Conference Papers

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    'Watch and Buy' and its impact on online retailers and manufacturer: A Stackelberg and Nash game analysis
    (Institute of Electrical and Electronics Engineers Inc., 2023) Raju, S.; Rofin, T.M.; Kumar, S.P.; Islam, S.M.N.
    'Watch and Buy' or live stream (LS) selling is a novel selling method where an influencer will showcase the product through an LS channel or social media; the consumer can interact with the influencer and buy the product online. Our study, for the first time, analyses the pricing decisions of an LS channel when they competes with the traditional online retailer (OR). We also analysed the impact of LS on OR and the product manufacturer (PM). Later, we examined the effect of conversion rate and revenue sharing contract on the supply chain partners' profit. This study's findings can act as a starting point for the analytical studies in LS selling and can aid the management practitioners of LS, OR and PM to optimise their profit. © 2023 IEEE.
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    Predicting the Determinants of Augmented Reality Technologies in E-commerce Applications: A SEM and ANN Modeling
    (Springer Science and Business Media Deutschland GmbH, 2025) Anand, K.; Sheena, S.
    Augmented reality (AR) technologies are the most promising technologies for e-commerce applications. This study has used the UTAUT theory, extending with privacy risk, which may impact the user’s decision to try AR features in e-commerce apps. The paper investigated factors using a two-stage approach comprising of Structural Equation Model (SEM) and an Artificial Neural Network (ANN). The validation of the model is performed with the IBM SPSS AMOS 24 and IBM SPSS 25 software. The results revealed performance expectancy as a noteworthy determinant through the analysis. Further privacy risks involved while using AR features of online shopping apps negatively impact the influence on the choice of the users to try out the technology. The proposed model has explained the user behavioral intention with 36.7%, whereas using ANN has predicted with an accuracy of 48.9%, indicating the model has high predictive power. This study’s theoretical and practical insights will contribute to developing and refining augmented reality systems in e-commerce applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.