TY - JOUR
T1 - Data-driven Digital Transformation for Uncertainty Reduction
T2 - Application of Satellite Imagery Analytics in Institutional Crop Credit Management
AU - Narayanamurthy, Gopalakrishnan
AU - Jayanth, R. Sai Shiva
AU - Moser, Roger
AU - Schäfers, Tobias
AU - Nagendra, Narayan Prasad
N1 - Published online: 16 December 2024.
PY - 2025/2
Y1 - 2025/2
N2 - 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.
AB - 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.
KW - Data-driven digital transformation
KW - Uncertainty
KW - Social innovation
KW - Big data analytics
KW - Institutional crop credit
KW - Satellite imagery
KW - Developing nations
KW - Data-driven digital transformation
KW - Uncertainty
KW - Social innovation
KW - Big data analytics
KW - Institutional crop credit
KW - Satellite imagery
KW - Developing nations
U2 - 10.1016/j.ijpe.2024.109498
DO - 10.1016/j.ijpe.2024.109498
M3 - Journal article
SN - 0925-5273
VL - 280
JO - International Journal of Production Economics
JF - International Journal of Production Economics
M1 - 109498
ER -