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Key challenges to digital financial services in emerging economies: the Indian context

Yes / Purpose: Digital Financial Services (DFS) have substantial prospect to offer a number of
reasonable, appropriate and secure banking services to the underprivileged in developing
countries through pioneering technologies such as mobile phone based solutions, digital
platforms and electronic money models. DFS allow unbanked people to obtain access to
financial services through digital technologies. However, DFS face tough challenges of
adoption. Realising this, the aim of this paper is to identify such challenges and develop a
framework.
Design/Methodology/Approach: We develop a framework of challenges by utilising
Interpretive Structural Modelling (ISM) and Fuzzy MICMAC approach. We explored eighteen
such unique set of challenges culled from the literature and further gathered data from two sets
of expert professionals. In the first phase, we gathered data from twenty-nine professionals
followed by eighteen professionals in the second phase. All were pursuing Executive MBA
programme from a metropolitan city in South India. The implementation of ISM and fuzzy
MICMAC provided a precise set of driving, linkage and dependent variables that were used to
derive a framework.
Findings: ISM model is split in eight different levels. The bottom level consists of a key driving
challenge V11 (i.e. high cost and low return related problem) whereas the topmost level
consists of two highly dependent challenges namely V1 (i.e. risk of using digital services) and
V14 (i.e. lack of trust). The prescribed ISM model shows the involvement of ‘high cost and
low return related problem (V11)’, which triggers further challenges of DFS.
Originality/value: None of the existing research has explored key challenges to DFS in detail
nor formulated a framework for such challenges. To the best of our knowledge, this is the first
paper on DFS that attempts to collate its challenges and incorporate them in a hierarchical
model using ISM and further divide them into four categories of factors using fuzzy MICMAC
analysis.

Identiferoai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/17475
Date25 October 2019
CreatorsRana, Nripendra P., Luthra, S., Rao, H.R.
Source SetsBradford Scholars
LanguageEnglish
Detected LanguageEnglish
TypeArticle, Accepted manuscript
Rights© 2019, Emerald Publishing Limited. This article is © Emerald Group Publishing and permission has been granted for this version to appear here: https://bradscholars.brad.ac.uk. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited.

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