ion sha ing, and PIC. In Sec ion 3, we explain he ma e ials and me hods used in he
s udy, including he da a collec ion and analysis echniques employed. The indings o he
s udy a e p esen ed in Sec ion 4, whe e he e ec s o BDA and in o ma ion sha ing on PIC
a e analyzed. In Sec ion 5, we discuss he implica ions o hese indings o p ac i ione s and
esea che s, including he po en ial o inc eased compe i i eness and pe o mance h ough
BDA and e ec i e in o ma ion sha ing. Finally, in Sec ion 6, we summa ize he s udy’s key
akeaways and iden i y oppo uni ies o u u e esea ch.
2. Theo e ical amewo k
2.1. Big da a analy ics
The concep o BDA has e ol ed, beginning wi h he de elopmen o la ge-scale da a p o-
cessing sys ems in he 1960s and 1970s (Bo ko ich & Noah, 2014). These ea ly sys ems
we e p ima ily used o scien i ic and go e nmen esea ch. Howe e , as echnology has
p og essed, he a ailabili y and a o dabili y o da a s o age and p ocessing powe ha e
inc eased, making BDA mo e accessible o o ganiza ions o all sizes. Wi h he ad en o he
in e ne and he explosion o digi al da a in he 21s cen u y, BDA has become an inc easingly
impo an a ea o esea ch and de elopmen . As a esul , a ious BDA ools and echnologies
ha e been de eloped o handle he olume, eloci y, and a ie y o big da a (McA ee e al.,
2012).
“Big da a” desc ibes he massi e amoun s o in o ma ion c ea ed and collec ed daily (Sun
& Liu, 2021). This da a can come om a ious sou ces, including social media, senso s, and
ansac ional sys ems. The high olume, eloci y, and a ie y o big da a make i challenging
o p ocess and analyze wi h con en ional da a managemen me hods. Da a can be ca ego-
ized in o h ee b oad ca ego ies: olume ( he o al amoun o da a being gene a ed), eloci y
(how quickly ha da a is being gene a ed), and a ie y ( he di e en o ma s in which ha
da a is being gene a ed, such as ex , images, and sound) (Gandomi & Haide , 2015; In eza i
& G essel, 2017; Liedong e al., 2020).
Jou nal o Business Economics and Managemen , 2024, 25(1), 66–84 69
In o ma ion is he d i ing o ce behind a company’s s a egic, ac ical, and ope a ional
decision-making. Howe e , he amoun o in o ma ion and da a companies collec apidly
inc eases, making i di icul o businesses o iden i y and ex ac he mos ele an in o -
ma ion o manage hei ope a ions and supply chain. The e m “BDA” has eme ged in his
con ex , poin ing o new oppo uni ies o explo ing and u ilizing la ge da a se s (Kache &
Seu ing, 2017).
BDA in ol es i e key s eps: da a access and s o age, p ep ocessing, in eg a ion, analysis,
and in e p e a ion, each c i ical o ealizing da a’s ull alue (Ye e al., 2021). Howe e , he
bene i s o BDA a e con ingen on he go e nance o p ocesses and s uc u es ha dic a e he
a ailabili y and analysis o in o ma ion, emphasizing he need o s a egic esou ce alloca ion
o enhance business capabili ies (Mikale e al., 2020). BDA applica ions in business s eamline
supply chain managemen by op imizing in en o y and o ecas ing, enhancing pe o mance,
and bols e ing secu i y h ough isk analysis (Raman e al., 2018). In manu ac u ing, BDA aids
in boos ing e iciency, cu ing cos s, and enhancing quali y con ol ac oss p oduc ion s ages
(Yin & Kaynak, 2015).
BDA can imp o e p oduc inno a ion by analyzing da a om a ious sou ces, such as
cus ome eedback, ma ke esea ch, and compe i o analysis. Addi ionally, i can imp o e
he speed and e iciency o he de elopmen p ocess by iden i ying pa e ns and ends in
de elopmen da a.
2.2. In o ma ion sha ing
In o ma ion sha ing e e s o exchanging in o ma ion among indi iduals, o ganiza ions, o
sys ems. In o ma ion sha ing be ween i ms e e s o exchanging in o ma ion among di e en
o ganiza ions. This p ocess can include sha ing knowledge, da a, and o he in o ma ion, such
as ma ke ends, bes p ac ices, and new echnologies (Ma ko ic & Baghe zadeh, 2018).
In o ma ion sha ing is pi o al in enhancing i m pe o mance, os e ing inno a ion, and
sha pening compe i i eness. I ca alyzes o ganiza ional collabo a ion and communica ion,
which a e essen ial o s eamlining business p ocesses and acili a ing e ec i e p oduc de-
elopmen (Hsu e al., 2008; Huo e al., 2021). By sha ing knowledge, expe ise, and echnolo-
gy among s akeholde s – including cus ome s, supplie s, and employees – i ms can le e age
esou ces hey lack in e nally, he eby boos ing hei compe i i e edge and pe o mance
(Şahin & Topal, 2019). The PIC amewo k unde sco es ha he exchange o in o ma ion mus
be imely, ele an , comple e, and accu a e, suppo ing he inno a ion p ocess, accele a ing
de elopmen imes, and imp o ing he success a es o new p oduc s. This s a egic sha ing
is ins umen al in iden i ying new oppo uni ies, educing de elopmen isks, and p eemp -
ing po en ial p oblems, he eby con ibu ing o a i m’s adap i e and inno a i e capabili ies
(Zhou & Ben on, 2007; Huo e al., 2021).
In o ma ion sha ing enables access o ex e nal knowledge and echnology, c ucial o
success ul new p oduc de elopmen . I os e s in e - i m collabo a ion, us , and e iciency
in p oduc de elopmen p ocesses (Raga z e al., 2002; Swink & Song, 2007; Bs iele , 2006).
Se e al ac o s can in luence he e ec i eness o in o ma ion sha ing in p oduc inno a-
ion. These include he echnology used o in o ma ion sha ing, he le el o us and com-
mi men among s akeholde s, and he le el o unce ain y in he in o ma ion sha ed (Le e al.,
2021). Addi ionally, ac o s such as cul u e (Ma as, 2017), o ganiza ional s uc u e (Che ian,
2007), and leade ship (Hoch, 2014) can also play a ole in de e mining he e ec i eness o
in o ma ion sha ing.

70 B. Yildiz e al. The nexus o big da a analy ics, knowledge sha ing, and p oduc inno a ion in manu ac u ing
2.3. P oduc inno a ion capabili y
PIC has been de ined and ope a ionalized in a ious ways in he li e a u e. Some esea che s
de ine PIC as de eloping and in oducing new p oduc s (Ma ko ic & Baghe zadeh, 2018).
O he s desc ibe i as imp o ing exis ing ou comes o con inuously c ea ing new p oduc
lines. S ill, o he s de ine i as he abili y o manage he en i e p oduc de elopmen p ocess
e icien ly and e ec i ely, om idea gene a ion o comme cializa ion. Despi e hese di e en
de ini ions, he e is a common unde s anding ha PIC is an in ica e and mul i- ace ed con-
cep inco po a ing echnical and o ganiza ional abili ies. As a esul , P oduc Imp o emen
Capabili y (PIC) desc ibes a business’s p opensi y o c ea e and launch inno a i e new p od-
uc s (Naja i-Ta ani e al., 2018). In oday’s ie cely compe i i e ma ke place, i is essen ial o
a company’s long- e m su i al and compe i i eness (Sla e e al., 2014).
The p oduc de elopmen p ocess, which can shed ligh on PIC’s undamen al aspec s,
is he s eps a business akes o c ea e and launch a new p oduc . These ac i i ies include
idea gene a ion, concep de elopmen , design and de elopmen , es ing and alida ion,
and comme cializa ion. A company’s PIC can be e alua ed based on i s abili y o e ec i ely
manage and coo dina e hese ac i i ies (Gonzalez-Zapa e o e al., 2016). Ano he way o
unde s and PIC’s key componen s o dimensions is o examine he company’s esou ces.
Resou ces include angible and in angible asse s, such as inancial esou ces, human e-
sou ces, echnology, and knowledge. A company’s PIC can be e alua ed based on i s abili y
o access and e ec i ely u ilize hese esou ces (AL-Kha ib, 2022; Naja i Ta ani e al., 2013;
Thomas, 2013).
In addi ion o he p oduc de elopmen p ocess and esou ces, he cul u e and leade ship
o a company also play a c i ical ole in i s PIC. A cul u e ha encou ages and suppo s inno-
a ion and leade ship ha is commi ed o inno a ion and p o ides di ec ion and suppo can
enable a company o c ea e and implemen new p oduc s (Szczepańska-Woszczyna, 2015).
2.4. De elopmen o hypo heses
BDA and in o ma ion sha ing ha e p o oundly impac ed p oduc inno a ion in ecen yea s.
By allowing companies o ga he and analyze as amoun s o da a, BDA has gi en i ms he
abili y o gain insigh s in o cus ome beha io and p e e ences ha we e p e iously una ain-
able. BDA helps in he de elopmen p ocess o new and imp o ed p oduc s ha a e be e
ailo ed o mee he needs o consume s.
BDA ans o ms o ganiza ional in o ma ion sha ing by acili a ing he collec ion, p ocess-
ing, and analysis o ex ensi e da a, leading o deepe insigh s and mo e s a egic decisions,
he eby enhancing collabo a ion (Capu o e al., 2021). I unco e s hidden pa e ns and
ends, enabling i ms o dissemina e mo e pe inen in o ma ion and collabo a e mo e e -
ec i ely, ensu ing ha all ele an pa ies ha e access o sha ed da a and insigh s o op imal
decision-making (Liedong e al., 2020; Liu & Wang, 2018).
BDA also allows o mo e e ec i e decision-making by p o iding eal- ime insigh s. Wi h
eal- ime da a, companies can make decisions in eal- ime in o ma ion, which leads o mo e
accu a e in o ma ion and, hus, be e decisions (Wan & Liu, 2021). BDA also leads o de-
eloping new echnologies and pla o ms ha acili a e in o ma ion sha ing. Wi h he help
o BDA, new echnologies ha e been de eloped, such as da a-sha ing pla o ms and da a
isualiza ion ools. These echnologies make sha ing in o ma ion and insigh s easie , leading
o be e collabo a ion and decision-making (Hade e al., 2022).
Jou nal o Business Economics and Managemen , 2024, 25(1), 66–84 71
O e all, BDA has had a signi ican impac on he way ha in o ma ion is sha ed and on
he quali y o i (Bah ami & Shokouhya , 2021) and is used wi hin o ganiza ions. By allowing
companies o ga he , p ocess, and analyze as amoun s o da a, BDA has enabled i ms o
gain insigh s and make mo e in o med decisions.
The e o e, he ollowing hypo hesis has been de eloped:
H1: BDA has a posi i e e ec on in o ma ion sha ing.
One o he i al bene i s o BDA in p oduc inno a ion is ha i allows companies o iden i y
ends and pa e ns in consume beha io ha we e p e iously hidden. Fo example, by analyz-
ing da a on cus ome pu chases and b owsing habi s, a company may iden i y pa e ns in he
ypes o p oduc s ha cus ome s a e mos in e es ed in (C. Lin e al., 2022). BDA can in o m he
de elopmen o new p oduc s o he imp o emen o exis ing ones. Addi ionally, BDA enables
companies o segmen hei cus ome s based on demog aphics, pu chase his o y, and o he
da a poin s, leading o mo e pe sonalized p oduc s and se ices (Capu o e al., 2021).
BDA aids in p oduc inno a ion by allowing o he swi and e icien es ing o new concep s,
using consume beha io da a o gauge po en ial success and guiding esou ce alloca ion. I also
enables ongoing p oduc pe o mance moni o ing, acili a ing ea ly de ec ion and esolu ion o
issues, he eby enhancing p oduc success and educing he isk o ailu e (Zhan e al., 2017).
BDA also allows companies o unde s and he compe i i e landscape mo e deeply. A com-
pany can iden i y a eas whe e i may gain an ad an age by analyzing da a on he p oduc s and
se ices o e ed by compe i o s (Calic & Ghasemaghaei, 2021). This can help a company de-
elop p oduc s be e sui ed o consume s’ needs while posi ioning hem mo e compe i i e in
he ma ke place. Addi ionally, BDA can enable companies o iden i y new ma ke oppo uni ies,
leading o new p oduc de elopmen and en y in o new ma ke s (Tunc-Abubaka e al., 2023).
Fu he mo e, BDA can enable companies o op imize hei supply chain, leading o mo e
e icien and cos -e ec i e p oduc de elopmen and deli e y. Fo example, by analyzing da a
on in en o y le els, deli e y imes, and o he ac o s, companies can iden i y bo lenecks and
ine iciencies in hei supply chain, which can hen be add essed o imp o e pe o mance
(AL-Kha ib, 2022).
The e a e s udies (Bah ami & Shokouhya , 2021; Con e as Pinoche e al., 2021; Fe nando
e al., 2018; C. Lin e al., 2022; Mikale e al., 2020; Muni e al., 2023) in he li e a u e ha e
ound ha BDA has an impac on p oduc inno a ion and de elopmen .
The e o e, he ollowing hypo hesis has been de eloped:
H2: BDA has a posi i e e ec on PIC.
One undamen al way ha in o ma ion sha ing can impac p oduc inno a ion is h ough
he de elopmen o new echnologies (Makkonen e al., 2014). When companies and o gan-
iza ions sha e in o ma ion abou hei esea ch and de elopmen ac i i ies, hey can lea n
om one ano he and build upon each o he ’s wo k (Ali, 2023). This can lead o he apid
ad ancemen o echnologies and he c ea ion o new p oduc s and se ices ha inco po-
a e hese echnologies. In manu ac u ing, in o ma ion sha ing can lead o he de elopmen
o new and mo e e icien p oduc ion me hods, ul ima ely leading o lowe cos s and mo e
a o dable p oduc s o consume s.
Mo eo e , in o ma ion sha ing can help companies and o ganiza ions iden i y new ma ke
oppo uni ies and de elop new business models. When companies sha e in o ma ion abou
hei cus ome s, hey can lea n abou hei needs, p e e ences, and ends in hei espec i e
indus ies (M. J. Lin & Chen, 2008). This can help hem o iden i y new p oduc s and se ices
72 B. Yildiz e al. The nexus o big da a analy ics, knowledge sha ing, and p oduc inno a ion in manu ac u ing
ha a e in high demand, as well as o de elop new business models ha be e mee hese
needs. Companies can sha e in o ma ion abou consume beha io and p e e ences, which
can help hem iden i y new p oduc s in high demand and de elop new business models ha
be e mee hese needs (R. Lin e al., 2012).
In o ma ion sha ing signi ican ly in luences p oduc inno a ion by os e ing collabo a ion
and he exchange o esou ces among companies, which is essen ial o de eloping new
echnologies and se ices ha bene i socie y and spu business g ow h (Ak oush & Awwad,
2018; Fayyaz e al., 2021; Keszey, 2018; Ma ko ic & Baghe zadeh, 2018). BDA s eng hens
his impac by enhancing he e ec i eness and e iciency o he in o ma ion-sha ing p ocess,
he eby boos ing he capaci y o p oduc inno a ion.
The e o e, he ollowing hypo heses ha e been de eloped:
H3: In o ma ion sha ing has a posi i e e ec on PIC.
H4: In o ma ion sha ing has a media ion e ec on he impac o BDA on PIC.
The model o he s udy is shown in Figu e 1.
Figu e 1. Resea ch model
3. Ma e ials and me hods
3.1. Sample and da a collec ion
A su ey was emailed o 1000 manu ac u ing companies o in es iga e how BDA a ec s he
capaci y o p oduc inno a ion and how in o ma ion sha ing media es his e ec . Du ing
Janua y and May o 2022, he su ey was a ailable. A e he ini ial emails we e sen o he
pa icipan s, 93 usable esponses we e collec ed. Only 29 alid esponses we e ecei ed a e
a second email was sen ou weeks la e o companies ha had ye o espond o he i s .
A o al o 122 obse a ions we e hus u ilized in he analysis.
3.2. Ques ionnai e
The e we e wo sec ions o he ques ionnai e used o his esea ch. We asked eigh demo-
g aphic ques ions abou businesses and esponden s in he i s sec ion. Twen y-one ol-
low-up ques ions we e used o quan i y he heo e ical amewo k. The second sec ion o
ques ions used a 5-poin Like scale o gauge how esponden s ag eed o disag eed wi h
each s a emen (1 – s ongly con lic , 5 – s ongly ag ee).
The ques ionnai e was adap ed om he ollowing s udies o assess he a iables:
1. Big Da a Analy ics (BDA); Wamba e al. (2020); based on en i ems.
2. P oduc Inno a ion Capabili y (PIC); Liao and Li (2019) based on i e hings.
3. In o ma ion Sha ing (IS); Saleem e al. (2021) based on six i ems.
Jou nal o Business Economics and Managemen , 2024, 25(1), 66–84 73
3.3. Da a analysis
The e we e h ee dis inc le els o analysis in his s udy.
To assess he scales’ alidi y and eliabili y, we conduc ed explo a o y and con i ma o y
ac o analyses. The Kaise -Meye -Olkin (KMO) measu e and Ba le ’s es we e used o en-
su e he app op ia eness o ac o analysis, wi h KMO alues abo e 0.7 indica ing sui abili y
o he analysis (Field, 2017). Con i ma o y ac o analysis (CFA) was hen used o es he
dis ibu ion o a iables ac oss o ganiza ional se ings. Cons uc alidi y and eliabili y we e
con i med by good i indices and he calcula ion o ac o eliabili y and a e age a iance
ex ac ed (AVE), wi h alues abo e 0.7 o eliabili y and 0.4 o AVE indica ing a eliable
s uc u e (Fo nell & La cke , 1981; Hai e al., 2016). No mali y was checked h ough skewness
and ku osis alues.
In S age 2, we applied a s uc u al equa ion model (SEM) o e alua e ou hypo heses (H1,
H2, and H3). SEM is a o ed o i s obus ness in handling complex models and i s capaci y
o adjus o measu emen e o , making i p e alen in di e se esea ch a eas. I employs
a ious s a is ical es s o alida e cons uc s, including es s o con e gen , disc iminan ,
and in e nal consis ency (Fo nell & La cke , 1981). Fi indices like he chi-squa ed es assess
he model’s da a i , and eg ession coe icien s we e analyzed o de e mine he suppo o
ou hypo heses.
Hayes’ (2017) p ocess mac o me hod, which u ilizes boo s apping, was employed o es
he media ion e ec , whe e media o s a e in e ening ac o s ha al e he ela ionship be-
ween independen and dependen a iables (Ba on & Kenny, 1986). Media ion is conside ed
when bo h independen and dependen a iables show a signi ican e ec . Howe e , a hi d
a iable may in luence hei ela ionship (Benne , 2000). The p ocess begins by es ablishing
a link be ween he independen a iable (X) and he dependen a iable (Y), and media ion
analysis can p oceed e en i X and Y a e no di ec ly ela ed, as a gued by some esea che s
(MacKinnon e al., 2000). To con i m a media o ’s ole, he indi ec e ec ’s signi icance is de-
e mined using Hayes’s me hod, which is deemed obus due o i s boo s apping echnique
(F i z & MacKinnon, 2007).
4. Findings
Some demog aphic cha ac e is ics o he pa icipan s a e gi en in Table 1.
Table 1. Demog aphic cha ac e is ics o he i ms
Sec o F equency Pe cen
Packaging / Glass 6 4.9
Pain / Chemis y 6 4.9
I on, S eel Coppe 32.5
Elec ic, Elec onics, Compu e 15 12.3
Ene gy 75.7
Food 19 15.6
Cons uc ion / Building Ma e ials 10 8.2
Machine 4 3.3
Fu ni u e / Fo es P oduc s 4 3.3
80 B. Yildiz e al. The nexus o big da a analy ics, knowledge sha ing, and p oduc inno a ion in manu ac u ing
Disclosu e s a emen
The au ho s ha e no compe ing inancial, p o essional, o pe sonal in e es s om o he pa -
ies ha a e ela ed o he subjec o his pape .
Re e ences
Ak oush, M. N., & Awwad, A. S. (2018). Enable s o NPD inancial pe o mance: The oles o NPD capabili-
ies imp o emen , NPD knowledge sha ing, and NPD in e nal lea ning. In e na ional Jou nal o Quali y
& Reliabili y Managemen , 35(1), 163–186. h ps://doi.o g/10.1108/IJQRM-08-2016-0122
Ali, Z. (2023). In es iga ing in o ma ion p ocessing pa adigm o p edic pe o mance in eme ging i ms:
The media ing ole o echnological inno a ion. Jou nal o Business & Indus ial Ma ke ing, 38(4),
724–735. h ps://doi.o g/10.1108/JBIM-07-2020-0342
AL-Kha ib, A. W. (2022). In ellec ual capi al and inno a ion pe o mance: The mode a ing ole o big da a
analy ics: e idence om he banking sec o in Jo dan. Eu oMed Jou nal o Business, 17(3), 391–423.
h ps://doi.o g/10.1108/EMJB-10-2021-0154
Bah ami, M., & Shokouhya , S. (2021). The ole o big da a analy ics capabili ies in bols e ing supply chain
esilience and i m pe o mance: A dynamic capabili y iew. In o ma ion Technology & People, 35(5),
1621–1651. h ps://doi.o g/10.1108/ITP-01-2021-0048
Ba on, R. M., & Kenny, D. A. (1986). The mode a o –media o a iable dis inc ion in social psychological
esea ch: Concep ual, s a egic, and s a is ical conside a ions. Jou nal o Pe sonali y and Social Psy-
chology, 51(6), 1173–1182. h ps://doi.o g/10.1037/0022-3514.51.6.1173
Benne , J. A. (2000). Media o and mode a o a iables in nu sing esea ch: Concep ual and s a is ical
di e ences. Resea ch in Nu sing & Heal h, 23(5), 415–420.
h ps://doi.o g/10.1002/1098-240X(200010)23:5<415::AID-NUR8>3.0.CO;2-H
Bo ko ich, D. S., & Noah, P. (2014). Big da a in he in o ma ion age: Explo ing he in ellec ual ounda ion
o communica ion heo y. In o ma ion Sys ems Educa ion Jou nal, 12(1), 15–26.
Bs iele , L. (2006). T us o ma ion in collabo a i e new p oduc de elopmen . Jou nal o P oduc Inno a-
ion Managemen , 23(1), 56–72. h ps://doi.o g/10.1111/j.1540-5885.2005.00181.x
Calic, G., & Ghasemaghaei, M. (2021). Big da a o social bene i s: Inno a ion as a media o o he e-
la ionship be ween bi g da a and co po a e social pe o mance. Jou nal o Business Resea ch, 131,
391–401. h ps://doi.o g/10.1016/j.jbus es.2020.11.003
Capu o, R., Fio en ino, R., Ga zella, S., & Giudici, A. (2021). Big da a analy ics in inno a ion p ocesses:
Which o ms o dynamic capabili ies should be de eloped and how o emb ace digi iza ion? Eu opean
Jou nal o Inno a ion Managemen , 25(6), 273–294. h ps://doi.o g/10.1108/EJIM-05-2021-0256
Chen, M., Mao, S., Zhang, Y., & Leung, V. C. M. (2014). Big Da a. Sp inge In e na ional Publishing.
h ps://doi.o g/10.1007/978-3-319-06245-7
Chen, X., Li, B., Chen, W., & Wu, S. (2021). In luences o in o ma ion sha ing and online ecommenda ions
in a supply chain: Reselling e sus agency selling. Annals o Ope a ions Resea ch.
h ps://doi.o g/10.1007/s10479-021-03968-7
Che ian, E. J. (2007). The impac o o ganiza ional s uc u e on in e o ganiza ional in o ma ion sha ing
du ing c isis esponse. In B. VandeWalle, X. Li, & S. Zhang (Eds.), ISCRAM China 2007: P oceedings o
he 2nd In e na ional Wo kshop on In o ma ion Sys ems o C isis Response and Managemen (pp. 451–
454). Ha bin Enginee ing Uni e si y. China. h ps://www.webo science.com/wos/woscc/ ull- eco d/
WOS:000250334100085
Chie ici, R., Mazzucchelli, A., Ga cia-Pe ez, A., & V on is, D. (2019). T ans o ming big da a in o knowledge:
The ole o knowledge managemen p ac ice. Managemen Decision, 57(8), 1902–1922.
h ps://doi.o g/10.1108/MD-07-2018-0834
Con e as Pinoche , L. H., Amo im, G. de C. B., Lucas Júnio , D., & Souza, C. A. de. (2021). Consequen ial
ac o s o Big Da a’s Analy ics Capabili y: How i ms use da a in he compe i i e scena io. Jou nal o
En e p ise In o ma ion Managemen , 34(5), 1406–1428. h ps://doi.o g/10.1108/JEIM-11-2020-0445

Jou nal o Business Economics and Managemen , 2024, 25(1), 66–84 81
Elgendy, N., & El agal, A. (2014). Big da a analy ics: A li e a u e e iew pape . In P. Pe ne (Ed.), Lec u e
no es in compu e science: Vol. 8557. Ad ances in da a mining. Applica ions and heo e ical aspec s
(pp. 214–227). Sp inge In e na ional Publishing. h ps://doi.o g/10.1007/978-3-319-08976-8_16
Fayyaz, A., Chaudh y, B. N., & Fiaz, M. (2021). Upholding knowledge sha ing o o ganiza ion inno a ion
e iciency in Pakis an. Jou nal o Open Inno a ion: Technology, Ma ke , and Complexi y, 7(1), A icle 4.
h ps://doi.o g/10.3390/joi mc7010004
Fe nando, Y., Chidamba am, R. R. M., & Wahyuni-TD, I. S. (2018). The impac o Big Da a analy ics and
da a secu i y p ac ices on se ice supply chain pe o mance. Benchma king: An In e na ional Jou nal,
25(9), 4009–4034. h ps://doi.o g/10.1108/BIJ-07-2017-0194
Field, A. (2017). Disco e ing s a is ics using IBM SPSS s a is ics (5 h ed.). SAGE Publica ions.
Fo nell, C., & La cke , D. F. (1981). S uc u al equa ion models wi h unobse able a iables and measu e-
men e o : Algeb a and s a is ics. Jou nal o Ma ke ing Resea ch, 18(3), 382–388.
h ps://doi.o g/10.1177/002224378101800313
F i z, M. S., & MacKinnon, D. P. (2007). Requi ed sample size o de ec he media ed e ec . Psychological
Science, 18(3), 233–239. h ps://doi.o g/10.1111/j.1467-9280.2007.01882.x
Gandomi, A., & Haide , M. (2015). Beyond he hype: Big da a concep s, me hods, and analy ics. In e na ion-
al Jou nal o In o ma ion Managemen , 35(2), 137–144. h ps://doi.o g/10.1016/j.ijin omg .2014.10.007
Gonzalez-Zapa e o, C., Gonzalez-Beni o, J., & Lannelongue, G. (2016). An eceden s o unc ional in eg a-
ion du ing new p oduc de elopmen : The pu chasing–ma ke ing link. Indus ial Ma ke ing Manage-
men , 52, 47–59. h ps://doi.o g/10.1016/j.indma man.2015.07.015
Gü sakal, N. (2017). Büyük Ve i. Do a Yayincilik.
Hade , M., Tcho a, D., Mhamedi, A. E., Ghodous, P., Dolgui, A., & Abouabdellah, A. (2022). Applying in-
eg a ed Blockchain and Big Da a echnologies o imp o e supply chain aceabili y and in o ma ion
sha ing in he ex ile sec o . Jou nal o Indus ial In o ma ion In eg a ion, 28, A icle 100345.
h ps://doi.o g/10.1016/j.jii.2022.100345
Hai , J., Ande son, R., Black, B., & Babin, B. (2016). Mul i a ia e da a analysis (7 h ed.). Pea son Educa ion.
Hayes, A. F. (2017). In oduc ion o media ion, mode a ion, and condi ional p ocess analysis: A eg ession-
based app oach (2 ed.). Guil o d Publica ions.
Hoch, J. E. (2014). Sha ed leade ship, di e si y, and in o ma ion sha ing in eams. Jou nal o Manage ial
Psychology, 29(5), 541–564. h ps://doi.o g/10.1108/JMP-02-2012-0053
Hsu, C., Kannan, V. R., Tan, K., & Keong Leong, G. (2008). In o ma ion sha ing, buye -supplie ela ionships,
and i m pe o mance: A mul i- egion analysis. In e na ional Jou nal o Physical Dis ibu ion & Logis ics
Managemen , 38(4), 296–310. h ps://doi.o g/10.1108/09600030810875391
Hu, D., Li, Y., Pan, L., Li, M., & Zheng, S. (2021). A blockchain-based ading sys em o big da a. Compu e
Ne wo ks, 191, A icle 107994. h ps://doi.o g/10.1016/j.comne .2021.107994
Huo, B., Ul Haq, M. Z., & Gu, M. (2021). The impac o in o ma ion sha ing on supply chain lea ning and
lexibili y pe o mance. In e na ional Jou nal o P oduc ion Resea ch, 59(5), 1411–1434.
h ps://doi.o g/10.1080/00207543.2020.1824082
In eza i, A., & G essel, S. (2017). In o ma ion and e o ma ion in KM sys ems: Big da a and s a egic
decision-making. Jou nal o Knowledge Managemen , 21(1), 71–91.
h ps://doi.o g/10.1108/JKM-07-2015-0293
Janssen, M., an de Voo , H., & Wahyudi, A. (2017). Fac o s in luencing big da a decision-making quali y.
Jou nal o Business Resea ch, 70, 338–345. h ps://doi.o g/10.1016/j.jbus es.2016.08.007
Jiaxi, W. (2009, No embe ). The impac o in e -o ganiza ional ela ionship on new p oduc de elopmen
pe o mance wi h he in e media e ole o in o ma ion sha ing. In 2009 Fou h In e na ional Con e -
ence on Coope a ion and P omo ion o In o ma ion Resou ces in Science and Technology (pp. 285–289).
Beijing, China. IEEE. h ps://doi.o g/10.1109/COINFO.2009.47
Kache, F., & Seu ing, S. (2017). Challenges and oppo uni ies o digi al in o ma ion a he in e sec ion o
Big Da a Analy ics and supply chain managemen . In e na ional Jou nal o Ope a ions & P oduc ion
Managemen , 37(1), 10–36. h ps://doi.o g/10.1108/IJOPM-02-2015-0078
Kaly as, J. R., & Albe son, D. R. (2015). A big da a p ime o execu i es. In J. R. Kaly as & M. R. O e ly,
Big da a: A business and legal guide (pp. 1–10). CRC P ess.
82 B. Yildiz e al. The nexus o big da a analy ics, knowledge sha ing, and p oduc inno a ion in manu ac u ing
Keszey, T. (2018). Bounda y spanne s’ knowledge sha ing o inno a ion success in u bulen imes. Jou -
nal o Knowledge Managemen , 22(5), 1061–1081. h ps://doi.o g/10.1108/JKM-01-2017-0033
Kulanga a, N. P., Jackson, S. A., & P a e , E. (2016). Examining he impac o socializa ion and in o ma ion
sha ing and he media ing e ec o us on inno a ion capabili y. In e na ional Jou nal o Ope a ions
& P oduc ion Managemen , 36(11), 1601–1624. h ps://doi.o g/10.1108/IJOPM-09-2015-0558
Le, C. T. D., Paku á , M., Kun, I. A., & Oláh, J. (2021). The impac o ac o s on in o ma ion sha ing: An
applica ion o me a-analysis. PLoS ONE, 16(12), A icle e0260653.
h ps://doi.o g/10.1371/jou nal.pone.0260653
Liao, Y., & Li, Y. (2019), Complemen a i y e ec o supply chain compe encies on inno a ion capabili y.
Business P ocess Managemen Jou nal, 25(6), 1251–1272. h ps://doi.o g/10.1108/BPMJ-04-2018-0115
Liedong, T. A., Rajwani, T., & Law on, T. C. (2020). In o ma ion and nonma ke s a egy: Concep ualizing
he in e ela ionship be ween big da a and co po a e poli ical ac i i y. Technological Fo ecas ing and
Social Change, 157, A icle 120039. h ps://doi.o g/10.1016/j. ech o e.2020.120039
Lin, C., Kunna hu , A., & Fo es , J. (2022). Supply chain dynamics, big da a capabili y and p oduc pe o -
mance. Ame ican Jou nal o Business, 37(2), 53–75. h ps://doi.o g/10.1108/AJB-08-2020-0136
Lin, M. J., & Chen, C. (2008). In eg a ion and knowledge sha ing: T ans o ming o long- e m compe i i e
ad an age. In e na ional Jou nal o O ganiza ional Analysis, 16(1/2), 83–108.
h ps://doi.o g/10.1108/19348830810915514
Lin, R., Che, R., & Ting, C. (2012). Tu ning knowledge managemen in o inno a ion in he high- ech indus y.
Indus ial Managemen & Da a Sys ems, 112(1), 42–63. h ps://doi.o g/10.1108/02635571211193635
Liu, S., & Wang, H. (2018). Analysis o supply chain collabo a ion wi h big da a supplie s pa icipa ing in
compe i ion. In J. Xu, M. Gen, A. Hajiye , & F. L. Cooke (Eds.), P oceedings o he Ele en h In e na ional
Con e ence on Managemen Science and Enginee ing Managemen (pp. 998–1006). Sp inge In e na-
ional Publishing. h ps://doi.o g/10.1007/978-3-319-59280-0_82
MacKinnon, D. P., K ull, J. L., & Lockwood, C. (2000). Equi alence o he media ion, con ounding and sup-
p ession e ec . P e en ion Science, 1(4), 173–181. h ps://doi.o g/10.1023/A:1026595011371
Makkonen, H., Pohjola, M., Olkkonen, R., & Koponen, A. (2014). Dynamic capabili ies and i m pe o -
mance in a inancial c isis. Jou nal o Business Resea ch, 67(1), 2707–2719.
h ps://doi.o g/10.1016/j.jbus es.2013.03.020
Ma as, M.-H. (2017). O e coming he in elligence-sha ing pa adox: Imp o ing in o ma ion sha ing
h ough change in o ganiza ional cul u e. Compa a i e S a egy, 36(3), 187–197.
h ps://doi.o g/10.1080/01495933.2017.1338477
Ma ko ic, S., & Baghe zadeh, M. (2018). How does b ead h o ex e nal s akeholde co-c ea ion in luence
inno a ion pe o mance? Analyzing he media ing oles o knowledge sha ing and p oduc inno a ion.
Jou nal o Business Resea ch, 88, 173–186. h ps://doi.o g/10.1016/j.jbus es.2018.03.028
McA ee, A., B ynjol sson, E., Da enpo , T. H., Pa il, D. J., & Ba on, D. (2012). Big da a: The managemen
e olu ion. Ha a d Business Re iew, 90(10), 60–68.
Mikale , P., Bou a, M., Lekakos, G., & K ogs ie, J. (2020). The ole o in o ma ion go e nance in big da a
analy ics d i en inno a ion. In o ma ion & Managemen , 57(7), A icle 103361.
h ps://doi.o g/10.1016/j.im.2020.103361
Mo imu a, F., & Sakagawa, Y. (2023). The in e media ing ole o big da a analy ics capabili y be ween
esponsi e and p oac i e ma ke o ien a ions and i m pe o mance in he e ail indus y. Jou nal o
Re ailing and Consume Se ices, 71, A icle 103193. h ps://doi.o g/10.1016/j.j e conse .2022.103193
Muni , S., Abdul Rasid, S. Z., Aami , M., Jamil, F., & Ahmed, I. (2023). Big da a analy ics capabili ies and
inno a ion e ec o dynamic capabili ies, o ganiza ional cul u e and ole o managemen accoun an s.
Fo esigh , 25(1), 41–66. h ps://doi.o g/10.1108/FS-08-2021-0161
Naja i Ta ani, S., Sha i i, H., Soleimano , S., & Najmi, M. (2013). An empi ical s udy o i m’s abso p i e
capaci y dimensions, supplie in ol emen and new p oduc de elopmen pe o mance. In e na ional
Jou nal o P oduc ion Resea ch, 51(11), 3385–3403. h ps://doi.o g/10.1080/00207543.2013.774480
Naja i-Ta ani, S., Naja i-Ta ani, Z., Naudé, P., Oghazi, P., & Zeynaloo, E. (2018). How collabo a i e inno-
a ion ne wo ks a ec new p oduc pe o mance: P oduc inno a ion capabili y, p ocess inno a ion
capabili y, and abso p i e capaci y. Indus ial Ma ke ing Managemen , 73, 193–205.
h ps://doi.o g/10.1016/j.indma man.2018.02.009
Jou nal o Business Economics and Managemen , 2024, 25(1), 66–84 83
Pe ks, H. (2000). Ma ke ing in o ma ion exchange mechanisms in collabo a i e new p oduc de elop-
men : The in luence o esou ce balance and compe i i eness. Indus ial Ma ke ing Managemen , 29(2),
179–189. h ps://doi.o g/10.1016/S0019-8501(99)00074-7
Raga z, G. L., Hand ield, R. B., & Pe e sen, K. J. (2002). Bene i s associa ed wi h supplie in eg a ion in o
new p oduc de elopmen unde condi ions o echnology unce ain y. Jou nal o Business Resea ch,
55(5), 389–400. h ps://doi.o g/10.1016/S0148-2963(00)00158-2
Raman, S., Pa wa, N., Ni anjan, I., Ranjan, U., Moo hy, K., & Meh a, A. (2018). Impac o big da a on sup-
ply chain managemen . In e na ional Jou nal o Logis ics Resea ch and Applica ions, 21(6), 579–596.
h ps://doi.o g/10.1080/13675567.2018.1459523
Sagi oglu, S., & Sinanc, D. (2013, May). Big da a: A e iew. In 2013 In e na ional Con e ence on Collabo a-
ion Technologies and Sys ems (CTS) (pp. 42–47). San Diego. IEEE.
h ps://doi.o g/10.1109/CTS.2013.6567202
Şahin, H., & Topal, B. (2019). Examina ion o e ec o in o ma ion sha ing on businesses pe o mance in
he supply chain p ocess. In e na ional Jou nal o P oduc ion Resea ch, 57(3), 815–828.
h ps://doi.o g/10.1080/00207543.2018.1484954
Saleem, H., Li, Y., Ali, Z., Ayyoub, M., Wang, Y., & Meh een, A. (2021). Big da a use and i s ou comes in
supply chain con ex : The oles o in o ma ion sha ing and echnological inno a ion. Jou nal o En e -
p ise In o ma ion Managemen , 34(4), 1121–1143. h ps://doi.o g/10.1108/JEIM-03-2020-0119
Sla e , S. F., Moh , J. J., & Sengup a, S. (2014). Radical p oduc inno a ion capabili y: Li e a u e e iew,
syn hesis, and illus a i e esea ch p oposi ions. Jou nal o P oduc Inno a ion Managemen , 31(3),
552–566. h ps://doi.o g/10.1111/jpim.12113
Su, X., Zeng, W., Zheng, M., Jiang, X., Lin, W., & Xu, A. (2022). Big da a analy ics capabili ies and o ga-
niza ional pe o mance: The media ing e ec o dual inno a ions. Eu opean Jou nal o Inno a ion
Managemen , 25(4), 1142–1160. h ps://doi.o g/10.1108/EJIM-10-2020-0431
Sun, B., & Liu, Y. (2021). Business model designs, big da a analy ics capabili ies and new p oduc de-
elopmen pe o mance: E idence om China. Eu opean Jou nal o Inno a ion Managemen , 24(4),
1162–1183. h ps://doi.o g/10.1108/EJIM-01-2020-0004
Swink, M., & Song, M. (2007). E ec s o ma ke ing-manu ac u ing in eg a ion on new p oduc de elop-
men ime and compe i i e ad an age. Jou nal o Ope a ions Managemen , 25(1), 203–217.
h ps://doi.o g/10.1016/j.jom.2006.03.001
Szczepańska-Woszczyna, K. (2015). Leade ship and o ganiza ional cul u e as he no ma i e in luence o
op managemen on employee’s beha iou in he inno a ion p ocess. P ocedia Economics and Finance,
34, 396–402. h ps://doi.o g/10.1016/S2212-5671(15)01646-9
Thomas, E. (2013). Supplie in eg a ion in new p oduc de elopmen : Compu e media ed communica-
ion, knowledge exchange and buye pe o mance. Indus ial Ma ke ing Managemen , 42(6), 890–899.
h ps://doi.o g/10.1016/j.indma man.2013.05.018
Tian, X. (2017). Big da a and knowledge managemen : A case o déjà u o back o he u u e? Jou nal o
Knowledge Managemen , 21(1), 113–131. h ps://doi.o g/10.1108/JKM-07-2015-0277
Tsang, Y. P., Wu, C. H., Lin, K.-Y., Tse, Y. K., Ho, G. T. S., & Lee, C. K. M. (2022). Unlocking he powe o big
da a analy ics in new p oduc de elopmen : An in elligen p oduc design amewo k in he u ni u e
indus y. Jou nal o Manu ac u ing Sys ems, 62, 777–791. h ps://doi.o g/10.1016/j.jmsy.2021.02.003
Tunc-Abubaka , T., Kalkan, A., & Abubaka , A. M. (2023). Impac o big da a usage on p oduc and p ocess
inno a ion: The ole o da a diagnos ici y. Kybe ne es, 52(9), 3178–3196.
h ps://doi.o g/10.1108/K-11-2021-1138
Vázquez-Casielles, R., Iglesias, V., & Va ela-Nei a, C. (2013). Collabo a i e manu ac u e -dis ibu o ela-
ionships: The ole o go e nance, in o ma ion sha ing and c ea i i y. Jou nal o Business & Indus ial
Ma ke ing, 28(8), 620–637. h ps://doi.o g/10.1108/JBIM-05-2011-0070
Wamba, S. F., Dubey, R., Gunaseka an, A., & Ak e , S. (2020). The pe o mance e ec s o big da a analy -
ics and supply chain ambidex e i y: The mode a ing e ec o en i onmen al dynamism. In e na ional
Jou nal o P oduc ion Economics, 222, A icle 107498. h ps://doi.o g/10.1016/j.ijpe.2019.09.019
Wan, W., & Liu, L. (2021). In ap eneu ship in he digi al e a: D i en by big da a and human esou ce man-
agemen ? Chinese Managemen S udies, 15(4), 843–875. h ps://doi.o g/10.1108/CMS-07-2020-0282
84 B. Yildiz e al. The nexus o big da a analy ics, knowledge sha ing, and p oduc inno a ion in manu ac u ing
Wang, Z., Wang, T., Hu, H., Gong, J., Ren, X., & Xiao, Q. (2020). Blockchain-based amewo k o imp o ing
supply chain aceabili y and in o ma ion sha ing in p ecas cons uc ion. Au oma ion in Cons uc ion,
111, A icle 103063. h ps://doi.o g/10.1016/j.au con.2019.103063
Ye, L., Pan, S. L., Wang, J., Wu, J., & Dong, X. (2021). Big da a analy ics o sus ainable ci ies: An in o ma ion
iangula ion s udy o haza dous ma e ials anspo a ion. Jou nal o Business Resea ch, 128, 381–390.
h ps://doi.o g/10.1016/j.jbus es.2021.01.057
Yin, S., & Kaynak, O. (2015). Big da a o mode n indus y: Challenges and ends [Poin o View]. P o-
ceedings o he IEEE, 103(2), 143–146. h ps://doi.o g/10.1109/JPROC.2015.2388958
Zhan, Y., Tan, K. H., Ji, G., Chung, L., & Tseng, M. (2017). A big da a amewo k o acili a ing p oduc
inno a ion p ocesses. Business P ocess Managemen Jou nal, 23(3), 518–536.
h ps://doi.o g/10.1108/BPMJ-11-2015-0157
Zhou, H., & Ben on, W. C. (2007). Supply chain p ac ice and in o ma ion sha ing. Jou nal o Ope a ions
Managemen , 25(6), 1348–1365. h ps://doi.o g/10.1016/j.jom.2007.01.009