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A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach
Authors
A Barve
A Behl
+77 more
A Gandomi
Abhishek Behl
Anukul Mandal
B Brown
BH Wixom
C Boja
C Hsinchun
C Valmohammadi
C Valmohammadi
CM Olszak
D Barton
D Fisher
Dong-Hee Shin
DR Farris
E Loukis
EM Morgado
EW Ngai
EWT Ngai
F Provost
FJ Ohlhorst
G George
G Vossen
H Khajouei
HV Jagadish
IAT Hashem
ID Constantiou
J.P. Saxena
JL Zhao
K Kambatla
K. Ramamurthy
KE Martin
L Chen
L Taylor
LA Zadeh
Lianyong Qi
M Barrett
M Maity
M Minelli
M Shukla
MA Shareef
Merja Mahrt
N Sachdeva
O Kwon
P Koutsabasis
P Whalen
Pankaj Dutta
Patrick Mikalef
PB Goes
R Agarwal
R Attri
R Csutora
R Dubey
R Dubey
R Sharma
RJ Kauffman
RK Yin
RM Chang
RR Nelson
S Akter
S Devaraj
S Mithas
S Mohanty
S Opricovic
Samarjit Kar
SF Wamba
SJ Gorane
Stefan Lessmann
Sushil
T Dinev
TH Davenport
TL Saaty
TM Jones
U Woudstra
VA Bhosale
Yogesh K. Dwivedi
Z Sun
ZH Zhou
Publication date
1 January 2019
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
Abstract
E-commerce start-ups have ventured into emerging economies and are growing at a significantly faster pace. Big data has acted like a catalyst in their growth story. Big data analytics (BDA) has attracted e-commerce firms to invest in the tools and gain cutting edge over their competitors. The process of adoption of these BDA tools by e-commerce start-ups has been an area of interest as successful adoption would lead to better results. The present study aims to develop an interpretive structural model (ISM) which would act as a framework for efficient implementation of BDA. The study uses hybrid multi criteria decision making processes to develop the framework and test the same using a real-life case study. Systematic review of literature and discussion with experts resulted in exploring 11 enablers of adoption of BDA tools. Primary data collection was done from industry experts to develop an ISM framework and fuzzy MICMAC analysis is used to categorize the enablers of the adoption process. The framework is then tested by using a case study. Thematic clustering is performed to develop a simple ISM framework followed by fuzzy analytical network process (ANP) to discuss the association and ranking of enablers. The results indicate that access to relevant data forms the base of the framework and would act as the strongest enabler in the adoption process while the company rates technical skillset of employees as the most important enabler. It was also found that there is a positive correlation between the ranking of enablers emerging out of ISM and ANP. The framework helps in simplifying the strategies any e-commerce company would follow to adopt BDA in future. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature
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