Consumers Adoption Behavior Prediction through Technology Acceptance Model and Machine Learning Models
This paper is to uncover the key factors that influence purchase intention of customers through analysing technology acceptance theories/models, in the current online-to-offline (abbreviated as O2O) mobile commerce, and to improve the prediction accuracy
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Statistics for Data Science and Policy Analysis
Statistics for Data Science and Policy Analysis
Azizur Rahman Editor
Statistics for Data Science and Policy Analysis
Editor Azizur Rahman School of Computing and Mathematics Charles Sturt University Wagga Wagga, NSW, Australia
ISBN 978-981-15-1734-1 ISBN 978-981-15-1735-8 (eBook) https://doi.org/10.1007/978-981-15-1735-8 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
To all who have supported, contributed and participated in the ASPAC2019.
Preface
This book constitutes the refereed proceedings of the Applied Statistics and Policy Analysis Conference 2019 (ASPAC2019), held on 5–6 September 2019, in Wagga Wagga, NSW, Australia. The overwhelming growth of data and its users is a reality, which has put new thoughts amongst the research community to devise new ideas for giving data-driven evidence-based policy decisions at local, state, national and international levels. In recent years, applied statistics and data science have received renewed interest from a broad range of stakeholders ranging from governments to corporations and end users of data and its analysis or modelling tools. As a result, applied statistics and data science research such as data mining and policy analysis have been placed high as a national priority in many countries including Australia. In this data-centric world with vastly growing demand situation, there is a need to ensure that reliable statistical and modelling solutions that address important and emerging policy issues at both public and private institutions are disseminated timely and widely amongst the research and industry communit
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