Basi , Abdul; Amiya Bhaumik; Niazi, Abdul Aziz Khan
A icle
Challenges o digi al ma ke ing adop ion in FMCG sec o
in Pakis an: A MICMAC-ISM app oach
Pakis an Jou nal o Comme ce and Social Sciences (PJCSS)
P o ided in Coope a ion wi h:
Joha Educa ion Socie y, Pakis an (JESPK)
Sugges ed Ci a ion: Basi , Abdul; Amiya Bhaumik; Niazi, Abdul Aziz Khan (2025) : Challenges o digi al
ma ke ing adop ion in FMCG sec o in Pakis an: A MICMAC-ISM app oach, Pakis an Jou nal o
Comme ce and Social Sciences (PJCSS), ISSN 2309-8619, Joha Educa ion Socie y, Pakis an (JESPK),
Laho e, Vol. 19, Iss. 3, pp. 468-494,
h ps://doi.o g/10.64534/Comme .2025.513
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Pakis an Jou nal o Comme ce and Social Sciences
2025, Vol. 19(3), 468-494
h ps://doi.o g/10.64534/Comme .2025.513
Challenges o Digi al Ma ke ing Adop ion in FMCG
Sec o in Pakis an: A MICMAC-ISM App oach
Abdul Basi 1, Amiya Bhaumik2* & Abdul Aziz Khan Niazi3
1,2,3 Lincoln Uni e si y College, Selango , Malaysia
*Co esponding au ho ’s Email: [email protected]
A icle His o y
Recei ed: 09 June 2025
Re ised: 12 Sep 2025
Accep ed: 16 Sep 2025
Published: 30 Sep 2025
Abs ac
The s udy is aimed o analyze he in e ela ionships o challenges o digi al-ma ke ing
adop ion in Fas Mo ing Consume Goods (FMCG) sec o in Pakis an. The design
comp ises o e iew o up- o-da e li e a u e, p ima y da a ga he ing, modeling and
analysis. The da a a e collec ed h ough su ey om an expe ’s panel ec ui ed om
s akeholde s on he basis o p ede e mined c i e ia by using ma ix ype ques ionnai e. The
li e a u e discou se o ex ac ion o lis o challenges, In e p e i e S uc u al Modeling
(ISM) o ex ac he unde lying model o in e ela ionships, and Ma iced' Impac s C oise's
Mul iplica ion Appliquée a UN Classemen (C oss Impac Ma ix Mul iplica ion Applied
o Classi ica ion) popula ly known as MICMAC o analysis a e employed as esea ch
me hods. Resul s o li e a u e su ey e eal ha he e a e o al i een challenges o digi al-
ma ke ing adop ion in FMCG-sec o . Resul s o ISM modeling show ha cus ome s’
digi al engagemen , consume us conce ns, engaging ele an con en , u ilizing mul i-
media channels, in eg a ion o AI, con inuous op imiza ion, building digi al capabili ies,
ma ke ing inno a ion, esponsi e cus ome se ice, and handling new sou ces o da a
occupy Le el I. Managing supplie & cus ome coo dina ion occupies Le el II.
O ganiza ional esis ance occupies Le el III. In eg a ion o online and o line channels
occupies Le el IV. Da a p i acy and secu i y occupies Le el V. Technological ba ie
occupies Le el VI. The esul s o da a-cen ic and scale-cen ic MICMAC analysis
subs an ia e he esul s o ISM modeling. I is a eal ime da a based unique ype o s udy
ha p o ides unde s anding o s akeholde s pa icula ly o ma ke e s, egula o s, FMCG
mange s, esea che s and echnologis s.
Keywo ds: Digi al Ma ke ing, FMCG-sec o , Pakis an, MICMAC, Ma ke ing Inno a ion,
AI In eg a ion in Ma ke ing.
1. In oduc ion
The FMCG-sec o is one o he i al sec o s o he economy because i supplies daily
necessi ies o consume s. I d i es economic g ow h h ough high- olume sales and job
c ea ion. I suppo s nume ous ancilla y indus ies e. g. packaging and ad e ising. I
Basi , Bhaumik & Niazi
469
p o ides consis en ax e enues o go e nmen s, and se es as an indica o o o e all
economic heal h. I s con inuous inno a ion o mee e ol ing challenges. The echnological
de elopmen s ha e changed a landscape o ma ke ing. The e up ion o COVID pandemic
in Decembe 2019 has exace ba ed he change in ma ke ing backd op. I has accele a ed
he adop ion o digi al-ma ke ing ac oss he boa d (Chowdhu y & Na h, 2024). The
digi aliza ion has become a i al phenomenon o be unde s ood by FMCG i ms, in es o s,
in es men analys s & manage s, he go e nmen , and policymake s. FMCG- i ms a e he
mos a ec ed s akeholde s.
Al hough he h ea s o new en an s in o FMCG-sec o a e low since i equi es la ge
in es men s o be compe i i e (O aman e al. 2011) bu s ill hey a e suscep ible o
challenges. Fo de eloping any s a egy, he ma ke e s i s need o assess ends in o
globaliza ion in he indus y which is d i en by ma ke dynamics like
economic/en i onmen al, and/o compe i i e condi ions. Thain and B adley (2014)
a i med ha CUBEical Thinking o FMCG can p o ide FMCG manu ac u e s i)
anspa en , ii) consis en and iii) easy- o-unde s and amewo k o insigh in o in e play
dynamics among b ands, communica ion channels, and consume s. Ma e al. (2020) using
semi-s uc u ed in e iews and applying ac o -mapping-g id a gued ha he e exis s a
gene al desi e o educe plas ic om FMCG-sec o bu he e also exis s a eluc ance o be
he i s one. The si ua ion ‘we will i you will’ is obse ed in eali y. The consume s a e
equally a ba ie in ansi ion and hey ac as a double-edged swo d in FMCGs sec o .
Khalil and Villace (2021) ound ha p ice, place and p omo ion excep p oduc has an
in e ace wi h pe cei ed quali y o FMCG cus ome s a he same ime hese ela ionships
a e also media ed h ough posi i e wo d o mou h. I is also a ho opic in Pakis an iz:
Dan as e al. (2023) conduc ed a esea ch s udy in he con ex o Pakis an and s a ed ha
g een packaging, mul isenso ing packaging, and consume pe cep ion di ec ly impac on
en i onmen al sus ainabili y. Ansa i e al. (2024) asse ed ha GHRM depend on g een
go e nmen policy, pa icula ly en i onmen al policies, g een leade ship, op managemen
suppo , and g een heal h & sa e y policies. Qu eshi e al. (2019) ca y esea ch in he
con ex o ela ionship be ween job sa is ac ion and job pe o mance o employees o he
FMCG-sec o o Pakis an and bols e ed ha no ma i e commi men is he mos in luen ial
o m o o ganiza ional commi men , whe eas, con inuous commi men is he leas
in luen ial in impac ing job sa is ac ion. I also eached o a conclusion ha he e is a
signi ican associa ion o h ee o ms o commi men o a ec job sa is ac ion o employees
posi i ely. Gup a e al. (2022) showed ha high cos o in es men , lack o mone a y
esou ces, inadequa e in e ne connec i i y, lack o IT in as uc u e and unclea economic
bene i o digi al in es men a e he op i e challenges o implemen inno a i e
digi aliza ion echnologies du ing and pos pandemic si ua ions. I e ealed ha insigh s
in o digi aliza ion can be o high alue o manage s and esea che s he e o e should be
explo ed deeply. I has become u mos necessa y o in es iga e he challenges o digi al
ma ke ing adop ion in FMCG sec o in Pakis an. Unde s anding he in e ela ionships o
Challenges o Digi al Ma ke ing Adop ion in FMCG Sec o
470
challenges in digi al-ma ke ing adop ion in FMCG-sec o is i al o businesses o succeed.
These challenges, i add essed imely and s a egically, can lead o enhanced cus ome
sa is ac ion, engagemen , b and isibili y & loyal y, and ul ima ely in o inc eased sales and
ma ke sha e (Chowdhu y & Na h, 2024).
The e is a esea ch gap in his a ea since i is ela i ely un-c ys allized a ea o esea ch,
he e o e, he e is a wide scope o explo a o y s udies. I has become impe a i e o explo e
pa icula ly he in e ela ionships o challenges o digi al-ma ke ing adop ion as an
a e ma h o COVID pandemic (Chowdhu y & Na h, 2024). In e dependencies and
hie a chies in he challenges a e i al phenomenon ha dese es immedia e a en ion o
ed essal. The esea ch p oblem unde in es iga ion is o analyze he in e ela ionships o
challenges o digi al-ma ke ing adop ion in FMCG-sec o in Pakis an. The esea ch
objec i es include i) o un eil he a ay o challenges o digi al-ma ke ing adop ion in
FMCG-sec o in Pakis an, and ii) o hie a chalize hem and de e mine he
in e ela ionships, ele ance and dependencies. An a ay o me hodological choices has
been conside ed o achie e hese objec i es like ELECTRE, FANP, GRA, MOORA,
Mul i-MOORA, IPA, IRP, ISM, MADM, MICMAC, TACTIC, TOPSIS, AHP, ANP,
COPRAS-G, DEA, DEMATEL, PROMETHEE, RIDIT, SWARA, VIKOR, WASPAS,
e c. ISM coupled wi h MICMAC analysis is ound o be he mos app op ia e. The s udy
has p o ound heo e ical and p ac ical implica ions o s akeholde s because i s indings
will be use ul in policy decisions. Findings will also be help ul o s akeholde s by o e ing
deepe insigh s o he phenomenon. I has impo an p ac ical implica ions o FMCG
i ms, in es o s, in es men analys s and manage s, he go e nmen , and policymake s
since i p o ides close iew and unde s anding o challenges posed in eal wo ld.
The s udy con ibu es, i) a comp ehensi e lis o challenges o digi al-ma ke ing adop ion
in FMCG-sec o in Pakis an, ii) a s uc u al model, iii) d i ing-dependence diag am (scale
cen ic), i ) d i ing-dependence diag am (da a-cen ic), and ) a lo o in o ma ion on
hie a chies, in e - ac o ela ionships, di ec ion o ela ionships, and dependencies e c. The
emaining s udy is ep esen ed as: li e a u e e iew, me hodology, da a, modeling,
analysis, esul , discussion, & conclusion.
2. Li e a u e Re iew
The li e a u e e iew is a necessa y pa o he esea ch s udies since i p e en s om
objec ionable duplica ion and ells wha is done so a ill da e abou he phenomenon unde
in es iga ion and wha needs o be done. I also se s he e y ou se o he s udy. The e o e,
he au ho s ha e e iewed he easonable quan i y o con empo a y li e a u e abou he
issue unde s udy. The esea ch da a bases o wo ld- enowned publishe s: Wiley Online
Lib a y, Eme ald Insigh , Else ie (Science Di ec ), Taylo & F ancis Online, JS o , and
Sp inge Link e c. ha e been explo ed o li e a u e su ey using he ad anced sea ch
il e s. Using he google as sea ch engine he keywo ds o sea ch ha e been used as:
challenges o digi al-ma ke ing, digi al-ma ke ing adop ion, digi al-ma ke ing adop ion in
FMCG, challenges o digi al-ma ke ing adop ion in FMCG, in e ela ionships o
Basi , Bhaumik & Niazi
471
challenges o digi al-ma ke ing, FMCG-sec o in Pakis an, p oblems o FMCG, issues o
FMCG-sec o in Pakis an, e c.
The sea ch gene a ed hund eds o esul s ou o ha selec ed ele an esea ch a icles a e
e iewed and ew o hem which seemed o be highly ele an a e epo ed he e. Remaining
a e no epo ed he e o he sake o b e i y. Fo na i e al. (2009) asce ained ha p oduc
inno a ion posi i ely con ibu es o sales pe o mance o e ail ou le s in ma u e
subca ego ies. Woodside and Summe s (2011) bu essed ha ma ke e s mus unde s and
o maximize hei packaging and sponso ship in es men s. Fo na i e al. (2013) OLS and
GMM echniques delinea ed ha asso men sha e plays a c ucial ole o de e mine p i a e
label-sales g ow h. Ali and Dubey (2014) bols e ed ha cus ome sa is ac ion is ela ed o
hei expec a ions. I is e y impo an o he o ganiza ion o unde s and he expec a ion
o he cus ome s and e aile and i he e aile ’s expec a ion is me , i can lead o cus ome
sa is ac ion. Kuche o and Zhil so a (2021) conduc ed a esea ch s udy in he con ex o
Russia and p oclaimed ha using social media o ec ui men & employe b anding needs,
expec ed con en , and he ole o pa icula social media ools o enhance employe
b anding p og ams in FMCG. Duong e al. (2022) sugges ed ha lagship s o es do no
se e as po en ial al e na i e o adi ional e ail ou le s o FMCG pa icula ly in ood
b ands. P imadasa, e al., (2025) sugges s SMEs adop ing e hical, en i onmen ally
conscious business s a egies.
Gup a e al. (2022) eached o a conclusion ha “high cos o in es men ”, “lack o
mone a y esou ces”, “inadequa e in e ne connec i i y”, “lack o IT in as uc u e” and
“unclea economic bene i o digi al in es men ” a e op i e ba ie s in implemen ing
inno a i e-digi aliza ion- echnologies in mos o he de eloping coun ies. Hudnu ka
(2022) exposed ha he need o sus ainable packaging is ob ious, howe e , he gap
be ween ha need and FMCG's abili y is also appa en . Ji -Singh-Mann and Kau (2013)
deployed MANOVA and ound ha hypo heses abou di e ences in b anding s a egies
ac oss he sec o s a y o e he ime. Malshe e al. (2022) in es iga ed ma ke ing s a egy
implemen a ion impedimen s and emedies and concluded ha bo h le el-speci ic and
cumula i e e ec s o impedimen s necessi a e p esc ip i e emedies o s abilizing he
implemen a ion p ocess. Naja i e al. (2022) bols e ed ha AIoT has p o ound indus ial
impac and can ha e a powe ul ans o ma ion on di e en businesses. I gained high
deg ee o impo ance especially in FMCG indus ies. Niyas and Ka ida (2022) s udied
FMCGs' inancial b and alues using an app op ia e inancial-b and- alua ion-model and
e ealed ha b and alue has a con empo aneous e ec on s ock-p ices, bu wi h nega i e
h ee-yea ime-lagged e ec . Wilkins and I eland (2022) ca ied a esea ch s udy in he
con ex o ma gin managemen and consume p e e ences and esul ed ha pack quan i y
is leas impo an p oduc a ibu e, he e o e, manu ac u e s may ha e a be e chance o
maximizing p o i by educing pack quan i y, a he han inc easing p ice o lowe ing
p oduc quali y.
Challenges o Digi al Ma ke ing Adop ion in FMCG Sec o
472
Azama e al. (2023) bu essed ha majo i y o FMCGs a e unde alued in e ms o he
alue o in angible asse s by compa ing he ma ke alue o in angible asse s wi h he
undamen al and heo e ical alue. The empi ical indings in a way suppo he posi i e
impac o in angible asse s on companies’ alue based using dynamic panel app oach.
Kamakela e al. (2023) collec ed da a om 14 FMCG manu ac u ing i ms indica ed ha
29% o he i ms a e using digi al- echnology o p edic and c ea e isibili y in supply
chain, 8% o he i ms a e s ill wi hin he eme ging le el o supply isk-capabili ies. Khaye
e al. (2023) employed Mixed-In ege Linea P og amming as esea ch me hodology and
asce ained he KPI sco es o p ocu emen , manu ac u ing, and dis ibu ion we e 90%,
86.67%, and 80.30% co espondingly. Ahuja and Tabeck (2024) asse ed ha ma ke e s
should le e age he social media p esence o enhance b and us wo hiness, b and
ela ionships, cong uence and posi ioning.
Biswas e al. (2024) employed Fuzzy TOPSIS o anking o he solu ions in anspo
sec o and a gued ha he app op ia e anspo policy is he op solu ions ha can play a
i al ole in imp o ing ehicle- ill a e in he long un in FMCG-sec o s. Indasa i and
Tjahyan o (2024) used La en Seman ic Analysis and e ealed ha he e ec o da a
p epa a ion yielded a esul o 0.092 ou pe o ming da a wi hou s op lis and wo d-based
s op lis s a ailable by he s op wo ds lib a y, e en hough he execu ion ime was 0.01
seconds longe han o he s. Long e al. (2024) employed mul i a ia e eg ession o es
eigh ac o s a ec ing cus ome engagemen and ound ha sel -cong ui y, social media
ma ke ing, b and wa m h, social in luence, economic bene i , and b and a achmen
posi i ely in luence he social cus ome engagemen . Makgabo e al. (2024) asse ed ha
he Indus y 4.0 does imp o e quali y in he FMCG-sec o . P ima y con ibu o s a e:
ope a ional & inancial pe o mance, cus ome ocus, educa ion & aining, and p oduc ion
p ocesses. Wang e al. (2024) claimed ha b and image and design-d i en a ibu e
signi ican ly a ec pu chase in en ion, whe eas, senso y expe ience does no ha e such
a ec . One can also ind s udies ha de eloped a concise checklis ha p o ides business
ma ke ing esea che s a sho cu o implemen a ion o s a egies in coming p ojec s.
Kaneko and Kajikawa (2025) sugges ed ha a quan i ica ion app oach o analyzing
echnological po olio in he basis o CIT. Mo gan e al. (2025) iden i ied ha inhe en
ade-o s be ween di e en componen s and s ages o ma ke ing s a egy-implemen a ion
a e impo an causes o implemen a ion p oblems and ailu es. I also con i med ha o
de e mine how bes o iden i y he kinds o implemen a ion capabili ies ha can bes enable
o iden i y o selec among he inhe en ade-o s. Rashid e al. (2025) a i med ha he
be e supplie us and he use o in eg a ed echnology enhance capabili y o he
o ganiza ion o be e espond o dis u bances. Based on he li e a u e e iew, a lis o
challenges (S ep 1 & 2) o digi al-ma ke ing adop ion in FMCG-sec o is gene a ed as
Table-1.
Basi , Bhaumik & Niazi
473
Table 1: Challenges o Digi al-Ma ke ing Adop ion in FMCG-Sec o
Code
Challenges
Desc ip ion
Sou ce
1
O ganiza ional
esis ance
Senio -leade ship is eluc an o
in es in digi al wi hou clea e u n
on in es men .
Moo man and
Day (2016)
2
Cus ome s’ digi al
engagemen
Digi al pe mi s o shape messages o
indi idual consume p e e ences.
Mei e e al.
(2019)
3
Technological ba ie s
The legacy sys em does no suppo o
ad ance digi al-ma ke ing ools.
Longoni and
Cian (2022)
4
Da a p i acy and
secu i y
Da a secu i y and p i acy is a
pe sis en challenge he FMCG-
sec o .
McCa hy e al.
(2017)
5
Consume us
conce ns
Cus ome s a e inc easingly
suspicious o how hei da a is being
used.
Hilleb and e al.
(2015)
6
Engaging and ele an
con en
Con en does no esona e wi h
consume s and d i es disengagemen .
Chowdhu y and
Na h (2024)
7
U ilizing mul imedia
channels
Video con en may no pa icula ly be
e ec i e in cap u ing audience
a en ion.
Chowdhu y and
Na h (2024)
8
Con inuous
op imiza ion
I is qui e challenging o con inuously
hone he campaigns based on
pe o mance me ics.
Chowdhu y and
Na h (2024)
9
In eg a ion o AI
In eg a ion o AI in ma ke ing
decision-making is somehow
challenging.
Ho man e al.
(2022)
10
In eg a ion o online
and o line channels
B idging he gap be ween digi al and
physical channel is a bi challenging.
Ba a and Kelle
(2016)
11
Building digi al
capabili ies
I is somehow challenging o in es in
con inuous aining o upskill he
ma ke ing eam.
Mei e e al.
(2019)
12
Ma ke ing Inno a ion
Accep ance and adop ion o new
digi al ools and pla o ms.
Longoni and
Cian (2022)
13
Responsi e cus ome
se ice
Swi esponses a e essen ial and
challenging in digi al-ma ke ing.
Hollebeek e al.
(2023)
14
Handling new sou ces
o da a
Handling new sou ces o da a o
cus ome acqui emen and e en ion
a e challenging.
McCa hy e al.
(2017)
15
Managing supplie &
cus ome coo dina ion
To p o ide he cus omized o e ,
he e coo dina ion is impo an .
Wang e al.
(2017)
Challenges o Digi al Ma ke ing Adop ion in FMCG Sec o
474
3. Resea ch Me hodology
Pos -posi i is esea ch philosophy is ollowed in he s udy wi h induc i e esea ch
app oach. The design comp ises o e iew o up- o-da e li e a u e, p ima y da a ga he ing,
modeling and analysis. Popula ion unde s udy is he olks o s akeholde s o FMCG-sec o .
The s udy ollows pu posi e sampling design and he sample size is wen y- h ee expe s
om he s akeholde s. The da a a e collec ed h ough su ey om a panel o expe s
ec ui ed om he s akeholde s on he basis o p ede e mined c i e ia by using ma ix ype
ques ionnai e ( he ques ionnai e is a ailable upon easonable eques om any eade ). The
li e a u e discou se o ex ac ion o lis o challenges, ISM o ex ac he unde lying model
o in e ela ionships, and MICMAC o analysis a e employed as esea ch me hods. Resul s
o li e a u e su ey e eal ha he e a e ( o al) i een challenges o digi al-ma ke ing
adop ion in FMCG-sec o . ISM and MISCMAC a e popula echniques o modeling and
analysis o simpli y he conund um si ua ions (A i e al., 2013; Nazi e al., 2024; Rashid
e al., 2025; Sushil, 2017; Wa ield, 1973).
3.1 Expe s’ Panel
Non-p obabili y based pu posi e sampling design and da a collec ion om an expe ’s
panel (S ep 3) pa icula ly ha o expe s on he phenomenon unde esea ch is op ed when
he e is no da a a ailable o he a ailable da a is no eliable (A i e al., 2013; Wa ield,
1973). Since he da a on he issue unde s udy is no a ailable in seconda y o m, he e o e,
i is impe a i e o elici some da a om he expe s om he ield (S ep 4). The e a e wo
ypes o panels o expe s i.e. homogenous and he e ogonous. The size o panel a ies om
eigh expe s (minimum) o wen y- i e-expe s (op imum) (Clay on, 1997; Khan & khan,
2013). The s udy has wen y- h ee alid esponses o expe s o building he modeling &
analysis he eon. The c i e ia o ec ui men o expe s on panel includes: i) uni e si y
g adua e as minimum quali ica ion, ii) en yea s as minimum ele an ield expe ience, iii)
some acumen o esea ch o unde s and he basics o he ini ia i e, i ) willingness o
pa icipa e in he s udy, and ) able o spa e easonable ime o pa icipa e o he s udy in
o ice se ing (Sushil, 2017; A i e al., 2013; Wa ield, 1974). The echniques o da a
collec ion used is ace- o- ace one-on-one semi-s uc u e in e iew ecoded on a VAXO
based ques ionnai es sui able o ISM based s udies (Li & Yang, 2014). The in e iews a e
conduc ed in o ice se ing o he expe s.
Fi een s eps classical p ocedu e o ISM and MICMAC is applied s ep-wise i.e.
asce ainmen o an a ay o challenges (S ep 1), e i ica ion o challenges (S ep 2),
ec ui men o expe s’ panel (S ep 3), collec ion o da a (S ep 4), agg ega ion o da a &
SSIM (S ep 5), con e sion o symbols in o bina y ma ix (S ep 6), making he bina y
ma ix in o ully ansi i e ma ix (S ep 7), pa i ioning he ansi i e ma ix in o sub-
ma ices o ex ac ing le els (S ep 8), ea anging he ansi i e ma ix o e lec he model
on diagonals (S ep 9), summa y ep esen a ion o ISM p ocess (S ep 10), p epa ing he
ISM Model (S ep 11), p epa ing scale-cen ic MICMAC diag am (S ep 12), p epa ing
Basi , Bhaumik & Niazi
475
da a-cen ic MICMAC diag am (S ep 13), jux aposing he esul s (S ep 14), and discussion
on esul s (S ep 15).
3.2 Da a
Da a collec ion on ques ionnai es is agg ega ed (S ep 5) in o MS-Excel shee s using he
ule ‘mino i y gi es way o majo i y’ (Abdullah & Si aj, 2014; Cai e al., 2018; Li e al.,
2019; Sushil, 2012).
3.3 ISM Modeling
S epwise p ocedu e o ISM modelling as de ised and used in Dhochak & Sha ma (2016);
Sushil (2017); A i e al., (2013); Wa ield, (1974) is applied o he da a collec ed i.e.
S uc u al Sel -In e ac ion Ma ix (SSIM) Table-2 is gene a ed om agg ega ed da a.
Table 2: SSIM (S ep 5)
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1
V
O
A
V
O
O
V
O
A
O
O
V
A
V
2
A
O
O
V
V
X
X
A
X
V
X
X
A
3
V
O
O
O
O
V
V
V
V
O
V
O
4
V
A
O
X
V
V
V
O
O
V
O
5
A
V
V
A
A
V
X
O
V
O
6
V
V
V
V
A
V
A
A
A
7
A
V
V
A
A
V
V
O
8
V
O
A
V
A
O
A
9
V
V
V
X
V
O
10
X
A
V
A
O
11
X
V
X
O
12
X
A
A
13
X
O
14
V
15
SSIM Table-2 is con e ed in o Ini ial Reachabili y Ma ix (S ep 6) Table-3 using he ules
o bina y coding.
Challenges o Digi al Ma ke ing Adop ion in FMCG Sec o
482
Figu e 1: ISM Model
ISM model (Figu e-1) show ha challenges coded as (2), (5), (6), (7), (8), (9), (11), (12), (13),
and (14) occupy Le el I ( op le el- con aining he leas c i ical ac o s). Challenge coded as (15)
occupies Le el II, challenge (1) occupies Le el III, challenge (10) occupies Le el IV and
challenge (4) occupies Le el V (middle o he model – ha ing mode a e c i ical e ec s
acco dingly). Challenge (3) occupy Le el VI (bo om le el – being he mos c i ical challenge).
3.4 MICMAC Analysis
Ma iced' Impac s C oise's Mul iplica ion Appliquée a UN Classemen (MICMAC) is a
s uc u al me hodology ha has he capabili y o subs an ia e he esul s o he ISM modeling
(Gode , 1986). The e a e wo app oaches o pe o m his analysis i.e. scale-cen ic app oach and
da a-cen ic app oach. The s udy uses bo h he app oaches (Figu e-2 and Figu e-3 espec i ely)
o gain deepe insigh s o he phenomenon.
Basi , Bhaumik & Niazi
483
Figu e 2: D i ing-Dependence Diag am (Scale Cen ic)
D i ing-dependence diag am (S ep 12 - scale cen ic) MICMAC analysis (Figu e-2) shows ha
challenge coded as (3) all in independen qua e , whe eas, all o he challenges i.e. (1), (2), (4)
o (15) all in linkage qua e .
Figu e 3: D i ing-Dependence Diag am (Da a Cen ic)
Challenges o Digi al Ma ke ing Adop ion in FMCG Sec o
484
D i ing-dependence diag am (S ep 13 - da a cen ic) MICMAC analysis (Figu e-3) shows ha
ha challenges (3), (4), and (15) all in independen qua e , whe eas, emaining all i.e. (1), (2),
(5), (6), (7), (8), (9), (10), (11), (12), (13), and (14) all in linkage qua e .
4. Resul s
Keeping in iew he esea ch p oblem (i.e. o analyze he in e ela ionships o challenges
o digi al-ma ke ing adop ion in FMCG-sec o in Pakis an), he li e a u e e iew is
pe o med, da a a e collec ed, and modeling & analysis p ocedu e is applied on he p ima y
da a. The esul s o li e a u e su ey e eal ha he e a e o al i een challenges o digi al-
ma ke ing adop ion in FMCG-sec o o ganiza ional esis ance (1), cus ome s’ digi al
engagemen (2), echnological ba ie s (3), da a p i acy and secu i y (4), consume us
conce ns (5), engaging and ele an con en (6), u ilizing mul imedia channels (7),
con inuous op imiza ion (8), in eg a ion o AI (9), in eg a ion o online and o line
channels (10), building digi al capabili ies (11), ma ke ing inno a ion (12), esponsi e
cus ome se ice (13), handling new sou ces o da a (14), and managing supplie &
cus ome coo dina ion (15). The esul s o ISM modeling show ha cus ome s’ digi al
engagemen (2), consume us conce ns (5), engaging and ele an con en (6), u ilizing
mul imedia channels (7), con inuous op imiza ion (8), in eg a ion o AI (9), building digi al
capabili ies (11), ma ke ing inno a ion (12), esponsi e cus ome se ice (13), and
handling new sou ces o da a (14) occupy Le el I. Managing supplie & cus ome
coo dina ion (15) occupies Le el II. O ganiza ional esis ance (1) occupies Le el III.
In eg a ion o online and o line channels (10) occupies Le el IV. Da a p i acy and
secu i y (4) occupies Le el V. Technological ba ie s (3) occupies Le el VI. The esul s o
scale-cen ic MICMAC analysis show ha challenge echnological ba ie s (3) alls in
independen qua e , whe eas, emaining all i.e. o ganiza ional esis ance (1), cus ome s’
digi al engagemen (2), da a p i acy and secu i y (4), consume us conce ns (5),
engaging and ele an con en (6), u ilizing mul imedia channels (7), con inuous
op imiza ion (8), in eg a ion o AI (9), in eg a ion o online and o line channels (10),
building digi al capabili ies (11), ma ke ing inno a ion (12), esponsi e cus ome se ice
(13), handling new sou ces o da a (14), and managing supplie & cus ome coo dina ion
(15) all in linkage qua e . The esul s o da a-cen ic MICMAC analysis show ha
challenges echnological ba ie s (3), da a p i acy and secu i y (4), and managing supplie
& cus ome coo dina ion (15) all in independen qua e , whe eas, emaining all i.e.
o ganiza ional esis ance (1), cus ome s’ digi al engagemen (2), consume us conce ns
(5), engaging and ele an con en (6), u ilizing mul imedia channels (7), con inuous
op imiza ion (8), in eg a ion o AI (9), in eg a ion o online and o line channels (10),
building digi al capabili ies (11), ma ke ing inno a ion (12), esponsi e cus ome se ice
(13), and handling new sou ces o da a (14) all in linkage qua e . The esul s o he s udy
a e obus because a classical p ocedu e o ma hema ical modeling, analysis and
ep esen a ion is used wi h assis ance o some compu e so wa e like Ed awMax, MS
Excel e c. The esul s o he s udy a e summa ized and jux aposed in Table-13 below o
be e comp ehension (S ep 14).
Basi , Bhaumik & Niazi
485
Table 13: Summa y o Resul s
Resul o Li e a u e
Re iew
Resul s o MICMAC Analysis
Resul s o
ISM
Commen s
Cod
e
Fac o s
D i in
g
Dependenc
e
E ec i eness
Clus e
Le el
1
O ganiza ion
al esis ance
13
14
-1
Linkage
III
2
Cus ome s’
digi al
engagemen
14
15
-1
Linkage
I
3
Technologica
l ba ie s
15
1
14
Independe
n
VI
Key Fac o
4
Da a p i acy
and secu i y
14
11
3
Linkage
V
5
Consume
us conce ns
14
15
-1
Linkage
I
6
Engaging and
ele an
con en
13
15
-2
Linkage
I
7
U ilizing
mul imedia
channels
13
15
-2
Linkage
I
8
Con inuous
op imiza ion
13
15
-2
Linkage
I
9
In eg a ion o
AI
13
15
-2
Linkage
I
10
In eg a ion o
online and
o line
channels
13
14
-1
Linkage
IV
11
Building
digi al
capabili ies
14
15
-1
Linkage
I
12
Ma ke ing
Inno a ion
12
15
-2
Linkage
I
13
Responsi e
cus ome
se ice
14
15
-1
Linkage
I
14
Handling new
sou ces o
da a
14
15
-1
Linkage
I
15
Managing
supplie &
cus ome
coo dina ion
13
12
1
Linkage
II
The s udy has achie ed i s objec i e i.e. i) o un eil he a ay o challenges o digi al-
ma ke ing adop ion in FMCG-sec o in Pakis an ( h ough e iew o li e a u e), and ii) o
Challenges o Digi al Ma ke ing Adop ion in FMCG Sec o
486
hie a chalize hem and de e mine he in e ela ionships, ele ance ( h ough ISM Modeling)
and dependencies ( h ough da a/scale cen ic MICMAC).
5. Discussion (S ep 15)
The s udy is aimed o analyze he in e ela ionships o challenges o digi al-ma ke ing
adop ion in FMCG-sec o in Pakis an and he indings o he s udy ha e impo ance o
FMCG i ms, in es o s, in es men analys s and manage s, he go e nmen , and
policymake s. The me hods employed o analyze include li e a u e discou se, ISM
modeling, and MICMAC analysis. I is c i ical o discuss ce ain aspec o he s udy
keeping in iew he esul s. This sec ion con ains discussion on esul s, con as ing he
esul s wi h con empo a y li e a u e, p ac ical & heo e ical implica ions o he s udy, and
on limi a ions o he s udy & di ec ions o u u e s udies.
5.1 Discussion on Resul s o he S udy
The esul s o li e a u e discou se a e summa ized in Table- 1 (i.e. i een impo an
challenges posed o digi al-ma ke ing adop ion in FMCG-sec o in Pakis an). This lis is
gene a ed om sca e ed and scan y li e a u e collec ed om wide a ie y o con ex s ha
is no claimed o be exhaus i e. Howe e , i is belie ed ha i co e s he mos o impo an
ones. ISM modeling shows ha ‘ echnological ba ie s (3)’ is he mos c i ical and key
challenge likely o be aced in adop ion o digi al-ma ke ing in FMCG-sec o in Pakis an
since his challenge occupies he bo om o he model which is conside ed o be he mos
c i ical one acco ding o he in e p e a ions o ISM models. Acco dingly, he challenges
appea ing middle and op o he model ha e ela i e mode a e se e e and leas c i ical
e ec s espec i ely. The e o e, he policy make s should deal wi h hem in o de o
p ecedence and impo ance in p ac ical si ua ions. MICMAC analysis shows ha challenge
‘ echnological ba ie s (3)’ (acco ding o scale-cen ic MICMAC) and challenges
‘ echnological ba ie s (3), da a p i acy and secu i y (4), and managing supplie &
cus ome coo dina ion (15)’ (acco ding o da a-cen ic MICMAC) all in o independen
qua e , he e o e, hese a e he d i e s. All o he a e ca ego ized as linkages he e o e o
ake any ac ion on any o he challenges need special ca e because any ac ion on he linking
challenges may ha e e ec on o he s as well as on hemsel es in u n. The e is no challenge
as au onomous, his ype o he esul a i ms ha he ac o s a e ele an and impo an o
he sys em unde s udy. Da a-cen ic analysis shows ha challenge ‘ma ke ing inno a ion
(12)’ has he po en ial o be ca ego ized as dependen one. The esul s o MICMAC, in
gene al, subs an ia e he esul s o ISM modeling.
5.2 Con as ing he Finding o he S udy wi h Con empo a y Li e a u e
The o e all esul s o he s udy con o m o he indings o con empo a y esea ch
pa icula ly o ha o Chowdhu y and Na h (2024). In his con ex a con as o he cu en
s udy is ep esen ed in Table-14.
Basi , Bhaumik & Niazi
487
Table 14: Compa ing wi h P io S udies
S .
S udy
Coun y
Focus
Va iables
Resul s
Me hod
1
Cu en
Pakis an
Challenges o digi al
ma ke ing adop ion
in FMCG sec o in
Pakis an
15
challenges
Technological
ba ie s a e he
key challenge
ISM wi h
MICMAC
analysis
2
P imadasa
e al.
(2024)
Indonesia
Halal-Sus ainable
Supply Chain
Managemen
(HSSCM) indica o s
o SMEs
16
indica o s
SMEs adop ing
e hical,
en i onmen ally
conscious
business
s a egies
DEMATEL,
ISM &
MICMAC
In eg a ed
app oach
3
Ansa i
e al.
(2024)
India
Modeling he
enable s o
implemen ing g een
HRM p ac ices
9 enable s
Go e nmen
policy is he key
enable
ISM-
MICMAC
app oach
4
Ahuja
and
Tabeck
(2024)
India
Impac o social
media on a ious
ma ke ing aspec s
e.g. b and
us wo hiness,
ela ionships,
cong uence and
posi ioning
5 a iables
o
exploi ing
social media
ma ke ing
in FMCG
sec o
The ma ke e s
should le e age
social media
p esence o
enhance b and
us wo hiness,
ela ionships,
cong uence &
posi ioning
Mul iple-case
me hod
5
Azama
e al.
(2023)
China
O ganiza ional
s a egic cybe
secu i y managemen
6 i.e.
Company
Value,
In angible
asse s,
Re u n on
asse s,
Cu en
a io, Size
le e age
The e is posi i e
impac o
in angible asse s
on companies’
alue
Dynamic
panel
app oach
Al hough his s udy is di e en om he con empo a y li e a u e on many coun s o say in:
opic o in es iga ion, popula ion unde s udy, me hods, con ex , app oach, indings, e c.
These ypes o he s udies a e mos ly in Indian con ex s. The s udies ha seem simila o
ha o cu en , in ac , a e on di e en opics and con ex s bu use me hodologies simila
o he s udy in hand e.g. s udy lis ed a se ial numbe 2 & 3 seem simila since he use o
MCDM echniques simila o cu en s udy bu hey add ess ‘Halal-Sus ainable Supply
Chain Managemen (HSSCM) indica o s o SMEs’ and ‘Modeling he enable s o
implemen ing g een HRM p ac ices’ espec i ely.
5.3 Discussion on Implica ions o he S udy
Findings ha e impo an p ac ical implica ions o FMCG i ms, in es o s, in es men
analys s and manage s, he go e nmen , and policymake s because i p o ides deepe
Challenges o Digi al Ma ke ing Adop ion in FMCG Sec o
488
unde s anding o challenges posed o hem in eal ime si ua ions. The s udy has p o ound
heo e ical and p ac ical implica ions o all hese s akeholde s because hey can use i s
indings in policy decisions bo h in business and economic mix in sho /long- un business
cycles. Findings can also se e indi iduals/companies/non-p o i o ganiza ions by o e ing
deepe insigh s in he phenomenon.
5.4 Discussion on Limi a ions and Fu u e Di ec ions o he S udy
The indings o he s udy can be used wi h ca e because i is subjec o ce ain limi a ions.
Fi s ly, limi ed numbe o esea ch s udies a e e iewed and he lis o challenges so
gene a ed is no claimed o be comp ehensi e, he e o e, u u e s udies mus be designed in
b oade scope. Secondly, he s udy ollows quali a i e pa adigm o esea ch, he e o e, i
is sugges ed ha some quan i a i e u u e s udies should be designed o a i m he esul s
he eo . Thi dly, i is conduc ed in he con ex o Pakis an, in u u e, he s udies should be
designed in di e en con ex o subs an ia e he gene aliza ion o he esul s o he s udy.
Fou hly, he au ho s used he basic e sion o ISM modeling and MICMAC and he
analysis does no o e cause & pole, he e o e, i is sugges ed ha he u u e s udies mus
be designed in o m TISM and pola ized-TISM in o de o enhance he endea o s o he
s udy.
6. Conclusion
Admi edly, by unde s anding and add essing he in e ela ionships o challenges o
digi al-ma ke ing in FMCGs can ha ness he powe o digi al-ma ke ing o enhance
cus ome sa is ac ion and engagemen , build s onge b ands, and achie e sus ainable
g ow h. The p oblem unde s udy is sca ce and scan y li e a u e on his issue, whe eas, his
is impo an issues ha needs immedia e a en ion, hence, he cu en s udy aims o ills
gap. The design comp ises o e iew o up- o-da e li e a u e, p ima y da a ga he ing,
modeling and analysis. The da a a e collec ed h ough su ey om expe ’s panel ec ui ed
om he s akeholde s on he basis o p ede e mined c i e ia by using ma ix ype
ques ionnai e. The li e a u e discou se o ex ac ion o lis o challenges, ISM o ex ac
he unde lying model o in e ela ionships, and MICMAC o analysis a e employed as
esea ch me hods.
Resul s o li e a u e su ey e eal ha he e a e o al i een challenges o digi al-ma ke ing
adop ion in FMCG-sec o : o ganiza ional esis ance (1), cus ome s’ digi al engagemen
(2), echnological ba ie s (3), da a p i acy and secu i y (4), consume us conce ns (5),
engaging and ele an con en (6), u ilizing mul imedia channels (7), con inuous
op imiza ion (8), in eg a ion o AI (9), in eg a ion o online and o line channels (10),
building digi al capabili ies (11), ma ke ing inno a ion (12), esponsi e cus ome se ice
(13), handling new sou ces o da a (14), and managing supplie & cus ome coo dina ion
(15).
Resul s o ISM modeling show ha challenges coded as (2), (5), (6), (7), (8), (9), (11), (12),
(13), and (14) occupy Le el I. Challenge coded as (15) occupies Le el II. Challenge (1)
Basi , Bhaumik & Niazi
489
occupies Le el III. Challenge (10) occupies Le el IV. Challenge (4) occupies Le el V.
Challenge (3) occupies Le el VI.
Resul s o scale-cen ic MICMAC analysis show ha challenge coded as (3) alls in
independen qua e , whe eas, all o he challenges i.e. (1), (2), and (4) o (15) all in linkage
qua e . Resul s o da a-cen ic MICMAC analysis show ha challenges (3), (4), and (15)
all in independen qua e , whe eas, emaining all i.e. (1), (2), (5), (6), (7), (8), (9), (10),
(11), (12), (13), and (14) all in linkage qua e . I is a eal ime da a based unique ype o
s udy ha p o ides unde s anding o s akeholde s pa icula ly o ma ke e s, FMCG
manage s, egula o s, esea che s and echnologis s. I con ibu es lis o challenges, a
s uc u al model, and scale-cen ic & da a cen ic analy ical diag ams o he con empo a y
li e a u e.
Resea ch Funding
The au ho s ecei ed no esea ch g an o suppo o his esea ch s udy.
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