ISBN-13: 9783319885032 / Angielski / Miękka / 2018 / 106 str.
ISBN-13: 9783319885032 / Angielski / Miękka / 2018 / 106 str.
Chapter 1. Introduction.- Chapter 2. Literature Review.- Chapter 3. Research Gap, Objectives and Scope.- Chapter 4. Methodology.- Chapter 5. Analysis of Demographic Indices.- Chapter 6. Developing a Causal Model using SEM.- Chapter 7. Developing A Classification Model using ANN.- Chapter 8. Developing a Classification Model using SVM.- Chapter 9. Summary and Conclusion.
Sanjay Mohapatra received his B.E. from NIT Rourkela, M.Tech from IIT Madras, PGDBM XIMB, India and has finished his Ph.D. from Utkal University, India under Management Department. At present, he is an Associate Professor in Information Systems in XIMB, India. Professor Mohapatra has more than 21 years of industry experience. He has worked in various capacities in organizations like Hindustan Aeronautics Limited, Larsen & Toubro, PricewaterHouse, Infosys, Polaris & J&B Software. His teaching interests are in IT Strategy and Management Information Systems and research interests are in the area of IT enabled processes. He has authored/co-authored nine books and more than twenty papers in peer reviewed international journals.
This book offers an elaborate and empirical look at service quality of hospitals in the emerging market of India. The poor quality of service is a major issue in a large number of hospitals (particularly in government hospitals), which forces patients to opt for private hospitals that are generally much more expensive than government hospitals. This book provides a comprehensive understanding of service quality antecedents in Indian hospitals. It focuses on patient satisfaction and includes valuable insights and implications for hospital management and government. The book is theoretically grounded in SERVQUAL literature and uses appropriate and sophisticated techniques and tools to analyse data. It highlights causal model development with Structural Equation Modelling (SEM) and introduces a classification model, developed using Artificial Neural Networks (ANNs), in order to benchmark specialty cardiac care. The book also deals with Support Vector Machines (SVMs) and compares the error rates between SVM and ANN to find the best classification technique among the two. Overall, this book is a timely and relevant work that contributes to the theory, practice and policy of service quality in hospitals.
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