ABSTRACT
The ole o e-consume empowe men on
pe sonal da a disclosu e in he digi al
economy
D Ayub Se ubugo
Facul y o Managemen Sciences
Du ban Uni e si y o Technology - Sou h A ica
h ps://o cid.o g/0000-0001-9296-7996
[email p o ec ed]
D Soob amoney Penceliah*
Facul y o Business and Managemen Sciences
Cape Peninsula Uni e si y o Technology - Sou h A ica
h ps://o cid.o g/0000-0002-3109-1780
[email p o ec ed]
* Co esponding au ho
The Re ail and Ma ke ing Re iew
Volume 21, Issue 2, No embe 2025, Pages 208-223
Doi: h ps://doi.o g/10.5281/zenodo.17579297
Digi al ans o ma ion has imp o ed online shopping om he beginning o he 20 h cen u y. Online businesses
ha e exponen ially expanded he le el o se ice deli e y wi h he help o decision suppo sys ems using use digi al
oo p in . E-consume online beha iou al da a is p og essi ely becoming a aluable asse o p ecise, g anula online
a ge ing. Consume s a e being moni o ed o p edic eal- ime demand based on b owsing beha iou al su plus. This
pape assessed he in luence o elec onic consume (e-consume ) empowe men on pe cei ed us , pu chase
in en ion, and he e ec s o da a ha es ing. A c oss-sec ional quan i a i e app oach was used wi h an online su ey o
400 esponden s. Da a was analysed using SPSS and R s a is ics wi h S uc u al equa ion modelling. The indings
showed ha elec onic consume empowe men in luenced pe cei ed us , pu chase in en ion, and he e ec s o da a
ha es ing, which led o online sel -disclosu es used in eal- ime p edic ions. The esul s e ealed ha while some e-
consume s we e awa e o ee da a exploi a ion, mos e-consume s did no no ice ha hei online da a was being
ha es ed and exploi ed by online e aile s. This pape illumina ed he ole o e-consume empowe men in op imising
da a ha es ing o imp o e pe sonalised cus ome se ice in he da a-d i en digi al economy. These indings may assis
digi alised companies o ini ia e loyal y p og ammes o compensa e e-consume s o da a esou ce inpu .
Keywo ds: digi al ans o ma ion, online shopping, consume beha iou , consume da a, da a mining.
209 The Re ail and Ma ke ing Re iew: Vol21 Issue 2 (2025) ISSN:2708-3209
1. INTRODUCTION
Digi al ans o ma ion is becoming a manage ial unc ion in an exponen ially g owing da a-d i en digi al economy.
Socie y is in e wined wi h he in e ne , pe mi ing digi al ci izenship, whe e pa icipa ion is impossible wi hou da a
sha ing (Lu z, Ho mann & Ranzini 2020). Con empo a y capi alism equi es online companies o be da a-d i en by
accumula ing e-consume da a o boos p o i able s a egies in he compe i i e digi al economy (Sadowski 2019). The
digi al e olu ion is now ex ending o de eloping economies, whe e he echnological enaissance is expanding in e ms
o ma e ialism, unmasking belie s, and pe sonali y ai s (Mu s ai o, Ragnedda & O ge e 2021). To compe e
a ou ably in a da a-d i en economy, digi al ma ke e s a e now wo king wi h da a scien is s o un eil he hidden
pa e ns o online shoppe s. The heo y o easoned ac ion (TRA) is c ucial in explaining how online shoppe s’ belie s
in luence online shopping decisions (Sha ma 2019). Ghosal, Biswa, P asad and Behe a (2020) sugges ed ha all
heo e ical in luences on online consume da a disclosu e a e cen ed on he echnology accep ance model (TAM),
he eby compelling online shoppe s o accep echnology. Agains his backg ound, he empowe men o e-consume s
is c ucial in ha nessing he ha es ing o consume da a, which is aw ma e ial o decision-making in he g owing da a-
d i en digi al economy. This pape assessed he in luence o e-consume empowe men on pe cei ed us , pu chase
in en ion, and he e ec s o da a ha es ing.
2. LITERATURE REVIEW
Online consume s a e usually ulne able o au oma ed moni o ing o b owsing oo p in — allowing online
companies o use ag acili ies ha ack, so , and ha es e-consume da a using a ilia ed websi es (Fa man, Comello
& Edwa ds 2020). P esen ly, ee e-consume da a is a aw ma e ial used as a building block o gene a e e enue o
capi alis s, as neo-Ma xis schola s c i ique he echnological glo i ica ion, which has c ea ed inequali ies be ween
digi al capi alis s and socie y (Mu s ai o e al. 2021). Ne e heless, algo i hm il e ing and exploi a ion a e p e en iously
done o help online shoppe s enjoy an easy shopping expe ience. Ne e heless, such unno iceable ope a ions may
c ea e discom o du ing he online shopping jou ney (Lombo g & Kapsch 2020). Consume s ha e no in en ion o
sha ing hei da a while hey a e sea ching o p oduc s and se ices online. Howe e , du ing hei online engagemen s
wi h e aile s, hei da a is ha es ed eely and u ilised in manage ial decision-making p ocesses. The mining o e-
consume da a and he empowe men o e-consume s as p o ide s o da a equi es addi ional a en ion and
ecogni ion. The li e a u e e iew did no e eal any esea ch ha ocussed on e-consume empowe men conce ning
pu chase in en ions, pe cei ed us , and he e ec s o da a ha es . E-consume empowe men , he e ec s o da a
ha es ing, pe cei ed us , and pu chase in en ions a e discussed in he nex subsec ions.
2.1 E-CONSUMER EMPOWERMENT
The economic alue o e-consume da a has s a ed a deba e on he commodi ica ion o consume da a which is
wo h billions o dolla s (Li, Liu & Mo iwalla2021). E-consume empowe men seeks o s eng hen he ela ionship
be ween online i ms and e-consume s in a da a-d i en digi al economy. Online companies delibe a ely use echnology
o a emp o mislead cus ome s by popping up an illusion o con ol, o ensu e secu i y (D ape and Tu ow 2019).
Based on ecen s udies, e-consume da a inpu has alue o online companies. The e o e, i online shoppe s a e
compensa ed o hei da a inpu , hey end o downplay he e ec s o sel -disclosu e due o he endency ha online
companies ha e co-owne ship igh s o e-consume da a. Howe e , auc ioning da a equi es speci ic owne ship o
assign alue depending on he willingness o exchange da a o compensa ion (Li e al. 2021).
The ole o consume empowe men on pe sonal da a disclosu e in he digi al economy 210
The g owing need o digi al ans o ma ion has compelled decision make s o ocus on digi al p owess wi h end- o-
end in eg a ion o e- e aile Apps on sma de ices o e-consume s, enabling eal- ime analy ics using click, collec ,
and analyse sys ems (Bonne & Wes e man 2021). I is e iden in he Uni ed S a es ha indi iduals a e willing o
disclose hei pe sonal da a in conside a ion o an economic e u n (Li e al. 2021). Empowe ing e-consume s can ake
a ious app op ia e o ms o ai ea men o consume s who p o ide da a o decision making. Consume s whose
online ac i i ies accumula e a aluable da a ese oi always ha e li le con ol and a e o en obli ious o he ha es
o hei da a esou ces o p o i able p edic ions. While consume s a e lea ing ails o b owsing da a on online shopping
pla o ms, hey emain unawa e abou he alue o hei sel -disclosu es.
2.2 EFFECTS OF DATA HARVEST
Da a ha es ing is associa ed wi h posi i e and nega i e e ec s. The nega i e e ec s posi a h ea o online ade
ela ions as consume s become sa ie and mo e awa e o he alue o hei da a, exploi ed h ough da a ha es ing.
Howe e , he posi i e e ec s a e ha his imp o es consume expe ience wi h inc eased pe sonalisa ion, which, in
u n, inc eases loyal y. Digi al ans o ma ion has also igni ed online su eillance ac i i ies ha b ing new consume
expe iences (Lang, Xia & Liu 2021). Wi h he su eillance o e-consume s, a ac i e oppo uni ies o en au oma ically
appea in e-consume s' inboxes, like p ice specials ha a e alloca ed based on e-consume p o iles (Cla ke 2019).
Fu he mo e, acking online shoppe s’ b owsing his o y p o ides in o ma ion abou hidden equi emen s o shoppe s
ha necessi a e pe sonalised di e en ia ed ma ke ing p ac ices which au oma ically ma ch wi h consume p e e ences
(Bo nschein, Schmid & Mai e 2020). Machine lea ning echnology classi ies simila e-consume beha iou pa e ns
and mo emen s o ep oduce ine-g ained beha iou al segmen s wi h algo i hms ha au oma ically p edic u u e
possibili ies o ma ke ing s a egy (Tong, Luo & Xu 2020). Al hough he posi i e e ec s o p edic ing consume demand
helps consume s o con enien ly access goods and se ices online, online e aile s accumula e eno mous e enue
om he same p edic ions. Since consume s a e he p o ide s o his use ul da a ha has p edic i e powe , hey should
be empowe ed o hei con ibu ion.
Mo eo e , public deba es abou e-consume acking a e in ensi ying due o conce ns abou declining consume
au onomy escala ed by da a ha es ing (Ruckens ein & G an o h 2019). Howe e , Bo nschein e al. (2020) a gued ha
e-consume s ha e no powe o choice in how websi es au oma ically ha es hei aluable da a while ansac ing
online. Cla ke (2019) ecommended ha he h ea o su eillance should be beyond ep oach, o a oid he
engende ing o ea owa ds he con olling o consume powe h ough sc eening beha iou al pa e ns. Kendell (2020)
wa ned ha beha iou al manipula ion dep i es e-consume s o choice by in luencing pu chase in en ions owa ds
e aile s’ p oduc o e ings, he eby diminishing consume s’ a ional beha iou . The e is a dea h o esea ch wi h
espec o empowe ing e-consume s in conside a ion o he e ec s o da a ha es ing. Also, due o he posi i e e ec s
ela ed o cus omisa ion, consume s de elop us which ul ima ely exposes hem o da a exploi a ion.
2.3 PERCEIVED TRUST
Online shoppe s ace a dilemma o us issues, seeking eno mous amoun s o choice- ela ed in o ma ion om
di e en companies o be e decision-making, leading o he ha es ing o b owsing oo p in s (Kuma , Rajan,
Venka esan & Lecinski 2019). Pe cei ed us is he p obabili y ha e-consume s belie e in he legi imacy o online
companies (Ca s ens, Unge e & Human 2019). Bo nschein e al. (2020) a gued ha posi i e pe cei ed us owing o
b and popula i y lu es e-consume s o delibe a ely accep acking. T us is an an eceden o pe cei ed isk, allowing
o a men al gua an ee owa ds isk- ee online shopping (Kau & A o a 2020). Fu he mo e, he in e linking o social
pla o ms wi h e- ailing websi es is p opelling e ec i e da a sha ing ha boos s online shopping’s pe cei ed us . This
211 The Re ail and Ma ke ing Re iew: Vol21 Issue 2 (2025) ISSN:2708-3209
ca elessly esul s in e-consume s e ealing ee beha iou al da a ha is exploi ed by e- e aile s wi hou he e-
consume ’s knowledge (Bugshan & A a 2020). E-consume s a e igno an ly succumbing o da a disclosu e in
conside a ion o posi i e expec a ions om he o e ings o he online e aile . Since us ing consume s a e willing o
p o ide aluable insigh s ha a e p o i ably used by online companies, hey should be empowe ed in a bid o p o ide
unlimi ed da a esou ces used in decision making.
Fu he mo e, due o digi al ma ke ing ac i i ies ha a e ocusing on ending con en on online pla o ms, success ul
ma ke e s p o ide popula b ands ha a ac e-consume s' un o eseen ee da a exploi a ion h ough online
engagemen s mo i ed by us (Ge ic & Dob inic 2020). Pu chase and Vole y (2020) ag eed ha inno a ion enhances
p oduc isibili y, c edibili y and no el y, and ul ima ely b and popula i y wi h cu iosi y-d i en online sea ches o
upg ades, which inad e en ly expose e-consume s o ee da a exploi a ion. B and popula i y lu es e-consume s o
con iden ly e eal beha iou al da a due o pe cei ed us , leading o ee da a exploi a ion. The e o e, once e-
consume s a e empowe ed wi h con ol, hey end o engage online wi h con idence, which inc eases he pull o
b owsing da a ha es ed o eal- ime decision-making and a ge ing. Recen s udies show no esea ch on he ole o
e-consume empowe men in in luencing pe cei ed us in acili a ing da a ha es . Once pe cei ed us is posi i e,
e-consume s de elop pu chase in en ions.
2.4 PURCHASE INTENTION
Hamouda (2021) de ined pu chase in en ion as he possibili y o a shopping ac ion a e e alua ing al e na i es.
Choi and Pa k (2021) s a ed ha he TAM ac i a es subjec i e easoning o ansac online, he eby exposing
unsuspec ing consume s o da a ha es ing. Lombo g and Kapsch (2020) belie ed ha pe sonalised algo i hms
shaping pu chase in en ion no mally go unno iced by online shoppe s. Fu he mo e, pu chase con enience (because
o TAM) abou he ease o echnology alongside i s use ulness, igge s online pu chase willingness leading o da a
ha es ing (Baeshen 2021). The p edic ions o p oduc need ecommended by algo i hms p esen a pleasan
beha iou al moni o ing expe ience (Mican, Si a -Tau & Moisescu 2020). Du ing online in o ma ion sea ches,
companies' algo i hms dis inguish u ili a ian mo i es om hedonic mo i es, educing in o ma ion o e load by upscaling
choices based on sea ch c i e ia (Nam & Kannan 2020). Once he bene i s o online shopping exceed he isks in ol ed,
shoppe s downplay he isks, emba king on pu chase in en ions linked o sel -disclosu e (Fehe 2021). In he da a-
d i en economy, e-consume s disclose da a e en wi hou eques s, owing o he in luence o he bene i s expec ed.
The e o e, us has an in luence on pu chase in en ion which p omp s online shoppe s o engage online, lea ing a ail
o b owsing beha iou al da a a ailable o exploi a ion by online companies. No ably, he empowe men o e-
consume s wi h secu e use - iendly echnology and aluing hei da a inpu can boos online pu chase in en ions and
enable sea ch beha iou ha es ing used o make accu a e p o i able decisions.
Fu he mo e, eal- ime communica ion in luences pu chase in en ions as online shoppe s unwi ingly all p ey o e-
e aile s who moni o hei beha iou al in en ions o a ge ing (G ewal, Gau i, Rogge een & Se u aman 2021). Real-
ime communica ion inc eases he willingness o de elop pu chase in en ions, allowing o he moni o ing o online
beha iou al in en ions o a ge ing. The e o e, con enience encou ages he p obabili y o sel - disclosu e as online
shoppe s become mo e amilia and de elop he us o eely egis e online in an icipa ion o ac ually making a
pu chase, he eby allowing he p ocess o expose shoppe s o ee da a esou ce exploi a ion wi hou ealising. Hidden
algo i hms connec ed o ecommende sys ems il e he b owsing beha iou and ma ch p oduc s o e-consume s’
p e e ences, inc ease consume con enience and he e o e enhancing hei willingness o disclose da a.
The ole o consume empowe men on pe sonal da a disclosu e in he digi al economy 212
3. RESEARCH METHODS
The s udy used a quan i a i e esea ch design me hod. The online su ey ques ionnai e was analysed using SPSS
and R s a is ics. The popula ion o his c oss-sec ional s udy consis ed o 400 online shoppe s om Du ban, KwaZulu-
Na al, Sou h A ica. Quo a sampling was used unde con enience se ings based on a ibu es o key ca ego ical
a iables o he popula ion. Sys em equencies, o sample e e y n h isi o o he websi e, we e inco po a ed in he
in e ne p o ocol add esses (IP) allowing speci ic sub-se s o isi o s and es ic ing mul iple submissions (F icke
2017).
P e es ing wi h 42 online shoppe s in Du ban was conduc ed o ensu e he eliabili y o he measu ing ool be o e
conduc ing he ull-scale su ey. The assessmen o a measu emen model was conduc ed by es ing eliabili y and
alidi y. In s uc u al equa ion modelling (SEM), explo a o y ac o analysis (EFA) was used owing o li le in o ma ion
abou he expec ed la en s uc u e o obse ed indica o s (Finch 2020).
4. RESULTS AND DISCUSSION
Nine demog aphic a iables we e conside ed o he su ey. The dimensions a e summa ised below:
Fo y- ou pe cen o esponden s epo ed ha hey shop mon hly. In esponse o gende , a sco e o 65.2% o
online shoppe s we e male. Howe e , exis ing li e a u e e ealed ha emales a e mo e likely o ca y ou online
shopping han males (Lipschul z 2020). Fi y- wo pe cen o online shoppe s we e employed ull- ime and could a o d
online shopping using hei bank ca d de ails. Almos hal o he online shoppe s we e in hei you h. The ace a iable
is mainly ep esen ed by 62.8% Black esponden s. Wi hin he ma i al s a us ca ego y, 68.2% o he esponden s
claimed o be single. This indica ed ha single indi iduals shop online mo e equen ly han o he g oups in his
ca ego y. In he educa ion ca ego y, 68.2% o he esponden s claimed be in possession o e ia y quali ica ions. All
o he abo e-men ioned esponden s a e in he ci cle o online shoppe s, and a e he e o e exposed o da a ha es ing,
making hem ulne able. A e analysing ca ego ical a iables, explo a o y ac o analysis was conduc ed o
con inuous a iables.
4.1 EXPLORATORY FACTOR ANALYSIS (EFA)
Validi y es ing uses ei he EFA o con i ma o y ac o analysis (CFA) bu no bo h. This is because EFA and CFA
a e opposi e on he con inuum and mus ne e be used in he same s udy (Finch 2020). The ques ionnai e used is new
and he ola ili y is due o he dynamics o he digi al phenomenon in his s udy, he e o e an EFA is sui able o ensu e
alidi y. Ensu ing he app op ia eness o ac o analysis equi es he Kaise -Meye Okin (KMO) sampling adequacy o
be ≥ 0.6 and a p- alue a p ≤ 0.01 le el o signi icance (Dawson 2018). Fac o analysis allows he educ ion o
dimensions by e aining a iables wi h an eigen alue o 1, he eby ex ac ing unco ela ed ac o s (o hogonal) (Finch
2020). Table 1 shows he sample adequacy es s.
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TABLE 1. SAMPLE ADEQUACY TESTS
KMO and Ba le 's Tes
Kaise -Meye -Olkin Measu e o Sampling Adequacy.
.904
Ba le 's Tes o Sphe ici y
App ox. Chi-Squa e
5654.596
d
666
Sig.
.000
As e lec ed in Table 1, he KMO is 0.9 which indica es sample adequacy, and he p- alue is signi ican a 0.00.
Table 2 shows he ac o s ex ac ed om ac o analysis es s.
Table 2 e lec s ha he Eigen alue o he i s ac o is 9.593, which educes as mo e ac o s a e added, wi h he
8 h ac o con ibu ing 1.087 o he eigen alues, making he 8 h ac o he cu -o baseline. Howe e , only ou ac o s
we e conside ed o he s udy, as indica ed in Table 3.
TABLE 2. PRINCIPAL AXIS FACTORING
Fac o
Ini ial Eigen alues
To al
% o Va iance
Cumula i e %
1
9.593
25.928
25.928
2
3.445
9.310
9.310
3
2.008
35.238
35.238
4
1.664
5.426
5.426
5
1.244
40.665
40.665
6
1.164
4.497
4.497
7
1.133
45.162
45.162
8
1.087
3.361
3.361
The ac o analysis g ouped i ems (see Table 3) and he esul s o he EFA we e used o conduc he SEM. The
eliabili y es esul s o he ou ac o s (E ec s, Pe cei ed us , Pu chase in en ion and E-consume empowe men )
conside ed in he s udy we e good. An alpha o 0.70 is okay and 0.60 o a new scale is good (Rou & Akh a uddin
2018).
Pa icipan s we e asked o espond o he s a emen s acco ding o he Like scale o s ongly disag ee o s ongly
ag ee. The ollowing sco es we e ob ained:
4.1.1 E ec s
Eigh y-one pe cen o esponden s ag eed ha hei in o ma ion is help ul o online companies, which sugges s ha
online companies use e-consume da a o economic bene i s wi hou ewa ding he owne s o he da a inpu . Rus
(2020) sha ed ha e-consume da a is use ul o in o med decision-making. When esponden s we e asked whe he
hei da a was used o a ge hem, 65% ag eed, while 60% ag eed ha hei da a was p o i able o company
The ole o consume empowe men on pe sonal da a disclosu e in he digi al economy 214
p edic ions. The e o e, empowe ing e-consume s wi h ewa ds o hei da a inpu can boos online shopping, by
suppo ing b owsing da a ha es ing o a ge ed p edic ions.
4.1.2 Pe cei ed us
Eigh y-nine pe cen o esponden s buy om c edible websi es. Bo nschein e al. (2020) a i med ha e-consume s
choose o shop om c edible websi es. A simila esul was ob ained when esponden s we e asked whe he hey buy
om c edible websi es and ecommended online companies. Responden s also conside ed p o ec ion, in ha 86.25%
o esponden s ag eed ha hey bough om companies ha p o ec ed hei p i acy. Resul s showed ha 75.25% o
esponden s we e happy wi h online cus ome ca e. O e all, he empowe men o e-consume s h ough cus ome ca e
and secu i y builds us , enhancing online da a ha es ing o eal- ime decisions as e-consume s con inue o engage
online.
4.1.3 Pu chase in en ion
Nine y pe cen o he esponden s conside ed con enience in on-line shopping, no ealising ha behind he scenes,
online companies we e acking hei aluable da a. Ce ainly, pu chase con enience because o he ease o
echnology mo i a es online pu chases leading o da a ha es ing (Baeshen 2021). The ac o , online shopping
sa is ies my p e e ences, sco ed 80.25%. Howe e , once hei p e e ences we e me , hey downplayed ee da a
esou ce ha es ing, which bene i s online companies mo e han e-consume sa is ac ion does, which comes a a cos .
Ne e heless, 87.75 o esponden s we e happy wi h online shopping. I is no ewo hy ha he empowe men o e-
consume s by acknowledging hei da a inpu could u he enhance online shopping, boos ing e enue because o
con inued pu chase in en ions.
4.1.4 E-consume empowe men
Eigh y- i e pe cen o esponden s ag eed ha hei online da a is aluable o online companies. Li e al. (2021)
ag eed ha he economic alue o e-consume da a is billions o dolla s. Fi h- i e pe cen o esponden s ag eed ha
hey should ha e con ol o e hei da a, which is con olled by online i ms o make p o i able p edic ions. Bo nschein
e al. (2020) concu ed ha e-consume s a e powe less and ha e no con ol o e hei da a as hey shop online.
Fu he mo e, 78% o esponden s ag eed ha online companies a e ha es ing hei da a which may cause consume
de ec ions. Howe e , he empowe men o e-consume s by ecognising he alue o hei da a and secu ing hei online
ansac ion can mo i a e us and boos online shopping. A e analysing he desc ip i e s a is ics, SEM was used o
conduc in e en ial s a is ics.
TABLE 3. FACTOR ANALYSIS TESTS
E ec s
EF1. I ecommend dependable online companies o my pee s
EF2. We p o ide ou ee in o ma ion, which is used o p o i able p edic ions by online companies.
EF3. Ou p e ious online eco ds a e used by online companies o a ge us
EF4. Disclosing ou in o ma ion du ing online shopping is ine i able
215 The Re ail and Ma ke ing Re iew: Vol21 Issue 2 (2025) ISSN:2708-3209
Pe cei ed us
PT1. I buy om ecommended online companies
PT2. I buy om online companies ha p o ec my p i acy
PT3. I shop om c edible online websi es
PT4. Online shopping sa es ime
PT5. I only disclose my in o ma ion o online companies ha dese e my loyal y
PT6. I am happy wi h online cus ome ca e
PT7. I ega d us when disclosing my in o ma ion du ing online shopping
PT8. I conside p ice when shopping online
PT9. Online eedback helps o make in o med shopping decisions
PT10. I always buy om online companies wi h quali y se ices
Pu chase in en ion
PI1. I am happy wi h online shopping
PI2. I conside epu able online companies when shopping online
PI3. Online shopping sa is ies my p e e ences
PI4. Online shopping is con enien
PI5. I bene i om shopping online
E-consume empowe men
EE1. Ou online in o ma ion is collec ed by online companies
EE2. We should ha e con ol o e ou online in o ma ion
EE3. Ou online in o ma ion has alue o online companies
EE4. We should be paid by online companies using ou in o ma ion
4.2 STRUCTURAL EQUATION MODELLING
Tan-lei and Lin (2018) p opounded ha co a iance s uc u es wi h SEM allowed o analysis o momen s uc u es.
Fac o loading is he measu e o he ela ionship be ween each la en a iable and he obse ed indica o , whe e he
unique a iances a e pa s o he indica o ha a e no concomi an o he obse ed a iable (Finch 2020). Co ela ion
and co a iance es s we e conduc ed using SEM. The esul s a e indica ed in he nex subsec ions. Figu e 1 ep esen s
he measu emen model which ocuses on he co ela ion es ima es.
The ole o consume empowe men on pe sonal da a disclosu e in he digi al economy 216
FIGURE 1. CORRELATION STRUCTURE
Figu e 1 indica es he ela ionships be ween he a iables. Co ela ion coe icien alues we e ex ac ed om Figu e
1 and a e summa ised in Table 4.
TABLE 4. CORRELATION ESTIMATES
Es ima e
EE
<->
PI
0.746
EE
<->
PT
0.770
EE
<->
EF
0.638
PI
<->
PT
0.879
PI
<->
EF
0.567
EF
<->
PT
0.613
As e lec ed in Table 4, he ela ionship be ween a iables a e posi i e. Fo example, e-consume empowe men
(EE) has a posi i e ela ionship wi h pu chase in en ion (PI) wi h a sco e o 0.746, which indica es ha he mo e e-
consume s a e empowe ed, he mo e he pu chase in en ion o ice e sa. Empowe men o e-consume s can in ensi y
online pu chase in en ion leading o online engagemen s ha a e ha es ed o u u e a ge ing. E-consume
empowe men (EE) and pe cei ed us (PT) a e also posi i ely ela ed, as e lec ed in Table 4, wi h a sco e o 0.770.
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