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Modeling the Intention to Use AI Healthcare Chabots in the Indian Context
Covid-19 has accelerated the need and use of artificial Intelligence-based healthcare Chabots. Penetration of the internet, smartphone, computational capability and machine learning technology brings healthcare services close to the patients. The penetration of AI healthcare Chatbot technology worldwide is on the rise. However, the healthcare ecosystem in India is unique and poses challenges in the adoption of healthcare chatbots. The demographic characteristics, economic conditions, diversity, belief systems on health-seeking, and alternative medical practices play a role in accepting and using chatbots. In this study, we attempt to model the factors influencing the intention and the purpose of using the chatbot. Through a literature review, we identify the variables related to the adoption of healthcare chatbots. We then focus on the more relevant concepts to the Indian context and develop a conceptual model. Through cases and literature, we frame the propositions of the study. We look at the awareness of chatbot features, perception towards the chatbot, trust and mistrust of the healthcare system, the doctors and the chatbots, health-seeking behavior, and the belief in traditional, complementary, and alternative medicine prevalent in India. This study contributes by developing an initial conceptual model for healthcare chatbots adoption in the Indian context. In the future, we plan to operationalize the study and test the propositions through an elaborate survey to validate the model empirically. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Modelling and analysis of split parallel hybrid electric vehicle based on 14 degrees of freedom
The paper studies the scope, performs the modelling and validation for conversion of any Convetional Vehicle to a Split Parallel Hybrid Electric Vehicle. The introduction of a smart Energy Management System for sucha setup is also evaluated. The EMS enables load sharing between the IC Engine and the Traction motor based on the gradient of the road. The gradient analysis is performed using the GPS based road gradient database. For the accurate modelling and the dynamic analysis of the designed model the performance of the vehicles Degrees of Freedom (DoF) for the variation in steering angle is analyzed. 14 DoF parameters are considered and the designed vehicle is subjected to variation in steer angle followed by the analysis on the response of the DoF parameters. BEIESP. -
Modelling and CFD simulation of vortex bladeless wind turbine
When the forces act on a bluff body in the wind flow direction, vortices are formed. Vortex bladeless wind turbine oscillates as a result of the vortices generated due to VIV. When the vortex shedding frequency is nearer to the natural frequency of the structure, maximum amplitude of vibration occurs and coincidentally power is generated. 3D models are designed to stimulate flow at a Reynolds number of 50000. This paper focuses on modelling the bladeless wind turbine based on semi-vortex angle and also 1) to study the vortices pattern and vorticity of different models 2) to study the drag and lift coefficients. In this paper vortex turbine is designed with certain parameters of dimension in Solid Edge and CFD analysis is carried out in Simscale software. Different model performance parameters like power, natural frequency and coefficient of power are compared among different models to opt for the best vortex bladeless wind turbine design. 2022 Author(s). -
Modelling and optimization of Rhodamine B degradation over Bi2WO6Bi2O3 heterojunction using response surface methodology
The Bi2O3/Bi2WO6 heterostructures of various compositions are prepared via the surfactant-assisted solgel method, which exhibits enhanced and synergistic photocatalytic activity towards the degradation of Rhodamine B (Rh B) using visible light irradiation. Characterization of these heterostructures has been done using X-ray diffraction, microscopic and spectroscopic methods. The 50% tungstate in bismuth oxide (BWO) nanocomposites having band gap of 2.85eV and an average size of 4080nm shows maximum dye removal up to 87% in 4h compared to pure Bi2O3 and other heterostructures of Bi2O3/Bi2WO6. The reusability studies demonstrate the excellent retention of photocatalytic activity without much loss in activity, implying the stability and efficiency of the prepared catalyst. The degradation of the Rh B dye is modeled mathematically to analyze the interactive effects of the key parameters like the time, amount of catalyst, and dye concentration, and to determine the optimal setting of these parameters to optimize the degradation process using the face-centered Central Composite Design (FC-CCD) of the Response Surface Methodology (RSM) analysis. An accurate full quadratic model has been developed with R2 = 99.41%. The sensitivity of the degradation was evaluated at all levels of the key parameters. At 0.1g of catalyst amount, it was found that the increment of the catalyst amount would be suitable for improved degradation as compared to allowing more time for the degradation. The maximum degradation was obtained for a dye concentration of 5ppm, and 0.1g catalyst for 4h. 2022, King Abdulaziz City for Science and Technology. -
Modelling Climate, COVID-19, and Reliability Data: A New Continuous Lifetime Model under Different Methods of Estimation
In this article, a new continuous probability distribution called Arvind distribution is developed and studied. The proposed distribution has only one parameter but it exhibits a wide variety of shapes for density and hazard rate functions. A number of important distributional properties including mode, quantile function, moments, skewness, kurtosis, mean deviation, probability-weighted moments, stress-strength reliability, order statistics, reliability and hazard rate functions, Bonferroni Lorenz and Zenga curves, conditional moments, mean residual and mean past life functions, and stochastic ordering of the Arvind distribution are derived. For point estimation of the parameter of the proposed distribution, six estimation procedures including maximum likelihood, maximum product spacings, least squares, weighted least squares, Cram-von Mises, and Anderson-Darling estimators are used. The interval estimation of the unknown parameter has also been discussed using observed Fishers information. A vast simulation study has been conducted to examine the behaviour of different estimation procedures. Finally, the applicability of the proposed model is demonstrated by using three real-life datasets. The results of the real data analysis clearly announce that the Arvind distribution can be a better alternative to several existing models for modelling different types of data from various fields. 2024, Society of Statistics, Computer and Applications. All rights reserved. -
Modelling for working capital efficiency: integrating SBM-DEA and artificial neural networks in Indian manufacturing
Purpose: This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN). Design/methodology/approach: A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME. Findings: Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME. Originality/value: The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively. 2024, Emerald Publishing Limited. -
Modelling Networks withAttached Storage Using Perfect Italian Domination
Network-attached storage (NAS) is how data is stored and shared among hosts through a configured network. This is cheaper yet the best solution for sharing and using any huge unstructured data in an organization. Optimal distribution of NAS in a network of servers can be done using the concept of Perfect Italian Domination (PID). PID is a vertex labelling where the vertices of a graph G are labelled by 0, 1, 2 such that a vertex with label 0 should have a neighbourhood where the summation of the labels is exactly 2. The minimum possible sum of the labels obtained for graph G is its PID number. A network in an organization can have any structure. It can be highly interconnected, like a graph obtained from the Join of two graphs or the Corona product of two graphs. Hence, this paper discusses the PID of different graphs generated by the Join and the Corona products. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Modelling of Cointegration with Students T-errors
Two or more non-stationary time series are said to be co-integrated if a certain linear combination of them be-comes stationary. Identification of co-integrating relationships among the relevant time series helps the researchers to develop efficient forecasting methods. The classical approach of analyzing such series is to express the co-integrating time series in the form of error correction models with Gaussian errors. However, the modeling and analysis of cointegration in the presence of non-normal errors needs to be developed as most of the real time series in the field of finance and economics deviates from the assumption of normality. This paper focuses on modeling of a bivariate cointegration with a students-t distributed error. The co-integrating vector obtained from the error correction equation is estimated using the method of maximum likelihood. A unit root test of first order non stationary process with students t-errors is also defined. The resulting estimators are used to construct test procedures for testing the unit root and cointegration associated with two time series. The likelihood equations are all solved using numerical approaches because the estimating equations do not have an explicit solution. A simulation study is carried out to illustrate the finite sample properties of the model. The simulation experiments show that the estimates perform reasonably well. The applicability of the model is illustrated by analyzing the data on time series of Bombay stock exchange indices and crude oil prices and found that the proposed model is a good fit for the data sets. 2022 by authors, all rights reserved. -
Modelling of critical success factors for blockchain technology adoption readiness in the context of agri-food supply chain
The agri-food supply chain is continuously facing several challenges; the most severe are food quality and safety issues. These issues debilitate the performance of the supply chain and often harm the consumer's health. Therefore, there is an urgent need to address food quality and safety assurance in the supply chain. Blockchain technology (BCT) holds the potential to resolve these issues by enhancing security and transparency. The present study explores the critical success factors (CSFs) of BCT adoption readiness in the AFSC. Initially, CSFs are identified through a literature survey and finalised by experts' opinion. The finalised factors are prioritised using the fuzzy best-worst method, followed by sensitivity analysis. The results reflect that 'food quality control', 'provenance tracking and traceability', and 'partnership and trust' as the top three success factors. The study's findings will assist policymakers, managers, and practitioners in strategising the decision-making process while BCT dissemination. Copyright 2023 Inderscience Enterprises Ltd. -
Modelling temperature-dependent malaria transmission vector model considering different levels of immunity in host population
Malaria is spread by female Anopheles mosquitoes, which complete their life cycle by feeding on human blood. Parasites from the mosquito's saliva enter the human bloodstream through a mosquito bite. Thus, the link between humans and mosquitoes to parasites is established. According to WHO statistics, malaria appears widespread in tropical and subtropical locations around the equator, including most of Sub-Saharan Africa, Latin America, and Asia. The most prevalent causes of malaria transmission might be amicable temperature, which aids in the growth of the mosquito life-cycle, and a failure to maintain the local socio-economic condition, which reduces individual immunity. This study proposes the vector-host model to understand the spread of malaria infection. A vector model is used to understand the effects of temperature on the development of egg, larval, pupal, and adult mosquito populations. Furthermore, the role of immunity is being explored using the host model. Numerical simulations support the influence of temperature on disease transmission. The study draws attention to the fact that, along with issues like global warming and climate change, managing the socio-economic conditions of the area (healthcare facilities, environmental improvement) is essential for malaria eradication. CSP - Cambridge, UK; I&S - Honda, USA, 2023 -
Modelling the energy dependent X-ray variability of Mrk 335
We present a technique which predicts the energy dependent fractional r.m.s. for linear correlated variations of a pair of spectral parameters and apply it to an XMM-Newton observation of Mrk 335. The broadband X-ray spectrum can be interpreted as a patchy absorber partially covering the primary emission, a warm and hot coronal emission or a relativistically blurred reflection along with the primary emission. The fractional r.m.s. has a non-monotonic behaviour with energy for segments of lengths 3 and 6 ksecs. For each spectral model, we consider every pair of spectral parameters and fit the predicted r.m.s. with the observed ones, to get the pair which provides the best fit. We find that a variation in at least two parameters is required for all spectral interpretations. For both time segments, variations in the covering fraction of the absorber and the primary power law index gives the best result for the partial covering model, while a variation in the normalization and spectral index of the warm component gives the best fit in the two corona interpretation. For the reflection model, the best fit parameters are different for the two time segment lengths, and the results suggests that more than two parameters are required to explain the data. This, combined with the extreme values of emissivity index and reflection fraction parameters obtained from the spectral analysis, indicates that the blurred reflection model might not be a suitable explanation for the Mrk 335 spectrum. We discuss the results as well as the potential of the technique to be applied to other data sets of different AGN. 2025 Elsevier B.V. -
Modelling the nexus of macro-economic variables with WTI Crude Oil Price: A Machine Learning Approach
Crude oil price shocks have a significant impact on aggregate macroeconomic indices like GDP, interest rates, investment, inflation, unemployment, and currency rates, according to empirical evidence. Various factors like GDP, CPI, and Gold prices show a considerable impact on the Crude old prices. The correlation analysis between these variables can help the machine learning model to find the highly impacting factor of the target variable. The advanced machine learning algorithms can be used to find the most relevant variable impacting the crude oil price followed by predicting the crude oil price. Time series analysis algorithms can forecast the crude oil prices for the specific period ahead. In the current study it was observed that US dollar and CPI show a high impact on Crude oil prices. The study has implemented six machine learning algorithms out of which the ARIMAX was found to be the most efficient model. VAR and ARIMA models are used to successfully forecast the crude oil prices for the next 5 years. From the current research, a machine learning model has been obtained as an outcome of the study, which will help economists in the future to understand the dynamics of crude oil prices driver and forecast it for the near future. 2022 IEEE. -
Modelling the role of institutional support in shaping the social behaviour of business administration students
The relevance and scope of teaching social responsibility and ethical behaviour to business students has been widely discussed among academicians worldwide (Giacalone & Thompson, 2006). Presently all business schools emphasize teaching social responsibility to the students. But the effectiveness of this education on the student's social responsibility was not evaluated in the past. This study tries to fill this gap by conducting an empirical study on the effectiveness of social responsibility projects undertaken by undergraduate business students for their overall development. The study hypothesized that the course support and institutional support would influence the student's perception of social responsibility, which in turn affects the student's academic performance. For this purpose, the study was conducted among 450 students who have undergone a social responsibility course. The path analysis method was used to test the hypothesized model. Further, the study also evaluated the moderation effect of gender on this model. The study's major finding indicated that the social responsibility course and the organizational support positively impacted students' social responsibility perceptions, which, in turn, influenced students' academic performance. The study suggests that business institutions should emphasize social responsibility initiatives. 2024 Nova Science Publishers, Inc. -
Modelling, Temperature Analysis, and Mechanical Properties of Friction Stir Welding of Al-Cu Joints with Hardened OHNS Steel Tools
Friction stir welding (FSW) is a nearly modern welding method with vital advantages over the conventional welding process, such as lower distortion, enhanced mechanical properties, and eco-friendly. In FSW, the joint characteristics mainly depend on heat development during the joining process due to its solid-state joining method. The basic principles of thermomechanical methods during FSW are unknown since it is a new metal joining method. In this investigation, the 2D and 3D models of the tools with different pin forms were designed using SOLIDWORKS. The ANSYS software was used to investigate the temperature distributions near the weld zones. The fixture was designed and made according to the machine conditions. The base plates used were AA6101 and C11000; the tool material used was the Hardened OHNS steel tool with square and circular pin form. The temperature values were measured in each trial while joining of Al-Cu base plates along the weld line. The results reveal that in the joint area, a trial with high temperature leads to high ultimate tensile strength (UTS) and Charpy impact strength (CIS). Made at tool rotation speed 1200 rpm and feed velocity 20 mm/min of Hardened OHNS steel tool with circular pin form. The obtained UTS value at joints was less than that of Al and Cu base plates. The microhardness value detected at the joint area was higher than the Al and Cu base plates, providing high strength, and irregularly dispersed. 2022, Books and Journals Private Ltd.. All rights reserved. -
Models for Social Responsibility Action by Higher Education Institutions
This book offers 18 chapters on replicable models for social responsibility actions for universities and other academic institutions. The chapters are broadly classified under two major areas: sustainable development models and social sensitisation programmes. The chapters capture the efficient and successful models of social responsibility practiced by Indian and foreign universities. The models are proposed based on the evidence from a rigorous research process. Universities across the world can benefit from the best practices and implement the same successfully. The models will be helpful to universities in achieving the United Nations' Sustainable Development Goals (SDGs) and rank higher on the Sustainability Tracking, Assessment & Rating System (STARS). The research-based chapters will have significant benefits to researchers in expanding the domain of social responsibility of higher education institutions. As a text, this book will serve students of higher education in sustainability and social responsibility related courses. 2024 by Nova Science Publishers, Inc. All rights reserved. -
Moderating effect of social media usage on technology barriers to technology adoption by teachers
The education and learning process is redefined with the mesmerizing impact of ever-volatile technology platforms. With the advent of the Industry 4.0, supported by the intelligent web 3.0 connectivity catalyzed the transformation of traditional education philosophies and pedagogies in tune with the Learning 4.0, to empower both learners and educators as co-producers of knowledge. The researches brought to light that social application platforms became an indispensable part of the digital learning process. The bio-inspired technology designs considerably cast off the issues related to ease of use, perceived usefulness, and reduced the perceived internal barriers of the teachers to improve their Technology Adoption substantially. This Technology Adoption research was conducted under the theoretical framework of the education change model of Michael Fullan integrated with educators Communities of Practice. This descriptive research study framed to address how the teachers Technology Adoption was affected by their use of social media platforms and how it moderated their perceived Technology Barriers. Standardized questionnaires from Joe W. Kotrlik and Donna H. Redmann were adopted with a pilot study. Stratified cluster sampling was used to gather 1029 responses from Higher Secondary School teachers of six educational districts in Kerala. The analysis was done with IBM SPSS v.21 and Process v.3.4. Teachers Social Media Use and Perceived Technology Barriers were significantly correlated with the Technology Adoption of the teachers. The perceived Technology Barriers were reduced with respect to their Social Media Usage. The relation of perceived Technology Barriers with Technology Adoption was significantly moderated with Social Media Use. Gender and school sectors were neither mediated nor moderated Technology Adoption. These results are helpful in the teachers technology training programs and for further research. 2019 SERSC. -
Moderating effects of academic involvement in web-based learning management system success: A multigroup analysis
While several educational institutions in India, in accordance to global practices, have adopted Web-Based Learning Management Systems (WLMS) to supplement classroom courses, it is largely seen that these WLMSs fail in their objectives, leading to little or no return on investments. The study aims to define the factors that affect students acceptance of a web-based learning management system and test the moderating effect of their academic involvement in the success of a WLMS. 477 valid questionnaires were collected from university/college students to empirically test the research model using the structural equation modelling approach. The results concludes that indirect and direct effects account for 49% of the variation in the intention to use, which is explained by technical system quality, information quality, educational quality, service quality of the technical support team and user satisfaction. High academic involvement moderates the impact of different service qualities of the WLMS on user satisfaction, intention to use the system, and success of the WLMS. Based on the findings, theoretical and managerial implications are discussed. 2021 -
Moderating influence of critical psychological states on work engagement and personal outcomes in the telecom sector
Organizations want their employees to be engaged with their work, exhibiting proactive behavior, initiative, and responsibility for personal development. Existing literature has a dearth of studies that evaluate all the three key variables that lead to optimal employee performancecritical psychological states (CPSs), work engagement, and personal outcomes. The present study attempts to fill that gap by linking the variable CPSs (which measures experienced meaningfulness, responsibility, and knowledge of results) with the other two. The study surveyed 359 sales personnel in the Indian telecom industry and adopted standardized, valid, and reliable instruments to measure their work engagement, CPSs, and personal outcomes. Analysis was done using structural equation modeling (SEM). Findings indicated that CPSs significantly moderate the relationship between personal outcomes and work engagement. The Author(s) 2014. -
Moderating influence of critical psychological states on work engagement and personal outcomes in the telecom sector /
Sage Journals, Vol.4, Issue 2, pp.584-592. -
Moderating role of firm characteristics on the relationship between corporate social responsibility and financial performance: evidence from India
Purpose: The effect of corporate social responsibility (CSR) on corporate financial performance (CFP) is shown to depend on both firm-specific and external factors. This study investigates the moderating role of two firm-specific factors the firm life-cycle stage and ownership structure on the CSRCFP relationship in a developing economy setting India. Design/methodology/approach: The study covers 1,419 listed companies in India during 201521. The firm lifecycle is represented using firm age and future growth prospects. Ownership is represented through a dummy variable and promoters holding percentages. Return on assets (RoA) is used as a measure of CFP, while CSR intensity, i.e. the ratio of CSR expenditure to profit after tax (PAT), is used to represent CSR. Fixed effect panel regression and generalized method of moments (GMM) models are used for data analysis. Findings: CSR expenditure has a significant negative impact on CFP. Firm age and future growth prospects amplify this negative impact, indicating that the firm life-cycle has a significant negative moderating effect on the CSRCFP relationship. Furthermore, the impact of CSR on CFP is worse for government companies than private ownership. Promoters holdings have a positive impact on the CSRCFP relationship. Research limitations/implications: The results question the validity of mandatory CSR expenditure on companies operating in developing countries and call for a differentiated policy approach to CSR expectations based on firm characteristics. This study also enhances the existing literature on CSRCFP. Originality/value: The growing research on CSRCFP has limited coverage of firm characteristics as contributing factors. Hence, this paper helps in enhancing the existing literature on CSRCFP and makes it more relevant to firms with specific characteristics. 2024, Nisha Prakash and Aparna Hawaldar.