Faculty Publications

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    Customer experience in social commerce: A systematic literature review and research agenda
    (John Wiley and Sons Inc, 2023) Dhaigude, S.A.; Mohan, B.C.
    Social commerce (SC) is an upcoming trend that has changed the online shopping experience by allowing e-retailers to develop long-term relationships with customers and increase sales. Empowered by Web 3.0, SC offers many-to-many interactions, enhancing the quality and quantity of social interaction related to the seller–customer, information searches, and product/service delivery. The customer experience (CEX) has been well developed both in the online and offline contexts. However, limited attention has been paid to examining CEX in the SC setting. This study aims to conduct a systematic review of the literature to develop a conceptual framework exploring both the antecedents and consequences of CEX in the SC setting. In the process, we make three significant contributions to academia and practice. First, the study contributes to our understanding of CEX in the context of SC. Second, it proposes a conceptual framework by identifying antecedents of CEX and potential consequences using the consumer culture theory. Finally, it highlights a subject relevant to academia and practice while proposing recommendations for further research. © 2023 John Wiley & Sons Ltd.
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    Smart Strategies for Improving Electric Vehicle Battery Performance and Efficiency
    (Nature Research, 2025) Tangi, S.; Vatsa, A.; Opam, A.; Bonthagorla, P.K.; Gaonkar, D.N.
    The increasing demand for Electric Vehicles (EVs) necessitates accurate range prediction and optimization of driving parameters to address range anxiety and improve user experience. This study proposes a machine learning-based framework for predicting EV range, optimum acceleration, and velocity using a synthetically generated dataset of 2,000 samples designed to reflect real-world driving scenarios. Four models—Random Forest (RF), Extra Trees (ET), Linear Regression (LR), and Long Short-Term Memory (LSTM)—were evaluated individually and in ensemble combinations. To ensure statistical reliability, all models were trained and tested over ten independent runs with randomized data partitions, and the results were reported as average performance with standard deviations. The ensembles consistently outperformed individual models, with the full ensemble (RF + ET + LSTM + LR) achieving the most robust performance across all metrics (MAE, MSE, and R²). Furthermore, a real-time web application was developed using the trained models to dynamically estimate driving parameters. The findings highlight the potential of integrating AI-driven predictive modelling into EV systems to support efficient driving behaviour and energy management. © The Author(s) 2025.