E-payments in the post-COVID-19: navigating uncertainty and forecasting trends
Abstract
Purpose
This study aims to investigate the impact of the COVID-19 pandemic on electronic payments and to predict future trends in this sector using advanced grey forecasting models. It seeks to fill the research gap in understanding how digital payment behaviors have evolved post-pandemic, particularly focusing on the Iranian market.
Design/methodology/approach
The research employs grey forecasting models, including grey model (GM(1,1)), rolling grey model (RGM(1,1)), unbiased GM (1,1), modified unbiased GM (1,1), discrete grey model (DGM(1,1)) and grey Verhulst, to analyze the e-payment indicators in the next three financial periods. Data were collected on electronic transactions and payment behaviors, and various forecasting models were compared to assess their accuracy and applicability.
Findings
The study finds that the unbiased GM (1,1) and modified unbiased GM (1,1) models offer superior accuracy in predicting electronic payment trends compared to the other models. These models are particularly effective in scenarios with limited data, showcasing their robustness in forecasting future trends in digital payments.
Originality/value
This research contributes original insights into the post-pandemic evolution of electronic payments, highlighting the effectiveness of grey forecasting techniques in emerging markets. It provides valuable information for policymakers and financial institutions, aiding in strategic planning and policy development related to digital transactions.
Keywords
Acknowledgements
In accordance with the journal's protocols, artificial intelligence has helped improve the language and readability of this article. The author remains responsible for the content.
Citation
Ahmadian, M. (2025), "E-payments in the post-COVID-19: navigating uncertainty and forecasting trends", Journal of Economic Studies, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JES-11-2024-0745
Publisher
:Emerald Publishing Limited
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