Data-driven digital transformation for uncertainty reduction – Application of satellite imagery analytics in institutional crop credit management
Journal
International Journal of Production Economics
ISSN
0925-5273
Type
journal article
Date Issued
2025-02
Author(s)
Narayanamurthy, Gopalakrishnan
;
Jayanth, R Sai Shiva
;
;
Schaefers, Tobias
;
Nagendra, Narayan Prasad
Abstract
Agriculture financing in developing countries is dominated by informal lending. One challenge in the expansion of institutional (formal) credit is the lack of reliable data on the historical performance of farmers. Due to the absence of data, financial institutions face uncertainties that obstruct the decision-making process, leading to sub-optimal credit disbursal. Based on the theoretical lens of uncertainty reduction, this study focuses on achieving two key research objectives: identifying uncertainties in institutional crop credit management processes and examining how a data-driven digital transformation for social innovation based on satellite imagery analytics could alleviate these hindrances. We longitudinally study a satellite imagery analytics firm and complement the case data with stakeholder interviews. The results capture state space, option, and ethical uncertainties institutional lenders face in expanding crop credit and explain how data-driven digital transformation can reduce these uncertainties. Adopting such a data-driven digital transformation promises to make different stakeholder groups interact and collaborate to achieve the common objective of financial inclusion of small-scale economic actors. Further, we show that satellite imagery in crop credit management can significantly reduce the uncertainties caused by the lack of independent data sources.
Language
English
Keywords
Data-driven digital transformation
Uncertainty
Social innovation
Big data analytics
Institutional crop credit
Satellite imagery
Developing nations
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
Elsevier BV
Volume
280
Number
Art. 109498
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
1-s2.0-S0925527324003554-main.pdf
Type
Main Article
Size
2.72 MB
Format
Adobe PDF
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