Consumer willingness to adopt digital coupons in post-demonetization and COVID-19 in India
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DOIhttp://dx.doi.org/10.21511/im.21(1).2025.13
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Article InfoVolume 21 2025, Issue #1, pp. 157-169
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Creative Commons Attribution 4.0 International License
This study aims to examine the factors influencing consumers’ willingness to adopt digital coupons in India. It focuses on the impact of two major events: demonetization in 2016 and the COVID-19 pandemic. Together, these events have caused a shift toward digital payments and digital coupons, changing consumer behavior in favor of digital solutions. This study specifically focuses on consumers in Jaipur, both urban and rural, to capture the unique dynamics of this geographical region. In this study, 110 respondents from different demographic groups were given a structured questionnaire. 12 respondents were selected for in-depth qualitative interviews to learn more about the factors that promote and hinder the use of digital coupons. Quantitative data analysis is conducted using SmartPLS 4 software, and the qualitative interview data are analyzed thematically. The regression analysis reveals that convenience and perceived value drives the use of digital coupons, with 75% of respondents reporting their adoption. The findings bring into perspective how the digital consumer landscape of India is evolving and what role incentives play in digital marketing in driving consumer preference and shaping the long-term feasibility of the strategy. The conclusion reinforces that the use of digital incentives for consumers will be influential in choices and underlines the feasibility of digital approaches in the new consumer environment in India.
Acknowledgment
The authors would like to thank the Deanship of Manipal University Jaipur for supporting this work.
- Keywords
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JEL Classification (Paper profile tab)M31, O33
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References38
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Tables6
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Figures1
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- Figure 1. Conceptual model
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- Table 1. Demographics of the respondents
- Table 2. Perception and impacts regarding digital payments in Jaipur
- Table 3. Perception of digital economy tools
- Table 4. Measurement model
- Table 5. Structural parameter estimates
- Table A1. Construct operationalization
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