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This paper aims to identify the factors affecting the adoption of plastic money and mobile wallets by comparing rural and urban perspectives. For this, the study uses a unified theory of acceptance and use of technology (UTAUT3) model with additional variables, perceived value and perceived risk, to predict use behavior.

This research incorporates two cross-sectional surveys, Study R, which includes 417 rural respondents and Study U, which includes 431 urban respondents, regarding plastic money and mobile wallet adoption. This paper used the Statistical Package for Social Science and partial least squares-structural equation modeling for data analysis.

Both studies showed that performance expectancy, social influence and hedonic motivation substantially influence behavioral intention and use behavior. In contrast, effort expectancy has an insignificant influence in rural (Study R) and urban (Study U). In addition, personal innovativeness and perceived value positively influence, whereas perceived risk negatively influences behavioral intention and use behavior in both studies. However, facilitating conditions had a positive effect in Study U, but it had a negative effect on behavioral intention and use behavior in Study R.

This research lays a practical foundation for governments, policymakers and marketers to encourage a phygital payment service that explicitly addresses the rural and urban context. In addition, the findings of this paper also help regulatory authorities develop effective strategies and campaigns to encourage the sustainable development of countries.

This paper attempts to address the gap in the prevailing literature by investigating the role of geographical differences in the technology adoption system, especially in emerging nations like India, where these studies are missing. The adoption differences between rural and urban areas, along with the insightful findings by the authors, help to highlight the unique aspects of the context. As one of the pioneering studies, this research tests the UTAUT3 model, incorporating two additional constructs, to provide a comprehensive framework for using plastic money and mobile wallets – valuable for both researchers and practitioners.

The Multi-Stakeholder Recommendation System learns consumer and producer preferences to make fair and balanced recommendations. Exclusive consumer-focused studies have improved the recommendation accuracy but lack in addressing producers' priorities for promoting their diverse items to target consumers, resulting in minimal utility gain for producers. These techniques also neglect latent and implicit stakeholders' preferences across item categories. Hence, this study proposes a personalized diversity-based optimized multi-stakeholder recommendation system by developing the deep learning-based diversity personalization model and establishing the trade-off relationship among stakeholders.

The proposed methodology develops the deep autoencoder-based diversity personalization model to investigate the producers' latent interest in diversity. Next, this work builds the personalized diversity-based objective function by evaluating the diversity distribution of producers' preferences in different item categories. Next, this work builds the multi-stakeholder, multi-objective evolutionary algorithm to establish the accuracy-diversity trade-off among stakeholders.

The experimental and evaluation results over the Movie Lens 100K and 1M datasets demonstrate that the proposed models achieve the minimum average improvement of 40.81 and 32.67% over producers' utility and maximum improvement of 7.74 and 9.75% over the consumers' utility and successfully deliver the trade-off recommendations.

The proposed algorithm for measuring and personalizing producers' diversity-based preferences improves producers' exposure and reach to various users. Additionally, the trade-off recommendation solution generated by the proposed model ensures a balanced enhancement in both consumer and producer utilities.