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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    Conceptual model for the safe withdrawal of freshwater from coastal aquifers
    (2009) Mahesha, A.
    The effect of subsurface barrier on the motion of the saltwater-freshwater interface in coastal aquifers is analyzed for wide ranging freshwater pumping scenarios. A Galerkin finite-element model considering sharp interface approach is used for this purpose. A semi-pervious subsurface barrier extending up to impervious bottom of the aquifer is considered at certain distance inland, parallel to the seacoast. The effect of barrier is analyzed in checking the advancement of the saltwater-freshwater interface under different scenarios of freshwater withdrawals at seaward and landward locations of the barrier and compared with nonbarrier conditions. The results indicated that barrier is able to check the advancement of the intrusion significantly and in certain cases, the progress is completely stalled for withdrawals on the landward side. Also, marked variations in the interface profile are observed as compared to no barrier condition, especially, for the seaward freshwater developments. From the model, nearest possible locations from the seacoast have been worked out for the safe withdrawal of freshwater where their effects are negligible on the saltwater advancement. © 2009 ASCE.
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    Impact of COVID-19 on individuals’ mental health and preventive health behaviours: a conceptual framework
    (Inderscience Publishers, 2022) Pai, R.R.; Chetty, N.; Alathur, S.
    The corona virus disease (COVID-19) is a pandemic that facilitate a confrontation space for scientific and social existence of human frontiers. The rapid spread and mortality rate of COVID-19 and the preventive measures including social distancing and its impact on economy, developed an unprecedented consciousness around the globe. It has created an effect on the mental health of individuals employed across various sectors and is outlined in this study. There is currently an inadequate theoretical model that focuses on the comprehensive understanding of the psychology of preventive behaviour during the outbreak of pandemics. In this study, a transnational model is delineated for assessing the adoption of preventive behavioural practices associated with COVID-19 pandemic. It uses the components derived from the theories of situational awareness and health belief model and literatures related to impact of containment strategies on various sectors. The contribution includes policy recommendations that can be helpful for the healthcare professionals and government to control the disease spread. © © 2022 Inderscience Enterprises Ltd.
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    Investigation of performance and technical assessments of hybrid source electric vehicles under different locations and driving conditions
    (Taylor and Francis Ltd., 2024) Sidharthan P, V.; Kashyap, Y.
    Sustainable transportation is a significant concept followed by nations implementing Nationally Determined Contributions (NDCs) that reduce emissions and adapt to climate change impacts. Electric vehicle (EV) adoption has accelerated; however, a trade-off exists between EV adoption and EV batteries-Battery charging from the grid (conventional energy sources) and e-wastes from retired batteries deposited in landfills. Thus, EVs associated with renewable energy sources (RES) are an alternate solution. This paper proposes a hybrid source electric vehicle (HSEV) with a high energy-dense supercapacitor (SC) as the primary source and PV energy as the secondary source. An energy management algorithm (EMA) with a modified controller is implemented in a Matlab/Simulink environment. Analysis of HSEV under varying locations (Australia, India, and Scotland), driving profiles (WLTP class-1, IDC, and ECE), and driving times (daytime, nighttime) highlights the importance of the proposed EMA. Grid charging instants are reduced to 3 times per month in Australia under WLTP class-1 cycle employing PV energy. Moreover, SC degradation is least compared to the lithium-ion battery in a BEV (Battery Electric Vehicle), hence avoiding the chances of maintenance and replacements. The proposed HSEV exhibits improved performance compared to BEVs of a similar type under different locations, driving, and environmental conditions. © 2023 Taylor & Francis Group, LLC.
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    A dynamic traffic assignment framework for policy analysis in cities with significant share of two-wheelers
    (Elsevier Ltd, 2024) Chapala, S.B.K.; Nair, P.; Sreekumar, M.; Bhavathrathan, B.K.
    High maneuverability of motorized two-wheelers amidst vehicles of bigger size and different dynamics invalidates FIFO to traverse through the gaps between other vehicles for faster mobility. The failure of existing dynamic traffic assignment frameworks with multi-class conditions to capture this behaviour results in inaccurate routing. The study proposes a simulation based two-class dynamic traffic assignment framework comprising of two-wheeler specific behaviour. These features when incorporated in the framework will add to the utility of the traditional dynamic traffic assignment framework in travel time prediction and planning level applications and is therefore relevant to regions with significant share of two-wheelers. The study gives a clear view of the effect of two-wheeler specific features on the route choice behaviour based on the dynamic travel time. The results of the study shows that there occurs an unintentional separation of vehicle classes during congestion; this effect can be utilized for a two-wheeler specific policy implication for congestion management in cities. The proposed framework can be employed in identifying the optimal provision of exclusive two-wheeler lanes. It is also observed that the provision of exclusive lanes may sometimes be counterproductive. © 2023
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    An automated deep learning pipeline for detecting user errors in spirometry test
    (Elsevier Ltd, 2024) Bonthada, S.; Pariserum Perumal, S.P.; Naik, P.P.; Mahesh, M.A.; Rajan, J.
    Spirometer is used as a major diagnostic tool for obstructive airway diseases and a monitoring tool for therapy response and disease staging over time. It is a sophisticated medical device employed to quantify flow and volume of air exhaled by a subject during a specific testing period. The essential metrics obtained from the spirometry test, play a crucial role in enabling healthcare professionals to thoroughly evaluate the respiratory health and condition of the individual under examination. Several spirometer measurements including Forced Vital Capacity (FVC) and Forced Expiratory Volume (FEV) serve as guidelines for diagnosis and prognosis of Chronic Obstructive Pulmonary Diseases (COPD) and asthma. However, user errors caused by different reasons, including improper handling of the equipment and poor performance during the maneuvers of the expiratory airflow, end up in incorrect treatment directions. To ensure accurate results, spirometry tests traditionally require the presence of a skilled professional to identify and address these errors promptly. A novel machine learning approach is proposed in this paper to automatically identify four such user errors based on Volume-Time and Flow-Volume graphs. By detecting specific errors and providing immediate feedback to patients, reliability and accuracy of spirometry results will be improved and the need for trained professionals will be reduced. The implementation facilitates the widespread adoption of spirometry, particularly in low-resource telemedicine settings. This work implements a binary classification model distinguishing between normal and error test samples, achieving a prediction accuracy of 93%. Additionally, a 4-way classification model is presented for identifying individual error sub-types, demonstrating a prediction accuracy of 94%. © 2023 Elsevier Ltd