Management Studies - Publications

Permanent URI for this collection

https://dspace.uohyd.ac.in/handle/1/15

Browse

Browsing Management Studies - Publications by Author "Acharyulu, G. V.R.K."
  • Item
    Examining ePWOM-purchase intention link in Facebook brand fan pages: Trust beliefs, value co-creation and brand image as mediators
    ( 2021-12-01) Rao, Kunja Sambashiva ; Rao, Bramhani ; Acharyulu, G. V.R.K.
    This study integrates the theory of reasoned action, value co-creation theory and attribution theory to investigate electronic positive word-of-mouth (ePWOM) and consumer's purchase intention link in the presence of trust beliefs, value co-creation, and brand image as mediators in the context of brand fan pages of smartphones on the social networking site, Facebook. Results indicate a positive association between ePWOM and consumer's purchase intention with trust beliefs, value co-creation, hedonic brand image and functional brand image partially mediating this relationship. Furthermore, the results supported the serial mediation model where ePWOM influenced purchase intentions through trust beliefs and value co-creation in a sequential manner.
  • Item
    Post deregulation trends in financial services - Challenges and opportunities for commercial banks
    ( 2017-01-01) Syed, Abdul Malik ; Acharyulu, G. V.R.K.
    Financial services play a pivotal role in the overall economic development of a country. With the initiation of economic liberalization in mid-1991, India's financial sector has undergone extensive structural changes and reforms which have positively influenced the stability and efficiency of the system. The present paper outlines challenges and opportunities faced by banking industry in India, taking into account the key trends in financial services. Specifically, it reviews the main forces that generate change in the financial services sector and the influence of these forces on the competitive landscape of the banking industry in India.
  • Item
    Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India
    ( 2022-03-01) Syed-Abdul, Shabbir ; Babu, A. Shoban ; Bellamkonda, Raja Shekhar ; Itumalla, Ramaiah ; Acharyulu, G. V.R.K. ; Krishnamurthy, Surya ; Ramana, Y. Venkat Santosh ; Mogilicharla, Naresh ; Malwade, Shwetambara ; Li, Yu Chuan Jack
    Introduction: India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at the time of discharge from hospital. Methods: The dataset included of 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree and random forest and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error. Results: The logistic regression, XGBoost and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. The best accuracy and precision (PPV) were 0.91 ± 0.026 and 0.67 ± 0.0526, respectively achieved by XGBoost, followed by logistic regression. This study also determined top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed positive impact towards the risk of mucormycosis. Conclusion: The developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study, holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.
  • Item
    Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India
    ( 2022-03-01) Syed-Abdul, Shabbir ; Babu, A. Shoban ; Bellamkonda, Raja Shekhar ; Itumalla, Ramaiah ; Acharyulu, G. V.R.K. ; Krishnamurthy, Surya ; Ramana, Y. Venkat Santosh ; Mogilicharla, Naresh ; Malwade, Shwetambara ; Li, Yu Chuan Jack
    Introduction: India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at the time of discharge from hospital. Methods: The dataset included of 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree and random forest and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error. Results: The logistic regression, XGBoost and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. The best accuracy and precision (PPV) were 0.91 ± 0.026 and 0.67 ± 0.0526, respectively achieved by XGBoost, followed by logistic regression. This study also determined top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed positive impact towards the risk of mucormycosis. Conclusion: The developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study, holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.