Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (Studies in Computational Intelligence, 951) 3030670074, 9783030670078

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Studies in Computational Intelligence 951

Roger Lee Jong Bae Kim   Editors

Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

Studies in Computational Intelligence Volume 951

Series Editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/7092

Roger Lee · Jong Bae Kim Editors

Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

Editors Roger Lee Software Engineering and Information Technology Institute Central Michigan University Mount Pleasant, MI, USA

Jong Bae Kim Graduate School of Software Soongsil University Seoul, Korea (Republic of)

ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-030-67007-8 ISBN 978-3-030-67008-5 (eBook) https://doi.org/10.1007/978-3-030-67008-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed 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 Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The purpose of 21st ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2021Winter) held on January 28–30, 2021, Ho Chi Minh City, Vietnam is aimed at bringing together researchers and scientists, businessmen and entrepreneurs, teachers and students to discuss the numerous fields of computer science, and to share ideas and information in a meaningful way. This Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing discussed the wide range of issues with significant implications, from Artificial Intelligence to Communication Systems and Networks, Embedded Systems, Data Mining and BigData, Data-driven business models, Data privacy and security issues, etc. This publication captures 18 of the conference’s most promising papers, and we impatiently await the important contributions that we know these authors will bring to the field. In Chapter “A Study on the Analysis and Classification of Security Threats According to the Characteristics of Public Service Computer Network”, MinSu Kim analyzed the security threats according to the characteristics of public service computer network and presented the technologies and policies required for each stage, to protect the intelligence assets from attackers’ security threats, based on the framework to cope with security threats according to the characteristics of public service computer network. In Chapter “Understanding Factors Influencing Intention to Use E-government Services in Vietnam: Focused on Privacy and Security Concerns”, Thi-ThanhThao Vo and Hung-Trong Van analyzed the factors influencing intention to use e-government services in Vietnam. This study may be useful for policymakers to establish and enforce strategies as well as policies to raise the proportion of people in using e-government services in Vietnam. In Chapter “An Empirical Study of Prioritization Decision on the Establishment of Distribution Public Big Data Systems”, Yong Gi Park, Seong Il Hur, and Jin Won Jang explored the prioritization decision on the establishment of distribution of public big data systems and suggested the need for experts and the general public to share their opinions on the protection and utilization of personal information that v

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would be sensitive to the general public and the co-prosperity of stakeholders in the distribution industry such as small business owners and large corporations. In Chapter “A Study on the Structural Relationship Between Social Capital, Dynamic Capability and Business Performance in the Supply Chain Environment”, Yongmuk Kim and Jongwoo Park empirically analyzed the structural relationships among social capitals, dynamic capabilities, and business performance in the supply chain environment in order to overcome the limitations of previous studies and seek ways to secure competitive advantages and realize sustainable growth of SMEs with relatively insufficient resources compared to large corporations. In Chapter “Design and Implementation of 3-Phase Home Plugin Gateway PLC Router System”, Sun-O Choi and Jongbae Kim designed and implemented training equipment to enable face-to-face and non-face-to-face training for both sustainability of education in even the Covid19 environment. We expect that this study will be applied to fields such as smart factories, smart farms, and smart buildings that utilize IoT and Robot into the industrial world from the educational world, and will become a highly scalable expandable technology. In Chapter “Study of Malicious Script Analysis Methodology via GBM”, SungHwa Han and Hoo-Ki Lee proposed a malicious script analysis methodology via GBM that can reduce the cost of detecting malicious codes that cause security threats to lightweight information services that are required in mobile services and IoT services. In Chapter “Model of Factors Influencing the EWOM Writing Intention of E-commerce Customers in Vietnam”, Thi-Kieu-Trang Nguyen, Thi-Quynh-Anh Vu, and Hung-Trong Van suggested the model of determinants influencing the intention to write eWOM of buyers on ecommerce sites. Through the theory of planned behavior, this work indicates three determinants of attitude and then the intention to write eWOM, discusses some contributions in terms of theory and reality management, especially for technology platforms in encouraging eWOM in shopping online. In Chapter “Study on the Impact of Activity-Based Flexible Office Characteristics on the Employees’ Innovative Behavioral Intention”, Jin Won Jang, Yong Gi Park, Seong Il Hur, and Yong Jun An verified empirically the impact of A-FO characteristics on the members’ intention to innovate. This study conducted a survey on 402 employees from 10 Korean companies operating A-FO and made empirical analysis of the survey. In Chapter “The Effects of Rapport-Building on Customer Attitude and Loyalty in Medical Service”, Hye Soo Choi and Dong Hyuk Jo empirically verified the structural relationships between rapport-building behaviors, customer attitudes, and customer loyalty in the domestic medical service environments in order to overcome the limitations of previous studies and to seek ways to secure competitive advantages and achieve sustainable growth of medical institutions. In Chapter “Design and Implementation of Open Source Based on IoT and Robot Manipulator Arm Training Equipment”, Sun-O Choi and Jongbae Kim designed and implemented a function to control and manage the telecommunication network of houses and buildings by utilizing the three-phase Home Plugin gateway PLC Router

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System. This study can apply to a smart grid, smart building management, and is expected to develop into a new PLC service model for optimizing energy use. In Chapter “A Study on Improved Authentication Technique in Cloud Computing”, Hwan-Seok Yang analyzed the vulnerabilities of the security element that cloud computing has and proposed an authentication technique suited to cloud computing. PDM performs the self-authentication of many computing devices after receiving authentication from the authentication agency in the proposed authentication technique. In Chapter “A Study on Drum Transcription Using Machine Learning Focused on Discrimination of Acoustic Drum and Electronic Drum Sound”, Sang Wook Lee, Jae Hyuk Heo, Sung Taek Lee, and Gwang Yong Gim attempted to discriminate the sound of electronic and acoustic drums, which had not been studied and found that the pixel size of the input spectrogram and the number of CNN filters affect the recognition rate. In Chapter “Remeasurement Dispatching Rule for Semiconductor EDS Process”, Jeongil Ahn and Taeho Ahn investigated the problem of schedule changes that arise due to retests among abnormal situations in the EDS process of non-memory semiconductors and presents a genetic algorithm with penalty method (GAPM) for scheduling under such uncertainties. In Chapter “Fall Detection Method Based on Pose Estimation Using GRU”, Yoonkyu Kang, Heeyong Kang, and Jongbae Kim proposed a feature extraction and classification method to improve the accuracy of fall detection using GRU. As a result of various experiments, the proposed method was more effective in detecting falls than unprocessed raw skeletal data which have not processed anything. In Chapter “Stacked-Autoencoder Based Anomaly Detection with Industrial Control System”, Doyeon Kim, Chanwoong Hwang, and Taejin Lee proposed a stacked-autoencoder (SAE), deep Support Vector Data Description (SVDD)-based data anomaly detection technique using an ICS dataset created based on a testbed similar to an actual operating environment, and derived detection accuracy for each threshold. In both models, the highest accuracy was derived when the threshold was 0.98, and the accuracy was 96.03% in the SAE model and 95.48% in the Deep SVDD model. In Chapter “A Study on Intention to participate in Blockchain-Based Talent Donation Platform”, Woo Young Lee, Duk Jin Kim, Byeong Ryun Jeon, and Gwang Yong Gim verified the causes of participation in volunteer work (motivation, social responsibility, compensation need) for the factors that affect the intention of participation in the talent donation platform through the system factors (service, information, system quality) and the block chain characteristics (security, transparency). And this study defined new type of model called talent donation platform, and the research model was designed through the theory of volunteer participation, the successful model of the information system from the system perspective, and the prior study of the characteristics of the block chain. In Chapter “The Effects of Product’s Visual Preview and Customer Review on Sale Performance in Mobile Commerce”, Eun Tack Im, Huy Tung Phuong, Myung Suk Oh, Jun Yeob Lee, and Simon Gim classified the characteristics of information

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on customer behavior in m-commerce, and it had been confirmed that images and reviews had a significant effect on the performance of m-commerce sales products through S-O-R model. In Chapter “A Study on Factors Affecting the Intention to Use Library Information System”, Ju Hyung Kim, Sang Bin Jeong, Kyoung Jin Lee, and Gwang Yong Gim defined the user service of the Library Information System and examined the variables that are needed for the construction of next generation Library Information System through a group of experts. The result of this study will be used as a systematic and basic data on factors affecting the intention to use the systematic library information system. It is our sincere hope that this volume provides stimulation and inspiration, and that it will be used as a foundation for works to come. Seoul, Korea (Republic of) January 2021

Jong Bae Kim

Contents

A Study on the Analysis and Classification of Security Threats According to the Characteristics of Public Service Computer Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MinSu Kim

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Understanding Factors Influencing Intention to Use E-government Services in Vietnam: Focused on Privacy and Security Concerns . . . . . . . Thi-Thanh-Thao Vo and Hung-Trong Van

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An Empirical Study of Prioritization Decision on the Establishment of Distribution Public Big Data Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yong Gi Park, Seong Il Hur, and Jin Won Jang

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A Study on the Structural Relationship Between Social Capital, Dynamic Capability and Business Performance in the Supply Chain Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yongmuk Kim and Jongwoo Park Design and Implementation of 3-Phase Home Plugin Gateway PLC Router System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sun-O Choi and Jongbae Kim Study of Malicious Script Analysis Methodology via GBM . . . . . . . . . . . . Sung-Hwa Han and Hoo-Ki Lee

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Model of Factors Influencing the EWOM Writing Intention of E-commerce Customers in Vietnam . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thi-Kieu-Trang Nguyen, Thi-Quynh-Anh Vu, and Hung-Trong Van

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Study on the Impact of Activity-Based Flexible Office Characteristics on the Employees’ Innovative Behavioral Intention . . . . Jin Won Jang, Yong Gi Park, Seong Il Hur, and Yong Jun An

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The Effects of Rapport-Building on Customer Attitude and Loyalty in Medical Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Hye Soo Choi and Dong Hyuk Jo ix

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Design and Implementation of Open Source Based on IoT and Robot Manipulator Arm Training Equipment . . . . . . . . . . . . . . . . . . . . 119 Sun-O Choi and Jongbae Kim A Study on Improved Authentication Technique in Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Hwan-Seok Yang A Study on Drum Transcription Using Machine Learning Focused on Discrimination of Acoustic Drum and Electronic Drum Sound . . . . . . 141 Sang Wook Lee, Jae Hyuk Heo, Sung Taek Lee, and Gwang Yong Gim Remeasurement Dispatching Rule for Semiconductor EDS Process . . . . . 155 Jeongil Ahn and Taeho Ahn Fall Detection Method Based on Pose Estimation Using GRU . . . . . . . . . . 169 Yoonkyu Kang, Heeyong Kang, and Jongbae Kim Stacked-Autoencoder Based Anomaly Detection with Industrial Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Doyeon Kim, Chanwoong Hwang, and Taejin Lee A Study on Intention to Participate in Blockchain-Based Talent Donation Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Woo Young Lee, Duk Jin Kim, Byeong Ryun Jeon, and Gwang Yong Gim The Effects of Product’s Visual Preview and Customer Review on Sale Performance in Mobile Commerce . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Eun Tack Im, Huy Tung Phuong, Myung Suk Oh, Jun Yeob Lee, and Simon Gim A Study on Factors Affecting the Intention to Use Library Information System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Ju Hyung Kim, Sang Bin Jeong, Kyoung Jin Lee, and Gwang Yong Gim Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

Contributors

Jeongil Ahn Department of Business Administration, Graduate School, Soongsil University, Seoul, Korea Taeho Ahn Department of Business Administration, Graduate School, Soongsil University, Seoul, Korea Yong Jun An Good Consulting Group, Seoul, Korea Hye Soo Choi Department of Business Administration, Soongsil University, Seoul, Korea Sun-O Choi Department of IT Policy and Management, Graduate School of Soongsil University, Seoul, Korea Gwang Yong Gim Department of Business Administration, Soongsil University, Seoul, Korea Simon Gim SNS Marketing Research Institute, Soongsil University, Seoul, South Korea Sung-Hwa Han ITPM, Soongsil University, Seoul, Korea Jae Hyuk Heo Department of IT Policy and Management, Graduate School, Soongsil University, Seoul, Korea Seong Il Hur Soar IT Corporation Co. Ltd., Seoul, Korea Chanwoong Hwang Department of Information Security, Hoseo University, Asan, South Korea Eun Tack Im Graduate School of Business Administration, Soongsil University, Seoul, South Korea Jin Won Jang Ernst & Young Han Young, Seoul, Korea Byeong Ryun Jeon Graduate School, Department of IT Policy and Management, Soongsil Univ., Seoul, South Korea

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Sang Bin Jeong Department of IT Policy and Management, Graduate School of Soongsil Univ, Seoul, Korea Dong Hyuk Jo Department of Business Administration, Soongsil University, Seoul, Korea Heeyong Kang Department of IT Policy and Management, Graduate School of Soongsil University, Seoul, South Korea Yoonkyu Kang Department of IT Policy and Management, Graduate School of Soongsil University, Seoul, South Korea Doyeon Kim Department of Information Security, Hoseo University, Asan, South Korea Duk Jin Kim Graduate School, Department of IT Policy and Management, Soongsil Univ., Seoul, South Korea Jongbae Kim Startup Support Foundation, Soongsil University, Seoul, Korea Ju Hyung Kim Department of IT Policy and Management, Graduate School of Soongsil Univ, Seoul, Korea MinSu Kim Department of Information Security Engineering, Joongbu University, Seoul, Korea Yongmuk Kim Department of Business Administrate, Graduate School, Soongsil University, Seoul, Korea Hoo-Ki Lee Department of Cyber Security Engineering, Konyang University, Nonsan, Korea Jun Yeob Lee College of Economics, SungKyunKwan University, Seoul, South Korea Kyoung Jin Lee Department of IT Policy and Management, Graduate School of Soongsil Univ, Seoul, Korea Sang Wook Lee Department of IT Policy and Management, Graduate School, Soongsil University, Seoul, Korea Sung Taek Lee Department of Computer Science, Yongin University, GyeonggiDo, Korea Taejin Lee Department of Information Security, Hoseo University, Asan, South Korea Woo Young Lee Graduate School, Department of IT Policy and Management, Soongsil Univ., Seoul, South Korea Thi-Kieu-Trang Nguyen Faculty of Digital Economy & E-commerce, VietnamKorea University of Information andCommunication Technology, The University of Danang, Danang, Vietnam

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Myung Suk Oh Graduate School of Business Administration, Soongsil University, Seoul, South Korea Jongwoo Park College of Business Administrate, Soongsil University, Seoul, Korea Yong Gi Park National Assembly of South Korea, Seoul, Korea Huy Tung Phuong Graduate School of Business Administration, Soongsil University, Seoul, South Korea Hung-Trong Van Faculty of Digital Economy & E-commerce, Vietnam-Korea University of Information and Communication Technology, Danang, Vietnam; Faculty of Digital Economy & E-commerce, Vietnam-Korea University of Information andCommunication Technology, The University of Danang, Danang, Vietnam Thi-Thanh-Thao Vo Faculty of Digital Economy & E-commerce, Vietnam-Korea University of Information and Communication Technology, Danang, Vietnam; The University of Danang, Danang, Vietnam Thi-Quynh-Anh Vu Faculty of Digital Economy & E-commerce, Vietnam-Korea University of Information andCommunication Technology, The University of Danang, Danang, Vietnam Hwan-Seok Yang Department of Information Security Engineering, Joongbu University, Seoul, Korea

A Study on the Analysis and Classification of Security Threats According to the Characteristics of Public Service Computer Network MinSu Kim

Abstract In the structure of general public service computer network, the infrastructure is established and operated in the form of securing the security for coping with the security threats. Thus, this study aims to analyze each type of external/internal security threats to computer network of public service established based on the dualistic infrastructure network of service network and business network, and also to present the framework to cope with security threats through the case-based scenario. Keywords Classification of security threats · Public service computer network · Information security management system · Framework to cope with security threats · APT

1 Introduction The general public service computer network is composed on the basis of dualistic infrastructure network of service network and business network, and the infrastructure in the form of securing the security is established and operated. The establishment of this computer network has brought about huge convenience and efficiency in the overall society and industry. In case of Korea, total 19.65 million households of the whole 19.75 million households have the internet connection. The household internet connection rate was shown as 99.5% (Basis of statistics in 2018), which means that almost every household had the internet connection. The internet utilization rate was 91.5% (Basis of statistics in 2018) of 50.39 million people in three-years old or up, so that Korea is evaluated as an IT (Internet Technology) power with the highest level in the world [1].

M. Kim (B) Department of Information Security Engineering, Joongbu University, Seoul, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_1

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Especially, in case of e-government service that could judge the level of digital public service informatization in the national level, the national awareness (92.5%), utilization rate (87.5%), and satisfaction (97.2%) were rising for six years in a row, which could bring about the reduction of social cost for national service. Even in the global level, the Korean government is maintaining the highest ranks for ten years like taking the shared lead in the sector of online participation, and also taking the 3rd place overall in the sector of e-government development, in the evaluation of UN e-government of 2018 [2]. However, as the equipments necessary for national life and corporate activities are connected to network, the whole economic and social areas are operated in the smart system, and the security threats are increased by the expansion of cyber space. Also, in the types of cyber attacks, the Advanced Persistent Threat (APT) is endlessly developing into socio-technological method which is more advanced by utilizing the situational information and taste of a specific target. The damages by cyber attacks are intensified as the channel for attacks is diversified to new channels like supply network besides the well-known penetration channels like making it difficult to distinguish cyber attacks from normal accesses by utilizing the cloud computing, evading the detection and tracking by discarding the cloud account after attack, automating cyber attacks by utilizing AI, detouring the detection by security equipments by applying the defense evasion algorithm to cyber attacks, and vulnerability of email, software, and website [3]. In case of public service, to cope with security threats that are explosively increasing based on the advanced network infrastructure, the security infrastructure is established on the basis of Information Security Management System [4]. Thus, this study aims to analyze each type of external/internal security threats to the public service computer network established based on the dualistic infrastructure network of service network and business network, and also to present the framework to cope with security threats through the case-based scenario.

2 Related Study 2.1 Characteristics of Security Threats In case of security threat, it is tough to find out its attacker due to the anonymity, so that it could maximize the damage by minimizing the risk to the attacker. It also has other characteristics such as it is difficult to capture the signs like time and target of attack in advance; it is cheap in cost; and it could be performed regardless of time and place. For those reasons, it is predicted that the cyber attacks would be continuously performed in the local aspect. Moreover, in case when having a conflict of interests or confrontation between countries, it is also possible to have large-scale cyber wars [5, 6].

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As the major targets of attack, it is highly possible that the key national infrastructure such as the military, electricity, finance, and transportation could become the targets. As the methods of attack, such DDoS attack, spread of virus, and electronic bomb are variously used. According to the subject of attack, the cyber attacks could be divided into the threat of personal intrusion like Ransomware, the threat of organizational intrusion including DDoS (Distributed Denial of Service) attack causing the loss by paralyzing the corporate computer network, and the threat of national intrusion by cyber war between countries, including the means of attack for the threat of organizational intrusion [3, 7].

2.2 Current State of Public Service Network Separation Technology As the technology to separate the service network from business network for preventing the intrusion from outside and also the leak of internal information, the network separation technology is classified into physical separation and logical separation. In case of physical network separation, the service network and business network are doubly separated by physically partitioning all the resources composing the network. Also, in case of logical network separation, the service network and business network are separated by using the virtualization technology with no changes in the existing working environment [8–10].

3 Research Method 3.1 Public Service Network Structure 3.1.1

Internet Network

The internet network of public service computer network has secured the work efficiency through the connection of network between public organizations. For example, it means the network that could be used for performing a simple work in case of personal mail or business trip (internet network—internet network).

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M. Kim

Service Network

The service network of public service computer network is the DMZ, so that the Web server, Mail server, and DNS server are located according to the types of relevant service.

3.1.3

Business Network

The business network of public service computer network has secured the security for performing important work through the connection of network between intranet and public organization, by using the public service communication network (public service communication network—public service communication network).

3.1.4

Control Network

The control network of public service computer network blocks the communication with outside through the network section composed separately based on the concept of closed network. However, to monitor the internal system of control network, it is also possible to establish the unidirectional communication environment with business network by using the network connecting system (Fig. 1).

3.2 Procedure to Establish the Scenario The procedure to establish the cyber threat scenario is carried out in the order of analysis on the current state, establishment of scenario, and verification of scenario just as Fig. 2. Based on the system related to the analysis scope, the scenario is established and verified based on the existing cyber threat case.

3.3 Scenario-Based Security Training 3.3.1

Internet Network

As an attack on the exposed important system, the internal business system could be directly accessed by using the port for maintenance that is open to the search service. For the inspection, it is required to check the search information exposed to outside, and also to check the services that could be directly accessed in the exposed ports. The Fig. 3 shows the diagram of attack on the exposed important system.

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Fig. 1 Public service network diagram

Fig. 2 AHP research model

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Fig. 3 Attack on the exposed important system

3.3.2

Service Network

As an attack on the vulnerability of representative homepage, it is possible to ac-quire the system authority of web server and DB server through the file upload and SQL Injection. For the inspection, it is required to check the possibility to ac-quire the authority of server by using the vulnerability of file upload in the representative homepage, to check the possibility to acquire personal information and to seize the authority of DB server through the SQL Injection attack on the representative homepage from outside, and also to check the possibility to directly access the internal important server in case when the authority of homepage or DB server is seized. The Fig. 4 shows the diagram of attack on the vulnerability of representative homepage.

Fig. 4 Attack on the vulnerability of representative homepage

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Fig. 5 Attack on the use of internet from important PC and the possibility of inflow of malignant code

3.3.3

Business Network

As an attack on the use of internet from important PC and the possibility of in-flow of malignant code, the malignant code is flown in and the internal business system is penetrated from the PC for work of public service operator or security staff through the external internet. For the inspection, it is required to check the possibility to directly access the external internet from the PCs of internal operator, security control, and security staff, to check the matter of installing the vaccine and performing the periodic update, to check the matter of performing the newest Window update, to check the matter of applying the network separation, and also to check the firewall blocking policy. The Fig. 5 shows the diagram of attack on the use of internet from important PC and the possibility of inflow of malignant code.

3.3.4

Control Network

Through the monitoring system or control server over the control system within control network, it attacks the vulnerability of control server of control network by accessing the control system infected by malignant code. For the inspection, it is required to check the service that could be directly accessed in the exposed ports, to check the matter of applying the network separation, to check the fire-wall blocking policy, and also to check the matter of installing the vaccine and performing the periodic update. The Fig. 6 shows the diagram of attack on the vulnerability of control server of control network.

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Fig. 6 Attack on the vulnerability of control server of control network

3.4 Framework to Cope with Security Threats to Public Service The framework to cope with security threats could be divided into prevention stage  preparation stage  coping stage  restoration stage. Just as shown in the security threat scenario, the attackers are watching for intelligence assets and human assets, so that it would be necessary to establish the permanent coping measures for security and management, and the technologies and policies required for each stage, to protect the assets from such attackers’ security threats [11]. The technologies and policies required for each stage of framework could be distinguished as the following Table 1.

4 Conclusions The public service computer network is composed of internet network, service network, business network, and control network, based on the dualistic infrastructure network of service network and business network. In the results of analyzing the existing major cases of cyber intrusion based on the matrix of security threats in each type, most of the incidents were related to the DDoS attack on service network or infection of malignant code through email, and the information leak or system paralysis by accessing the system through the unauthorized access to business network or control network. Actually, there are various and complex attacks on service network like homepage defacement and ransomware. Moreover, the DDoS attacks abusing the IoT equipment or cyber threats taking advantage of the popularity of mobile games are also rapidly increasing. This study presented the technologies and policies required for each stage, to protect the intelligence assets from attackers’ security threats, based on the framework to cope with security threats according to the characteristics of public service computer network.

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Table 1 Technologies and policies required for each stage of framework Stage

Required technology

Required policy

Prevention

Security education training support technology

Cyber defense manpower

Security plan establishment support technology Vulnerability identification/analysis Threat prediction/identification Preventive intelligence technology Malignant code collection/analysis

Improvement of cyber defense education training and perception Acquisition and R&D of cyber defense technology

Risk analysis/management technology Cyber security governance Mock penetration test technology Independent operating system development technology

R&D of cyber security technology

Security operating system development technology Independent microchip development technology

Cyber security plan

Independent network composition technology Network separation technology

Risk management

Code system security system Hardware security system

Software security

Software security system Preparation Deception technology Intelligence gathering prevention technology Network security system Network security protocol

Cyber security management policy

Internal control policy Regulation compliance policy

Internet entity security

Internal/external security inspection policy

Internet service security

Human security policy

Web security Wireless security Authentication technology

Outsourcing agency security policy

Access control technology

Physical security policy

Management control technology Database security

Insider security rules

Log technology Insider security Personal smart device security

Technological/managerial protective action mandatory policy (continued)

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Table 1 (continued) Stage

Required technology

Required policy

Password leak prevention

Mobile equipment security management

Contents security Physical access prevention Coping

Integrated security management Cyber risk information analysis and sharing Malignant code inspection system

Security set-up manual Copying with Crisis-coping framework cyber Crisis-coping plan security incident Incident-coping governance

Intrusion blocking technology

Coping guidelines

Intrusion tolerance technology

Classification of attacks and assignment of duties

Intrusion detection/analysis/inference/prediction technology

Security incident-coping procedure

Intrusion forecast/warning

Intrusion radio wave Botnet-coping technology Digital forensic-based technology Digital forensic application technology Digital forensic support system Damage evaluation Source trace-back technology Identification of physical point of attack

Investigation of cyber security incident Forensic policy

Cyber incident investigation and analysis manual Cyber attack damage evaluation methodology Forensic governance Forensic procedure standard guidelines Forensic preparation

Anti-forensic Encryption control policy policy Anonymity technology control policy

Identification of attacker’s identity

National identity system

Attack technology

Proxy control policy

Acknowledgements This is paper was supported by Joongbu University Research & Development Fund, in 2020.

References 1. Korea Internet Security Agency (KISA). 2018 Internet Usage Survey_Korean 2. http://www.kisa.or.kr/public/library/etc_View.jsp?regno=0011998 (2018) 3. Korea Internet Security Agency (KISA). 2019 National Information Security White Paper

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4. https://www.kisa.or.kr/public/library/etc_View.jsp?regno=0012001&searchType=&search Keyword=%EB%B0%B1%EC%84%9C&pageIndex=1 5. Eun, L.D.: Cyber threats trends. Korea Multimedia Soc. 22(2), 13–18 (2018) 6. Kim, J.-S., Lee, S.-Y., Lim, J.-I: Comparison of the ISMS difference for private and public sector. J. Korea Inst. Inf. Secur. Cryptol. 20(2), 117–129 (2010) 7. Kisoo, K.: National cyber security cyber attack response for study. In: Proceedings of Symposium of the Korean Institute of communications and Information Sciences, pp.782–783 (2013) 8. Kim, S.W., Shin, Y.T.: A method of establishing the national cyber disaster management system. J. Korea Inf. Sci. Soc. 37(5), 351–362 (2010) 9. Common Cybersecurity Vulnerabilities in Industrial Control Systems DHS, May 2011. 10. King, S.T., Chen. P.M., Wang, Y.-M., Verbowski, C., Wang, H.J., Lorch, J.R.: SubVirt: implementing malware with virtual machines. Proc 2006 IEEE 11. An, C.Y., Yoo, C.: Comparison of virtualization method. In: Proceedings of the KIISE Korea Computer Congress 2008, vol. 35, no. 1(B), pp. 446–450 (2008) 12. Lai, G., Song, H., Lin, X.: A service based lightweight desktop virtualization system. In: 2010 International Conference on Service Sciences (ICSS), pp. 277–282 (2010) 13. Dong-Hoon, S.: A study on the evaluation methodology to verify the response scenario for cyber threat assessment in nuclear facilities. In: Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, pp. 323–324 (2017)

Understanding Factors Influencing Intention to Use E-government Services in Vietnam: Focused on Privacy and Security Concerns Thi-Thanh-Thao Vo and Hung-Trong Van

Abstract Currently, the rapid growth of Information and Communication Technologies (ICTs), the explosive expansion in the use of Internet, and the distribution of E-commerce in the private sector, have put increasing pressure on the public sectors to serve people electronically. The application of ICTs and Internet in the government operations is termed as “E-government” and has become a global phenomenon recently. E-government stands for the complete optimization of the governance, service delivery, and constituency by transforming internal as well as external relationships through new technology and Internet. E-government has been adopted in many countries to provide digital government services to citizens, enable additional communication channels with the government, and improve transparency between government and citizens. This study examines determinants of people’s intention to use E-government services in Vietnam. Based on Technology Acceptance Model, privacy, security, and trust concept, a research model was proposed to discover the successful utilization of E-government in Vietnam. Keywords E-government · TAM · Trust · Privacy · Security · Intention to use

1 Introduction An electronic government, known as E-government, is the term representing the use of ICTs in public administration to provide citizens, businesses, and government agencies with convenient access to government information. According to the World Bank, E-government is defined as “The use by government agencies of information technologies (such as Wide Area Networks, the Internet, and mobile computing) that have the ability to transform relations with citizens, businesses, and other arms T. T. T. Vo · H. T. Van (B) Faculty of Digital Economy & E-commerce, The University of Danang - Vietnam-Korea University of Information and Communication Technology, Danang, Vietnam e-mail: [email protected] T. T. T. Vo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_2

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of government [1]. These technologies can serve a variety of different ends: better delivery of government services to citizens, improved interactions with businesses and industries, citizen empowerment through access to information, or more efficient government management. Moreover, the resulting benefits can be less corruption, increased transparency, greater convenience, revenue growth, and/or cost reductions” [1]. E-government initiatives have been established not only in developed countries but also in developing countries to get opportunities and advantages of ICTs, and the knowledge society [2]. Especially, in many developing countries, where corruption occurs as the most considerable obstacle to the economy development, E-government can fight against corruption, increase economic growth, and improve its services’ quality to people as well as provide good governance [3]. In addition to the potential and positive impacts of E-government, its complex in implementation is considered as challenges to many governments. Security and privacy are the most important features of any successful E-government. The growing adoption of the new technologies in organizations, with the aim to collect and process data on customer characteristics and behaviors, has caused the concerns in customers about the use, handling and possible transfer of their private information as well as security in information systems [4, 5]. A high level of trust and confidence of all E-government users will become the important foundation of any successful E-government initiatives [6]. Vietnam—a developing country in Southeast Asia has initiated several programs to promote e-Government [7]. Since 2009, the E-government system of Vietnam has been developed step by step and currently this system has brought a lot of benefits to customs services as well as to citizens, businesses nationwide, and worldwide. Vietnam E-government is currently in the middle-ranking of E-government growth in the world. Nevertheless, the embodiment of the services delivery and the services’ maturity of the world’s online E-government applications as well as modern public services are still limited. Both citizens and government want these services to be established and delivered to all the public agencies. In order to keep up with the fast-moving world of digitization and automation, the government of Vietnam needs to provide better E-government services to increase the confidence of citizens to use the E-services more efficiently and effectively. As the challenges arising from Egovernment implementation with security and privacy issues which therefore reduce the participation among people are critical, this study wants to discover the effect of such issues on intention to use E-government in Vietnam. Therefore, the purpose of this study is to figure out the answers for four key research questions: (1) What are determinants of intention to use E-government services in Vietnam?; (2) What are factors affecting trust in E-government services in Vietnam?; (3) How do security and privacy affect trust in Vietnam E-government services?; and (4) How do security and privacy affect intention to use E-government services in Vietnam?

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2 Literature Review 2.1 Privacy and Security in E-government In the E-government context, privacy refers to the protection of the individuals and organizations’ information during their interactions with online public administration services. Privacy may cause significant concerns for E-government adoption because the government database contains a large amount of personal information [8]. Secured privacy allows individuals and organizations to access safely as well as create a healthy environment for them to use E-government services. Internet users are now concerned about misuses and frauds including unauthorized tracking, information sharing with third parties, and especially financial information leaking; therefore, public administrators must ensure that the use of personal information is kept private and safe, as well as, cannot be used without users’ approvals. If so, users would believe in E-government interactions that will ensure the development of E-government system as well as secure users’ privacy [8]. Accordingly, the need for effective security mechanisms is very important for ensuring users’ online privacy. Since a higher extent of security comes along with an increase in the number of authentication procedures, security measures need to be built in a way that can efficiently protect the privacy of people and, at the same time, minimize the annoyance of people when using E-government services [9]. Given that security measures are aimed at helping to enhance users’ trust in using E-government services through Internet, thus, attract more users to use E-government services, as a result, the availability of security measures is another key element of services support.

2.2 Trust in E-government In the E-government context, trust is described as the users’ belief that the government has implemented a reliable and secured E-government system and the observation of faith in using as well as having transactions with various kinds of E-government services [10]. A number of researchers have examined the effect of trust on Egovernment acceptance as a process in which trust antecedents create momentum to believe in E-government services [11–13]. Indicators of users’ beliefs are given by trust in privacy, security, and confidence that can proceed through the presence of personal information and/or financial transactions [14]. Trust of people, as well as organizations, plays an important role in the adoption of E-government, the lack of trust of users may cause significant challenges to the E-government system acceptance [15]. Indeed, trust in the organization has a strong impact on the acceptance of technologies diffused by that agency [8]. Lack of trust is seen to be one of the most dangerous obstacles to E-service adoption, especially when there is the involvement of personal and financial information. Before accepting any E-government initiatives, people tend to have trust in government agencies in showing the technological skills

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and knowledge required to implement and secure E-government system. Furthermore, dishonest behaviors and broken promises by government officials would not only weaken users’ trust but also increase the resistance to these initiatives [16].

2.3 TAM and E-government The Technology Acceptance Model (TAM) [17] is one of the well-known IT/IS theories that have been used by many researchers to clarify and predict the fundamental factors that inspire users to accept as well as adopt new technologies. According to TAM, the acceptance of any novel technology is decided by two perceptions: (1) the perceived ease of use and (2) the perceived usefulness of the new technology. In the context of E-government, TAM has been used as a conceptual model in several studies [8, 18–20]. Perceived usefulness of E-government refers to the extent to a citizen believes that using E-government services would improve his or her work performance. Users may find E-government services useful if the system allows them to find the information or perform administrative transactions easily [18]. Consequently, the higher degree of usefulness of E-government services users perceive, the greater the rate of adoption of E-government services by users is. Additionally, perceived ease of use is related to how much a citizen believes that it would be effortless to use the E-government services. Once users feel that the system transparent and understandable, as well as, they do not have to put too much effort into interacting with the system, they may perceive E-government system as easy to use.

3 Research Model and Hypotheses Based on E-government services’ characteristics and literature review, as well as considering prior studies’ limitations, the research model was proposed as follows (refer to Fig. 1). In the E-government services sector, the results of previous studies have shown the positive relationship between perceived ease of use, perceived usefulness, and intention to use [8, 20, 21]. However, perceived ease of use has been found to increase the users’ perceptions about the control, organization credibility, and weaken perceived risks [21, 22]. An easy-to-use system may help users to comprehend the use of the services better and lift their self-confidence while using it, and all of this can boost the perceived security level of the system [23]. Improvements in perceived ease of use will minimize the risk of errors as well as simplify procedures for users, which are considered to be essential aspects of the provision of public services [24]. So, the following hypotheses are proposed: H1: Perceived ease of use positively affects Privacy of E-government. H2: Perceived ease of use positively affects Perceived usefulness of E-government.

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Fig. 1 Research model

H3: Perceived ease of use positively affects Security of E-government. Privacy in this study refers to the level of security and protection of users’ personal information provided by the E-government websites. In the operations with E-government system, people have to provide their personal details on the technology interface while communicating, paying, or receiving via E-government websites. As a consequence, E-government users may feel experience a lack of privacy. During the interactions in E-government system, the privacy of users can be improved by their trust in the E-government websites [25]. According to Kim, trust plays an important role in every successful E-government system, and privacy is a key element in building users’ trust in E-government services [26]. People would stop interacting with E-government services if their privacy is violated, and then E-government projects would fail. So, the following hypotheses are proposed: H4: Privacy positively affects Perceived usefulness of E-government. H5: Privacy positively affects Trust in E-government. H6: Privacy positively affects Intention to use E-government. In the context of E-government, security has been investigated to be a critical factor influences citizens’ behavioral intention [21]. If a user is aware of a high level of security when using a service, they can infer that the service provider intends to meet their requirements for a good relationship as well as reduce perceived risks [27]. Security is related to users’ belief that a transaction would be safely conducted, and, in this situation, it will be easier for the users to assume that the use of such service will be useful [28]. These security factors, such as the protection of disposed information and the authentication of financial transactions in E-government are seen as potential contributors to the development and improvement of trust among people. So, the following hypotheses are proposed: H7: Security positively affects Perceived usefulness of E-government. H8: Security positively affects Trust in E-government. H9: Security positively affects Intention to use E-government.

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Perceived usefulness is an important element in any successful IT adoption since, in many cases, people only accept a new technology mainly because of its functions in performing tasks that are not inherent in the use of the IT itself. A technology that is considered to be of high usefulness is one that a user trusts in the presence of a positive use-performance relationship. People tend to use or not to use technology regarding the extent to which they trust it would help them perform their jobs better. Additionally, perceived usefulness has been concluded as having a significant positive influence on not only attitude but also usage intention [17]. Thus, the following hypothesis is proposed: H10: Perceived usefulness positively affects Trust in E-government. H11: Perceived usefulness positively affects Intention to use E-government. Trust is a defining characteristic in most of social relationships and uncertain economies. Moreover, trust is seen as an essential factor in any long-term business relationship, wherever risk, uncertainty, and interdependence exist, trust is crucial because it reduces perceived risk. In E-government context, users’ trust refers to a collection of beliefs held by an online user involving certain characteristics of the E-suppliers and their potential future behaviors [29]. By increasing the better interactions between citizens and E-government, as well as entrusting personal data to the government, a better relationship would be kept up. Users’ intention to use Egovernment services would be affected by the perception of trust. Thus, the following hypotheses are proposed: H12: Trust positively affects Intention to use E-government.

4 Data Analysis Data analysis methods were used in this study included descriptive statistical analysis, exploratory factor analysis, reliability and validity tests, confirmatory factor analysis, and structural equation modeling were conducted by SPSS version 22 and AMOS version 21. For data collection, the survey method was used. The questionnaire was designed to collect data consisting of questions related to the profile of respondents, possible determinants of intention to use E-government of people in Vietnam. The respondents were asked to rate their agreements’ level in the questionnaires with statements using Likert’s seven-point scales, ranging from “strongly disagree” (1) to “strongly agree” (7). Respondents of this study are people who have used E-government services in Vietnam including applying for certificates, licenses, or filing taxes online, etc. Out of 350 self-administered questionnaires distributed, 327 questionnaires were useful for the data analysis. In this research, based on the average inter-item correlations, Cronbach’s coefficient alphas were calculated to check internal consistency. As be shown in Table 1, all the factor loadings were above the threshold of 0.5 and ranged from 0.799 to 0.927, with 6 research concepts:

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Table 1 The Cronbach’s Alpha Sec

Pri

PEU

PU

Trust

Int

0.927

0.874

0.799

0.926

0.904

0.907

Model fit indices

Cmin/df

GFI

AGFI

CFI

TLI

RMR

RMSEA

Recommended value

0.8

>0.8

>0.8

>0.8

0.5, sig. < 0.05 and cumulative value > 50% in models, so factors analysis is suitable [36].

3.4.3

Regression Analysis

The characteristics of eWOM communication are different from traditional WOM regarding its technology media factor. In fact, devices and their ease of use have strong influences in customers using rate. Therefore, this work makes an effort to study the differences in model in two popular devices: ecommerce website and the mobile application.

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Table 1 Design of scale Construct

Items

A reference scale

Attitude towards writing eWOMs

(AT1) Writing eWOMs is pleasant

[6]

(AT2) Writing eWOMs is good (AT3) Writing eWOMs is a positive thing Subjective norm

(SN1)My friends and family write eWOMs

[6]

(SN2) The numerous of people who influence significant on me write eWOMs for a product/service purchasing online (SN3) I strong suppose that most of people send feedback to e-commerce companies through eWOMs Perceived behavioral control

(PC1) I have control of writing eWOMs for a product/service purchasing online

[6]

(PC2) According to me, writing eWOMs is absolutely easy (PC3) I could easily write eWOMs for a product/service purchasing online without any trouble Satisfaction

(SA1) I write eWOMs to indicate my disappointed about a product/service purchasing online

[34]

(SA2) Writing eWOMs is one of many ways to demonstrate my satisfaction with a product/service purchasing online (SA3) I often show my thinking and emotion about items that I buying from a product/service purchasing online when I write eWOMs (SA4) Writing eWOMs for a product/service purchasing online give me a good chance to express my mind Rewards

(RE1) I can get some benefit by writing eWOMs for a product/service purchasing online

[6]

(RE2) Writing eWOMs for a product/service purchasing online is a good occasion to be virtually remunerated (e.g. in-app points, e-voucher, etc.) (continued)

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Table 1 (continued) Construct

Items

A reference scale

(RE3) I have a chance to get financial awards if writing eWOMs for e-commerce website Altruism

(AL1) Writing eWOMs for e-commerce website makes me feel respected

[34]

(AL2) I write eWOMs because I think that my opinion appriciate highly (AL3) I strong believe that it is necessary to write eWOMs Behavioral intentions

(BI1) I will write eWOMs for a product/service purchasing online

The authors

(BI2) I have an extreme tendency to write eWOMs for for a product/service purchasing online in the coming soon (BI3) I do not meet any difficult in writing eWOMs for for a product/service purchasing online next time (BI4) I will commonly send eWOMs for an a product/service purchasing online in the future

Table 2 Reliability coefficients Cronbach alpha Model 1

Model 2

Variable

Cronbach alpha

Attitude towards writing eWOM

0.725

Subjective norm eWOM

0.745

Perceived behavioral control

0.603

Intention to write eWOM for electric commercial websites

0.789

Satisfaction

0.705

Utilitarian

0.688

Altruism

0.710

Regression analysis was conducted by two kinds of devices—PC and mobile application, in which tests the level influencing of types of devices on the writing eWOMs intention.

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Model 2 In model 2, all of three factors including satisfaction, rewards and altruism impact on attitude towards writing eWOM with beta coefficients at 0.415, 0.073 and 0.215 respectively. In addition, R-square = 0.703, it means three of elements (satisfaction, utilitarian and altruism) explained 70.3% dependence variable attitude towards writing eWOM. Attitude towards writing eWOM = −0.17 + 0.415 Satisfaction + 0.073 Utilitarian + 0.215 Altruism.

Model 1 The intention of writing eWOM by PC—Model 1.1 The result of testing Durbin—Watson with d = 1.755 (dU < d < 4 – dU ) indicated that there is no correlation in model 1.1 because the model with three independent variables and 127 samples has dL = 1.482 and dU = 1.604. Moreover, variance inflation factor (VIF) values of three independent variables are under 5, therefore, we can conclude that there is no multicollinearity between independent variables in the model 1.1. Consequence, the model does not infringe the regression hypothesis. The Sig. < 0.05 illustrates that there is some linear relationship. An R-square value of 0.605 means that the independent variable accounts for 60.5% of the variation in the dependent variables. Another way of looking at it is that the independent variables supplies 60.5% of the information which need to accurately predict the dependent variable. In other words, the linear regression model is 60.5% complete. The result in model 1.1 indicated that two variables including attitude towards writing eWOM and subjective norm eWOM have a positive effect on the intention to write eWOMs with beta coefficients at 0.535 and 0.145, respectively, while variable of perceived behavioral control has an opposite trend with a negative effect with beta coefficients at −0.075. It means that the motivation of the eWOMs writing intention will be fall down by using PC because of its inconvenience. Intention to write eWOM = 0.073 + 0.535 Attitude towards writing eWOM + 0.145 Subjective norm eWOM –0.075 Perceived behavioral control The result of testing Durbin–Watson with d = 1.730 (dU < d < 4 – dU ) indicated that there is no correlation in model 1.2 because the model with 3 independent variables and 291 samples has dL = 1.713 and dU = 1.753. Moreover, variance inflation factor (VIF) values of three independent variables are under 5, therefore, authors can conclude that there is no multi-collinearity between independent variables in the model 1.2. Consequence, the model does not infringe the regression hypothesis. The Sig. < 0.05 illustrates that there are some linear relationships. The R-square value of 0.775 means that the independent variable accounts for 77.5% of the variation in the dependent variables. Another way of looking at it is that the independent variables supplies 77.5% of the information which need to accurately predict the dependent variable. In other words, the linear regression model is 77.5% complete.

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The result in model 1.2 indicated that both of three variables including attitude towards writing eWOM and subjective norm eWOM perceived behavioral control and have a positive effect on the intention to write eWOMs with beta coefficients at 0.489, 0.155 and 0.131. Therefore, in comparison with the model 1.1, using mobile application generates the motivation of writing eWOMs of customers in e-commerce website. This result explains why an R-square of model 1.2 is higher 12.7% than of model 1.1. Intention to write eWOM = 0.036 + 0.489Attitude towards writing eWOM + 0.155 Subjective norm eWOM − 0.131 Perceived behavioral control

4 Discussion Results from hypotheses checking confirm the conceptual model. In which, this research has recognized and proved the important role of attitude among the two other factors in TPB influencing to intention of the buyers in writing eWOM on an e-commerce site. This point shares common with [20], which confirmed that the level of impact from attitude equals to three times as it from subjective norm. The noticeable thing is that, perceived behavioral control also has no influences on the intention to write eWOM in that work. It can be understood that, even the buyers do not know or know limitedly how to write eWOM, their intention to write an eWOM is not influenced. This result nearly is same with [28]. In terms of determinants of attitude toward writing eWOM, satisfaction on purchasing an online product account for largest part (0.415), followed by altruism (0.215). The influence of utilitarian purpose is not really clear. The result can be used to remind corporates taking care of their customers, to have their satisfaction. Therefore, regarding theoretical implication, our conclusion has made some major contributions to understand the significance of the eWOM intention. This work seeks to provide an integrated system for learning about eWOM design process. In particular, the results highlight the important role of foundational functions in eWOM sharing. Furthermore, the authors also tried to prove the impact of devices for writing eWOM by dividing them into two cases of computer website and mobile application. From the managerial application, this study gives the recommendations for ecommerce website marketers. In order to promote eWOM, especially the positive ones, they need to understand how customers utilize eWOM to gain product and service information. The website design practitioners should inform the users about rewards or benefit they may get from trigger the feedback after purchasing. They also need to enhance the ease of use for their mobile application.

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5 Conclusion The finding has suggested the model of determinants influencing the intention to write eWOM of buyers on ecommerce sites. Through the Theory of Planned Behavior this work indicates three determinants of attitude and then the intention to write eWOM, discusses some contributions in terms of theory and reality management, especially for technology platforms in encouraging eWOM in shopping online. The more importance of satisfaction compared to utilitarian and altruism purpose in contributing in attitude toward writing eWOM after shopping online. The results also emphasize the three factors from TPB creating the intention of writing eWOM, especially the attitude of the buyers. The two most popular. However, this article can be broaden by studying the direct influencing of buyers’ satisfaction, altruism and utilitarian purpose on intention to writing e-commerce. Furthermore, some moderators, like ages, gender or occupation would be tested.

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Study on the Impact of Activity-Based Flexible Office Characteristics on the Employees’ Innovative Behavioral Intention Jin Won Jang, Yong Gi Park, Seong Il Hur, and Yong Jun An

Abstract With the development of Information and Communications Technology (ICT) and the entry of the Millennial generation, organizations are increasingly introducing activity-based flexible offices (A-FO). A-FO refers to a working environment where people can do the job anytime and anywhere using digital devices without restrictions on time or space. The main purpose of companie’s introduction of AFO is to give employees job autonomy and to promote horizontal communication, ultimately encouraging members to innovate. In this study, the impact of A-FO characteristics on the members’ intention to innovate was empirically verified through the medium of job autonomy and horizontal communication. The concept of A-FO consists of physical, technical, and behavioral characteristics. This study conducted a survey on 402 employees from 10 Korean companies operating A-FO and made empirical analysis of the survey. Keywords Activity-based flexible office · Smart office · Innovative behavioral intention · Job autonomy · Horizontal communication · Enterprise social networks · Proximity · Privacy · IT infrastructure

J. W. Jang (B) Ernst & Young Han Young, Seoul, Korea e-mail: [email protected] Y. G. Park National Assembly of South Korea, Seoul, Korea e-mail: [email protected] S. I. Hur Soar IT Corporation Co.Ltd, Seoul, Korea e-mail: [email protected] Y. J. An Good Consulting Group, Seoul, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_8

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1 Introduction The rapid development of Information & Communications Technology (ICT) is blurring the boundaries of business and changing the hierarchical organizational structure horizontally [1]. In addition, the nature of work is becoming more complex and demanding, the need for collaboration is increasing, the reliance on individual social skills and technical competencies is increasing, and the transition to mobile is becoming rapid [2]. Millennials are good at digital devices, 64 percent of which prefer a flexible work style, and productivity should be measured based on work performance results, not office hours. With the development of ICT and the advent of the millennium generation, the traditional offices where small numbers of people used to work can no longer meet a variety of business activities [3]. Therefore, many companies have begun to shift their existing offices to Activity-Based Flexible Office (A-FO) that can support a variety of activities [4]. In A-FO, activity refers to work activity, not physical activity. A-FO’s structure is open-type based, but it has a space for collaboration and collaborative work. For example, personal workspaces, conference spaces, videoconferencing spaces, lounges, and telephone booths are available [5]. What sets A-FO apart from a typical open office is that it offers a variety of environments that support different business activities, allowing users to choose a workspace based on their personal preferences under the desk-sharing system [6]. This study noted that creativity and innovation are essential elements for organizational development and continuous success in the era of digital transformation [7]. This is to empirically verify the effect of A-FO’s Physical and Technological Characteristics on the Employees’ Innovative Behavioral Intention. In addition, considering the fact that many companies are pursuing innovation through changes in the way their employees work, I want to verify whether Job Autonomy and Horizontal Communication have a mediating effect between the characteristics of A-FO and the intention of the employees to act in innovation.

2 Theoretical Background 2.1 A-FO Model Many studies have generally comprised the concept of A-FO with physical, technical, human and cultural elements. A-FO concepts in many literature studies include the following shown in Table 1. Wohlers & Hertel [6] presented the A-FO model by organizing how the characteristics of A-FO affect the working conditions of individuals, groups, and organizations, and how job conditions result in long-term and short-term outcomes. The

Study on the Impact of Activity-Based Flexible … Table 1 A-FO concepts

89

Author

A-FO concepts

Lefebvre [8]

Perceived Space (Actual space) Conceived Space (Virtual space) Lived Space (Relational space)

Hernes [9]

Physical (Physical space) Mental (Mental Space) Social (Social space)

Baane et al. [10]

Bricks (Physical resource) Bytes (Technological resource) Behaviors (Personal resource)

van Koetsveld & Kamperman [4]

Physical (Physical space) Virtual (Virtual space) Behavioral (Behavioral space)

Veldhoen & Company [11]

Place Technology People

Blok et al. [12]

Office Technology Culture

Coenen & de Kok [13]

Layout ICT (Information Communication Technology) HR (Human resource)

Dery, Sebastian and Meulen [14]

Space System Social

job conditions presented in the A-FO model are (1) autonomy, (2) territoriality, (3) proximity & visibility, and (4) privacy. De Croon et al. [15] presented a working condition in which these A-FO characteristics affect (1) the workload (such as noise and irregular working hours), (2) communication (such as seat sharing), (3) privacy (decreasing privacy), (4) selfregulation (improving autonomy such as remote work scheduling), and (5) working relationships (from colleagues).

2.2 Socio-Technical Systems The socio-technical system theory refers to optimizing the performance of the job system by integrating sub-systems on the interdependent technical side and subsystems on the human side. The version used today is more comprehensive, including

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four interdependent subsystems: (1) Technical, (2) Personnel, (3) Organizational, (4) External Environment [16]. From a socio-technical system perspective, the introduction of A-FO and flexible workflow involves fundamental changes in each subsystem as well as interaction between the subsystems [17].

2.3 Need–Supply Fit Theory Need–Supply Fit refers to the suitability of individuals to meet their needs [18]. Applying the demand-supply suitability theory) to an office is that the office supplies space (concentrated workspace or communication space) to meet the employee’s job needs. Satisfaction is highly recognized when workers’ needs are met at the office environment level.

2.4 Job Demands-Resources Theory According to the Job Demands-Resources Theory, task characteristics are divided into two categories: Job Demands and Job Resources. Job Demands require great effort as a physical, psychological, social, and organizational aspects of work and involve physiological and psychological costs [19].

2.5 IT Infrastructure The Foundation Course Glossary of IT Infrastructure Library (ITIL) defines IT infrastructure as the set of hardware, software and network facilities needed to develop, test, communicate, and monitor IT services. Henderson & Venkatraman [20] divided the IT infrastructure into two categories: resource-based view, resource component and organizational capability, again as a standardized technology available in the market: (1) Hardware Platform, (2) Software Platform, (3) Network/Telecommunication technology, and 4) Data-base. The technical characteristics of A-FO consist of (1) IT Hardware, (2) Office Management Application, and (3) Social Software, based on its direct relevance to A-FO. In this study, we would like to unify terms of Social Software into Enterprise Social Networks (ESN).

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2.6 Media Richness Theory Media richness refers to the media’s ability to deliver information that can provide abundant cues in the context of mediated communication. Given the richness of the media, it is possible to increase the accuracy and efficiency of communication by reducing the uncertainty of messages that can be interpreted vaguely or polysemously [21].

2.7 Investment Theory of Creativity In “Defying the Crowd, Free Press” [22], Sternberg presented the Investment Theory of Creativity, meaning “to buy cheap and sell expensive.” There are six key elements in this investment process: (1) Intelligent Skills, (2) Knowledge, (3) Thinking Styles, (4) Personality, (5) Motivation, (6) Environment. Sternberg said that these six factors work organically together, generating creativity.

3 Research Design 3.1 Variable Settings In this study, the physical and technical characteristics of A-FO were set up as independent variables. The characteristics are (1) physical characteristics, (2) technical characteristics, and (3) behavioral characteristics, which Baane et al. [10] presented as the concept of composition of A-FO. In this study, the parameters of the A-FO are (1) Job Autonomy and (2) Horizontal Communication, the behavioral characteristics of the A-FO that are influenced by the physical and technical characteristics of the A-FO and simultaneously affect the dependent variables. Based on a variety of prior studies, this study established employee’s Innovative Behavior Intention as a dependent variable, focusing on the effect of the physical and technical characteristics of A-FO on individual behavior, especially the way they work.

3.2 Research Model In this study, the study model for analyzing the effects of A-FO characteristics on members’ intention of innovative behavior is as follows, with a total of 14 hypotheses (Fig. 1).

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Fig. 1 Research model

3.3 Definition of Variables The operational definitions of the variables presented in this study model are as follows, and the survey items are organized based on them (Table 2).

4 Empirical Analysis 4.1 Data Collection This survey was conducted for five weeks from January 28 to February 28, 2020. A total of 482 copies of the questionnaires were collected, and 402 copies were used for the final analysis, excluding 80 questionnaires in which the measurement items were unfaithful.

4.2 Sample Characteristics A frequency analysis was conducted to identify demographic characteristics. The characteristics of the sample are as follows (Table 3).

4.3 Exploratory Factor Analysis and Reliability Verification Exploratory factor analysis (EFA) is an analysis method used to identify factors that are commonly contained between variables. It is used to identify variables that can

Study on the Impact of Activity-Based Flexible …

93

Table 2 Definition of variables Variable

Operative definition

Spatial flexibility

The degree of the existence of various Wohlers spaces required for work activities and and Hertel [6] the freedom to select and use them

References

Perceived distance between team members

The degree to which team members are perceived to be far apart from each other in the workplace

Perceived level of privacy

The degree to which individuals are Oldham perceived to be protected in the and Rotchford [23] workplace in terms of visual, auditory, informational, and territorial privacy

Quality of IT hardware

The degree to which the IT hardware is thought to be effective in use and thus affects its outcome

Quality of office management application

The degree to which the office-operated application is thought to be effective in use and thus affects its outcome

Perceived usefulness of ESN

The degree to which ESNs are perceived to be helpful in work efficiency and ease of communication within the organization

Attaran et al. [25]

Job autonomy

The degree to which individuals perceive that sufficient freedom, independence, and respect for judgment are assured to make their own decisions on how to conduct business, procedures and plans

Wohlers and Hertel [6]

Horizontal communication

The degree of communication between Robbins [26] members or departments of similar positions in the organization

Employees’ innovative behavioral intention

The degree to which an effort to find and share new ideas and to develop optimal plans and schedules for the realization of selected ideas in performing duties within the organization

Ahuja and Thatcher [24]

Sternberg [27]

represent the value of the data among variables used in the study by grasping the correlations such as covariance and correlation. The result of exploratory factor analysis and reliability analysis was that the factor loading was 0.5 or more, and the validity between each variable was secured, and the final measurement items were selected. Finally, in order to test the internal consistency, the Cronbach alpha’s test was performed. As the result, all values of 0.7 or higher were confirmed to ensure internal consistency [28] (Table 4).

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J. W. Jang et al.

Table 3 Characteristic of sample Gender Age

Industry

Frequency

Percentage

Male

257

63.9

Female

145

36.1

20–29

36

9.0

30–39

165

41.0

40–49

142

35.3

50–

59

14.7

Service industry

90

22.4

254

63.2

Manufacturing industry Wholesale/retail trade

6

1.5

52

12.9

314

78.1

Team leader

65

16.2

Executive

23

5.7

Other Position

Team member

4.4 Confirmatory Factor Analysis Confirmatory factor analysis (CFA) was checked to determine whether the extracted factors could explain the research model. In order to secure the reliability and validity of the factors, the standardization coefficient of the questionnaire items should be 0.5 or more. In addition, the value of the conceptual reliability (CR), which is a measure of internal consistency, should be 0.7 or higher. In this study, the result was 0.7 or higher. The average variance extraction value (AVE) should be greater than 0.5, and in this study, it was found that the average variance extraction value was greater than 0.5 without any abnormality [29] (Table 5). The value of the square root of average variance extracted value is greater than the correlation coefficient between conceptual variables. To test the discriminant validity, Fornell & Larcker’s method [30] was used to analyze that there is discriminant validity between the variables. In this study, it was confirmed that there was no problem in the discriminant validity between the concepts of constituent variables (Table 6).

4.5 Verification of Structural Equation Model Fit All of the fitness indices of the research model of this study satisfied the acceptance criteria (Table 7).

Study on the Impact of Activity-Based Flexible …

95

Table 4 Exploratory factor analysis, reliability analysis result C’A 1

2

3

4

5

6

7

8

9

Spf_1

0.197

0.138

0.106

0.135

0.062

0.100 −0.084 −0.085

0.882 0.927

Spf_4

0.250

0.145

0.129

0.126

0.050

0.105 −0.116 −0.112

0.870

0.219

0.110

0.142

0.178

0.073

0.068 −0.188 −0.132

Spf_5

0.807

Pdt_1

−0.034 −0.025 −0.030 −0.046 −0.012

0.018 0.918

0.113

−0.089 0.937

Pdt_2

−0.012 −0.029 −0.034 −0.033 −0.060

0.025 0.935

0.124

−0.085

Pdt_3

−0.026 −0.006 −0.038 −0.003 −0.044

0.063 0.914

0.092

−0.129

Plp_1

−0.031 −0.041

0.049

Plp_2

−0.065 −0.042

0.059 −0.005

Plp_3

−0.042 −0.024

0.029 −0.021 −0.011 0.132 0.042

0.934

−0.070 0.933

0.003 0.132

0.941

−0.083

0.897

−0.108

0.052

0.026

0.017

0.001 0.069

Qih_1

0.134

0.852

0.170

0.060

0.121

0.066 −0.022 −0.015

Qih_2

0.161

0.866

0.089

0.016

0.052

0.082 0.012

Qih_3

0.215

0.859

0.077

0.067

0.066

0.109 −0.051 −0.032

0.066

Qih_4

0.228

0.830

0.163

0.076

0.129

0.107 −0.004 0.007

0.109

Qih_5

0.228

0.818

0.164

0.089

0.091

0.114 −0.018 0.005

0.155

Qom_1

0.858

0.221

0.130

0.088

0.063

0.105 −0.048 −0.038

0.129 0.948

Qom_2

0.862

0.195

0.133

0.122

0.058

0.076 −0.074 −0.046

0.172

Qom_3

0.867

0.201

0.134

0.086

0.031

0.053 −0.001 −0.055

0.122

Qom_4

0.847

0.133

0.127

0.075

0.012

0.149 0.031

−0.012

0.102

−0.090

0.042 0.937 0.053

Qom_5

0.820

0.257

0.203

0.072

0.065

0.099 −0.012 −0.030

0.169

Pue_1

0.041

0.047

0.087

0.075

0.875

0.093 −0.026 0.035

0.056 0.912

Pue_2

0.071

0.080

0.108

0.057

0.860

0.088 0.019

Pue_3

0.066

0.100

0.164

0.088

0.889

0.050 −0.054 −0.007

0.032

Pue_5

0.003

0.168

0.174

0.042

0.844

0.059 −0.066 0.038

0.028

Ja_2

0.152

0.126

0.306

0.163

0.090

0.717 0.053

0.074 0.867

Ja_3

0.149

0.055

0.163

0.104

0.142

0.806 −0.006 0.006

Ja_5

0.043

0.146

0.315

0.151

0.064

0.790 0.054

−0.039

Ja_6

0.103

0.139

0.198

0.185

0.032

0.769 0.037

−0.032

0.027

Hc_1

0.095

0.185

0.770

0.128

0.136

0.281 0.013

0.044

0.134 0.917

Hc_2

0.105

0.150

0.813

0.132

0.121

0.224 −0.055 0.007

0.053

Hc_3

0.191

0.096

0.789

0.104

0.125

0.183 −0.057 0.002

0.092

Hc_4

0.136

0.139

0.790

0.096

0.155

0.158 −0.047 0.092

0.054

Hc_5

0.193

0.119

0.822

0.044

0.111

0.152 0.007

0.083

−0.027

0.060

0.055

0.047

0.074 0.109

(continued)

96

J. W. Jang et al.

Table 4 (continued) C’A 1

2

3

4

5

6

7

8 0.011

9

Iib_1

0.040

0.084

0.099

0.851

0.082

0.165 0.014

Iib_2

0.108

0.043

0.071

0.878

0.042

0.147 −0.045 0.015

0.102 0.911 0.098

Iib_3

0.108

0.058

0.134

0.872

0.096

0.130 −0.041 0.008

0.100

Iib_4

0.111

0.070

0.112

0.827

0.049

0.086 −0.022 0.019

0.083

EV

4.19

4.07

3.82

3.24

3.23

2.81

2.69

2.68

2.49

%

11.65

11.29

10.61

9.00

8.96

7.82

7.46

7.43

6.93

CUM

11.65

22.94

33.55

42.56

51.52

59.33

66.80

74.23

81.16

N/A

Spf: Spatial Flexibility, Pdt: Perceived Distance between Team Members, Plp: Perceived Level of Privacy, Qih: Quality of IT Hardware, Qom: Quality of Office Management Application, Pue: Perceived Usefulness of ESN, Ja: Job Autonomy, Hc: Horizontal Communication, Lib: Employees’ Behavioral Intention towards Innovation EV: Eigen Value, %: % of Variance, CUM: Cumulative, C’A: Cronbach alpha

4.6 Verification of the Research Model The results of the empirical analysis of the hypothesis are shown in Fig. 2 and Table 8. Adoption or rejection of the research hypothesis is judged based on the C.R (Critical Ratio) value of ±1.96 or more and P-Value of 0.05 or less.

5 Conclusion First, the hypothesis (H1-1) that spatial flexibility has a positive effect on job autonomy and horizontal communication was adopted. This can be seen as A-FO workers experiencing work autonomy by selecting various spaces suitable for their jobs and performing their tasks. Second, the hypothesis that spatial flexibility has a positive effect on horizontal communication (H1-2) was adopted. This means that the members’ radius of action is expanding from the team to the entire organization by providing various spaces for A-FO to communicate and collaborate. Third, the hypothesis (H2-1) that the perceived distance between team members has a positive effect on job autonomy was adopted. This is the hypothesis that the perceived distance between Team Members as the physical distance between team members increases has a positive effect on job autonomy (each person is responsible and effectively performing his or her tasks). Fourth, the hypothesis (H2-2) that the perceived distance between team members has a positive effect on horizontal communication was rejected. It is estimated that the average A-FO experience period of the survey respondents is less than two years on average, and that they are still working around the team. Moreover, most of the

1.01

Pdt_3

Perceived usefulness of ESN

Quality of office management application

Quality of IT hardware

1.115

1.022

Qom_5

Pue_2

0.908

Qom_4 1

0.998

Qom_3

Pue_1

1.027

1.059

Qih_5 1

1.051

Qih_4

Qom_2

1.076

Qih_3

Qom_1

0.992

Qih_2

0.861 1

Plp_3

Qih_1

1.069

Plp_2

1

1.067

Pdt_2

Perceived level of privacy Plp_1

1

0.904

Spf_5

Pdt_1

1.001

Spf_4

Perceived distance between team members

1

Spf_1

Spatial flexibility

Unnormalization Coefficient

Measurement item

Latent variable

Table 5 Confirmatory factor analysis result

0.057

0.038

0.038

0.036

0.034

0.046

0.045

0.049

0.049

0.035

0.03

0.038

0.035

0.037

0.031

S.E

19.551

26.902

23.743

27.504

29.936

22.796

23.115

21.898

20.289

24.398

35.086

26.607

30.439

24.165

32.236

C.R

***

***

***

***

***

***

***

***

***

0.000***

***

***

***

***

***

P

0.815

0.839

0.88

0.833

0.888

0.918

0.905

0.891

0.898

0.869

0.829

0.833

0.825

0.972

0.925

0.887

0.953

0.897

0.834

0.957

0.915

Standardization coefficient

0.913

0.947

0.936

0.934

0.937

0.929

CR

(continued)

0.726

0.787

0.747

0.827

0.833

0.816

AVE

Study on the Impact of Activity-Based Flexible … 97

Employees’ behavioral intention towards innovation

Horizontal communication

Job autonomy

Latent variable

Table 5 (continued)

1.149 1.178 1.185

Iib_3

Iib_4

0.966

Hc_5 1

0.958

Hc_4

Iib_2

0.945

Hc_3

Iib_1

1.04

1.051

Ja_6

Hc_2

1.224

Ja_5 1

0.895

Hc_1

1

1.165

Pue_5

Ja_3

1.156

Pue_3

Ja_2

Unnormalization Coefficient

Measurement item

0.063

0.06

0.059

0.048

0.05

0.046

0.047

0.067

0.069

0.058

0.057

0.05

S.E

18.801

19.661

19.467

20.282

19.193

20.351

21.962

15.61

17.807

15.38

20.492

23.052

C.R

***

***

***

***

***

***

***

***

***

***

***

***

P

0.852

0.883

0.876

0.786

0.821

0.793

0.822

0.861

0.852

0.762

0.865

0.752

0.777

0.841

0.912

Standardization coefficient

0.912

0.917

0.868

CR

0.722

0.689

0.624

AVE

98 J. W. Jang et al.

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99

Table 6 Discriminant validity analysis result Spf

Pdt

Plp

Qih

Qom

Pue

Ja

Hc

Spf

0.816

Pdt

0.068

0.833

Plp

0.050

0.069

0.827

Qih

0.125

0.005

0.006

0.747

Qom

0.231

0.008

0.013

0.261

0.787

Pue

0.033

0.011

0.001

0.078

0.035

0.726

Ja

0.100

0.002

0.000

0.136

0.120

0.073

0.624

Hc

0.117

0.007

0.006

0.173

0.173

0.140

0.403

0.689

Iib

0.109

0.006

0.000

0.052

0.080

0.045

0.178

0.112

Iib

0.722

Table 7 Goodness-of-fit test of structural equation model Goodness-of-fit index Absolute fit index

Goodness-of-fit across the model

Model explanatory power

Incremental fit index

Simplicity conformity Index

Indicator value

Threshold criterion

References

x2 (CMIN)p

1272.942 (P = 0.000)

p0.05–0.10

Muthen · Kaplan [31]

x2 (CMIN)/df

2.253

1.0CMIN/df2.0–3.0

Carmines · Mciver [32]

RMSEA

0.056

0.05–0.08

Browne · Cudeck [33]

RMR

0.078

0.08

Hair et al. [34]

GFI

0.851

0.8–0.9

Joreskog · Sorbom [35]

AGFI

0.824

0.8–0.9

Hair et al. [34]

PGFI

0.722

0.5–0.6

Mulaik et al [36]

NFI

0.901

0.8–0.9

Bentler · Bonett [37]

NNFI(TLI)

0.936

0.8–0.9

Bentler · Bonett [37]

CFI

0.942

0.8–0.9

Bentler [37]

PNFI

0.808

0.6

James et al. [38]

PCFI

0.845

0.5–0.6

James et al. [38]

J. W. Jang et al.

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Fig. 2 Hypothesis test result

Table 8 Hypothesis test result H

Hypothesis

Path coefficient Sandard C.R (non-standardized) error

P

Adoption/ Not adoption

H1-1 SPF

→ JA

0.132

0.04

3.314 0.000*** O

H1-2 SPF

→ HC

0.113

0.039

2.883 0.004**

O

H2-1 PDT

→ JA

0.078

0.033

2.35

O

H2-2 PDT

→ HC −0.014

0.032

−0.425 0.671

H3-1 PLP

→ JA

0.024

0.031

0.768 0.442

X

H3-2 PLP

→ HC

0.098

0.031

3.154 0.002**

O

H4-1 QIH

→ JA

0.153

0.048

3.186 0.001**

O

H4-2 QIH

→ HC

0.166

0.047

3.5

H5-1 QOM

→ JA

0.119

0.051

2.322 0.02*

H5-2 QOM

→ HC

0.185

0.051

3.667 0.000*** O

H6-1 PUE

→ JA

0.174

0.051

3.372 0.000*** O

H6-2 PUE

→ HC

0.261

0.051

5.083 0.000*** O

H7

JA

→ IIB

0.398

0.064

6.228 0.000*** O

H8

HC

→ IIB

0.149

0.056

2.681 0.007**

0.019*

X

0.000*** O O

O

companies surveyed are large companies, which are believed to require more time to engage in exchanges among the entire organization. Fifth, the hypothesis (H3-1) that perceived level of privacy has a positive effect on job autonomy was rejected. The results of the validation of this hypothesis support the study results [5] that A-FO does not always have high employee satisfaction in terms of workload, privacy, and productivity support. The results of the verification of this hypothesis may not be seen by members, including the Millennial generation, as a major factor in privacy affecting work autonomy.

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Sixth, the hypothesis (H3-2) that perceived level of privacy has a positive effect on horizontal communication was adopted. It is judged that members recognize that privacy is somewhat protected by the installation of furniture and smart glass that can select suitable spaces for meeting and interact and minimize auditory or visual exposure. Seventh, the hypotheses (H4-1, H4-2, H5-1, H6-1, H6-1, H6-2) that technical characteristics (the quality of IT hardware, the quality of office-operating applications, and the perceived usefulness of ESN) have positive effects on job autonomy and horizontal communication (H4-2, H5-1, H6-2) have all been adopted. The companies surveyed in this study are believed to be creating an environment where members can work without restrictions of time and place by providing maximum support for high-speed wired and wireless communication networks, high-definition video conferencing systems, cloud computing, desktop virtualization, and the latest mobile devices. Also, it is estimated that respondents are experiencing job autonomy and smooth communication using IT hardware. The perceived usefulness of ESN has been shown to have the greatest influence on parameters among the six independent variables of A-FO, meaning that ESN among the characteristics of A-FO has the greatest influence on the behavior of its members. Eighth, the hypothesis (H-7) that job autonomy has a positive effect on employees’ behavioral intention towards innovation was adopted. The results of the verification of this hypothesis are consistent with Glassman’s empirical study [39] of R&D staff finding that employees performing highly autonomous tasks are highly creative, and Amabile et al.’s research [40] suggests that employees are highly creative when they have the option of performing their tasks. Scott and Bruce [41] also supports the argument that the empowerment of leaders and the granting of trust and autonomy affects the creation of creative ideas and innovative behavior of subordinates. Ninth, the hypothesis (H-8) that horizontal communication has a positive effect on employees’ behavioral intention towards innovation was adopted. The verification results of this hypothesis are consistent with Nordfors’s [42] claim that communication is an important action for creativity and innovation [43], and that innovation is a collaborative work. In this study, it was found that the group of executives and team leaders considers horizontal communication as an important factor in the members’ intention of innovative behavior rather than the group of team members. It is expected that of the six independent variables in this study, ESN will contribute to future A-FO and organizational communication research as they are found to have the most important impact on business autonomy and horizontal communication. The limitations of the study and the future direction of the study are first thought to be more clearly understood by establishing a control group of traditional office workers within the same organization to compare and analyze them with A-FO workers. Second, this study corresponds to cross-sectional research. Therefore, a longitudinal study is required on the behavioral changes of members, changes in organizational operating systems, and the creation of a new corporate culture that may occur in the medium and long term following the introduction of A-FO.

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References 1. Cartwright, S.: New forms of work organization: issues and challenges. Lead. Organ. Dev. J. (2003) 2. Nolan, P., Wood, S.: Mapping the future of work. Br. J. Ind. Relat. 41(2), 165–174 (2003) 3. Davis, M.C., Leach, D.J., Clegg, C.W.: The physical environment of the office: contemporary and emerging issues (2011) 4. van Koetsveld, R., Kamperman, L.: How flexible workplace strategies can be made successful at the operational level. Corp. R. Estate J. 1(4), 303–319 (2011) 5. de Been, I., Beijer, M.: The influence of office type on satisfaction and perceived productivity support. J. Facil. Manag. (2014) 6. Wohlers, C., Hertel, G.: Choosing where to work at work–towards a theoretical model of benefits and risks of activity-based flexible offices. Ergonomics 60(4), 467–486 (2017) 7. Samani, S.A., Rasid, S.Z.B.A., bt Sofian, S. : Individual control over the physical work environment to effect creativity. Ind. Eng. Manag. Syst. 14(1), 94–103 (2015) 8. Lefebvre, H.: The Production of Space. Oxford, Blackwell (1991) 9. Hernes, T.: The spatial construction of organization, vol. 12. John Benjamins Publishing (2004) 10. Baane, R., Houtkamp, P., Knotter, M.: The new world of work unravelled–het nieuwe werken ontrafeld–over bricks, bytes & behaviour, 1–168. Koninklijke Van Gorcum BV (2010) 11. Veldhoen and Company.: Veldhoen company (2017). https://www.veldhoencompany.com/en/ organisation/ 12. Blok, M.M., Groenesteijn, L., Schelvis, R., Vink, P.: New ways of working: does flexibility in time and location of work change work behavior and affect business outcomes? Work 41(Supplement 1), 2605–2610 (2012) 13. Coenen, M., Kok, R.A.: Workplace flexibility and new product development performance: the role of telework and flexible work schedules. Eur. Manag. J. 32(4), 564–576 (2014) 14. Dery, K., Sebastian, I. M., van der Meulen, N.: The digital workplace is key to digital innovation. MIS Q. Exec. 16(2) (2017) 15. de Croon, E., Sluiter, J., Kuijer, P.P., Frings-Dresen, M.: The effect of office concepts on worker health and performance: a systematic review of the literature. Ergonomics 48(2), 119–134 (2005) 16. Hendrick, H.W.: Macroergonomics: The analysis and design of work systems. Rev. Hum. Factors Ergon. 3(1), 44–78 (2007) 17. Rolfö, L.: Activity-based flexible office work environments. Design and implementation processes and outcomes. Stockholm, KTH Royal Institute of Technology (TRITA-CBH-FOU, 2018: 52) (2018) 18. Kristof, A.L.: Person-organization fit: an integrative review of its conceptualizations, measurement, and implications. Pers. Psychol. 49(1), 1–49 (1996) 19. Demerouti, E., Bakker, A.B., Nachreiner, F., Schaufeli, W.B.: The job demands-resources model of burnout. J. Appl. Psychol. 86(3), 499 (2001) 20. Henderson, J.C., Venkatraman, N.: Strategic alignment: a model for organizational transformation via information technology, pp. 202–220. Oxford University Press, New York (1994) 21. Daft, R.L., Lengel, R.H.: Information richness: a new approach to managerial behavior and organization design (No. TR-ONR-DG-02). Texas A and M Univ College Station Coll of Business Administration (1983) 22. Sternberg, R.J., Lubart, T.I.: Defying the crowd: Cultivating creativity in a culture of conformity. Free Press (1995) 23. Oldham, G.R., Rotchford, N.L.: Relationships between office characteristics and employee reactions: a study of the physical environment. Adm. Sci. Q. 542–556 (1983) 24. Ahuja, M.K., Thatcher, J.B.: Moving beyond intentions and toward the theory of trying: effects of work environment and gender on post-adoption information technology use. MIS Q. 29(3), 427–459 (2005)

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25. Attaran, M., Attaran, S., Kirkland, D.: The need for digital workplace: increasing workforce productivity in the information age. Int. J. Enterp. Inf. Syst. (IJEIS) 15(1), 1–23 (2019) 26. Robbins, S.P., Judge, T.: Essentials of organizational behavior, vol. 7. Prentice Hall, Upper Saddle River, NJ (2003) 27. Sternberg, R.J.: A propulsion model of types of creative contributions. Rev. Gen. Psychol. 3(2), 83–100 (1999) 28. Bland, J.M., Altman, D.G.: Statistics notes: cronbach’s alpha. BMJ 314(7080), 572 (1997) 29. Hair, J.F., Ringle, C.M., Sarstedt, M.: PLS-SEM: indeed a silver bullet. J. Mark. Theory Pract. 19(2), 139–152 (2011) 30. Fornell, C., Larcker, D.F.: Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981) 31. Muthén, B., Kaplan, D.: A comparison of some methodologies for the factor analysis of nonnormal Likert variables. Br. J. Math. Stat. Psychol. 38(2), 171–189 (1985) 32. McIver, J., Carmines, E.G.: Unidimensional scaling (No. 24). Sage (1981) 33. Browne, M.W., Cudeck, R., Bollen, K.A., Long, J.S.: Testing structural equation models (1993) 34. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L.: Multivariate data analysis, vol. 5, no. 3, pp. 207–219. Upper Saddle River, NJ: Prentice hall (1998) 35. Joreskog, K.G., Sorbom, D.: LISREL: analysis of linear structural relationships by the method of maximum likelihood. Version VI. Chicago: Nationai Education Resources. Parameswaran, Ravi and A. Yaprak (1987), A Cross-National Comparison of Consumer Research Measures. J. Int. Busimss’ Stud. 18, 35–49 (1984) 36. Mulaik, S.A., James, L.R., Van Alstine, J., Bennett, N., Lind, S., Stilwell, C.D.: Evaluation of goodness-of-fit indices for structural equation models. Psychol. Bull. 105(3), 430 (1989) 37. Bentler, P.M., Bonett, D.G.: Significance tests and goodness of fit in the analysis of covariance structures. Psychol. Bull. 88(3), 588 (1980) 38. James, L., Mulaik, S., Brett, J.M.: Causal analysis: assumptions, models, and data (1982) 39. Glassman, E.: Managing for creativity: back to basics in R&D. R&D Manag. 16(2), 175–183 (1986) 40. Amabile, T.M., Gryskiewicz, N.D.: The creative environment scales: work environment inventory. Creat. Res. J. 2(4), 231–253 (1989) 41. Scott, S.G., Bruce, R.A.: Determinants of innovative behavior: a path model of individual innovation in the workplace. Acad. Manag. J. 37(3), 580–607 (1994) 42. Nordfors, D.: Innovation journalism, attention work and the innovation economy. A review of the innovation journalism initiative 2003–2009. Innov. Journalism, 6(1), 1–46 (2009) 43. Toker, U., Gray, D.O.: Innovation spaces: Workspace planning and innovation in US university research centers. Res. Policy 37(2), 309–329 (2008)

The Effects of Rapport-Building on Customer Attitude and Loyalty in Medical Service Hye Soo Choi and Dong Hyuk Jo

Abstract This study aims to empirically verify the structural relationships between rapport-building behaviors, customer attitudes, and customer loyalty in the domestic medical service environments in order to overcome the limitations of previous studies and to seek ways to secure competitive advantages and achieve sustainable growth of medical institutions. To that end, this study reviewed the concepts and dimensions of rapport-building behaviors of medical service providers, customer attitudes, and organizational loyalty through literature research, proposed a research model based on the results, and empirically verified the model. According to the results of the study, first, in the relationship between rapport-building behaviors and customer attitudes, uncommonly attentive behaviors and information-sharing behaviors were found to have positive effects on cognitive attitudes. In addition, uncommonly attentive behaviors, courteous behavior, and connecting behaviors were found to have positive effects on affective attitudes. Second, in the structural relationships among customer attitudes’ sub-dimensions, cognitive attitudes were found to have positive effects on affective attitudes. Third, in the relationship between customer attitudes and customer loyalty, cognitive attitudes and affective attitudes were found to have positive effects on customer loyalty. Finally, in the relationship between rapport-building behaviors and organizational loyalty, organizational attitudes were found to have indirect effects. Therefore, the fact that by understanding the rapidly changing medical service industry’s competitive environment, and identifying the mechanism by which medical institutions’ patient-oriented service provision leads to the organizational performance of enhancing customer loyalty, this study presented a strategic directivity for the enhancement of the competitiveness of domestic medical institutions can be said to be the significance of this study. Keywords Rapport-building behaviors · Customer attitude · Customer loyalty · Medical service H. S. Choi (B) · D. H. Jo Department of Business Administration, Soongsil University, Seoul, Korea e-mail: [email protected] D. H. Jo e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_9

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1 Introduction Recently, as the average life expectancy has been increasing and the quality of life has been improved thanks to the continuous economic development and increases in income levels, the medical service industry has been rapidly growing. Along with the rapid increases in populations throughout the world, the life expectancy of humans is increasing, and the quality of life is improving. Therefore, the medical service industry has been evaluated as an industry with high added value per person and a great job creation effect in the recent low-growth environment without employment in general, and is recognized as a core industry in the future of the twenty-first century [1]. Medical services are generally a series of processes ranging from disease treatment to prevention and refer to professional medical treatment to protect the noble life of humans and care services to enable the patients to lead a healthy life. Medical institutions that provide medical services should endeavor to provide care services that at least meet patients’ expectations. If a medical institution provides services that do not meet patients’ expectations as such, the patients will stop using the medical institution and use other medical institutions [2]. In the service area, when the relationship between the service provider and the customer is more repetitive and continues for a longer period of time, the service provider provides more beneficial services to the customer. For such a repetitive and lasting relationship to be maintained, a bond between the service provider and the customer must be formed. The bond can be reinforced with trust, intimacy, and rapport, and the rapport formed between the service provider and the customer can improve the quality of interaction [3, 4]. However, despite that rapport must be formed inevitably for the treatment of patients in medical services in which interactions are important, not many studies applied rapport to medical services. In particular, studies that empirically analyzed what the predisposing factors that lead to the formation of rapport are, and how the formation of rapport affects customer attitudes are very scarce [5, 6]. Therefore, this study aims to empirically verify the structural relationships among Rapport-Building Behaviors, customer attitudes, and customer loyalty in the domestic medical service environment in order to overcome the limitations of previous studies and seek ways to secure competitive advantages and realize sustainable growth of medical institutions. Through this study, the path through which Rapport-Building Behaviors of medical service providers mediate customer attitudes leading to customer loyalty will be empirically verified with a view to identifying the mechanism by which the provision of patient-centered services by medical institutions leads to organizational performance in the rapidly changing environment. To this end, this study will examine the concepts and dimensions of medical service providers Rapport-Building Behaviors, customer attitudes, and organizational loyalty through literature review, proposes a research model based on the foregoing, and empirically verify the model. Through this study, the competitive environment of the medical service industry will be understood and the strategic

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directivity for enhancing the competitiveness of domestic medical institutions will be presented based on the understanding.

2 Theoretical Background and Hypotheses 2.1 Medical Service Medical services are essential services that can be used and should be received by anybody and are an indispensable service product [7]. A service is an activity or benefit provided by one person to other subjects and is defined as a physical product that is intangible and cannot be owned [8]. As an intangible service product as such, medical services include human services such as interaction behaviors at service encounters in addition to physical services such as technical expertise and facilities of medical institutions that provide services. The interaction behaviors as such include control appearing in the service process, consideration for customers, dependence on service providers, temporary playful escape from reality, acknowledgment of dissatisfaction, apology for the formation of dissatisfaction, sociability for relationship formation, respect for the subjects of relationship, and obedience to service expertise [9]. The formation of interrelations at service encounters is the exchange of information about the service and a process of conversion that occurs between service providers and users [10]. In general, due to the inseparability of services, customers participate in the interacting process of service exchanges being implemented. Since customers are not users who simply receive only services, but are producers who create services jointly, the level of interaction between the service provider and the customer at the service encounter has a great effect on the service outcome. In addition, the responses of responding employees to the customer’s demands and requests at service encounters become the causes of customer satisfaction or dissatisfaction depending on the characteristics of the responses [11]. In particular, unlike other services, medical services correspond to services that strongly require relationships with customers [12]. An Affect Theory of Social Exchange argues that emotions are formed as a result of social exchange relations, that is, service exchange processes [13]. Since much stronger uncertainty is dormant in medical services compared to other services, the existing image of the provider formed in customers during the service exchange process can act as a catalyst that can adjust the threshold value of the formation of initial emotion, and acts as a halo effect in the moments of truth (MOT) in the encounter situation, but on the other hand, it can be reinforced or changed into the level of affirmation or negation of the image at any time [12].

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2.2 Rapport-Building Behaviors Rapport refers to a customer’s perception that he/she pleasantly interact with the service provider [14]. Rapport building refers to sharing emotions or information by increasing intimacy with expression, positive reactions, sympathetic attitudes, etc. during conversation including forming a relationship by showing that the relevant person is concentrating on the other person through diverse methods such as following body language such as postures and gestures, constant eye contact, or adjusting the breathing rate following the other person [15, 16]. Rapport can be built during one or more interactions between a service provider and a customer and is an indicator of the quality of the relationship [14, 17]. Rapport is a core factor to understand the interpersonal relationships of service quality and determine customer satisfaction and loyalty. Customers’ evaluation of encounters mediates the relationship between employees’ behavior and their attitudes (e.g. satisfaction) and behaviors (e.g. loyalty) [17–19]. Rapport-building behaviors to form rapport include the exhibition of attention to the other person, imitating behaviors, building common grounds, and courteous behavior. Uncommonly attentive behavior means the behavior perceived by the customer in situations where the service provider conducts unordinary behaviors or even more uncommon behaviors [20]. Common grounding behavior occurs when one person tries to discover a field similar to that of another person or a field of mutual interest, and means the effort of service employees to find common grounds with the customer by intentionally or unintentionally checking issues of mutual interest [21– 23]. Courteous behavior is respecting and courteously treating customers beginning with a mind to sincerely care for customers and can be said to be an important element in arousing emotional attachment in relationships with customers and forming positive relationships with customers [20, 24]. Connecting behavior is a clear and intentional behavior of service providers to improve their relationships with customers, through which they feel united relationships with customers or feel as if they became members of a group together with customers [20]. Information-sharing behavior is an interactive behavior with which information is shared or collected between service employers and customers in the situation of a service encounter [20], and refers to the behavior of service providers to obtain information from customers and share information with customers in order to better understand customers and manage customers’ needs efficiently [25].

2.3 Customer Attitude An attitude refers to a learned tendency to respond to an object, event, or stimulus consistently in a favorable or undesirable manner [26]. The reason why attitudes are important to service providers is that they help define and measure consumer

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attitudes to identify consumer markets and establish product development or promotion strategies. Previous studies on attitudes as such identified that there are positive relationships among customer attitudes, purchase intentions, and purchase behaviors [27, 28]. People measure attitudes toward a service or brand through what they believe and how they think about the service or brand. Attitudes have been used in diverse situations in diverse fields [28]. Attitudes are not structured in a single dimension [29], but consist of diverse dimensions such as cognition, emotions, values, and consciousness [30, 31]. A cognitive attitude refers to the degree to which an individual likes or dislikes an object based on the usefulness and function performed by the object [30, 32], based on the belief the customer has about the object [33]. That is, a cognitive attitude is an individual’s cognitive evaluation of an object and can said to be the degree to which the individual knows about the service [33, 34]. Affective attitudes mean the emotions and emotional experiences obtained by using or experiencing objects [30]. Affective attitudes refer to favorable or unfavorable attitudes toward, intimacy to, and interest in a product or service, and include positive or negative emotions. Therefore, affective attitudes can affect a wide range of evaluations, ranging from satisfaction with services, evaluation of others, and selected activities to past events [35].

2.4 Relationships Among Rapport-Building Behaviors, Customer Attitudes, and Customer Loyalty The rapport between the customer and the service provider is recognized as an important issue of service organizations because it greatly affects customers’ perception of service provision and service organizations. In the service area, when the relationship between the service provider and the customer is more repetitive and continues for a longer period of time, the service provider provides more beneficial services to the customer. For such a repetitive and lasting relationship to be maintained, a bond between the service provider and the customer must be formed. The bond can be reinforced with trust, intimacy, and rapport, and the rapport formed between the service provider and the customer can improve the quality of mutual interaction [14, 17, 36–38]. That is, it has been stated that when the level of the relationship between the service provider and the customer has been enhanced, the level of satisfaction with the service exchange is enhanced, and intimate personal bond and sharing of personal information improve the accuracy of customer’s expectation to make a close relationship between expectations and performance thereby improving customer satisfaction [39]. In previous studies, it has been identified that the rapport-building behaviors of service providers significantly affects customer attitudes such as customer satisfaction [14, 17, 19, 37, 39–41], customer trust [42], commitment [37], attitude [42],

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attachment [24], and relationship quality [43]. Therefore, expecting that the rapportbuilding behaviors of service providers will significantly affect customer attitudes in the medical service environment based on the previous studies mentioned above, this study established hypotheses as follows. Hypothesis 1 (H1). Rapport-building behaviors will have a positive effect on cognitive attitudes. Hypothesis 1 (H2). Rapport-building behaviors will have a positive effect on affective attitudes. In a service environment, customer emotions are a series of emotional responses experienced by customers in their consumption of products and services [44]. Since cognitive attitudes provide a base for affective attitudes, cognitive attitudes must be formed before affective attitudes develop [45]. Durbin et al. [46] argued that there is a tendency that emotions are formed, and attitudes and behaviors are generated by cognition. Johnson and Grayson [45] identified that in the context of service, cognitive trust positively affects emotional trust and behavioral intentions. Therefore, expecting that cognitive attitudes of customers will significantly affect affective attitudes in the medical service environment based on the previous studies mentioned above, this study established a hypothesis as follows. Hypothesis 3 (H3). Cognitive attitudes will have a positive effect on affective attitudes. Customer behaviors after using services are a concept that encompasses revisit intention, recommendation intention, word of mouth intention, etc., and refers to the behaviors of which consumers are voluntarily conscious, that is, subjective behaviors, after the consumers formed an attitude toward an object [16]. Pleasant emotions felt by customers in their interactions with the service provider positively affect the customers’ behavior and attitude. That is, the emotional responses experienced by customers in services occur due to interactions with diverse environments, and have important effects on future behaviors of customers [16, 27, 28, 45, 47–49]. Therefore, expecting that customer attitudes will significantly affect customer loyalty in the medical service environment based on the previous studies mentioned above, this study established hypotheses as follows. Hypothesis 4 (H4). Cognitive attitudes will have a positive effect on customer loyalty. Hypothesis 5 (H5). Affective attitudes will have a positive effect on customer loyalty.

3 Research Method In this study, in order to empirically analyze the proposed research model, data was collected by surveying customers with experience in using domestic medical service institutions, and statistically empirical analysis was conducted. The types of

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customers of medical institutions were classified by referring to previous studies, and a survey was conducted using online e-mail for about 2 months from January 2020 to February 2020. A total of 335 questionnaires were collected, data with missing values or unscrupulous responses were removed, and the final 307 cases were selected and analyzed as valid samples for this study. To ensure the content validity of the measurement tool, this study used the measurement items verified in the existing literature by revising and supplementing them according to the purpose of this study. This study has developed the measurement items for ‘rapport-building behaviors’, ‘customer attitudes, and ‘customer loyalty’ as measurement tools by referring to the study of [20, 24] and [16]; the study of [30, 35] and [28]; and the study of [50] and [51], and then measured them by the Likert Seven-point scale (from Not at all to Very Much). This study conducted a questionnaire survey on customers with experience in using domestic medical service in Korea, collected data, and tested the hypothesis of this study using a statistical analysis method. For the collected data, SPSS Statistics 24.0 and AMOS 24.0 were used as analysis tools to check the data, and structural equation modeling was applied as an analysis method to analyze the results and test hypotheses. The analysis of structural equation models adopted a two-step approach to estimate the measurement model first, and then estimate the structural model, to gradually verify the suitability of the measurement model and the structural model, and to analyze the results based on it.

4 Analysis and Results This study conducted confirmatory factor analysis to ensure the content validity of the measurement tool. As a result of confirmatory factor analysis of measurement model, χ2 = 659.582 (P = 0.000), χ2 /df = 2.434, RMSEA = 0.068, AGFI = 0.821, TLI = 0.921, CFI = 0.934, all indices suggested the measurement model used were fit. After verifying measurement model’s fitness, reliability and validity were analyzed. For reliability, construct reliability (C.R.) should appear above 0.7, and average variance extracted (AVE) should be above 0.5. Additionally, for validity, two latent variables’ AVE1 and AVE2 should bigger than squared value (∅2) of its correlation. As a result of analysis, reliability and validity were verified and the detailed results are presented in Tables 1 and 2. As a result of structural model’s fitness test, χ2 = 684.950 (P = 0.000), χ2 /df = 2.482 was above threshold 3, and RMSEA = 0.070 was below standard of 0.08. Moreover, TLI = 0.918, CFI = 0.931 was above recommended value of 0.9 and therefore, the structural model’ goodness of fit of the research model was verified. The research hypotheses were tested after the structural model’s fitness was confirmed. As a result, first, for relationship between Rapport-building behaviors and customer attitude, uncommonly attentive behaviors (β = 0.377, C.R. = 4.224, p = 0.000) has a positive effect on cognitive attitude, common grounding behaviors (β = −0.054, C.R. = −1.043, p = 0.297) did not have a significant effect on cognitive

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Table 1 Confirmatory factor analysis based on reliability Variable

Measurement items

Factor L.D

C.R

Crb Alpha

Uncommonly attentive behavior

Provide better than expected Services

0.789

0.835

0.877

High interest in me

0.794

Trying to find (know) my needs

0.810

Trying to provide the right service

0.813

Trying to find common interests for conversation

0.886

0.826

0.893

Understand customers through efforts to find common interests

0.913

Pursuing the same values as mine

0.782

Polite and feeling

0.855

0.857

0.880

Feeling kind

0.893 0.825

0.834

0.826

0.859

0.822

0.858

0.823

0.823

0.903

0.927

Common grounding behavior

Courteous behavior

Connecting behavior

Information-sharing behavior

Cognitive attitude

Affective attitude

Customer loyalty

Feeling warm emotions

0.785

Trying to get close to me through witty and humorous conversations

0.798

Trying to have a good relationship with a conversation first

0.839

Know what’s important to the customer

0.751

Provides good information on 0.872 health care Sharing expertise in healthcare

0.834

Providing useful information for the use of medical institutions

0.756

Useful for my health management

0.747

Worth more than what you paid

0.845

Feeling rewarding to use

0.869

Happy to use

0.699

The visit feels good

0.886

Visit is a pleasure

0.810

Continuous use

0.854

(continued)

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Table 1 (continued) Variable

Measurement items

Factor L.D

Continue to use if possible

0.826

Intention to recommend to people around you

0.894

Talking favorably to people around you

0.911

C.R

Crb Alpha

Table 2 Discriminant Validity Variable

1

1. Uncommonly attentive

0.558*

2

3

4

5

6

7

2. Common grounding

0.416

3. Courteous

0.446

0.195

0.667*

4. Connecting

0.479

0.484

0.445

0.611*

5. Informationsharing

0.314

0.198

0.244

0.319

0.614*

6. Cognitive attitude

0.421

0.196

0.268

0.304

0.411

0.607*

7. Affective attitude

0.531

0.300

0.442

0.511

0.289

0.475

0.611*

8. Customer loyalty

0.393

0.118

0.325

0.329

0.370

0.579

0.419

8

0.614*

0.699*

Note *Square root of AVE (Average Variance Extract)

attitude, courteous behaviors (β = = 0.047, C.R. = 0.702, p = 0.483) has a positive effect on cognitive attitude, connecting behaviors (β = 0.081, C.R. = 0.829, p = 0.407) has a positive effect on cognitive attitude and information-sharing behaviors (β = 0.339, C.R. = 5.862, p = 0.000) did not have a significant effect on cognitive attitude; therefore, H1-1 and H1-5 were supported and H1-2, H1-3 and H1-4 were not supported. In addition, uncommonly attentive behaviors (β = 0.225, C.R. = 2.687, p = 0.007) has a positive effect on affective attitude, common grounding behaviors (β = −0.006, C.R. = −0.125, p = 0.901) did not have a significant effect on affective attitude, courteous behaviors (β = 0.147, C.R. = 2.452, p = 0.014) did not have a significant effect on affective attitude, connecting behaviors (β = 0.275, C.R. = 3.096, p = 0.002) did not have a significant effect on affective attitude and information-sharing behaviors (β = −0.020, C.R. = −0.505, p = 0.614) has a positive effect on affective attitude; therefore, H1-1 and H1-5 were supported and H1-2, H1-3 and H1-4 were not supported. Second, for relationship between cognitive attitude and affective attitude, cognitive attitude (β = 0.293, C.R. = 3.851 p = 0.000) has a positive effect on affective attitude; therefore, H3 was supported. Third, for relationship between customer attitude and customer loyalty, cognitive attitude (β = 0.707, C.R. = 7.870, p = 0.000) has a positive effect on customer loyalty, affective

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attitude (β = 0.299, C.R. = 3.562, p = 0.000) has a positive effect on customer loyalty; therefore, H4 and H5 were supported.

5 Conclusions This study aimed to empirically verify the structural relationships among rapportbuilding Behaviors, customer attitudes, and customer loyalty in the domestic medical service environment in order to seek ways to secure competitive advantages and realize sustainable growth of domestic medical institutions in the rapidly changing business environment. As a result of the study, first, thoughtful considerate behaviors and informationsharing behaviors as rapport-building Behaviors were shown to have positive effects on cognitive attitudes. In addition, thoughtful considerate behaviors, courteous behaviors, and bonding behavior were shown to have positive effects on affective attitudes. Rapport improves relationship quality by enhancing the interpersonal relationship between the service provider and the customer by accompanying relationshiporiented emotions [17]. In particular, in the service industry, where customer’s evaluation of service experiences is determined by the interaction between the service provider and the customer, rapport-building Behaviors such as the expression of positive emotions and consensus-forming behaviors shown by service providers to customers are recognized as an important success factor because they promote the formation of positive attitudes and behavior intentions of customers [16, 18, 20]. Second, cognitive attitudes were shown to have positive effects on affective attitudes. It can be understood that cognitive attitudes provide a basis for affective attitudes, and that in service situations, cognitive attitudes have positive effects on affective attitudes and behavior intentions [45]. Third, cognitive attitudes and affective attitudes were shown to have positive effects on customer loyalty. Positive emotions felt by customers in a service environment can induce follow-up actions, such as making customers want to spend more time and stay the service environment [52]. Therefore, it can be said that the result of the study suggests that in the medical service environment too, the attitude of a customer according to service experiences is an important driver that determines the future behavior of the customer. Finally, it was identified that thoughtful consideration, bonding, and information sharing had positive effects on customer loyalty by mediating cognitive and affective attitudes. Therefore, through this study, the structural path through which the behavior to form rapport between service providers and customers leads to customer loyalty through customer attitudes in the medical service environment was identified. In recent years, in the rapidly changing medical service market environment, the role and importance of Rapport building between service providers and customers are increasing as an alternative for medical institutions to respond to these changes and to secure sustainable competitiveness, but empirical studies on medical services are insufficient. Therefore, through this study, the mechanism by which the behavior to form rapport between the service providers’ medical institutions and customers is

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linked to customer attitudes and loyalty in the rapidly changing medical service environment was identified. Therefore, the fact that this study suggested a strategic directivity for enhancing the competitiveness of domestic medical service institutions by understanding the rapidly changing competitive environment of the medical service industry and identifying the mechanism by which the provision of patient-centered services by medical institutions leads to the improvement of customer loyalty can be said to be the significance of this study.

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Design and Implementation of Open Source Based on IoT and Robot Manipulator Arm Training Equipment Sun-O Choi and Jongbae Kim

Abstract Hyper-connected, hyper-intelligent, hyper-realistic and hyperconvergence technologies; the core of 4th industrial revolution, are acting as innovative engine. Especially, The Internet of Things and robot technology have become main convergence players in STEAM education to foster creative computing thinking skills. Now, educational field purchases various sensors, breadboards, Arduino, Raspberry Pi and Robot Manipulator Arm respectively for practical training because there is no integrated training equipment in educational field. However, purchasing them simultaneously for one practical training is fairly inefficient. Therefore, in this study, author have developed integrated design, implementation of IoT and Robot Manipulator Arm by HW method, and applied Internet web technology in the SW method so that the training equipment can be controlled and managed from remote location. Also, it is designed to maintain the characteristics of Open Source, operating on microcomputers, extended HW systems, and Separate operating system. It has been evaluated for environment and performance test in relation to integrated power supply design by integrated wiring, the operation. Performance for managing open source-based controls has been verified. The advantage of training equipment in this study is user-friendly design implementation, which is expected to improve the quality of EduTech industry development and convergence education due to better efficiency. Keywords Open source · Internet of things · Robot manipulator arm · IoT platform · EduTech · Training equipment

S.-O. Choi Department of IT Policy and Management, Graduate School of Soongsil University, Seoul, Korea e-mail: [email protected] J. Kim (B) Startup Support Foundation, Soongsil University, Seoul, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_10

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1 Introduction The Internet of Things and robots are representative core technologies that bring about changes in our lives in the era of the 4th Industrial Revolution. The 4th Industrial Revolution is a real situation facing the whole world. In the short term, “artificial intelligence (AI)” will appear, and in the medium long term, it will mean changes in society as a whole through development speeds that are unparalleled by “science technology.” Artificial intelligence differs greatly from the past in that machines that can learn vast amounts of data in hyper connected societies come to perform intellectual tasks [1]. The core technologies of the 4th Industrial Revolution include big data, artificial intelligence, robots, the Internet of things, and virtual reality. Robotics technology will be a practical completion that combines the keywords of the 4th Industrial Revolution. Internet of Things (IoT) is a technology that satisfies the needs of users in various fields by giving IP to things around us and networking them to exchange information and intelligence [2]. Meanwhile, according to ITU-T recommendations for participation division in telecommunications standardization under the International Telecommunications Association, IoT is defined a global infrastructure for the information society, enabling advanced services by interconnecting physical and virtual, based on existing interoperable information and communication technologies [3]. Recently, IoT has been converged with AI to further develop an intelligent IoTbased robot, in order to prevent corona 19 virus, it is being applied and introduced in public hygiene and medical fields, logistics, delivery, and education fields, which are service robots [4]. Currently, the face-to-face group education system has collapsed in the world due to the influence of Corona 19. EduTech appeared with e-learning in the early 2000s based on Internet, web and video technology. EduTech is developing into an innovative [5] industry by converging with major technologies of the 4th industrial revolution such as VR·AR, artificial intelligence (AI), and big data. Besides, this became an opportunity to actively use EduTech as a means to overcome Corona 19 and normalize education. Edutech before Corona 19 was predicted to grow to a market size of 500 trillion by 2025. Currently, it is predicted that EduTech will be more than double with the recent re-proliferation of Corona 19 worldwide [6]. However, the problem of the existing EduTech training equipment is that it was developed based on the face-to-face collective education system before Corona. Besides, the weakness of IoT and Robot Manipulator Arm [7] educational equipment is that there appear some problems in scalability and compatibility. Because they were made only for specific educational purpose. Namely, in order to practice EduTech education, it means that you must be purchase various components of sensors, Breadboards, Arduino, raspberry pi, and robot arms individually [8, 9]. In addition, since the existing IoT and Robot Manipulator Arm directly connects each component module of the training device to the control device, a separate training computer is required. Thus, in this study, we designed and developed an open source-based IoT and Robot Manipulator Arm training device that does not depend on a specific product or company as a method

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to solve the problem of existing IoT and Robot Manipulator Arm training device. The development method of this training equipment focus on philosophy of pursuing open source hardware (OSHW) [10], enhancing openness and expandability. Besides, various sensors and component modules such as Arduino [11], raspberry pi [12], and Robot Arm are detachably manufactured, we implement and design the display-integrated IoT and Robot Manipulator Arm practical equipment that does not require a separate PC attached to the equipment. This study is to present the method of design and implementation of open source hardware (OSHW) based IoT and Robot Manipulator Arm. The whole composition of this paper is as follows. In sect. 2, we describe open source hardware and IoT, robots, etc. In sect. 3, we explain the results of the design and implementation process of the system. In sect. 4, we describe the results of the performance evaluation of the designed and implemented system. And finally in sect. 5, we summarize the conclusions.

2 Background In this study, based on the principle of open source hardware (OSHW), a function to integrate and control IoT and Robot Manipulator Arm was designed and implemented. Arduino and Raspberry pi. It is a representative open source hardware (OSHW). Open source hardware is hardware whose design is open to the public so that anyone can learn, modify, distribute, manufacture, and sell hardware based on the design. Arduino is an easy-to-use open-source electronics platform based on hardware and software. The Raspberry Pi can be equipped with a business card-sized OS that is smaller than a mobile phone and has the same functions and performance as the computers we use in real life, and various application SWs are installed to provide computing services. It is a small computer. Robot [13] is a mechanical device that can operate as a machine with a human-like appearance and function or as a single computer program, and automatically performs a complex series of tasks. Manipulator [14] is a mechanical device that provides similar movements to the human arm. Its main function is to provide a special robot’s movement so that the tool can do the required at the end of the arm. The manipulator is part of a robot that performs tasks physically. The point at which the manipulator bends, slides, and rotates is called the joint or position coordinate axis. The manipulator is run by using mechanical devices such as links, gears, actuators and feedback mechanisms. Representative robot operating systems are ROS [15] and MSRDS [16]. The development tool for the Robot Manipulator Arm in this study utilized the open source Arduino Sketch IDE SW. In this paper, the open cloud IoT platform method was applied, and the IoT and Robot Manipulator Arm system was designed and implemented based on the principle of open source hardware (OSHW). The IoT platform is a supporting software

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that connects everything to the IoT system [17]. IoT is a communication network things that can communicate, and the three elements of service that judge and control with collected information should be effectively combined [18]. Meanwhile, the role of IoT platform is to connect hardware such as sensors and devices, provide security and authentication for devices and users, handle heterogeneous hardware and software communication protocols, collect, visualize, and analyze data. And it has been applied to various fields ranging from integrated data web services to smart factories [19]. The IoT platform’s logical architecture can be expressed as the following figure [20] (Fig. 1). In this study developed IoT and Robot Manipulator Arm training device based on the principle of open source hardware (OSHW) from the viewpoint of user service of the IoT platform. In addition, the CoAP (Constrained Application Protocol) [21] and MQTT (Message Queuing Telemetry Transport) [22] protocols that support the Cloud IoT Platform environment are linked to the system. Recently, global IT companies around the world have been developing technology and continuing business to secure market leadership beyond the IoT Platform domain to Robot Platform. Until recently, IoT platform business models around the world have been growing at a very fast speed. Overseas IoT platform companies include Google, Amazon, Microsoft, Facebook, and Alibaba, and Korea is doing IoT platform service business at Samsung, LG, kt, sk, naver, and kakao. In this study, the design and implementation of the Robot Manipulator Arm was integrated with IoT training equipment as an Open H/W platform design method. The data verification and remote integrated management function development system adopted KT’s GIGA IoT Makers Platform [23]. Fig. 1 IoT reference architecture

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3 Design and Implementation of the IoT Platform Education System Based on Open Source Hardware 3.1 System Structure The block structure diagram of the open source hardware-based IoT and Robot Manipulator Arm practical equipment system designed and developed in this study is shown in Fig. 2. The direction of system design in this study is implemented by the development of open source hardware-based electronic, mechanical, and instrument hardware and the design method of open source software-based IoT and Robot Manipulator Arm training equipment. The ultimate goal of the design of the system structure in this study is to design and develop practical equipment that supports education of the convergent creative STEAM (Science, Technology, Engineering, Arts, Mathematics) [24] incorporating EduTech-based face-to-face/non-face features. Most of all, in terms of H/W, the Main PCB is Board designed so that a separate training PC is not required in consideration of the conditions of the training site. First, the system is integrated so that not only sensors but also various IoT device modules can be practiced in one equipment. Second, external devices such as Robot Manipulator Arm are fixed and connected.

Fig. 2 System block structure diagram

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Besides, the central processing unit, which is wirelessly connected to external devices, is designed to facilitate the practice of external devices such as the Robot Manipulator Arm as well as the sensor module. Next, in terms of S/W, it is developed using open IoT platforms such as Raspberry Pi and Linux OS as well as Arduino. The software training is designed so that you can practice various IoT and Robot Manipulator application programs in one device by selecting the corresponding sensor and device module. For this purpose, this training device functionally applies the IoT platform API so that each device (sensor module, device, module, communication module) can be controlled in a cloud environment.

3.2 Hardware Design Figure 3 is a schematic diagram of some parts modules and the entire HW MAIN PCB CAD [24] of the All-In-One IoT and Robot Manipulator Arm training equipment that does not require a separate PC. In this study, it designed as a display-integrated hardware system so as not to require a separate training PC. The MAIN PCB was designed to mount a variety of detachable and attachment sensor modules, detachable and attachment Robot Manipulator Arm and device modules. Besides, the Raspberry Pi designed with AC/DC on chip (12 bit 8 port) so that it can be connected to Arduino data conversion. A breadboard mounted in a form capable of training electronic circuits, and a PLC [25] device is designed to enable practical training at a smart factory [26].

Fig. 3 IoT and Robot Manipulator Arm HW MAIN PCB schematic diagram

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In addition to this, it was designed to secure storage space for not only wireless keyboards and mice, but also other parts boxes. Besides, while providing a layout that allows wired/wireless network connection, the power supply was designed to provide AC, 220 V, and 50 Hz in consideration of minimizing external cables.

3.3 Software Design Figure 4 is a conceptual diagram of the cloud-based IoT and Robot Manipulator Arm training device that controls various sensors, and device modules of the training device designed in this research in real-time and enables data confirmation. This system was designed and implemented on the software side as a training device so that the IoT communication SW and Robot Manipulator Arm could operate the Web client application SW. According, the Gateway Server designed by installing it on the training equipment and applying the Raspberry pi module, and the open IoT and Robot Manipulator Arm training equipment designed to interface with the Internet communication network and the cloud via the Web. Through this, IoT and Robot Manipulator Arm training equipment can be controlled and managed from a remote location with computers and smart phones at any time. Fig. 4 IoT Educational System SW concept diagram

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Table 1 IoT education system device registration Device information registration Basic information (required) Device name

Enter the name of the device to register according to the naming convention

Device ID

ID is automatically generated

Device password

Used for authentication when interlocking devices with IoTMakers

protocol classification Choosing platform standards Protocol type

When selecting the platform standard in protocol classification—Select the primary protocol type (kt standard interface) and the secondary protocol type (TCP/HTTP/MQTTT)

3.4 Implementation of Raspberry Pi Gateway The gateway server (Raspberry pi GateWay Server) of this study designed and implemented as a Raspberry Pi application program to transmit data collected from various IoT sensors, device modules, Arduino, and PLC installed in the training equipment to the server system through the Internet. By doing this, the training equipment is implemented for remote control management through the Internet anytime, anywhere in interface with KT’s open cloud IoT Platform (IoTMakers). Table 1 explains that the training device developed in this study is the configuration item for the first registering IoTMakers, a cloud-based open IoT platform of kt, and inputting the communication protocol with the device. LED on/off control used the Open API system of kt’s IoT Makers, which is an open source IoT platform. In addition, Table 2 shows the environment setting items that register and input the LED device information to the practice equipment and kt’s open IoT Platform Cloud IoT Makers and Connecting interface functions, and to the gateway for web-based remote data control management and control. Figure 5 shows the screen where the IoT and Robot Manipulator Arm function actually operates by programming the Arduino IDE Sketch SW using the training equipment developed in this study.

4 Testing and Evaluation In this study, the Open Source-based IoT and Robot Manipulator Arm system designed and developed utilizes kt’s open GIGA IoT Makers Platform. The remote control management driving experiment of the LED module implements a WEB environment using kt’s open GIGA IoT Makers and confirms that it is operating normally as shown in Fig. 6.

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Table 2 IoT education system gateway registration Register gateway device information Basic information (required) Device name

Enter the name of the device to register according to the naming convention

Device ID

ID is automatically generated

Device password

Used for authentication when interlocking devices with IoTMakers

User model and manufacturer name

Customizing the HW of the gateway and the gateway

Gateway connection ID

Create a gateway ID and enter it directly as required.

Protocol classification

Choosing platform standards

Protocol type

When selecting the platform standard in protocol classification—Select the primary protocol type (kt standard interface) and the secondary protocol type (TCP/HTTP/MQTTT)

Fig. 5 System device execution screen Fig. 6 LED on/off control UI

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Table 3 System performance test Development System Performance Test and Evaluation Item Key Performance Spec Hard Ware

Environment Test

Unit

Development Goals

Pre-development level

High/low temperature storage test

Time

24

16

Temperature-humidity immunity test

Time

24

16

Normal temperature tolerance test

Time

24

24

Vibration test Soft Ware

Time

10

24

Open Source Portability

Y/N

Y

N

Open Source Functionality

Y/N

Y

N

Communications Network Compatibility

Y/N

Y

Y

IOT Platform Remote Integrated Control Y/N

Y

N

DOF Robot Manipulator Performance

Y/N

Y

Y

System Communication Protocol Performance

Y/N

Y

Y

Besides, the performance test and evaluation results of the Open Source based IoT and Robot Manipulator Arm system designed and developed in this study are shown in Table 3. The system performance evaluation type is divided into H/W and S/W using the laboratory evaluation method. The environmental evaluation items of the hardware are: First, how long has the system been exposed in high temperature, low temperature, humidity environmental variables, and second, we observe that the system operates normally for a long time without malfunction or system defect due to environmental variable factors. And evaluated the items. Also, do in software environment evaluations such as open source-based portability, functionality, compatibility, and remote integrated control first, does the open source software work properly on this system? Second, are open source hardware parts, sensors, device modules, etc. implanted without defects in various open source application software, so that the functions work normally and are compatible with the system without conflict? Thirdly, is the degree of freedom in the evaluation of the item of operation the DOF and the robot manipulator in a normal driving state? Fourth, it was confirmed and evaluated that this system of the open source system was operated normally using GIGA IoT Makers, an IoT platform of kt.

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5 Conclusion The development of IoT and Robot technology is becoming the centerpiece of the 4th industrial revolution around the world. In addition, the open source platform, fused with various IoT and Robot technologies, plays an important role as a driving force for the 4th Industrial Revolution. In this study, we develop an integrated training device system by utilizing the open source base IoT and Robot Manipulator Arm. The recent spread of Covid19 has created a situation in which the collective face-to-face education system in the whole world collapses. The emerging industry to addressing this phenomenon is the online-based untact education-related EduTech industry. The EduTech industry is playing a catalyst role in effectively implementing STEAM education, known as creative convergence education. In this study, we designed and implement training equipment to enable faceto-face and non-face-to-face training for both sustainability of education in even the Covid19 environment. The practical equipment of this study adopts an open source platform and uses EduTech technology for face-to-face and non-face-to-face training of IoT and Robot Manipulator Arm education. In particular, it is developed so that non-face-to-face training can be conducted by checking the data of training equipment installed in remote areas using the Internet communication network and controlling it with a computer or smartphone. The open source-based IoT and Robot Manipulator Arm training equipment system designed and developed in this study pursues advantages such as openness, compatibility, scalability, and availability. However, in the real situation of the educational field, IoT and Robot Manipulator Arm can be used in various majors, but there is a limitation to application into all the scenarios of specialized training and differentiated in curriculum practical training. This training device include all the requirements to break down the barrier of inter-major and inter-subject, and to rear creative integrated human resource. Therefore, in the future, based on this study, it is expected that further open source-based expansion modules, expansion devices, applications, etc. that meet the educational demands of various fields will be developed. The meaningfulness of this study is that it can respond to various educational needs. Beyond this study, it is expected that it will be applied to fields such as smart factories, smart farms, and smart buildings that utilize IoT and Robot into the industrial world from the educational world, and will become a highly scalable expandable and technology.

References 1. Kim, S.-W., Lee, M.-S.: Understand the current status of teaching and learning informatization and develop indicators in the 4th industrial revolution. J. Digit. Converg. 18(4), 67–74 (2020) 2. Kim, J.-W.: A smart home prototype implementation using raspberry Pi. J.Korea Inst. Sci. 10(10), 1139–1144 (2019)

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3. ITU-T Recommendations, (2012). Overview of the Internet of things, Rec.ITU-T.2060, p. 1, https://www.itu.int/ITU-T/recommendations/rec.aspx?rec=y.2060. Accessed 28 Oct 2020 4. Nipa (2020). Issue Report No. 2020-5, Intelligent IoT-based Service Robot Use Cases and Implications. https://www.nipa.kr/main/selectBbsNttView.do?key=116&bbsNo=11&nttNo= 7594&bbsTy=bbs. Accessed 28 Oct 2020 5. Nipa (2019). Issue Report No. 2029-34, Trends in Artificial Intelligence-Based Edu-Tech Companies and Services. https://www.nipa.kr/main/selectBbsNttView.do?key=116&bbsNo= 11&nttNo=7013&bbsTy=bbs. Accessed 28 Oct 2020 6. Holon, I.Q.: EdTech is growing at 16.3% and will grow 2.5x from 2019 to 2025, reaching $404B in total global expenditure. https://www.holoniq.com/. Accessed 28 Oct 2020 7. Moran, M.E.: Evolution of robotic arms. J. Robot. Surg 2(9), 103–111 (2007) 8. Plaza, P., Sancristobal, E., Fernandez, G., Castro, M., Perez, C.: Collaborative robotic educational tool based on programmable logic and Arduino. In: Proceedings of 2016 Technologies Applied to Electronics Teaching, Seville, Spain vol. 8, pp. 1–8 (2016) 9. Novak, M., Kalova, J., Pech, J.: Use of the arduino platform in teaching programming. In: Proceedings of 2018 IV International Conference on Information Technologies in Engineering Education, Moscow, Russia, vol. 4, pp. 1–4 (2018) 10. Open Source Hardware Association, Principles of Open Source Hardware (OSHW) 1.0”. May, 30, 2020, form https://www.oshwa.org/definition/korean/. Accessed 28 Oct 2020 11. Arduino, What is Arduino. https://www.arduino.cc/en/Guide/Introduction. Accessed 28 Oct 2020 12. The Raspberry Pi Foundation, Raspberry pi “Ourmission”. Accessed 28 Oct 2020 13. Wikipedia.: A robot is a machine. https://en.wikipedia.org/wiki/Robot. Accessed 28 Oct 2020 14. Wikipedia.: Manipulator (device). https://en.wikipedia.org/wiki/Manipulator_(device). Accessed 28 Oct 2020 15. ROS.: The Robot Operating System (ROS). https://www.ros.org/about-ros/. Accessed 28 Oct 2020 16. Microsoft.: Microsoft Robotics Developer Studio. https://www.microsoft.com/en-us/dow nload/details.aspx?id=29081. Accessed 28 Oct 2020 17. Korea IDG From ITWorld Fredric Paul, Why the definition of an IoT platform is so confusing. http://www.itworld.co.kr/news. Accessed 28 Oct 2020 18. Calum McClelland, IoT For All. What is an IoT Platform? https://www.iotforall.com/what-isan-iot-platform. Accessed 28 Oct 2020 19. SAS® insight, Definition and Importance of Internet of Things (IoT) IoT. https://www.sas. com/ko_kr/insights/big-data/internet-of-things.html. Accessed 28 Oct 2020 20. Guth, J., Breitenbücher, U., Falkenthal, M., Leymann, F., Reinfurt, L.: Comparison of IoT Platform Architectures: A Field Study based on a Reference Architecture. In: Proceedings of the International Conference on Cloudification of the Internet of Things (CIoT). IEEE (2016) 21. CoAP.: The Constrained Application Protocol (CoAP). https://coap.technology/.Accessed. 28 Oct 2020 22. MQTT.: MQTT: The Standard for IoT Messaging. http://mqtt.org/.Accessed. 28 Oct 2020 23. kt,GIGAIoTMakers. https://iotmakers.kt.com/openp/index.html#/introduce.Accessed. Accessed 28 Oct 2020 24. KOFAC.: Concept and Definition of STEAM. https://steam.kofac.re.kr/?page_id=11269. Accessed 28 Oct 2020 25. Wikipedia.: Programmable logic controller. https://en.wikipedia.org/wiki/Programmable_ logic_controller. Accessed 30 Oct 2020 26. Kim, H.-G.: A study of the effect of smart factory quality on efficiency and utilization. Korean Corpor. Manage. Rev. 27(4), 145–161 (2020)

A Study on Improved Authentication Technique in Cloud Computing Hwan-Seok Yang

Abstract Cloud computing has emerged as a new field of IT industry as it has made many changes in the computing environment by acceleration of communications environment and the rapid spread of mobile devices recently. Many IT companies are providing various types of cloud services and have switched a variety of services that the companies have into the cloud service. However, Cloud computing has a slow growth rate and technology development related fields are being behind due to several security vulnerabilities that cloud computing has. In this paper, we analyze the vulnerabilities of the security element that cloud computing has and propose an authentication technique suited to cloud computing. PDM performs the self-authentication of many computing devices after receiving authentication from the authentication agency in the proposed authentication technique. The safety and efficiency of the device authentication can be increased in this way. Keywords Cloud computing · 2-tier authentication · Key exchange · Mobile device authentication

1 Introduction Cloud computing is a technology of the future competitive advantage. The IT environment can be changed greatly. It has been adapted in many areas. In particular, it has brought many changes in the environment of the companies because there is no need to build an infrastructure and a variety of environments can be received. Cloud computing having many advantages like this is not vitalized. This is because of anxiety about reliability of cloud service and security and confidentiality of their data. The users for receiving cloud services can connect via a wired or wireless network using a variety of IT devices such as smart phones and laptops. Recently, the technology of mobile cloud has been spread of service and offered the convenience and scalability of the cloud. It also represents non-specific characteristics of H.-S. Yang (B) Department of Information Security Engineering, Joongbu University, Seoul, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_11

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the device platform and the operating system. In particular, the normal service in the cloud is impossible when it has problems in one resource of the wireless networks and cloud services. It is because a blended threat may occur in mobile cloud services using cloud resources. The user has been conveniently by multiplexes and the scope of attacks using these vulnerabilities becomes also widened. The authentication technique having high security for devices used to cloud computing should be provided in order to provide secure cloud service to security [1–3]. In this paper, we proposed and efficient and secure 2-tier authentication method on a large of mobile devices used by users in the cloud environment. The proposed method consists of the 2 steps. The first step is that the CA (Certificate Authentication) authenticates CSS (Cloud Service Server) and AMS (Authentication Management Server). The CA receives the information on servers providing service and issues certificate. And it transmits the certificate public key authenticating the server to PMD (Personal Main Device). The users will use the certificate public key stored to PMD when verifying the servers. The second step is to register the user mobile device. It enables mobile device uses Cloud services easily and securely by registering mobile devices which the user owns to AMS. For this purpose, the PMD requests registration using the identification value of the mobile device and the public key of the CA. This process enhances the security of cloud services and improves efficiency. This paper is organized as follows. We show the characteristics and security threats of cloud computing in Sect. 2. The 2-tier authentication method proposed in this paper is describes in Sect. 3. We confirmed the excellent performance of the proposed method in Sect. 4. Finally, Sect. 5 concludes this paper.

2 Cloud Computing 2.1 Cloud Computing Models Cloud computing uses computing resources such as software, storage and network as needed. It has the feature of high scalability and virtualized resource. The high scalability is to allow this process fast and flexibly when traffic or the number of user is increased rapidly. The virtualized resource is that the resource on different position is integrated physically through the virtualization and is provided to the user on the cloud computing environment. The user cannot access or manage the physical properties of the resource in the virtualization environment and can access to resource only in a logical form and manage. Table 1 shows the characteristics according to the physical computing resource and service-oriented architecture. These cloud computing is a type of the public infra paying the cost as using service. It is that anyone with a variety of terminals can use service by connecting to service provider using a wired or wireless network and there is no limit to object using the service [4].

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Table 1 The characteristic of virtualization Division

Characteristic

Application virtualization Many applications are easily managed and available on-demand Desktop virtualization

The heterogeneous other desktop can be owned virtually and the separation in the workspace become possible

Server virtualization

The server resource utilization can be increased and many servers can be consolidated into virtual servers

Storage virtualization

Service realization by allocating a minimum of virtual space using thin provisioning

Network virtualization

Technology that simplifies network configuration by providing close relationship physically between the plurality of physical computing resources to logical relationship

Cloud computing model is classified as public cloud, private, and hybrid cloud. Public cloud operates based on internet for multiple users and data center exists outside of the company using the cloud. Resources provided in this cloud are shared and used by many companies. The virtual computing area of each company is assigned and this area is based on physical resource in common. Private cloud is constructed for use of only a company and utilizes the same technology with public cloud. It is often constructed when a company maximizes the use of computing resources and reacts more sensitively than is possible in a traditional IT model for the need of the company. Hybrid cloud is a mix of public cloud and private cloud and is made by the special needs of customer. In particular, service and data which need security in business is placed under the control of the private cloud and less important information or treatment use public cloud. Cloud computing must consider much more Security element than existing other computing. In particular, information disclosure by hacking or interfere of service use may occur because every cloud computing depend on internet connection [5]. Security element considered in cloud computing environment is different by cloud service property. Cloud computing can be divided to infra service (IaaS, Infrastructure as a Service), the platform service (PaaS, Software as a Service), and software service (SaaS, Software as a Service) by the type of services provided. Figure 1 shows the structure of the cloud computing service model.

2.1.1

Infra Service

In order to infra service, IDC (Internet Data Center) space and server or storage, and network equipment in the space are needed. Implementation technology of infra service is virtualization technology and dynamic resource allocation technology. The security elements to be managed in this service model are a virtual machine and user management. Thorough preparation for this is required because hacking attack of different virtual machine through spread of malicious code which attacks the virtual machine and virtual network attack can occur. Personal information of cloud service

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Fig. 1 The structure of cloud service model

user could be leaked by a malicious employee and the illegal use of the private cloud can be done. Therefore, a strict management security system for the user must be established.

2.1.2

Platform Service

Platform service is a service which can borrow platform for developing in the web and can be divided into platform as a basic component and service greatly. Platform which user selects must be accessed anytime and anywhere and the insertion or integration of multiple applications built in the same platform or other platform must be provided. So, in the process which user develops application on platform, process of cross site scripting, SQL injection, denial of service and traffic encryption must be applied. Data is encrypted when cloud service user and data are transmitted. Therefore, the way which is ensured safety should be considered while system performance does not degrade as data transmission delay and decryption by encryption.

2.1.3

Software Service

Software service as an advanced form can provide the software which suits to user’s environment if existing ASP (Application Service Provider) is a form of incomplete service that it provides software which has standardized interface and capability and user which uses it adjusts to the environment and uses. Company which uses a software service should be responsible for most of the security elements in order to reliability from service provider because it attempts to rely on the software service provider from generation to disposal of document, artifacts, and business elements. Software provided in the software service must be implemented to prevent the vulnerability not to be exposed to attack. Reliability for service provider should

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be guaranteed for sure. It should be prepared to attacks such as SYN flooding and DDoS through real time monitoring of network traffic because service is provided through internet. User authentication system which accesses to service should be enhanced in order to monitor thoroughly attack using vulnerability of security which each terminal has and block the access of unauthorized user because cloud allows the connection of the various terminals.

3 Cloud-Based User Authentication Technique 3.1 2-Tier User Authentication Technique 3.1.1

Authentication Model

In this paper, we proposed a user authentication method using the structural characteristics of cloud computing for SaaS in the public cloud computing services. The proposed authentication is based on the environment of PMD for storing the certificate, CSS for providing the cloud service, and AMS for managing the authentication. And the user should be authenticated. This authentication method provides secure and efficient methods for the various computing devices that the user owns. In Fig. 2, the CSS and the AMS belong to the virtualization domain. The first user performs service entity authentication with CSS 1:1. The proposed method should satisfy the following conditions. First, the user should have a mobile terminal for using the mobile communication. Second, the user and the CSS should share the encryption algorithm to use beforehand. Third, the inside of the virtualization domain should provide secure security. Fig. 2 The proposed authentication model

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Fig. 3 The process of the server authentication

3.1.2

Authentication Process (Server Authentication)

The authentication is performed whether the server providing the service to the user is safe. For this, a secure channel before a service is provided is required. The trusted CA generates a certificate which can verify validity at the time of requesting a service. The CSS or the AMS is performed a pre-authentication through offline or a separate secure channel. And then, the user verifies the cloud authentication server and the AMS using the public key of the CA. step 1: The CSS submits its information and public key of the server to the CA for issuing the certificate. step 2: The CA generates a certificate after a transmitted information is verified. step 3: A certificate is issued to the server. step 4: The user is issued a public key to determine the validity of the servers. Figure 3 shows the process of the server authentication. The user accesses to the CSS to receive the service. The CSS transmits the certificate issued by the CA to the user. The user decrypts the certificate received from the CSS to the public key issued by the CA. The user verifies the server by confirming the information of the CSS. The symmetric key provides the encryption channel between the user and the CSS by using the public key of the CA. This symmetric key is transmitted to the service server after encrypted. The CSS decrypts the symmetric key received from the user to the private key. And then, it constitutes a secure channel encrypted with a symmetric key (Fig. 4).

3.1.3

Authentication Process (Mobile Device Authentication)

The mobile device authentication step is to use many mobile devices that the user owns safely and efficiently. In order to receive a service using a mobile device, the mobile device must be authenticated first.

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Fig. 4 The procedure of PMD access

The identification value requests in the PMD when the mobile device request a service. The mobile device registration is requested to the authentication management server using this the identification value and the public key of the CA. The AMS accepts the request after the requested information is checked. Many mobile devices which the user owns are authenticated through this process. Figure 5 shows the mobile device authentication procedure. Fig. 5 The procedure of mobile device authentication

138 Table 2 Experimental parameters

H.-S. Yang Parameter

Value

OS

Windows 10 Enterprise 64bit

CPU

Intel(r) Core(TM) [email protected] GHz

RAM

32 GB

Virtualization S/W

VMware Workstation 8

4 Performance Evaluation 4.1 Experimental Environment In this section, we evaluated the efficiency and security of the proposed authentication method. We compared and analyzed the authentication processing time with the PKI (Public Key Infrastructure) method in order to measure the efficiency of the authentication method. The user’s mobile device registration tests 300 times. The experiment repeated five times. Table 2 shows the parameters used in the experiment.

4.2 Performance Analysis In order to evaluate the performance on efficiency of the proposed authentication method, we compared with PKI-based authentication method. The standard for the performance evaluation was the processing time of user’s mobile device registration. Figure 6 shows the result of measuring the registration processing time on mobile devices. This result means how much this method can support authentication efficiently as the mobile device which the user owns increases. The proposed method Fig. 6 Time measurement results for device registration

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processes the device registration using the public key received from the CA. Therefore, the method showed excellent performance because there is no additional round establishing the existing PKI-based public key and session key. The security verification of the proposed authentication method is classified largely to identity confirmation and confidentiality of messages. The user confirms the identity of the server by checking the certificate because the service server authentication issues by the trusted CA. There are methods such as RSA, DSS, X.509 which are the main algorithms used for authentication. However, the terminal may be attacked directly or the falsification on operating system and network traffic may occur. In the proposed method, authentication on service server or AMS is performed. And the user progresses the authentication of the server again. So security is increased. The security of the message between the user and the cloud service server establishes the session key between the two entities and encrypts. This is encrypted to the public key of server included in the certificate to transmit this session key securely and one session key is used only once. It is more secure. Therefore, the proposed method has a characteristic which provides safe security communication.

5 Conclusion Cloud computing may seem similar to service form of the existing ASP. But it has a difference that user based on the virtualization can configure one’s environment. The application areas of this cloud computing has also widened and the business environment even of companies is changing according to provide the service satisfying the need of users by using IT resources efficiently. But the biggest reason that cloud computing is not vitalized more is safety of service and security of data. The user authentication and systematic rights management is essential in the cloud environment that data of many users are mixed. So far, the systematic plan to perform authentication key management has not proposed in the IT environment that the individual uses multiple computing devices. The 2-tier authentication method proposed in this paper consists of two steps. One is the step authenticating servers performing cloud service and authentication management. The other is the step registering several mobile devices owned by the user. The user using the cloud service uses a public key of a certificate received from a CA. Identification value of a device and a public key of a certificate is used for registration of mobile device. This proposed in this paper. The proposed method is compared with the PKI method and the excellent result is confirmed by experiments. Acknowledgements This is paper was supported by Joongbu University Research and Development Fund, in 2020.

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References 1. Bhosle, P.I., Kasurkar, S.A.: Trust in cloud computing. Int. J. Adva. Res. Comput. Eng. Technol. (IJARCET) 2(4), 1541–1548 (2013) 2. Mahajan, P., Setty, S., Lee, S., Clement, A., Alvisi, L., Dahlin, M., Walfish, M.: Depot: cloud storage with minimal trust. ACM Trans. Comput. Syst. 29(4), 12 (2011) 3. Armbrust, M., et al.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010) 4. Jansen, W., Grance, T.: Guidelines on Security and Privacy in Public Cloud Computing, pp. 800– 144. Technical Report Special Publication, NIST (2011) 5. Jeong, M.S., Lee, D.B.: Analysis of security threats and security requirements in smartwork. J. Korea Inst. Inform. Secur. Cryptol. 23(3), 30–37 (2011) 6. Shaikh, R., Sasikumar, M.: Trust framework for calculating security strength of a cloud service. IEEE Int. Conf. Commun. Inform. Comput. Technol. (ICCICT) (2012) 7. Banirostam, H., Hedayati, A., Khadem Zadeh, A., Shamsinezhad, E.: A trust based approach for increasing security in cloud computing infrastructure. In: 15th International Conference on Computer Modelling and Simulation (2013) 8. Ahmed, M., Xiang, Y., Ali, S.: Above the trust and security in cloud computing: a notion towards innovation. In: IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, Australia (2010) 9. Li, T., Lin, C., Ni, Y.: Evaluation of user behavior trust in cloud computing. In: International Conference on Computer Application and System Modeling-ICCASM, China (2010)

A Study on Drum Transcription Using Machine Learning Focused on Discrimination of Acoustic Drum and Electronic Drum Sound Sang Wook Lee, Jae Hyuk Heo, Sung Taek Lee, and Gwang Yong Gim

Abstract A drum is a polyphonic instrument composed of several instruments such as Snare, Symbol, Tom, and Hi-hat. Each constituent instrument has a different sound (pitch, tone), and it sounds differ depending on the playing method, tuning condition, drum manufacturer, and whether it is an acoustic or electronic drum. In addition, it is an instrument in which frequencies are overlapped between the instruments constituting the drum. For this reason, in order to contribute to the improvement of the drum sound recognition rate that previous researches were not high, this paper attempted to discriminate the sound of electronic and acoustic drums, which had not been studied. This paper used the IDMT-SMT-Drums data set (https://www.idmt. fraunhofer.de/en/business_units/m2d/smt/drums.html), which was already used in several previous studies as the learning and test data of this study, and 70% of the drum sound WAV files of the data set were used for learning and 30% used for testing. For the analysis, a Convolutional Neural Network (CNN) was used, and WAV files, which are drum sound files, were converted into spectrogram files (Image files) using Mel Spectrogram technology for input of CNN. As a result of this study, the recognition rate was 78–92% for each type of hyper-parameter application. It was found that the pixel size of the input spectrogram and the number of CNN filters affect the recognition rate. In future research, it is necessary to study the improvement of recognition rate by applying various hyper-parameters by securing additional learning and test data. S. W. Lee · J. H. Heo Department of IT Policy and Management, Graduate School, Soongsil University, Seoul, Korea e-mail: [email protected] J. H. Heo e-mail: [email protected] S. T. Lee Department of Computer Science, Yongin University, Gyeonggi-Do, Korea e-mail: [email protected] G. Y. Gim (B) Department of Business Administration, Soongsil University, Seoul, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_12

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Keywords Music information · Retrieval · Drum · Transcription · Machine learning

1 Introduction Today, in the era of the 4th industrial revolution, computers are using artificial intelligence technologies to become a world where computer can understand text, identify pictures, hear and understand sounds, and make voices like humans. Among the various fields of artificial intelligence research, music information retrieval studies such as recognition of musical genres when computers listen to music, test for similarity in melody, discrimination of musical instruments, and creation of music scores have been conducted. Melody similarity check services such as SHAZAM1 have already been commercialized and have already achieved great corporate value. Among various research fields of music recognition, this study is a study on the recognition of drum sounds. In detail, it has focused on research to discriminate the sound of electronic and acoustic drums. The background of this study, which was conducted for the purpose of increasing the recognition rate of drum sounds, is that although electronic drums are widely used in recent music, the existing studies to increase the drum sound recognition rate have not been addressed. A recognition model that distinguishes the acoustic and electronic drum sounds as the result of this study was used. In the future research, it is determined whether it is an acoustic drum sound or an electronic drum sound, and then the recognition model learned with the acoustic drum sound is used for songs played on the acoustic drum, and the recognition model learned with the electronic drum is used for songs played on the electronic drum. By using it, the research plans to conduct a study to see if it improves the drum sound recognition rate than existing studies. Subsequently, as the recognition rate of the drum sound increases, it may become easier to grasp the reference point for grasping the beat and speed when recognizing the sound of several different instruments in polyphonic music where several other instruments and vocals are played at the same time. In this respect, it is judged that it may be helpful in the future research on the perception of polyphonic music.

1 https://www.shazam.com/ko.

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Table 1 MIR research fields (https://musicinformationretrieval.com/why_mir.html) Research field

Research detail

Fingerprinting

The field of compressing sound source files

Cover song detection

The field of recognizing whether it is an original song or a cover song

Genre recognition

A field that recognizes genres of music such as rock, jazz, and blues

Transcription

A field that recognizes music and uses it for editing, arrangement, etc.

Symbolic melodic similarity

A field that searches for the similarity of melody in music

Source separation

Separating the sound of each instrument from polyphonic music

Instrument recognition

The field of recognizing the sound of a specific instrument

Score alignment

The field of generating sheet music by recognizing music

Key detection

A field to recognize the pitch of music

Pitch tracking

A field that recognizes and utilizes the relative sound difference felt by a person

Tempo tracking

A field that recognizes and utilizes the Beat Per Minutes (BPM) of music

Beat tracking

A field that recognizes and uses the beat of music

Query by humming

The field of finding music through the sound of people’s humming

2 Research Background 2.1 Music Information Retrieval(MIR) Research Music Information Retrieval (MIR), a research field in which a computer recognizes music, uses signal processing and machine learning as inputs from digital media sound source files such as MP3 and WAV or real-time audio stream data. The field is to implement functions such as recognition of musical genres, test of melody similarity, discrimination of musical instruments, and creation of music scores. The detailed research fields are shown in Table 1. Most of the MIR research fields currently in commercial service are cognitive fields through pattern recognition using Machine Learning. However, most of the commercialized services are monophonic music played only with a single instrument or vocal (Table 2). Although there is a service for polyphonic music2 in which several instruments are played at the same time, it is the level of recognizing only the vocals or the melody of the instruments that make up the Predominant Play among the several instruments

2 https://www.lunaverus.com/.

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Table 2 Major commercialized services by MIR research field Application field Service detail Transcription

Shazama Service A service that finds the title of a song when a person hums or listens to music • Main implementation technology: SVM, CNN • Acquired by Apple at the end of 2018

Score alignment AnthemScoreb Service By inputting an MP3 file, etc., the melody flow of the main musical instrument or vocal is created as a score • Main implementation technology: STFT, CNN Spectrogram Transcription

Songs2see Servicec Educational game with scores when played in front of the computer microphone • Main implementation technology: Onset Detection Regroover Serviced A program that creates a drum loop again after separating the drum recording file for each instrument through manual operation of each drum composition. • Main implementation technology: Onset Detection Melodynee Service+ Program to edit music by reading sound source files • Main implementation technology: SVM, CNN

a https://www.shazam.com/ko b https://www.lunaverus.com/ c https://www.songs2see.com/en/ d https://accusonus.com/products/regroover e https://www.celemony.com/en/start

played at the same time. In addition, there is no commercial service that recognizes drums.

2.2 Drum Transcription Research A drum is an instrument composed of several components as shown in Fig. 1. In addition to the constituent instruments shown in Fig. 1, more various constituent instruments such as cow bell, chime bell, and China symbol can be added depending on the music. In some cases, it is played in a simple configuration using only Kick Drum, Hi-hat, and Snare Drum. Each musical instrument has a different sound (pitch, tone), and even if the musical instrument is the same (for example, a snare drum), the pitch and tone are different depending on the drum manufacturer, playing method, and tuning status. In addition, the electronic drum sound, which is widely used as a convenience in recent years, produces a different sound from the existing acoustic drums. These points make it difficult to recognize drum sounds on a computer. Musical instruments such as pianos always sound the same pitch (frequency) when a certain

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Fig. 1 Instruments that make up the drum [1]

key is pressed (for example, the sole scale of a piano keyboard), regardless of the manufacturer, but drums do not. Also, in the drum, as shown in Fig. 2, notes can be overlapped between the instruments constituting the drum. Unlike each keyboard of a piano that generates sounds of different frequencies (pitches), it is difficult to recognize the frequency domains of each other generated by the components of the drum which overlap. Figure 3 is a spectrogram of polyphonic music in which several instruments are played together, not just drumming. The X-axis is the frequency, the Y-axis is the time, and the color change represents the intensity of the sound played at that point. Ultimately, the purpose of this study is to recognize the music played by each instrument in the composition of these sounds and to be able to separate the playing part by each instrument, but it is true that the academic level has been limited to date. Fig. 2 Frequency overlapping between instruments composing drum [1]

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Fig. 3 Spectrogram in polyphonic music (https:// www.lunaverus.com/cnn)

In particular, unlike other instruments, the drum does not produce a certain frequency and, when played with other instruments, creates a phenomenon of overlapping pitches, which makes it difficult to recognize other instruments. Therefore, recognizing the sound of drums can be helpful in recognizing other instruments in polyphonic music.

2.3 Issue on Drum Transcription The drum is a polyphonic instrument in which Snare, Symbol, Hi-hat, Ride, and Tom are played simultaneously. In addition, unlike Melody instruments, research results are poor due to technical difficulties because of the characteristics of instruments that generate sounds of multiple frequencies with a single strike. In previous studies, the difficulty of recognizing drum sounds can be found. It depends on a variety of playing techniques, such as Flam, which is a technique of striking two drum sticks at a time difference, Rim, which strikes the frame of the drum, and Roll, which plays like a drum stick. Although there have been prior studies on recognizing drum sounds, there is a limitation in research that there is no sound sample using various playing techniques [2]. Various previous studies [2–7] trying to solve the difficulty of overlapping sounds between drum-constructed instruments still have a recognition rate of 75%, which is not commercialized [7]. In addition, a recognition study based on the difference in sound of each drum manufacturer, a recognition study according to the tuning state of the instrument, a recognition study according to the use of various drum composition instruments (Ride symbol, Ozone symbol, Splash symbol, Chime bell,

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Cow bell, etc.), acoustic drum Recognition studies based on the difference between and electronic drums have not been studied so far. Among the fields that did not have such prior research, it was judged that it was necessary to recognize the sound of electronic drums that are widely used recently. In the IDMT-SMT data set used in the study, the electronic drum sound and the acoustic drum sound data set existed, so it was possible to conduct a study to determine the acoustic and electronic drum sound. Since the drum is an instrument responsible for the overall rhythm of music in polyphonic music, if the recognition rate of the sounds of each component of the drum itself can be improved, the academic foundation for distinguishing various instruments played in polyphonic music will be made.

3 Research Methods 3.1 Data Collection This research model utilizes a total of 741 WAV files (468 acoustic drum sounds, 273 electronic drum sound files) of IDMT-SMT-Drum Dataset, which have both acoustic and electronic drum sound files among the open drum sound file data sets. Analysis was conducted by distributing them for learning and testing at a 70:30 ratio. The WAV file for training was used as an input of CNN (Convolutional Neural Network) by changing it to a Spectrogram image using MLS (Mel-scale Log Magnitude Spectrogram). After learning the three training model types with the same training data, accuracy was measured for each model using the test data in the Python Application Program (Fig. 4). Fig. 4 Design of experiment

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3.2 Experimental Environment The experimental environment used in this study utilized the Machine Learning Cloud Development Platform. IDMT-SMT dataset for learning and testing was stored in Google Drive, and the learning and testing data in Google Drive3 was read and processed using Google Collaboration Machine Learning Cloud Development Platform,4 and related Python codes were managed using GitHub5 in the form of Jupyter notebook. In addition, Librosa Python Package,6 which is widely used for music and audio analysis, was used for Spectrogram image work using MLS (Mel-scale Log Magnitude Spectrogram).

3.3 Data for Machine Learning Drum sound samples(ENST-Drums,7 DREANSS,8 200 Drum Machines9 etc.), which can be used for the learning and test data required for recognizing drum sounds through machine learning. However, there is a lack of sound samples for each technique needed to recognize various techniques. In addition, among the various drum-component instruments, sound samples such as Snare, Kick, and Hi-hat are mainly composed of sound samples. Not only that, there is a shortage of sound samples for drum-component instruments such as TOM, China Symbol, Ozone Symbol, and Splash Symbol. The number of Sound Samples in the public datasets may seem high, but not many datasets classify electronic drum sounds. In addition, since most of the Sound samples are small, research was conducted using IDMT-SMT Drums Dataset, which has the largest number of electronic drum sound samples among them, and detailed Dataset utilization is as shown in Table 3.

3 https://www.google.com/intl/en_zm/drive/. 4 https://colab.research.google.com. 5 https://github.com/. 6 https://librosa.org/doc/latest/index.html. 7 https://perso.telecom-paristech.fr/grichard/ENST-drums/. 8 https://zenodo.org/record/1290739#.XYvGjigzaiN. 9 http://www.hexawe.net/mess/200.Drum.Machines/.

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Table 3 Status of train and test data used in this study Drum type No of train data

No of test data

Acoustic drum Electronic drum Total Acoustic drum Electronic drum Total Snare

120

70

190

36

21

57

Kick

120

70

190

36

21

57

Hihat

120

70

190

36

21

57

Total

360

210

570

108

63

171

4 Experiment Result 4.1 Spectrogram Build First of all, the drum sound WAV file was created using MLS (Mel-scale Log Magnitude Spectrogram) and actual using of Librosa Python package and CNN’s Spectrogram image for Input were created like Figs. 5 and 6. As shown in the figure above, the reverberation of the Acoustic Snare Drum sound is much more irregular and the waveform is longer than the reverberation of the Electronic Snare Drum sound, which can also visually determine the load.

Fig. 5 Acoustic snare drum spectrogram

Fig. 6 Electronic snare drum spectrogram

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Table 4 CNN hyper-parameter on training model types

Training model type CNN hyper parameter

Value

A

(1) Input spectrogram pixel size

(32, 32, 3)

(2) Layer filter setup

(32, 64, 128)

(3) Fitting epoch

20

(1) Input spectrogram pixel size

(32, 32, 3)

(2) Layer filter setup

(32, 64, 128)

(3) Fitting epoch

25

(1) Input spectrogram pixel size

(128, 128, 1)

(2) Layer filter setup

(128, 256, 512)

(3) Fitting epoch

20

B

C

4.2 Training CNN (Convolutional Neural Network) was organized in three layers. Pool size by layer is ‘2, 2’., drop-out is ‘0.25’, padding is ‘same’, activation function used ‘relu’ in common. The accuracy was determined using softmax as the last activation function. In addition, a multi-class image classification method was used and Model build, Compile. Fitting were performed in three types of Table 4. In addition, “categorical_crossentropy” was used as an active function in the learning model compile, and “adam” was used as an optimizer.

4.3 Test Result As seen in model A and B, it has been identified that the size of the epoch also has a slight effect on model fitting (Table 5). Table 5 Model test result Training model

Training data

Test data

Test result

Acoustic

Electronic

Acoustic

Electronic

A

360

210

108

63

0.782

B

360

210

108

63

0.794

C

360

210

108

63

0.921

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5 Conclusions 5.1 Research Summary and Implication Prior studies [3–6, 8–20] using the same IDMT-SMT-Drums Dataset used in this study, electronic drum sounds have never been used to build a learning model. But this research used electronic drum and acoustic drum sounds. And the results of this study on the distinction of acoustic drum and electronic drum sounds are as follows. Through this experiment, the computer was able to distinguish acoustic drum and electronic drum sounds with 92% accuracy. This means that more learning data and subsequent various hyper-parameter settings will improve the recognition rate above the commercial level of 95%. As electronic drum music is increasing, it will greatly help improve the recognition rate of drum sound in the future. Moreover, technical considerations regarding the configuration of CNN hyperparameter to increase acoustic and electronic drum sound recognition can be summarized in the following section. First, the drum sound WAV file should be at least 128 Pixel for imaging as a Spectrogram for machine learning (Convolutional Neural Network). Second, in order to improve the recognition rate, it is important to adjust the number of filters hyper-parameters as the pixel value of the input image input increases. Third, if there are several sound samples in the third drum sound WAV file, it is important to analyze the timing of the image strike accurately (onset detection) and isolate the sound samples back and forth at regular intervals to prevent the deformation of the image during the analysis. (The importance of ground truth processing of learning and test data.) Finally, in the Spectrogram, colors indicating the intensity of the blow did not significantly affect the recognition rate even if treated in black and white. In the Spectrogram, the part that indicates the strength of the strike is expressed in color, and the shape of the image (Pattern) is more important in determining the acoustic drum and the sound of the electronic drum, meaning that the intensity of the strike does not mean much. If future drum sound recognition research is carried out as a result of this research to determine the sound of double acoustic drums and electronic drums, the overall drum sound recognition rate will be improved if the acoustic drum is recognized as an acoustic drum sound learning model and the electronic drum learning model is recognized as an electronic drum.

5.2 Future Challenges and Discussion Topics First of all, further research is needed to determine acoustic drum and electronic drum sounds. In this study, the amount of learning data is small, especially compared to

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468 acoustic drum sound WAV files, while the number of electronic drum sound WAV files is relatively insufficient at 273. This may affect the recognition rate due to an imbalance in the construction of the learning model, and it is estimated that the number of electronic drum sound WAV files in the future will also be better than the 92% accuracy of the current study if various training models are tested with the same number of acoustic drum sound WAV files. Second, when establishing an acoustic drum sound recognition model, the acoustic drum sound data is used only with acoustic drum recognition model, and when building an electronic drum sound recognition model, the electronic drum sound is used only with electronic drum recognition model. Then using the results of this study when recognizing drum sound in certain songs, the acoustic drum sound is recognized through acoustic drum recognition model and the electronic drum sound is recognized through electronic drum recognition model. Future studies will be needed to see how different these models and recognition model without distinction between the acoustic drum and electronic drum. Third, research is needed to build a drum sound recognition model by securing various drum-playing techniques as learning data. A drum is a musical instrument that produces different sounds depending on the location or method of playing even if it is the same drum instrument. In real drumming, many drummers used various methods of performing. This could have a bad effect on current drum sound recognition rates and this is also studied by Wu and Lerch [2]. However, due to the absence of sound samples based on various methods of playing, the recognition rate cannot be secured enough. In the future, this part will be an important part to improving drum sound recognition rate if a learning model is established by securing sufficient learning data for various performance methods (Flam, Bell hit, Mallet hit, Symbol Crescendo, etc.) under the concept of structuralizing and Ground Truth. Finally, research is needed to create a drum recognition model by securing sound samples of various drum-composed instrument sounds. Currently, most public drum sound data sets have learning data for only three drum-composed instruments: Snare, Hi-hat, and Kick Drum. For this reason, prior studies were limited to the recognition of three drum-composed instruments. However, the drum’s composition instruments are not only varied in size, but also diverse, including Crash Symbol, Ride Symbol, Ozone Symbol, Splash Symbol, Cow Bell, and Chime Bell. In particular, Crash Symbol, Ride Symbol, etc. are compositional instruments that must be present in the drum set, so in fact, without recognition models for these necessary compositional instruments, it may be an important reason why the recognition rate of drum sounds in Polyphonic music does not improve.

References 1. Wu, C.W., Dittmar, C., Southall, C., Vogl, R., Widmer, G., Hockman, J., Lerch, A.: A review of automatic drum transcription. IEEE/ACM Trans. Audio Speech Lang. Process. 26(9), 1457– 1483 (2018)

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2. Wu, C.W., Lerch, A.: On drum playing technique detection in polyphonic mixtures. In: ISMIR, pp. 218–224 (2016) 3. Vogl, R., Dorfer, M., Knees, P.: Drum transcription from polyphonic music with recurrent neural networks. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 201–205. IEEE (2017) 4. Gajhede, N., Beck, O., Purwins, H.: Convolutional neural networks with batch normalization for classifying hi-hat, snare, and bass percussion sound samples. In: Proceedings of the Audio Mostly 2016, pp. 111–115 (2016) 5. Hockman, J., Southall, C., Stables, R.: Automatic drum transcription for polyphonic recordings using soft attention mechanisms and convolutional neural networks (2017) 6. Rossignol, M., Lagrange, M., Lafay, G., Benetos, E.: Alternate level clustering for drum transcription. In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 2023–2027. IEEE (2015) 7. Wu, C.W.: Mirex 2017 Drum Transcription Submissions (2017). [online]. http://www.musicir.org/mirex/abstracts/2017/CW.pdf 8. Han, Y., Kim, J., Lee, K.: Deep convolutional neural networks for predominant instrument recognition in polyphonic music. IEEE/ACM Trans. Audio Speech Lang. Process. 25(1), 208– 221 (2016) 9. Wu, C.W., Lerch, A.: Automatic drum transcription using the student-teacher learning paradigm with unlabeled music data. In: ISMIR, pp. 613–620 (2017) 10. Wu, C.W., Lerch, A.: Drum transcription using partially fixed non-negative matrix factorization. In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 1281–1285. IEEE (2015) 11. Wu, C.W., Lerch, A.: Drum transcription using partially fixed non-negative matrix factorization with template adaptation. In: ISMIR, pp. 257–263 (2015) 12. Souza, V.M., Batista, G.E., Souza-Filho, N.E.: Automatic classification of drum sounds with indefinite pitch. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2015) 13. Stables, R., Hockman, J., Southall, C.: Automatic Drum Transcription using Bi-directional Recurrent Neural Networks (2016) 14. Roebel, A., Pons, J., Liuni, M., Lagrangey, M.: On automatic drum transcription using nonnegative matrix deconvolution and Itakura Saito divergence. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 414–418. IEEE (2015) 15. Ueda, S., Shibata, K., Wada, Y., Nishikimi, R., Nakamura, E., Yoshii, K.: Bayesian drum transcription based on nonnegative matrix factor decomposition with a deep score prior. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 456–460. IEEE (2019) 16. Vogl, R., Dorfer, M., Knees, P.: Recurrent neural networks for drum transcription. In: ISMIR, pp. 730–736 (2016) 17. Vogl, R., Dorfer, M., Widmer, G., Knees, P.: Drum transcription via joint beat and drum modeling using convolutional recurrent neural networks. In: ISMIR, pp. 150–157 (2017) 18. Vogl, R., Widmer, G., Knees, P.: Towards multi-instrument drum transcription (2018). arXiv: 1806.06676 19. Jacques, C., Roebel, A.: Data augmentation for drum transcription with convolutional neural networks. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1–5. IEEE (2019) 20. Yoshii, K., Goto, M., Okuno, H.G.: Drum sound recognition for polyphonic audio signals by adaptation and matching of spectrogram templates with harmonic structure suppression. IEEE Trans. Audio Speech Lang. Process. 15(1), 333–345 (2006)

Remeasurement Dispatching Rule for Semiconductor EDS Process Jeongil Ahn and Taeho Ahn

Abstract Recently, the Fourth Industrial Revolution has rapidly accelerated with the development of IT technology. Demand for state-of-the-art semiconductors sharply increased due to these market changes, and improvement of production capacity is particularly important to gain a competitive advantage. The semiconductor manufacturing process can be divided into three stages such as fabrication, probe (EDS), packaging. Electrical die sort (EDS) process is an important testing process in quality control between fabrication and assembly processes and is the point where the manufacturing capacity and supply chain can be affected. For these reasons, the demand for optimal operation is continuously increasing. However, schedule changes occur due to unexpected abnormal situations, such as machine failures or repairs owing to the nature of the testing process, that require retests to accurately analyze defects in products, or problems in the testing process. Therefore, scheduling considering uncertainty is especially important for smooth production. This study investigated the problem of schedule changes that arise due to retests among abnormal situations in the EDS process of non-memory semiconductors and presents a genetic algorithm with penalty method (GAPM) for scheduling under such uncertainties. The EDS process has the characteristics of a flexible manufacturing system (FMS), and it can be scheduled using the solution to the flexible job-shop scheduling problem (FJSP). Since the FJSP is an NP-hard class problem among combinatorial problems, a meta-heuristic method that can find the optimal solution in a short time is used. GAPM uses a genetic algorithm as the exhaustive search algorithm that is widely used as FJSP solutions and uses neighborhood search techniques for local search. In addition, the penalty method was used to make effective scheduling possible even under uncertainties such as retests that occur during the manufacturing process. Keywords FJSP · Scheduling · Genetic algorithm · Penalty method · Simulation J. Ahn · T. Ahn (B) Department of Business Administration, Graduate School, Soongsil University, Seoul, Korea e-mail: [email protected] J. Ahn e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_13

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1 Introduction 1.1 Research Background and Purpose The development of information and communication technology (ICT) has led to artificial intelligence, robots, the Internet of Things (IoT), mobile devices, autonomous cars, and the bio industry, and has become the core element of the Fourth Industrial Revolution. Accordingly, the demand for the state-of-the-art semiconductors in which various functions are integrated is continuously increasing. The need and demand for not only memory semiconductors but also high value-added system semiconductors are increasing. Companies in South Korea, which lead the memory semiconductor industry, are also strengthening the system large-scale integration (SLSI) and Foundry to diversify semiconductor products and secure competitiveness [15]. Foundry businesses fabricate semiconductors for other companies that provide their own design. Such businesses require large-scale investment, and it is important to have process technology that customers can trust. The semiconductor manufacturing process can be divided into three stages: a fabrication process in which an integrated circuit is formed on a wafer, a testing process in which normal formation of these chips is checked, and an assembly or packaging process in which the product is packaged. In EDS, chips are tested to determine their normal operation by measuring the electrical characteristics of integrated circuits. EDS is a part of the back-end process of the fabrication process; however, in the case of Foundry products, both PUSH and PULL methods are used because ordered products and on-going production are mixed. The ratio of products varies according to the market situation and establishing production schedules accordingly is an important part of production management. For stable production management, processing capacity should be increased to minimize uncertainty. However, eliminating abnormal situations is difficult. Therefore, scheduling that takes uncertainty occurring during the EDS process into account is a significant aspect of production and operations management [2, 17]. This study investigated scheduling algorithms under uncertainties considering retest situations among abnormal situations that occur during the EDS process of Foundry semiconductors. A scheduling algorithm is an optimization algorithm that appropriately allocates planned production to each machine and is like the dispatching rule. Due to the complex combinatorial problems of semiconductor process scheduling, which are difficult to solve, heuristics is generally used [16].

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Machine operation in the EDS process is a flexible manufacturing system (FMS) that can process various products by changing parts and measurement programs, and it has the characteristics of the flexible job-shop scheduling problem (FJSP) that can be used to solve production planning problems.

1.2 Research Method and Scope Various solutions have been presented for the FJSP; among these, an evolutional algorithm, which is a meta-heuristics method, is used in various forms. Various application methods have been presented for the genetic algorithm due to its advantages in applications such as encoding and decoding that is easy for chromosome arrangement combinations and computer programming [4–6, 14, 20, 29, 32, 34]. A genetic algorithm is investigated to determine the possibility of applying it as the scheduling algorithm of the EDS process. Additionally, a simulation system that is similar to the actual production process is established to evaluate the effectiveness of the algorithm, and scheduling is dynamically performed by generating in-production retest situations according to the probability distribution. Problems regarding the scheduling algorithm are analyzed through repeated experiments, and the possibility of stable scheduling even under uncertainties is evaluated. This study assumed a situation in which products are deterministic and retest is only considered under uncertainty. Since the experiment is conducted under a limited hypothetical condition for scientific research, the simulation model should be expanded and reevaluated to apply the findings to actual industrial fields.

2 Previous Research Research on scheduling in the semiconductor industry has been developed with a focus on manufacturing processes, which is related to corporate performance [13, 24]. Lee and Jeong classified machine abnormalities and different lot release and dispatching rules and determined that performance depended on the situation [16]. Seo and Bruce showed that productivity efficiency can be improved by simultaneously transporting multi-load lots in transport equipment [23]. Jeong presented a dynamic dispatching rule for a group of equipment that has time to switch processes [9]. The rule created an optimal combination of product progression according to work priority assessment criteria possible once the manufacturing process is classified based on the predetermined situation judgment criteria. Seo et al. demonstrated that effective production progress is possible by applying a reservationbased method as a dispatching rule to meet the manufacturing process deadline for make-to-order production, which is one of the characteristics of the non-memory semiconductor industry [23]. Yang et al. used a genetic algorithm (GA) simulation

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approach for the problem of dispatching decision-making of multi-attribute combinations [32]. Furthermore, researchers have examined various problems in manufacturing processes such as the method of dynamic machine allocation in response to deadlines for urgent orders, flexible job-shop scheduling using a decision tree, and scheduling for situations in which machines are designated for exclusive use depending on the machine characteristics [11, 12, 33]. Sivakumar and Chong modeled the works in process (WIP) status and various situations such as machine preventive maintenance that can occur in semiconductor back-end process, machine repair time, and setting up for product manufacturing, and analyzed the distribution and throughput of theoretical cycle time [26]. Comparisons were made against actual production performance, and scheduling accuracy was improved by adjusting simulation parameters. Chen et al. presented a dynamic dispatching approach (DDA) framework that can actively dispatch using events such as entering urgent lots or when the machine is on standby as major events [3]. The DDA centers on two types of events for the machine and job order, and applied techniques to classify lot types (normal or urgent) and determine the priority. Weigert et al. presented a simulation-based optimization technique for semiconductor backend process scheduling, and comparatively analyzed various optimization algorithms such as threshold accepting (TA), old bachelor acceptance (OBA), and recordto-record travel (RRT) [30]. Hildebrandt et al. performed event simulations using heuristic optimization algorithms and presented dispatching rules [8]. Pezzella et al. presented a representation method by combining work, processes, and machines after organizing the processing time of each machine in tables and presented a method to solve the FJSP using GAs after coding chromosomes for easy computer programming [20]. Gao et al. solved the FJSP using genetic descent and neighborhood search algorithms, reflecting the order of priority, and creating better solutions by changing movable processes of chromosomal arrangements of the selected best solution [5]. Moslehi and Mahnam presented a method to find solutions of the multi-objective FSJP (MOFJSP) using PSO, which enabled the rapid exploration of optimal solutions using a neighborhood search technique that considers the shortest run time and the fastest end time while changing the machine allocation of each particle when performing a local search [18]. To improve the FJSP, Xing et al. proposed a knowledge-based ant colony optimization (KBACO), which improved the ACO using knowledge-based heuristic searching architecture (KBHSA) that consists of a knowledge model [31]. Tan and Aufenanger presented the real-time rescheduling method of flexible flow shop (FFS) using a distributed knowledge-based decisionmaking method, and the rescheduling was performed after the decision-making so that the dispatching rule is properly applied to the condition of the machine [28].

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There are various ways of applying the penalty technique, but a form, which adds the penalty coefficient representing the penalty weight and the product of user-defined penalty function to the objective function, is generally used to solve problems without restrictions [1, 21]. Hamda and Schoenauer presented the adaptive segregational constraint handling evolutionary algorithm (ASCHEA), which separates restrictions. The algorithm (1) reduces the scope of search using the ratio function of a feasible solution in the population, (2) applies the selection operator using the strategy that feasible solution between feasible and unfeasible solutions is selected and mated, and (3) separates restrictions by applying a system that gives the advantages of feasible solutions in the evaluation in searching for the optimal solution of ASCHEA [7]. Nanakorn and Meesomklin presented a method that applies an adaptive penalty function to the GA. Their method presented always obtains the level of penalty for all feasible solutions explored by applying the penalty and adaptive evaluation functions to the fitness function that evaluates feasible solutions in the search algorithm [19]. Methods of using dynamic scheduling were presented for rescheduling when an abnormal situation occurs [3, 9, 10, 14, 16, 27, 33]. Various methods such as the state-action-reward-state-action (SARSA) algorithm and a method that presented multiple objective particle swarm optimization (MOPSO) are used to cope with the uncertainty [2, 22, 25]. Chaari et al. reviewed various literature and classified scheduling approaches under uncertainties into four types: (1) proactive, (2) reactive, (3) proactive-reactive, and (4) predictive-reactive [2]. The best preparation for the uncertainty is applying an appropriate method for the situation rather than considering a certain method as superior to others.

3 Definition of the Problem The present study investigated scheduling under uncertainties by retesting that occurs during the EDS process, which is one of the manufacturing processes of semiconductors. Considering schedule changes due to retesting, the problem of appropriately assigning products to each machine was investigated. The problem investigated herein was similar to the solution for the FJSP but penalty techniques are used in consideration of the uncertainty by retesting. In the semiconductor industry, terms such as lot, step, and tester are generally used, but because they are related to FJSP terms such as job (lot), operation (step), and machine (tester), FJSP terms are used herein to be consistent with previous research literature. Object Function Minimize max f _timei,j,k + coef × pfunc

(1)

f _timei,j,k = s_timei,j,k + PRC_TIMEi,j,k ∀i, j, k, k ≥ 1

(2)

(i,j,k)

Subject to

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s_timei,j,1 ≥ SETUPTIME × Yi,j,1 ∀i, j s_timei,j,k ≥ ftime i,j,k−1 + SETUPTIME × Yi,j,k H  

∀i, j, k, k ≥ 2

xi,j,k,h ≤ 1∀h, t

(3) (4)

(5)

(i,j,k)∈St h=1

Yi,j,k ∈ {0, 1}∀i, j, k

(6)

  St = (i, j, k)|s_timei,j,k ≤ t < f _timei,j,k ∀i, j, k

(7)

where, I J k h t M s_timei,j,k f _timei,j,k coef p_func PRC_TIMEi,j,k Yi,j,k St xi,j,k,h

is an index representing the product., i = 1, 2, . . . , N . is the jth job of the product., (i, j) = (i, 1), (i, 2), . . . , (i, Ni ), ∀i. is the kth operation of job (i, j)., (i, j, k) =   (i, j, 1), . . . , i, j, Ni,j , ∀i, j. is an index representing the machine, h = 1, 2, . . . , M . is a specific point in time. is the number of machines available. is the start time of (i, j, k). is the end time of (i, j, k). is the penalty coefficient. is the penalty function. is the time required to perform operation (i, j, k). is the variable marking the occurrence of SETUP_TIME. is a set of operations performed at time t. If (i, j, k) is assigned to machine h and work is performed, the value is 1, otherwise 0.

The objective function (1) minimizes the sum of penalty functions considering the uncertainty of retesting while minimizing the ending time of all jobs. The weight of the penalty function is determined by the penalty coefficient. The objective function is applied under the same conditions during production even if retest job occurs.

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In Eq. (2), the ending time means that the operation of a job is assigned to a machine and started, and then ended after the operation time has passed. Equation (3) shows that a is required before the operation of the job starts, and the is determined by. Equation (4) shows that precedence relations exist in the job process. Equation (5) shows that exists only in machine h. Equation (6) indicates that has the value of either zero or one. In Eq. (7), refers to a set of operations that are performed at a specific time.

4 Algorithm 4.1 The Solution Spaces Problem solution is composed of the solution space that is assigned to machine while maintaining the lead-lag relationship of job operations. Since the number of cases that can be a solution space has the restriction of following the order of each job operation, arranging the operations of jobs is a distinguishable permutation. Since all operations can be assigned to all machines, it is the product of the total number of operations and the permutation of machines. Accordingly, the following equation can be used. The number of feasible solutions = where, n total number of jobs of all products. rj number of operations of job j. m number of machines.

n! n! × r1 ! × r1 ! × · · · × r1 ! (n − m)!

(8)

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4.2 GAPM Algorithm

The GAPM algorithm applied to Eq. (1), which is the fitness function, when evaluating the chromosomes obtained by exploration with uncertainty considered after applying the genetic algorithm as the exhaustive search algorithm and the neighborhood search algorithm as the local search algorithm. The GAPM algorithm selects superior chromosomes using the fitness function after generating the initial population. Crossbreeding and mutation are selected probabilistically, and local best solutions are selected by the neighborhood search algorithm applied as a local search. The local best solution, in comparison to the global best solution, selects superior chromosomes and designates them as the global best

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solution. In a search process, if the chromosome compositions of the global and the local best solutions are identical or the number of repetitions is greater than the specified number of repetitions, the search is terminated and the final global best solution is designated as the optimal solution.

5 Simulation 5.1 Simulation Modeling A simulation model is used to explore the optimal for the production planning schedule by applying a simulation optimization technique. WIP, Input Stocker, Hold Stocker, Output Stocker, Scheduler, Dispatcher, and Analyzer required for scheduling were composed. Simulation was conducted using the schedule created through the GAPM algorithm based on production planning. The analyzer that judges retest was proceeded as a probability model.

5.2 Evaluation of GAPM Algorithm This simulation evaluates the usefulness of the GAPM algorithm using experimental data. To determine the influence of changes in the penalty coefficient on the makespan, the optimal value of a penalty coefficient was determined by performing the simulation with retest job excluded. To evaluate scheduling for situations with retest jobs under uncertainty, comparisons were made between the case in which the penalty coefficient was set to zero and the case in which the optimal penalty coefficient obtained from the simulation was applied. The algorithm evaluation data consisted of a total of 100 jobs, which includes 10 types of products and 10 jobs for each product, the total number of operations of each job was 230, and the number of machines was 10. In the experimental data, the ratio of the number of operations of all products was 10% one operation, 50% two operations, and 40% three operations, and the was 25 units between 125 and 750 (5–30 when converted into one wafer) selected from randomly selected results. The algorithm was evaluated by comparing the averages and distributions of the makespan of the optimal solutions investigated by changing the coef values, which were the penalty coefficients in the fitness function of the GAM algorithm. Simulation was performed by applying values from setting the coef value to zero and the optimal coef value obtained through simulation and applying retest job probability model. To compare the averages and distributions of the simulation results, two-sample F-test was performed to confirm homoscedasticity, and two-sample t-test was performed to analyze mean differences. All experimental conditions were repeated 100 times.

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(1) Comparison of GAPM scheduling results according to the penalty coefficient Simulation was performed in the absence of uncertainty by retest. The penalty coefficients were increased from zero to 2.9 in 0.1 increments. Changes in the makespan by changes in the penalty coefficient were confirmed as shown in Fig. 1a. Changes were in a U-shape in which the makespan was rapidly decreased in the beginning and then gradually increased as the penalty coefficients were increased. Because the pfunc(penalty function) has the characteristics of mediating the operations of jobs due to the characteristics of function configuration parameters, pfunc was found to influence not only uncertainty but also the overall scheduling. The results of pfunc in this experiment were about 65% of the makespan, which indicated that the calculated value of pfunc was sufficiently reflected even for a 10% change in the penalty coefficient. For coef = 0.3, the average of the makespan was minimal, and the makespan was gradually increased with the increase in the values of the penalty coefficients. Figure 1b presents the result of the comparison of makespan distribution between when coef was set to zero based on the experimental data and the optimal coef value of 0.3 obtained through the simulation. As confirmed above, both averages and distribution were improved. The comparison showed that the average and standard deviation were decreased by 2% and 47.5%, respectively. That is, since the objective function Eq. (1) was minimization, the efficiency of coef = 0.3 was good and stable. (2) Comparison of the GAPM scheduling results under uncertainty This experiment compared the simulation results with 5% retest rate between two algorithms using the setting of coef = 0 of the GAPM and the optimum penalty coefficient, coef = 0.3, which was confirmed through the process in (1). The production planning in the simulation was deterministic, and because jobs with completed operations were deleted as the simulation progressed, the number of jobs to be scheduled was reduced rather than maintained. In addition, the averages and distributions of the simulation makespan were increased compared to simulation to which retest rates were not applied because both the number of retest events occurred, and temporal locations were randomly determined. Different results can be obtained since actual production is continuous rather than deterministic. As can be seen in the

Fig. 1 a The penalty coefficient curve, b comparison of the makespan distribution

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results, good performance was found when the penalty method was applied rather than not applied. The results of two-sample hypothesis test with F-test for coef = 0 and coef = 0.3 for the result of the simulation showed p-value = 0.002 with 95% confidence interval, which shows that distributions between the two samples were different. The results of two-sample hypothesis test using t-test was p-value = 0.000 with 95% confidence interval, which indicates that the averages of the two samples were not the same. The results of the simulation showed that the number of the operations of jobs retested was three on average with the minimum and maximum of zero and nine, respectively.

6 Conclusions In the manufacturing operations in which products are produced, various abnormal situations such as machine failure, lost parts, and defective products occur. Such abnormal situations may cause problems in product quality or decreased productivity resulting in various problems for production and operations management such as inability to deliver products in a timely manner. Most manufacturing firms make significant efforts and investment to minimize the effect caused by abnormal situations that are difficult to predict, but eliminating all problems is impossible. Scheduling that can cope with abnormal situations is important for the stable production of products even under uncertainty but scheduling for unpredictable situations is difficult. This study investigated rescheduling under the uncertainty of the EDS operations of semiconductors, which has rarely been investigated before. The GAPM algorithm presented herein is a scheduling algorithm that can properly respond when schedule change is necessary by a retest job, which is one of the unpredictable situations in test operations. The major approach is the FJSP solution, which explores feasible solutions using the GA used in many studies and allows the exploration of optimal solutions considering uncertainty by the fitness function to which penalty method is applied. The penalty coefficient that determines the weight of the penalty function can determine the optimal coefficients that fit production planning by applying a simulation optimization technique. It can be completed into an automated scheduling system by integrating a schedule system and a simulation system. The GAPM algorithm showed good performance in scheduling with consideration given to uncertainties caused by retest jobs. The penalty method applied to the GAPM can be used as an adaptive system, and its expansion is easy because it can be used in other situations such as machine abnormalities, parts problems, and work problems by adjusting the penalty coefficients and the penalty function. The GAPM algorithm developed herein is a scheduling technique considering retest jobs that occur in the EDS operations of non-memory semiconductors, and

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its usability in scheduling under uncertainty, such as adding and deleting jobs for retests, is high. However, the penalty function to which penalty method was applied needs to be reviewed when it is extended into additional studies or the managing method of production operation is different since the function academically approached a fragmentary situation of the EDS operation, which was the target of the present study. In addition, the optimal value of the penalty coefficient is only effective for the situation used as the experimental data herein, and if production planning is different, the optimal value should be investigated through simulations. Since the production planning data used in the experiment assumed a deterministic situation and were randomly generated, a review is necessary for the continuous production. Only necessary functions were implemented for the simulation system to evaluate the GAPM algorithm, and analysis should be performed after constructing the simulation system with more realistic situations reflected. Finally, cases using a knowledge-based learning algorithm are increasing among studies related to the FJSP due to the development of artificial intelligence technology. Generalizing research findings to date to all problems is difficult, but the possibility of practical use is increasing given the efforts of many researchers, and additional studies using these techniques appear to be necessary.

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Fall Detection Method Based on Pose Estimation Using GRU Yoonkyu Kang, Heeyong Kang, and Jongbae Kim

Abstract Falls are a major cause of injuries or deaths in the elderly over the age of 65 and a factor in social costs. Various detection techniques have been introduced, but the existing sensor base fall detector devices are still ineffective due to user inconvenience, response time, and limited hardware resources. However, since RNN (Recurrent Neural Network) provides excellent accuracy in the problem of analyzing sequential inputs, this paper proposes a fall detection method based on the skeleton data obtained from 2D RGB CCTV cameras. In particular, we proposed a feature extraction and classification method to improve the accuracy of fall detection using GRU. Experiments were conducted through public datasets (SDUFall) to find featureextraction methods that can achieve high classification accuracy. As a result of various experiments to find a feature extraction method that can achieve high classification accuracy, the proposed method is more effective in detecting falls than unprocessed raw skeletal data which are not processed anything. Keywords Human pose estimation · Skeleton · Fall detection · Deep learning · GRU

Y. Kang · H. Kang Department of IT Policy and Management, Graduate School of Soongsil University, Seoul, South Korea e-mail: [email protected] H. Kang e-mail: [email protected] J. Kim (B) Startup Support Foundation, Soongsil University, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_14

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1 Introduction As the world has become an aging society, fall has emerged as a social problem. In the elderly, muscle strength and postural balance are reduced, and 25% of the elderly who are able to move between 65 and 74 years of age each year experience falls, and 10–15% of them suffer serious injuries, leading to death. Accurate and rapid detection of falls and rapid response are required through detailed continuous accumulation of motion data and data-based motion analysis for the elderly/disabled. The existing fall detection system is divided into sensor-based and vision-based systems. The dual sensor base [1–3] is an inexpensive sensor, and economical system construction is possible, but there is a limit to attaching the sensor to the body. For medical and commercial purposes, there is a need for a vision-based fall detection system that uses inexpensive cameras that do not require sensors to be attached to the patient’s body.

2 Related Work A vision-based fall detection system acquires images through video equipment, detects and uses images to detect a fall. Deleting the background from the image [1, 4] by extracting and characterizing the human skeleton data from the video, it detects a fall by characterizing the shape or silhouette of a person in the video [4, 5]. In recent years, the method of recognizing human activities based on skeletal data [6] has become the mainstream of fall detection technology. Skeleton data is extracted using a CNN-based technology such as PoseNet [7] from an image or video acquired using a 3D depth camera such as Kinect developed by Microsoft or a 2D RGB video. The extracted skeleton data is spatial–temporal data that changes with time. The Recurrent Neural Network is a powerful method for classifying time series data, but it takes a long time to learn and has a disadvantage of a vanishing gradient problem or exploding gradient when the input data becomes large. To solve this problem, LSTM (Long-short term memory) and GRU (gated recurrent unit) were introduced. GRU is the simplest structure as a modification of RNN, and unlike LSTM, it is composed of two gates, Reset Gate (r) and Update Gate (z), and has the advantage of short learning time such as a long sequence. The study in this paper is to improve the performance of an analysis and classification system for estimating human posture by extracting skeleton data from images or videos using PoseNet and applying artificial intelligence to the extracted posture data. We propose a Fall Detection Method Based on Human-Pose using GRU (FDHG) that classifies and infers the extracted skeleton data, and detects falls/falls based on the results.

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3 Vision Based Fall Detection 3.1 Algorithm The architecture of the FDHG fall detection technique proposed by this study is shown in (Fig. 1) and consists of 5 steps. Step 1, it is a data collection process that collects 2D RGB video data from the camera. Step 2, raw skeleton data related to falls is extracted from image or video through PoseNet as shown in Fig. 2. To detect posture, 17 key points are extracted from the head to the feet of a person. Step 3, the extracted skeleton data is pre-processed by the proposed feature extraction method before analysis by GRU. Step 4, the input data preprocessed according to the feature extraction method is learned by the learned GRU for inference. Step 5, it is a process of detection whether fall or not based on the GRU inference result of data extracted from the input image or video. AI Hub and URFD [1] dataset were used for the fall posture learning dataset [8]. Learning sources details are motion capture datasets and Annotated 2D images.

Fig. 1 Architecture of FDHG

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Fig. 2 17 key points of human body

3.2 Feature Extraction for HSGC The proposed feature extraction method used the following two methods. The first is the head and shoulder group key point coordinates (HSGC). In the case of a bed fall in ward of hospital, the change in the position of the head and upper body is the most critical factor in detecting the patient fall. HSGC is the X and Y coordinates that correspond to the body falling from the highest point when falling in the direction of gravity. HSGC consists of 7 X,Y coordinates. The coordinates consist of 7 coordinates including the nose (0), the eyes (1, 2), the ears (3, 4) of the face and the left (5)/right (6) of shoulders. The second feature extraction method is the velocity of head and shoulder group key point coordinates (VHSGC). The speed is obtained by subtracting the first position and the end position of a window/frame of a image or video by time [9], and the head position of each frame and the sequential change of HSGC are used as a threshold function. A sliding window of length “n” frames is used as a training data set, and the mean values x and standard deviations (SD) of x in a defined frame are as shown in Eq. (1).     2 2  S =  (xi−1 − yi ) + (yi−1 − yi ) 

(1)

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Equation (1) using the Euclidean distance method, Acc is the acceleration of the head position of X, Y and Z values provided by the Kinect sensor. S in Eq. (1) is the acceleration of the head position by applying the Euclidean distance calculation method to the X and Y values provided by the Kinect sensor. 1 Dataset i n i=1 n

v=

(2)

In Eq. (2), v is the average value of the head position velocity in each sliding window, and n is the number of frames in the data set. 1 RDS i n i=1 n

Srd =

(3)

Srd in Eq. (3) is the average value of the head position conversion speed, n is the number of frames per second of the sliding window, and real-time acceleration RDSi is the head position conversion speed of each sliding window in the series.

4 Experiments 4.1 Pose Dataset The data set for the experiment used 50 kinds of human motions of domestic AI HUB in Korea and UR Fall Detection Dataset (URFD). URFD contains 70 sequences (30 falls + 40 activities in everyday life). Fall events were recorded with data from two Microsoft Kinect cameras and corresponding accelerometers. The specification of each data set is shown in Table 1. Table 1 Specification of AI HUB and URFD dataset Specification

AI HUB

URFD

Configuration

RGB video (640 × 480, 50 fps),

RGB video (640 × 480, 50 fps), depth video, accelerometer data

Number of falls

9600 (3360 falls + 6240 ADLs)

30 (30 falls + 40 ADLs)

Fall direction

Forward, backward, left, right, top, bottom

Forward, backward, left, right

Scenario

The subject falls down while lying on the floor left/right/forward

Falls while sitting down, falls while walking

Camera viewpoint

Twelve

One

Brightness change

Yes

No

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Upper body raising posture

Lying or falling posture

Fallen posture

Time

Fig. 3 Sequential frame of human pose

4.2 Sequential Dataset The dataset for Gated Recurrent Units(GRU) training to detect fall detection, which is the goal of this study, is a detailed sequential fall motion frames as illustrated in Fig. 3 by using the continuous shooting technique for the continuous motion of the fall. Figure 3 is composed of labels for each sitting posture, upper body rising posture, falling posture, and collapsed posture. In the sequence of 17 frames, the collapsed posture sub-frame and the collapsed posture label were classified as falls and used as learning and experimental data. It was used in the inference to sum the successive values of the window. Figure 3 is composed of a sitting, upper body raising, lying or falling and fallen posture. In the sequence of 17 frames, the lower frame of the falling pose and the fallen pose label were used for learning and falling pose classification as experimental data. In other words, the sequential values of the window was used for the pose inference.

4.3 GRU Training and Classification Figure 4 is a learning algorithm that detects a fall in an image after trained GRU. This is an example of a method of detecting a fall from Human pose skeleton data, that is, sub-sequence data of an image which is enabled to the learned. The inference result of the method of summing the continuous values of the input data window is “falling posture”, and if the fallen posture remains unchanged for a certain period of time, it is estimated as fallen. The size of the window in Fig. 4 changes according to the duration of ‘lying pose’. If it is estimated to fallen when ‘lying pose’ is maintained for more than 5 s, the sum of the windows must be 10 or more. When lying position is (1), the upper body only standing position is (2), the legs lowered out of the bed is (3), and the fallen position is (10), if the sum of the windows is 10+, it is a fall.

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Fig. 4 Fall detection algorithm

4.4 Experimental Configuration In order to redirect the output value of PoseNet’s key points to a.json file, you need to change an annotation format of AI Hub Dataset to.mat. The method devised to make it enable to MatLab is to convert all output values into.json format and save as shown in Fig. 5. In order to consider the execution time and classification accuracy, and to increase the fall detection efficiency, the GRU is set to two stacks and 256 hidden layers. AI Hub and URFD DataSet were used as a data set for classifying falls, and the feature Fig. 5 Key points extraction and coordination using PoseNet

176 Table 2 PoseNet configuration

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Single-pose

Architecture

ResNet50

Input resolution

250

Output stride

32

Multiplier

1

Quant bytes

2

extraction method was a fusion of HSGC and VHSGC with the skeleton key points (SD) extracted by PoseNet. PoseNet configuration is shown in Table 2. The parameters of GRU are batch_size, epoch, and learning_rate, and batch_size is 128 as the size of the data to be enabled. The parameter Epoch representing the number of repeated learning of the training data was set to 5000. Learing_rate is Adam [1, 5] optimizer, and the initial learning rate is set to 0.0001.

5 Results Camera equipment was installed to create the image data set, and the experimenter directly demonstrated and input various postures for falling. Out of the types of various pose, frequently occur poses in wards data sets such as falling from bed to floor and falling from chair to ground were input and saved as PoseNet output coordinates. A lot of data was generated even with small movements, so the data was refined by selecting a representative poses. Falls often occur when an elderly person tries to puts on his shoes or to stand by her/himself on the floor with his or her legs down from the bed, rather than falling directly from the bed. Table 3 is 17 raw skeleton data (SD) of sitting position, sitting position on a chair, falling posture and Fallen pose) which contain x, y coordinates and its confidence scores. The key point coordinates of HSGC with the head and shoulders as a group are shown in Table 4. The experiment uses public data set AI hub and URFD to GRU learning purpose such as walking, sitting, falling and fallen. For fall classification, SD using only the raw skeletal data of the image input, HSGC method that grouped the head and shoulders, and VHSGC method that added fall velocity of decent of HSGC were compared. Table 5 shows the classification and fall detection accuracy of the results of applying SD, HSGC and VHSGC method using GRU after learning the dataset. Without additional method, the SD feature classification accuracy averaged 97.52%, and the HSGC application result showed that the average classification accuracy was 98.36%, and the VHSGC classification accuracy averaged 99.06%. Compared to SD

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Table 3 17 skeleton x, y coordinates and confidence score of fallen pose (PoseNet) Skeleton

x (width)

y (height)

Sore (confidence)

0

Nose

50.7867378654276

33.7355435843783

0.9326699798112

1

Left eye

45.6546546542537

27.3837837830000

0.9298401624043

2

Right eye

54.3478326575665

28.6546546542765

0.9561378209289

3

Left ear

44.8378536542765

29.6835843543787

0.9705154655595

4

Right ear

53.4555556454655

30.6547252783783

0.9788288003904

5

Left shoulder

38.6546546542765

17.6546546542765

0.9859890651409

6

Right shoulder

85.6783534537825

59.7837345345300

0.9821316993541

7

Left elbow

65.8753425222545

41.7345342125375

0.9829989015543

8

Right elbow

45.5378215783780

25.9365245745354

0.9775779430792

9

Left wrist

75.5783527858000

57.2156378637370

0.9632383321224

10

Right wrist

55.8353788112353

37.8727542452425

0.9483201818045

11

Left hip

65.4537834527527

44.7837354345378

0.9240573949614

12

Right hip

35.7873737837834

15.2378378227837

0.9343152817972

13

Left knee

15.6546587373543

97.5828528280000

0.9479046811346

14

Right knee

65.6546546542765

49.1586374837837

0.9616139279595

15

Left ankle

45.6783753738765

27.2878245347583

0.9774531335194

16

Right ankle

85.9375278542535

59.8235348378000

0.9245698832161

Table 4 SGC key point’s x, y, coordinate and confidence score of fallen pose SGC

x (width)

y (height)

Score (confidence)

1

Nose + shoulder

44.8378536542765

29.6835843543787

0.9496944310516

2

Left elbow

65.8753425222545

41.7345342125375

0.9584347662985

3

Right elbow

45.5378215783780

25.9365245745354

0.9838287909636

4

Left wrist

75.5783527858000

57.2156378637370

0.9299632750517

5

Right wrist

55.8353788112353

37.8727542452425

0.9698890879774

6

Left hip

65.4537834527527

44.7837354345378

0.9515728329719

7

Right hip

35.7873737837834

15.2378378227837

0.9820470642522

8

Left knee

15.6546587373543

97.5828528280000

0.9729722913580

9

Right knee

65.6546546542765

49.1586374837837

0.9418288558089

10

Left ankle

45.6783753738765

27.2878245347583

0.9557989619224

11

Right ankle

85.9375278542535

59.8235348378000

0.9721334947212

without applying the proposed method, the average fall classification accuracy was higher at 1.56%, and the fall detection accuracy was increased by 1.13%.

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Table 5 Comparison of classification and detection accuracy Method

Data Data size set

Classification accuracy (%) Average

Fall Sitting Standing Lying/Falling Fallen detection (%)

SD

34

AI 97.52 HUB + URFD

98.52

97.39

96.93

97.25

98.87

SD + HSGC

38

AI 98.36 HUB + URFD

99.78

99.3

97.41

96.95

98.99

SD + 40 VHSGC

AI 99.06 HUB + URFD

99.84

99.84

98.43

98.15

99.50

6 Conclusions This paper presents a new fall detection method based on 2D RGB video to apply the GRU neural network technique for classifying between activity of daily lives and falls. As a result of experimenting with the combination of the proposed feature extraction method based on human skeleton data, it was found that learning of various datasets including falling from a sitting position improved the accuracy of fall detection. Also, we verified that the proposed method FDHG is the most suitable feature extraction method for fallen detection in a given environment. Various studies are needed to obtain better drop detection accuracy.

Reference 1. Chen, W., Jiang, Z., Guo, H., Ni, X.: Fall detection based on key points of human-skeleton using OpenPose. Symmetry 12, 744 (2020). https://doi.org/10.3390/sym12050744 2. Bourke, A.K., O’brien, J.V., Lyons, G.M.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture (2007) 3. Cheng, K.W.C., Jhan, D.M.: Triaxial accelerometer-based fall detection method using a selfconstructing cascade-Ada Boost-SVM classifier. IEEE J. Biomed. Health Inform. (2013) 4. Abobakr, K.A., Hossny, M., Nahavandi, S.: A skeleton-free fall detection system from depth images using random decision forest. IEEE Syst. J. 12 (2018) 5. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. ICLR (2015) 6. Lie, W.-N., Le, A.T., Lin, G.-H.: Human fall-down event detection based on 2D skeletons and deep learning approach. In: International Workshop on Advanced Image Technology (2018) 7. Adhikari, K., Bouchachia, H., Nait-Charif, H.: Deep learning based fall detection using simplified human posture. Int. J. Comput. Syst. Eng. 13(5) (2019). Heinecke, T., Wolfe, M.: The role of Bluetooth low energy for indoor positioning application. Computer Science Department, Montana State University, Bozeman, MT USA

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8. Ma, X., Wang, H., Xue, B., Zhou, M., Ji, B., Li, Y.: Depth-based human fall detection via shape features and improved extreme learning machine. IEEE J. Biomed. Health Inform. 18(6), 1915–1922 (2014) 9. Wu, G.: Distinguishing fall activities from normal activities by velocity characteristics. J. Biomech. 33(11), 1497–1500 (2000)

Stacked-Autoencoder Based Anomaly Detection with Industrial Control System Doyeon Kim, Chanwoong Hwang, and Taejin Lee

Abstract The Industrial Control System (ICS) is a system for controlling industrial systems. It is mainly a national infrastructure, and if it is shut down, it can have a huge impact on our lives. Therefore, ICS is mainly operated in a closed network to minimize security threats. However, ICS has also increased its Internet connection points as the IoT advances, which has increased security threats. Until now, it was difficult to secure a data set from an actual operating environment in ICS, so it was difficult to study effective security techniques. In this paper, we proposed a stacked-autoencoder (SAE), deep Support Vector Data Description (SVDD)-based data anomaly detection technique using an ICS dataset created based on a testbed similar to an actual operating environment, and derived detection accuracy for each threshold. In both models, the highest accuracy was derived when the threshold was 0.98, and the accuracy was 96.03% in the SAE model and 95.48% in the Deep SVDD model. Keywords Stacked autoencoder · Deep SVDD · Anomaly detection · Industrial control system

1 Introduction Recently, the importance of industrial control systems is emerging. ICS is a system for effectively controlling and monitoring systems of major national infrastructure and industrial sectors. It consists of Supervisory Control and Data Acquisition (SCADA), and Distributed Control System (DCS) [1]. In general, it controls physical processes D. Kim (B) · C. Hwang · T. Lee Department of Information Security, Hoseo University, Asan, South Korea e-mail: [email protected] C. Hwang e-mail: [email protected] T. Lee e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_15

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Fig. 1 Number of vulnerable products used in different industries (according to US ICS-CERT classification)

related to infrastructure that are important in our lives, such as power, water and gas. Therefore, since it has an essential function related to our lives, it is impossible to shut down the system at all times, and when it is interrupted due to unavoidable circumstances, it greatly affects millions of people. One-third of domestic ICSs do not have security patches. In the case of nuclear power plants, the operation cycle is set to 18 months, and when the operation is stopped, it costs about £33,000 per hour [2]. According to a 2016 Research & Market report, the size of the ICS market in the Asia–Pacific (AP) region is growing at an annual rate of 47.2%, and is predicted to reach about 1.85 trillion won in 2020 [3]. In line with the growing market size, security incidents using this are also increasing. In 2019, Kaspersky Corporation announced that 46.4% of ICS cyber attacks were conducted based on the ICS in which its products were introduced. Attacks have been carried out in various ICS fields such as building automation, automobile manufacturing, power and energy, oil and gas [4] (Fig. 1). In order to cope with such attacks, many studies on ICS security are being conducted. However, in the case of ICS, since most of the ICS are composed of closed networks and data of important national facilities, it is difficult to obtain a data set reflecting actual data. Accordingly, the National Security Research Institute (NSR) released ICS security data set for AI learning similar to the actual control system operating environment in December 19th [5]. In this study, a single event detection study based on SAE and Deep SVDD was conducted using ICS dataset. Chapter 2 describes various studies related to ICS security, and Chap. 3 proposes a single event detection technology based on SAE and Deep SVDD. Chapter 4 describes the experimental results for each proposed model, and Chap. 5 concludes.

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2 Related Work 2.1 ICS Dataset The ICS dataset is a dataset suitable for developing AI-based industrial control system security technology. It guarantees data reliability by improving the problem of data labeling of the existing well-known ICS dataset, and generates data for each attack level so that the performance can be accurately evaluated. It consists of normal and abnormal data corresponding to 34 attack scenarios in a testbed consisting of 4 processes in boiler process, turbine process, water-treatment process, and hardware-in-the-loop (HIL) simulator. According to the passage of time of the testbed, 59 major point data from operation start time to end and attack label for each data are recorded. Key point data include Temperature cascade control, Water level in upper tank, and High/Low water level setpoint. NSR proposed a stacked-recurrent neural network (RNN)-based detection method [6]. Scores were calculated using Time-Series Aware Precision and Recall (TaPR). Since there is no label of normal data, unsupervised learning is used. Part of the timeseries data was imported through the sliding window and the window pattern was remembered. The sliding window was set to 90 s. In addition, data pre-processing was performed through Min-Max Scaling. Three-stage bidirectional GRU was used, and the batch size was set to 512 and the epochs were set to 32. As input, the value corresponding to 89 s in the front part of the window was entered, and as the output, the value of the last second (90th second) of the window came out. At the time of detection, the difference between the value output from the model (predicted value) and the value actually entered was viewed, and if the difference was large, it was considered as abnormal. An F1-score of 0.792 was derived, a TaP of 0.924, and a TaR of 0.693 were derived. Figure 2 is a graph showing the result of determining whether to attack. The orange line represents the attack location, and the blue line represents the size of the (average) error. Overall, it can be seen that there is a large error in the attack position.

2.2 Anomaly Detection In study [7], unknown attacks were identified and detected in flow-based network traffic through unsupervised deep learning, Autoencoder (AE) and Variational Autoencoder (VAE). In addition to general attack types, flow-based features extracted from network traffic data including various types of attacks were used. In addition, the area under the line of the (Receiver Operating Characteristics (ROC) curve was calculated and compared with the One-Class Support Vector Machine. The data set for detection and research of abnormal symptoms was Kyoto 2006+, CTU-13, UNSWNB15, CIDDS. We used -001 and CICIDS2017. Two hidden layers of 512 and 256 dimensions in encoder of AE and VAE, and the decoder consists of two hidden layers

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Fig. 2 Graph of the result of determining whether to attack

of 256 and 512 dimensions, respectively. Figure 3 shows the architectural diagram of AE and VAE used in the proposed model. In research [8], an abnormality detection technique based on Local Outlier Factor (LOF) and an attack profile analysis on the detected anomalies were conducted to detect suspicious behavior in the endpoint environment. An outlier score indicating the difference between the data was calculated. LOF points per event are allocated to Fig. 3 Architectural diagram of AE and VAE used in models

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Fig. 4 Structure of model

LOF, and loss per event is allocated to AutoEncoder. The LOF score and loss value per event was used to identify abnormal data after calculating the cumulative distribution function (CDF) using the standard normal distribution. The CDF value was used as an outlier score to detect a single suspicious event to detect outliers. Features were extracted based on logs collected from endpoints, and features include process name, file name, event type, local IP address, remote IP address, IP address field, and Unix timestamp. In addition, when an attack log corresponding to an attack scenario occurs, a malicious behavior detection rule is created through attack profiling. This is effective in detecting advanced attacks such as APT attacks on endpoints. The main attack scenarios are described as Drive by download, Ransomware, Cryptojacking, etc. Single event rules are classified into known and unknown attack patterns. These include Unusual network connection, Unusual download and upload of data. The abnormal score must be higher than the set threshold, and if it occurs more than 10 times within 10 min, it is judged as a single event. Figure 4 shows Structure of proposed model in research [8].

3 Proposed Model This paper conducts an experiment using ICS dataset. Detects single anomaly based on SAE and Deep SVDD. We calculated the loss value for each event to be analyzed. Loss value was expressed as a value between 0 and 1 using the Cumulative Distribution Function (CDF). The calculated CDF value and the randomly designated

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threshold value are compared, and if the CDF value is greater than the threshold value, it is judged as a single suspicious event.

3.1 SAE Based Anomaly Detection Sequence Data refers to countless types of data, including those generated by Natural Language, Text and IoT, and ICS. It is data that is meaningful in order, and that is impaired when the order is different. Temporary Sequence data is referred to as TimeSeries data if there is a temporal meaning time-Series data is referred to as a given time difference. To convert Sequence data to Time-Series data, you must perform a Resample. The value of 59 major pointers over time was used as features for the Anomaly detection in the ICS. If the data values of the pointers are too large or too small, a pointer with a higher value or converging to zero in the calculation process of the algorithm may have a greater effect on the result value, so proper learning does not proceed. To prevent this, a data scaling process is required. In this paper, data scaling through standardization was conducted. Standardization is one of the data scaling techniques using standard deviation, and all characteristics have the same scale. Learning was carried out using Stacked-Autoencoder (SAE) in the form of multiple layers of Autoencoder using scaling data. SAE is characterized by having the same structure on both sides and a Deep Belief Network (DBN) structure based on the middle layer. Unlike Single Autoencoer, it is an autoencoder with multiple hidden layers, and it can express a much more diverse function than when the hidden layer is composed of one and shows good performance. In addition, the more layers are added, the more complex coding can be learned. Figure 5 shows Structure of the proposed Autoencoder model.

3.2 Deep SVDD Based Anomaly Detection Deep SVDD is a technique that combines SVDD and deep learning, and detects SVDD-based anomaly by extracting features through a neural network [9]. Deep SVDD has similar characteristics to 1-SVM by providing a boundary for performing binary classification. If 1-SVM aims to find the hyperplane boundary farthest from the origin, SVDD aims to find the least optimal sphere surrounding normal data. Also, it provides boundary for two outlier detection. Figure 5 shows an example of SVDD. Figure 6 shows Example of SVDD.

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Fig. 5 Structure of the proposed SAE model

Fig. 6 Example of SVDD

4 Unknown Attack Detection Results Semi-supervised learning is a method of learning when there is data with and without labels [10]. In this paper, the model was trained using only normal data, and all data outside the boundary of the normal data were determined as abnormal data. After that, abnormal data were used as analysis data to calculate outliers and detect abnormalities. We predicted that the loss value would be large when the analysis target event was judged to be a model that only learned normal.

188 Table 1 Detection rate by threshold based on SAE

Table 2 Detection rate by threshold based on Deep SVDD

D. Kim et al. Threshold

Accuracy

0.5

0.9341

0.7

0.9590

0.9

0.9598

0.98

0.9603

Threshold

Accuracy

0.5

0.7672

0.7

0.9179

0.9

0.945

0.98

0.9548

4.1 Result of SAE Table 1 shows the detection rates by threshold when the SAE model is used. In the case of SAE-based anomaly detection, when the threshold was 0.5, an accuracy of 93.4% was derived, and when the threshold value was set as high as 0.98, an accuracy of 96.03% was derived.

4.2 Result of Deep SVDD Table 2 shows the detection rate by threshold when using the Deep SVDD model. In the case of deep SVDD-based anomaly detection, when the threshold was 0.5, an accuracy of 76.72% was derived, and when the threshold value was set as high as 0.98, the highest accuracy was derived at 95.48%.

4.3 Overall Result It can be seen that the detection rate increases as the threshold value increases in both models, and the highest accuracy is derived when the threshold value is 0.98. Figures 7 and 8 are graphs showing the result of determining the attack status for each model. The orange line represents the attack location, and the blue line represents the loss /score value. It was predicted by setting the threshold based on the top ten thousand points of the loss value of SAE and the score value of Deep SVDD. ROC Curve was created by putting the actual and predicted values, and the AUC representing the area under the ROC curver was calculated, and the higher the AUC, the better the model. Figure 9

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Fig. 7 Graph of the result of SAE

Fig. 8 Graph of the result of Deep SVDD

shows the ROC curves of SAE and Deep SVDD models. In this paper, when the detection rate and the results of AUC are combined, SAE is more effective than Deep SVDD in detecting a single event.

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Fig. 9 ROC curve

5 Conclusions In this study, a technique for detecting abnormal symptoms in ICS datasets using SAE and Deep SVDD was proposed, and after learning using normal data, a loss value for each event subject to analysis data was calculated based on this to detect a single suspicious event. When looking at the threshold of 0.98, the detection rate of SAE was higher than that of Deep SVDD. It was found that SAE-based semi-supervised learning is effective in ICS environments where it is difficult to secure abnormal data compared to normal data. In addition, it is thought that deep SVDD can sufficiently detect an attack if the sequence is reflected for actions that could not be detected as a single event, such as combining 1D-CNN and SVDD. Based on this, the proposed technique is expected to be effective in detecting attacks on time-series data and sequence data as well as ICS in the future. Acknowledgements This work was supported by the Institute for Information & communication Technology Planning & evaluation(IITP) funded by the Government (Ministry of Science and ICT) in 2020 (No. 2018-0-00276, Automated malware-pattern ruleset generation based on deep-learning).

References 1. Stouffer, K., Falco, J., Scarfone, K.: Guide to industrial control systems (ICS) security. NIST Spec. Publ. 800(82), 16 (2011) 2. Min-gyun Kang.: Cyber security status of industrial control systems by country (2019) https:// www.itfind.or.kr

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3. Global Industrial Control Systems Security Market 2016–2020 (2016). https://www.researcha ndmarkets.com/ 4. Kaspersky.: Threat Landscape for Industrıal Automatıon Systems, H2 (2019) 5. Shin, H.K., Lee, W., Yun, J.H., Kim, H.: HAI 1.0: HIL-based augmented ıcs security dataset. In: 13th, USENIX Workshop on Cyber Security Experimentation and Test (2020) 6. NSR.: HAI 1.0 Baseline Model (2020). https://dacon.io/competitions/official/235624/codesh are/1458?page=1&dtype=recent&ptype=pub 7. Zavrak, S., ˙Iskefiyeli, M.: Anomaly-based intrusion detection from network flow features using variational autoencoder. IEEE Access 8, 108346–108358 (2020) 8. Kim, S., Hwang, C., Lee, T.: Anomaly based unknown intrusion detection in endpoint environments. Electronics 9(6), 1022 (2020) 9. Ruff, L., Vandermeulen, R.A., Görnitz, N., Binder, A., Müller, E., Müller, K.R., Kloft, M.: Deep semi-supervised anomaly detection (2019). arXiv:1906.02694 10. Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3(1), 1–130 (2009)

A Study on Intention to Participate in Blockchain-Based Talent Donation Platform Woo Young Lee, Duk Jin Kim, Byeong Ryun Jeon, and Gwang Yong Gim

Abstract Recently, interests in spreading the culture of sharing, especially at the social level, are increasing due to the income polarization, poverty, the rapid growth of civil society, and the increase of importance of public interest in private organizations. According to the Ministry of Government Administration and Home Affairs, Korea’s volunteer participation rate of 2015 has steadily increased to 114% compared to 2011, and according to the World Donation Index evaluation, the ranks in interest and participation in non-monetary sharing have increased to 75th in 2016 and 60th in 2018. Even though social interest is increasing, not many empirical studies on the contribution to talent donation have been made yet. In 2016, a survey was conducted on the change of donation attitude and recognition of donation. The major topics in the area for spreading the donation culture in general were the convenience of donation methods and the transparency of donor organizations. In order to solve the convenience of donation, the mobile and Web based platforms such as Wikipedia, Airbnb, Delivery nation, and Yogiyo, make interactions possible without constraints on place, conditions and the number of user to induce the public to participate in talent donation. In addition, block chain is the distributed computing-based technology of a data forgery and tamper proofing. One person cannot modify the data arbitrarily, but anyone can see the result of the data change. The reason that block chain technology is in the spotlight is because anyone can access the result of data changes. That is, the transparency of the transaction can be secured. In this thesis, we intend to apply these block chain’s characteristics to the talent donation platform. W. Y. Lee · D. J. Kim · B. R. Jeon Graduate School, Department of IT Policy and Management, Soongsil Univ., Seoul, South Korea e-mail: [email protected] D. J. Kim e-mail: [email protected] B. R. Jeon e-mail: [email protected] G. Y. Gim (B) Department of Business Administration, Soongsil Univ., Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_16

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Keywords Talent donation · Volunteer · Platform · Blockchain · Donation · Volunteer activity · Sharing

1 Introduction Recently, due to deepening income polarization and poverty at the global level, interest in the spread of the culture of sharing has been increasing at the social level. With the Seoul Olympics, voluntary sharing culture has spread from government-led volunteer work to the foundation of a beautiful foundation at the private level in 2000. The sharing method is also diversifying, increasing interest and participation in non-financial sharing activities or talent donation, and the sharing activities or talent donation by various subjects are being activated through the segmentation and characterization of volunteer activities. As the core of existing services is shifting from suppliers to consumers, it is changing to platform services that consumers need, centered on consumers and Platform-based services such as Wikipedia, Airbnb, BAEMIN, and Yogiyo are expected to become key competitiveness in the fourth industrial era. Distrust in donation organizations due to embezzlement of donations by certain non-profit organizations and violation of the law by them. As a result, trust in nonprofit organizations and donation organizations is decreasing due to the uncertainty of donation history and use of donations. According to a survey of 620 adult men and women in their 20s and older in 2019, it is expected that the use of the latest innovation technology in the block chain will greatly affect the creation of a healthy donation culture by improving transparency [1]. The purpose of the study is as follows. First, the perception of talent donation is also increasing in proportion to the social interest and trend of talent donation, but there is not much research or discussion accordingly. Second, spread awareness of talent donation and seek practical ways of participation. Third, it presents a theoretical foundation for platforms where talent donors and beneficiaries can easily interact. Fourth, by utilizing the characteristics of the block chain technology, we can check the details of talent donation and path by securing security and transparency, and seek practical ways to participate in talent donors by sharing the flow of funds transparently.

2 Theoretical Background 2.1 Talent Donation Definition Talent donation is a new form of donation that contributes to society through the talents of enterprises, organizations, and individuals. It refers to not only using one’s capabilities for technology development or marketing, but also to contribute to society

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by conducting active activities through donations. In other words, donating one’s talent to society by donating to a new form [2]. Talent often refers to knowledge acquired in life, professional skills, or individual innate abilities. Recently, the word ‘sharing’ has been used interchangeably with volunteering as it attaches to talent. It has the meaning of sharing one’s innate talent or acquired expertise with others for a public purpose. Talent donation is a subset of volunteers, with a focus on expertise, experience, and talent, and is an evolution of general volunteering [3]. In addition, talent donation means that donors share their acquired expertise or innate talent. In other words, it can be said that donation and volunteering are combined in that individual talents that cannot be converted into money are shared, and furthermore, the talents of individuals are not provided as simple labor force like general volunteers. It has the characteristic of being able to contribute to society [4].

2.2 Similar Concept of Talent Donation 2.2.1

Volunteer

Volunteering is derived from the Latin word ‘voluntas’, and was coined with the words, volun-tus in English ‘Volo(Will)’. In that sense, it is voluntary and voluntary free will. Volunteer activity is an act in which an individual or group voluntarily provides time and effort for the community, state and human society at no cost. There are four main characteristics of volunteering. First, spontaneity. Second, nonpayment. Third, public interest. Fourth, durability. Spontaneity refers to selfparticipation in forming a social community with neighbors in need based on talent, time, and experience as one’s own intention. Nonpayment is related to compensation and does not receive financial compensation for volunteering. Public interest is an activity to improve the quality of life to solve problems scattered in neighbors and communities. Durability means participating in volunteer activities continuously for a certain period of time. Traditional general volunteers may have basic training to effectively utilize volunteer resources according to the volunteer environment, but are basically volunteers that do not require special skills or training specialized in volunteer activities. Generally, traditional volunteering is an act of administrative assistance, or finding the need to provide immediate direct service without any special preparation [3].

2.2.2

Pro Bono

Pro Bono is shortened word for Latin ‘for the good of the public’ (Pro Bono Publico). In the United States, the public interest that lawyers provide for the socially disadvantaged is commonly referred to as pro bono. It means providing unpaid legal services

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to underprivileged people who cannot afford to hire lawyers, and the American Bar Association encourages them to contribute at least 50 h to pro bono activities during the year, while South Korea has revised its 2003 law to encourage 30 h of public service annually. Pro bono and talent donation have something in common: volunteer work utilizing one’s talents, But the biggest difference is that at the organizational level, the talent donation serves the non-profit sector with talent or professional skills at the individual level [3].

2.2.3

Talent Donation Trend

The history of private volunteering in Korea has grown in quantity over 30 years in various events, starting with the Asian Games in 1986 and the Seoul Olympics in 1988. The ranking of the world donation index is also on the rise, and according to statistics from the National Statistical Office, the voluntary participation rate for adults over the age of 20 in Korea increased to 4.5% in 2007 and 16.5% in 2013, increasing participation and interest in non-monetary sharing.

2.2.4

Volunteer Participation Theoretical Background

The concept of donation is simple. It is a comprehensive concept that encompasses giving, philanthropy, and charity. Giving means the transfer of tangible and intangible resources owned by the person trying to help in one direction to the person in need, charity is an act based on personal interest and compassion, such as tolerance and compassion for those around us, and feels the satisfaction of the donor itself rather than the result of charity [3]. Philanthropy is a voluntary service act for the purpose of a planned public interest [5], and includes the meaning of an organized donation act at a transcendental level towards humanity beyond the level of personal needs satisfaction [6]. These complex types of donation can be divided into temporal donations to donate personal talent or time, and material donations to donate money or goods, that is, monetary donations. Temporal donation is divided into volunteer work in terms of providing non-monetary human services, although there are academic differences over the lower area so far. In a study on contributing determinants, Yoo Soo-jin conceptualized volunteering as a time donation based on the fact that volunteering is an act of giving time and money to donors, just like donating materials, and that when volunteering is done, financial spending is reduced [7]. Talent donation, which is the subject of research, is also a type of time donation based on one’s expertise, special talent or long experience. As we saw earlier, talent donation is a subset of volunteer work that can be divided into differentiated time donations and is a kind of technology-based volunteer work. Academic research on talent donation has yet to be established as a clear starting point. Talent donation is a time donation derived from volunteer work, which is basically a combination of volunteer work and system.

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2.3 Talent Donation Platform 2.3.1

Platform Concept

A platform is a place that passes by, not a destination, where groups with different intentions share and broker the value they want to obtain services and content. The advantage of the current platform is that the development of information and communication technology enables interaction without restrictions on the number of users, space or situation in a network environment, such as PC or mobile [8].

2.3.2

Donation Platform Trend

Korea’s leading donation crowdfunding platform is operated by IT giants as social contribution projects, including Naver (Happy Bean) and Kakao (Kakaotogether). Users of these platforms can select the projects they want from each portal site to check their stories, voluntarily donate as much as they want, and also receive income deductions and donation receipts. As such, the platform has become easily accessible in our daily lives, and by utilizing the platform, talent donation should also be a talent donation that can be easily connected by talent donors and beneficiaries.

2.3.3

Information System Success Model Prior Research

A platform is an information system, as an element of the information system perspective of the talent donation platform, the research of this study, the quality of the information system success model was cited as the main factor. DeLone and McLean’s information system success model [9] starts from the initial model and continues to be studied until now and continues to develop as the model changes through many verification by researchers (Fig. 1).

2.4 Blockchain 2.4.1

Blockchain Definition

Blockchain was first introduced by Satoshi Nakamoto with the advent of Bitcoin, which was invented in 2008. Bitcoin was introduced in a paper titled ‘Bitcoin: A peer-to-peer Electronic Cash System’ published in October 2008 in the Cryptographic Technology Community Maine, in this paper, the block chain technology was described, proposing a method to prevent double payments using the P2P network [10].

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Fig. 1 DeLone and McLean’s information system success model [9]

Bitcoin service was the first case implemented and commercialized as a block chain technology in 2009, and it was an innovative technology that allowed transactions between individuals without a third party recognized through verification by P2P participants, but it received little attention at that time. Recently, however, the block chain has become an innovative technology and its potential has become known as a next-generation technology for financial transactions, and the scope of its use is gradually expanding. Blockchain is a compound word of chain that means

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connection with a block. Each containing data is connected to the previous block through validation, and the block chain can be said to be a connected structure of this data. In other words, a block chain can be seen as a form of distributed database, and the structure of processing and storing data consists of a block’s connection structure.

2.4.2

Blockchain Characteristics

Among the block chain characteristics identified through prior research, the typical characteristics are reliability, security, availability, diversity, economics, decentralization, and transparency. These features indicate that the most active and urgent application of the block chain technology is the donation and the relationship exists, but they are “non-profit organizations (NGOs), “public aid (ODA) projects,” and “social investment” that have not been transparent or systematized in the existing system. If a sponsor, donor, or talent donor confirms a clear intention of participation or confidence to make a donation to a non-profit organization (NGO), they can identify and apply the appropriate characteristics of the block chain. Talent donation intentions related to transparency and reliability can be found in many studies of non-profit organizations (NGOs) or donations. Donating money is generally not a rewarding concept for giving money and having goods, and because it is an ‘altruistic action’ for others or society, it relies on ‘trust’ based on objective grounds and experiences for the donor to evaluate non-profit institutions, even decisions for donation [11]. Transparency of non-profit institutions (NGOs) means that organizations should be objective and sincere, learning, recognizing, and accountable for their own mistakes and actions as well as good achievements [12].

2.4.3

Use of Blockchain

Transparency is what is needed most to promote donation culture. Only when the process of managing and using the profits and profits generated by the talent donation platform is transparent, that it can the form reliability of the talent donation platform need to improve. Blockchain is a technology that can prevent forgery and tampering with distributed computing technology-based data and can ensure transparency in transactions by a structure in which everyone participating in the block chain network owns a ledger for transactions. In addition, when a block is added through additional transactions, illegal transactions can be prevented because the validity of the added transaction must be verified by everyone participating in the blockchain network. Utilizing these block chain features, it applies block chain technology to the platform so that talent donors and participants can transparently see how all the talent and goods traded on the talent donation platform are used.

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Fig. 2 Research model

3 Research Design Section 3 presents a research model based on prior research and theoretical background discussed in Sect. 2 to conduct empirical research on the intention to participate in the Blockchain-based talent donation platform, and after establishing a hypothesis to identify the correlation between each research variable in the research model, a survey item was organized to measure the operational definition of the measurement variable and the concept of the variable based on the preceding study.

3.1 Research Model This research was intended to verify the quality of the information system called the talent donation platform and to conduct empirical research on security and transparency acceptance among the characteristics of the block chain technology (Fig. 2).

3.2 Research Hypothesis Through prior research on the theoretical background of volunteer participation and the theoretical basis of volunteer behavior research model, two factors such as social psychological factors and personal background factors could be derived as factors that influence the intention to participate in talent donation. Social cognitive theory,

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Table 1 Research hypothesis Research hypothesis H1

The motivation for participation in talent donation will have a positive (+) effect on the intention to participate in the talent donation platform

H2

Talent donation social responsibility will have a positive (+) effect on the intention to participate in the talent donation platform

H3

The need for compensation for talent donation will have a positive (+) effect on the intention to participate in the talent donation platform

H4

System quality will have a positive (+) effect on platform user satisfaction

H5

Information quality will have a positive (+) effect on platform user satisfaction

H6

Service quality will have a positive (+) effect on platform user satisfaction

H7

User satisfaction will have a positive (+) effect on the intention to participate in the talent donation platform

H8

The security of blockchain technology will have a positive (+) effect on the intention to participate in the talent donation platform

H9

The transparency of blockchain technology will have a positive (+) effect on the intention to participate in the talent donation platform

personal background is motivation-behavioral inconsistency, Social psychological factors were mainly based on social exchange theory. Based on the relevant studies using the multi-dimensional model of Schanning, the Trot psychological contract model, Smith’s general behavior and continuous phase model, and the information system success model of DeLone and McLean (2003), the research model was designed by setting volunteer participation, information system factors, and block chain characteristics as independent variables, with user satisfaction parameters, and the degree of talent donation platform participation as dependent variables. In this research, the dependent variable refers to the willingness to participate in talent donation, and the following hypotheses were derived through the construction of the research model (Table 1).

3.3 Operational Definition of Variables The operational definitions for independent variables, parameters, and dependent variables included in the research model and the research theory are summarized as shown in Table 2.

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Table 2 Operational definition of variables Variable Volunteer participation factors

Definition

The degree to which you [12–15] want to help others or improve the well-being of others (altruistic motives) and the extent to which you want to use your own knowledge or skills (selfish motives)

Social responsibility

Degree of willingness to [3, 13, 15–18] benefit others belonging to a wide range of society

Need for compensation

Degree of necessity of financial and non-monetary compensation for talent donation

Platform system factors System quality

Blockchain characteristics

References

Motivation for participation

[3, 13, 15–17]

Convenience of using the [19–25] platform system, simplicity of procedure, and degree of safety for use

Information quality

The accuracy and [19, 20, 22–25] usefulness of information provided by the platform, and the degree to which the latest information is easily understood

Service quality

The ease of communication, efficiency and reliability of the services provided by the platform, and the degree of providing a segmented and customized service

[20–22, 25]

User satisfaction

The overall level of satisfaction users feel with the platform system

[26, 27]

Security

Degree of belief that it is safe from external hacking and can prevent fraudulent transactions and data forgery

[28–32]

Transparency

The degree to which you [30, 32–34] believe that by sharing all information, you can confirm accurate information (continued)

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Table 2 (continued) Variable Dependent variables

Intention to participate in the talent donation platform

Definition

References

Degree of willingness to use or introduce a talent donation platform

[3, 35]

4 Empirical Data Analysis In this study, research models were developed based on theoretical background and prior studies to understand the intention of participating in the Blockchain-based talent donation platform, and research hypotheses were established. An empirical analysis of each hypothesis was performed for verification. Demographic characteristics were identified through frequency analysis, feasibility analysis, and reliability analysis. Analysis were conducted on the measurement variables, SmartPLS3.0 and SPSS18 were used for statistical analysis. First, the PLS Algorithm was performed to evaluate the measurement model, and the internal inertia reliability, concentrated feasibility, and discriminative feasibility were verified. Next, bootstrapping and blindfolding were performed for the verification of mediated effects, evaluation of structural models, and hypothesis testing. 195 survey data collected during October 2019 were analyzed for the hypothesis verification of this paper. Among the respondents, 138 (70.8%) were male and 57 (29.2%) were female. Twenty-seven (13.8%) were in their 20s, 55 (28.2%) in their 30s, 90 (46.2%) in their 40s, 22 (11.3%) in their 50s, and one (0.5%) in their 60s. 109 college graduates (55.9%), followed by 82 graduate students (42.1%) and four high school graduates (2.1%).

4.1 Exploratory Factor Analysis Factor analysis and reliability were analyzed for exploratory factor analysis using SPSS 18 to explore the inherent relationship between the measurement variable and the relative variable. Factor analysis used the varimax rotation method and principal components analysis and deleted if the factor loading value is less than 0.5 [36]. The reliability of the late variable was measured by calculating the Cronbach’s alpha value, and all variables showed the desired confidence of 0.7 or higher [37, 38].

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Table 3 Internal consistency reliability Internal consistency reliability

Convergent validity

Categorization

Criteria

References

Cronbach α

≥0.7

[37, 38]

rho

≥0.7

[39]

CR (Composite reliability)

≥0.7

[38]

Outer loading relevance

≥0.7

[40]

AVE (Average Variance Extracted)

≥0.5

[41]

4.2 Confirmation Factor Analysis Using Smart PLS 3.0 which uses the PLS method, a conformation factor analysis was performed. The analysis of whether Internal Consistency Reliability, Convergent Validity, meets the criteria in Table 3 and all of them are met.

4.3 Discriminant Validity Discriminant validity should be specified in order to measure the ability between variables in this study. Fornell-Larker’s criteria for evaluation of the discriminant equivalence by potential variables should be greater than the highest of the correlations between potential variables [42]. As in the Table 4, the value of AVE square root for each potential variable was greater than the highest value of 0.554, which is the correlation between the latent variables, so the discriminant value was met. Table 4 Result of discriminant validity NC

Sec

US

SR

SerQ

SysQ

InfQ

MP

NC

0.848

Sec

0.094

0.843

US

0.101

0.326

0.714

SR

0.125

0.182

0.236

0.768

SerQ

0.08

0.48

0.441

0.27

0.728

SysQ

0.205

0.446

0.38

0.089

0.462

0.783

InfQ

0.206

0.0405

0.305

0.23

0.472

0.554

0.758

MP

0.251

0.279

0.273

0.229

0.274

0.336

0.305

0.671

IP

0.107

0.222

0.49

0.058

0.328

0.361

0.216

0.312

IP

0.809

NC Need For Compensation; Sec Security; US User Satisfaction; SR Social Responsibility; SerQ Service Quality; SysQ System Quality; InfQ Information Quality; MP Motivation For Participation; IP Intention to Participate

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4.4 Hypothesis Test First, the hypotheses H1 (t-value 1.379, p-value 0.084) and H3 (t-value 0.095, p-value 0.462) that the independent variables such as social responsibility and the need for compensation influence the intention to participate in the talent donation platform were rejected. Hypothesis H5 (t value 0.361, p value 0.359) that information quality affects user satisfaction was rejected. Finally, the hypothesis H8 (t value 0.024, p value 0.491) that security affects the intention to participate in the talent donation platform was rejected. The remaining hypotheses all had a significant defined effect. Therefore, hypothesis H1 (t value 2.377, p value 0.009), hypothesis H4 (t value 2.79, p value 0.003), hypothesis H6 (t value 3.7, p value 0), hypothesis H7 (t value 6.103, p value 0), hypothesis H9 (t value 0.024, p value 0.491) were all adopted. This means that system quality and quality of service affect user satisfaction for talent donation participants. Also, motivation for participation, user satisfaction, and transparency are affecting the intention to participate in the talent donation platform (Table 5). Table 5 Results of hypothesis testing Original sample (O)

Sample mean (M)

Standard deviation (STDEV)

T values

P values

Adoption

Participation motivation > participation intention

0.188

0.181

0.079

2.377

0.009

Selection

Social Responsibility > intention to participate

0.121

0.074

0.088

1.379

0.084

Dismiss

Need for compensation > intention to participate

0.008

0.027

0.081

0.095

0.462

Dismiss

System quality > user satisfaction

0.211

0.222

0.076

2.79

0.003

Selection

Information quality > user satisfaction

0.033

0.048

0.092

0.361

0.359

Dismiss

service quality > user satisfaction

0.328

0.335

0.089

3.7

0

Selection

User satisfaction > intention to participate

0.427

0.412

0.07

6.103

0

Selection

Security > participation intention

0.002

0.004

0.103

0.024

0.491

Dismiss

Transparency > intention to participate

0.132

0.128

0.079

1.68

0.046

Selection

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5 Conclusion In this research, the causes of participation in volunteer work (motivation, social responsibility, compensation need) were verified for the factors that affect the intention of participation in the talent donation platform through the system factors (service, information, system quality) and the block chain characteristics (security, transparency). To this end, a new type of model called talent donation platform was defined, and the research model was designed through the theory of volunteer participation, the successful model of the information system from the system perspective, and the prior study of the characteristics of the block chain. Also, Data from a total of 195 copies of the questionnaire were collected to verify the hypothesis through empirical analysis. Based on the results, the following results was obtained. In participating in talent donation platforms, convenience (system quality, service quality) for platform systems based on transparency in block chain technology is more important than social responsibility and need for compensation. First, among the factors affecting the willingness to participate in talent donation, the stronger the motivation for participation as a personal background factor, the higher the willingness to participate. The social responsibility and the need for compensation in terms of social and psychological factors do not have a direct impact on the willingness to participate in talent donation. Second, among the factors of information system, the system quality and service quality, except for information quality, were shown to have taken a positive note in relation to user satisfaction. This seems to recognize the convenience of using the talent donation platform rather than the information provided by the talent donation platform. Third, talent donation systems should operate more transparently than other systems, and in the case of blockchains, the biggest feature is transparency. Among the two characteristics (security, transparency) of block chain technology, the hypothesis that it affects participation in talent donation platforms has been confirmed that only transparency has a positive effect. As mentioned earlier, with the rising wall of distrust in donor organizations being emphasized day by day, the block chain, which prevents fraudulent transactions and ensures data integrity, seems to be expected to be a necessary technology for talent donation.

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The Effects of Product’s Visual Preview and Customer Review on Sale Performance in Mobile Commerce Eun Tack Im, Huy Tung Phuong, Myung Suk Oh, Jun Yeob Lee, and Simon Gim

Abstract Due to the development of mobile communication and the spread of mobile devices, the use of mobile commerce (m-commerce), which enables commercial activities anytime, anywhere, through the Internet, is becoming more active. Compared with e-commerce, m-commerce has excellent convenience, but consumer behavior such as product searching, preference and purchase are performed on a relatively smaller screen. Therefore, this paper extracted images attributes through vision API and Deep-CNNs, and sentiment analyzed customer reviews by separating them into Material, Size, Price, and Delivery. In order to study the effect of such information on consumer behavior, regression analysis and mediating effect analysis based on the Stimulus-Organism-Response (S-O-R) model. The model states that external stimuli affect the individual’s psychological state and ultimately the actual behavior. This paper has classified the characteristics of information on customer behavior in m-commerce, and it had been confirmed that images and reviews had a significant effect on the performance of m-commerce sales products through S-O-R model. Keywords Mobile-commerce · Customer attraction · Sale performance · Image information processing · Computer vision

E. T. Im (B) · H. T. Phuong · M. S. Oh Graduate School of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] H. T. Phuong e-mail: [email protected] M. S. Oh e-mail: [email protected] J. Y. Lee College of Economics, SungKyunKwan University, Seoul, South Korea e-mail: [email protected] S. Gim SNS Marketing Research Institute, Soongsil University, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_17

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1 Introduction Mobile-commerce (m-commerce) refers to a commercial activity centered on mobile devices. As a new business centered on mobile appeared due to the proliferation of mobile devices, m-commerce is also spreading [1, 2]. Since m-commerce enables customers to search for products anytime and anywhere through wireless devices, it induces more purchases than in e-commerce in the web environment [3]. However, m-commerce has an inherent limitation of having to display various information such as photos and texts on a relatively small screen compared to ecommerce [4]. In addition, according to the information overload theory, if customers are exposed to a large amount of information, their memory may be burdened and interest in products may decline [5]. Research on the systemic characteristics of mobile commerce related to customer satisfaction, acceptance, and trust [6–8] has been continuously conducted, but research on how the attributes of information on product pages sold in m-commerce affect customer behavior are insufficient. This paper was conducted on the effect of information attributes on customer behavior such as preference and purchase in mobile commerce, which is rapidly growing in commerce. So it extracted data such as price, favorite, sale image and review of t-shirts sold in shopee, m-commerce that has the most users in Southeast Asia. Considering that m-commerce is performed on a smaller screen than e-commerce, the attributes of the image were extracted. An object such as “number of model” in the image was detected using Vision API. In addition, image attributes such as “color harmony” and “Depth of Field” were extracted using the Deep-CNNs model proposed in the study of Malu et al. [9]. Reviews written by customers as well as “Start-point” and “Rating volume”, which are actively used in research fields related to customer decision-making in existing commerce, were divided by attribute and sentiment analysis. As a framework for analyzing collected data, the S-O-R model [10] was introduced. The S-O-R model is the theory that customers’ response actual behaviors such as purchasing through emotional internal evaluations (Organism) such as positives and negatives from stimuli related to the shopping environment. Based on this theory, this paper tried to reveal whether the characteristics of the previously extracted information affect customer behavior such as customer preference and purchasing behavior through regression analysis and mediating effect analysis Theoretical Background.

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2 Literature Review 2.1 Visual Preview Attributes and Sale Performance Kim and Lennon [11] indicated that product information played an important role in customer purchase decisions and played a critical role in satisfying consumer needs for adequate product information for purchase decisions [11]. Since product photos directly aid consumers’ understanding of products, retailers often put a lot of effort into polishing them. However, even though the information, offered from online vendor, could enhance customer satisfaction, there is a drawback. According to the theory of information overload, if customers suffer from a large amount of information which overload their perception tolerance, the information detract attention of customer. This could negatively influence purchase decisions of customers [12]. Furthermore, Xia et al. [5] research shows that fulsome visual clue could burden client memory [13], because of time and perception limit that consumers have [14]. There is limited research on the impact of product photos on purchase decisions. Most previous studies took an experiment-based approach, which delivered strict theories on some aspects of product photos [15]. In this study, by using image processing technique-based approach, this research found out which attributes of product images offered by m-commerce sellers are related to customers’ intention of purchases and even actual purchases. Due to the display screen interface of mcommerce are generally different from e-commerce, the effects of images might be different from the research with e-commerce setting.

2.2 Review Attributes and Sale Performance As a part of electronic word-of-mouth (eWOM), online reviews carry important information about products and services. The reviews enable consumers to shape awareness about the overall quality of the goods as well as specific attributes related to the goods and purchase process in online shopping platform [16–18]. Online reviews usually consist of numerical ratings and textual reviews [19]. The numeric ratings, which are the most common format and consumer can find easily, indicate the overall assessment of the product, and the textual review can reveal the detail experience or sentiment of consumers who have already purchased and used it [16]. As the information contained in ratings and textual review and the effort required to obtain the information are different, consumers dealing with decision making may adopt certain heuristic strategies to simplify their own considerations and then proceed with more effortful information processing to make a final decision [20]. This means that consumers refer to the both components appropriately even they work separately in the decision. Thus, including both numeric ratings and textual reviews or sentiments in economic modeling is essential for research.

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It has been extensively studied about the impact of the reviews on performance. Ketelaar et al. [21] found that star ratings affect consumers’ intention to buy. Hu et al. [22] suggested that consumers with a higher preference for a products make more purchases and reviews than other consumers, so the reviews tend to be more positive. In the research of Choi et al. [23] which examined interactive effects of ebook previews and reviews on individual purchase decisions, they found both when consumers viewed previews and reviews, the purchase decisions rose. Furthermore, the exposure of previews and review characteristics had a moderating effect on the performance. More in depth, Li et al. [16] examined the direct effect of online reviews on performance and the indirect effect of the reviews that mediated star point using a Joint Sentiment-Topic model (JST) which can extract the topics and those sentiments in textual review. In the analysis, they suggested that only the positive aspects of the product reviews have the mediation and direct impact on sales and the negatives are not found to have any impact on sales. They suggested one possible reason that the additional effect may be not detected because negative aspects were heavily factored in the variable of average sentiment. Hu et al. [20] suggested that sentiments in the textual reviews may have a greater impact on the final decision of consumers than star ratings. They found that the ratings have only an indirect impact on sales through review sentiments and the sentiments have direct impact on sales. Based on the analysis above, this research decide to use numeric ratings and textual sentiments of the product aspect together in our regression model without overall sentiment of the product, and then estimate the direct and indirect impact of the reviews on economic performance.

2.3 Stimulus-Organism-Response(S-O-R) Model The Stimulus-Organism-Response (S-O-R) model, which is partially similar to an information processing model focused on customer’s cognitive system how inputs processed and lead to reaction [24, 25], explains decision-making by consumers affected by external stimulus [10]. It is a conceptualized theory that consumers are stimulated by the external environment and this stimulation affect organism such as the customer’s emotions, thoughts, and psychological behavior. Ultimately, it affects the customer’s actual behavior (Response) through organism [26]. The S-O-R model is a theory that explains consumer emotion internalization and actual behavior in the online environment in the field of commerce. Mo et al. [27] conducted a study based on the S-O-R model to analyze the impact of the review system on the C2C website on customer purchase behavior. It was confirmed that reviews such as picture review, positive text review, and star-rating have a significant effect on customers’ purchasing behavior. Bigne et al. [28] conducted a study on customer evaluation and decision-making for online reviews of restaurants through eye-tracking and surveys. Through the credibility, informativeness, persuasiveness, and helpfulness of online reviews, customers brought positive internal evaluations such as trust. Through this, it was confirmed

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that they ultimately made a positive evaluation of the restaurant and would like to visit. Kim [29] conducted a study on the information processing process of consumers based on the degree of detailed product description and the size of photos provided in commerce through the S-O-R model. Through this study, it was confirmed that information such as photos and product descriptions provided by companies in commerce influences customer decision making.

3 Research Design This paper’s research model is designed based on previous researches on S-O-R model to study the effect of image preview and text review on consumer behavior. This paper was aimed to the effect of image preview and review on customer behavior. For this study, research model was designed based on previous researched on S-OR model [24–29]. This previous researches provides a theoretical basis for research that previews and reviews as external stimuli to affect customer’s positive inner evaluation and ultimately lead to purchasing behavior. Image attributes as stimuli to customer are divided into four groups (information, emotion, aesthetic and social presence) by referring to previous studies on image attributes [5, 15, 30–33]. The information of the image means whether information is provided to the customer through the image, and to measure this, it was detected whether the “Great Shopee Sale (GSS)”, which informs the customer of promotion information in Shopee, is exposed on the image. The emotion and aesthetic of the image were extracted using the Deep-CNNs model, and color harmony and vivid color related to color were extracted to measure the emotion variable, and depth of the field and object emphasis were extracted to measure the aesthetic. For the image’s social presence attributes measurement, research was based on whether the photo contains number of human models. As for the attributes of reviews, star-points and rating volumes, which are widely used in previous studies, were extracted at the HTML parsing step. In addition, in order to measure the attributes of verbal reviews, the product description and review data conducted term-frequency analysis, and were divided into 4 aspects: material, size, price, and delivery. For divided aspects, keywords were extracted for each topic through LDA analysis [34, 35]. Finally, sentiment analysis was performed for each aspect using the sentiment analysis API. The favorite extracted through HTML parsing was used to measure organism, which is an intrinsic evaluation of customer stimulus. Favorite is a display of preference for a product, allowing you to easily access the product page even if you do not purchase it right away. Finally, total sale was used to measure the final response of the customer, and this was also extracted through HTML. The derived research model can be confirmed in Figs. 1 and 2 is the process of extracting data.

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Fig. 1 Research model based on S-O-R model

Fig. 2 Data mining process

Section 4 details the process of extracting attributes.—“Empirical Data Analysis”. Table 1 is the operational definition of the variables used.

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Table 1 Operational definition Variable

Definition

Control variables

Sale price

Price of product, classified as 4 groups, ordered from the cheapest group to the most expensive group

Quantitative reviews

Mean of star-points

Mean rating (1–5) of product, contributed by clients who actually buy the products

Rating volume

The amount of rating records, classified as 4 groups from the fewest group to the largest group

Material

The average sentiment on material keywords from review text

[37]

Size

The average sentiment on size keywords from review text

[17]

Price

The average sentiment on price keywords from review text

[17]

Delivery

The average sentiment on delivery keywords from review text

[16]

Image’s information

GSS

Shopee calls its promotion programs by the name “Great Shopee Sale” (GSS). Products that applied these promotion programs will be affixed with a badge on the bottom left corner of their feature image (the first-order image)

Image’s social presence

Model

Dummy variable represents existence of model in product photograph 1: exist, 0: not exist

[15, 33]

Image’s emotion

Color harmony

Harmony of colors. The large number states colors in image are harmonious

[9, 15, 38]

Vivid color

Diversity of colors. The more colors used in image, the variable approaches 1

[9, 15, 38]

Depth of field

It represents depth of image. [9, 15, 38] The number close to 1 means the image include more perspective by blur the background

Object emphasis

Degree of emphasizing an object [9, 15, 38] compared to the other objects in image

Topical sentiment

Image’s aesthetics

References

(continued)

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Table 1 (continued) Dependent variables

Variable

Definition

References

Favorite

Cumulative favorite of the product, customers can indicate their preferences by clicking on the heart mark when they are interested in the product

[15]

Sales

Cumulative sales of the product, [5] transforms as natural logarithm

4 Empirical Data Analysis 4.1 Data Collection Founded in 2015, Shopee is a mobile application-oriented m-commerce company. It was judged as a suitable research target because it is based on the page where sales products are displayed to consumers. Images of 1,000 products and data necessary for research in the product sales page of t-shirts were collected through crawling for 2 days from August 20th to 21th, 2020. To achieve data, HTML parsing, the general way to extract data from website, is conducted to get image url, mean of star-points, price, rating volume, product verbal preview, customer review favorite and sales.

4.2 Image Attributes Extraction 4.2.1

Image Attributes Extraction Using Vision API

Advantages of image processing techniques allow us to investigate a large set of photo characteristics simultaneously in an empirical study. Python programming and Google Vision API were used for human recognition in product images. First, we ran multiple objects detection function of the API to get information (list of objects and bounding location) data for the objects in the image. We marked the image that has “person” with “human model available” label. A demo of using API can be seen in Fig. 3.

4.2.2

Image’s Attributes Using CNNs

This research used Deep Convolutional Neural Networks to solve this task. The model was proposed by Kong et al. [37], improved and fine-tuned by Malu et al. [9] and kevinlu1211 [38]. The final model was recognized for its high efficiency and

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Fig. 3 Demo for human recognition using Google Vision API

accuracy in evaluating the level of these attributes: “Color Harmony”, “Vivid Color”, “Depth of Field”, “Object Emphasis” [9]. Approach of CNN model proposed Malu et al.[9] can be founded Figs. 4 and 5 is sample extracted from this research.

4.3 Review Aspect Extraction and Sentiment Mining This research extract the aspect from the review text of the product and estimate sentiment scores. The whole process of that is shown in Fig. 6. First, to extract the aspects, a frequency analysis of the words was conducted in the product description corpus and the whole review corpus for T-shirts obtained through web crawling. How the word were used in the review was checked and this research selected only the word within the top 20 of frequency ranking. After that, we classified them to each aspect. In this process, the pattern of word frequency in product description and review came out similar but slightly different. For example, the color of T-shirts appeared frequently in product descriptions, but the frequency of reviews was low, and we only chose the word that appeared in both frequency rankings. To verify the keywords was placed at right aspect, latent Dirichlet allocation (LDA) [34, 35] was used. In extracting keywords for preselected topics, unsupervised classifications are not necessary. Thus, to classify the keywords by pre-determined topics, a corpus was separated and grouped by each topic with sentence units, and this method was

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Fig. 4 Approach for generating attributes of images by Malu et al. [9]

referred to in the literature of Wang et al. [39]. Next, researchers have selected the final keywords and examples of that are shown in Table 2. The natural language API of the Google Cloud Platform was used to perform an entity-level sentiment analysis. Each pair of ‘entity-polarity’ carried from the analysis was grouped through the keywords we already extracted and the average of sentiment polarity in each aspect was used for the final variables.

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Fig. 5 Sample of Shopee product images labeling (Top row: High rated images, bottom row: Low rated image)

Fig. 6 Process of extracting keywords of aspects and mining sentiments

4.4 Data Preprocessing and Coding 1,000 products were extracted through HTML parsing. Among them, 442 products with no product image, no review, no favorite, or no sale were removed and analyzed for a total of 558 products. Descriptive statistics of the extracted variables can be checked in Table 3. Favorite and Sale were changed to nature logarithm for reducing the difference between product pages.

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Table 2 Keywords of each topic and examples in review text Topic

Keywords

Examples in review text

Material

Material, cotton, fabric, spandex, …

“The material is stretchable and looks cooling” “Fabric material is different from the ads photos.”

Size

Size, fit, length, width, L size, …

“I’m so happy with the cutting and size.” “Ordered a L size, actual 2 cm wider than size chart.”

Price

Price, promotion, money, sale, …

“Worth for the price and well received.” “Not worth the price even if it’s cheap.”

Delivery

Delivery, shipping, time, shipment, …

“Prompt delivery by Ninja team.” “Delivery was quite slow but received well.”

Table 3 Descriptive statistics of the data Frequency

Min

Max

Mean

Sale price

558

2.39

129.45

Favorite

558

4.0

2800.0

188.489

Sale

558

9.0

8300.0

212.353

Mean of star-points

558

3.4

5.0

Ratings volume

558

5

1082

Material

558

−0.9000

0.9000

0.3839 0.1570

15.8743

4.648 33.27

Size

558

−0.8000

0.9000

Price

558

−0.9000

0.9000

0.5760

Delivery

558

−0.3667

1.0000

0.7929

GSS (0/1)

(58/500)

0.0

1.0

0.896

color harmony

558

−0.0578

0.7547

0.378623

vivid color

558

−0.3440

0.8260

0.174860

Depth of field

558

−0.2582

0.6782

0.199621

Object Emphasis

558

−0.4037

0.8898

0.612066

Model_number

558

0.0

8.0

0.914

price, rating volume: Dividing the degree into quartile Favorite, Sale were changed to nature logarithm Among 1,000 pieces of data, the following items were deleted (No images, no reviews, no ratings, no sales)

4.5 Regression Analysis This paper was designed based on the S-O-R model to analyze the effect of preview and review characteristics on sales performance in m-commerce.

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In Sect. 4.5.1, the regression analysis of the direct effects of preview and review attributes as stimuli on favorite as organisms, an intrinsic evaluation of customers, was conducted. In Sect. 4.5.2, a regression analysis was conducted on the direct effect of preview and review on sale, which is the result of the final response from customers. In Sect. 4.5.3, the indirect effect of stimulus (preview and review’s attributes) on response (sale) organism (favorite) was analyzed.

4.5.1

Effects of Preview and Review on Customer Preference

Review is an indirect experience through evaluation by a person who has already purchased, and material, price, and delivery attributes can be internalized through indirect experience of review about goods and services. However, size is a relative experience of the size of another person’s body and clothes. As a result, it was difficult to internalize it as his own evaluation. In the emotional aspect of the image, color harmony was adopted (β = 0.894, p < 0.05) as a positive influence, but vivid color was adopted (β = −0.387, p < 0.005) as a negative influence. This is because, as in the information overload theory, various colors (information) cause eye strain, so the important thing is to use less colors and use them harmoniously. Depth of Field was rejected but Object Emphasis was adopted (β = −0.885, p < 0.001) due to negative influence. A lower Object Emphasis means that the T-shirt the seller sells is showing in the same image. This allows consumers to efficiently judge products sold by sellers from one image without the inconvenience of viewing another image. The model number was negatively adopted (β = −0.097, p < 0.01), and this is a similar result to the referenced previous study [33]. The sale price increases the favorite as the price increases (β = 0.139, p < 0.001). Low involvement products can be defined as products with low price and low importance [40], and the process of searching and comparing other products that meet customer expectations compared to high involvement products in purchasing behavior may be omitted [41]. As the price increases, the product has a higher risk. When the price rises, consumers mark their favorite rather than immediate purchase, then explore other similar products to make the final purchase decision. The analysis results can be found in Table 4.

4.5.2

Effects of Preview and Review on Sale Performance

Price has been adopted as a risk as a effect (β = −0.168, p = 0.004). All reviews, except the size, have a direct effect on sale. The reasons for rejection are inferred for the same reasons described in Impact on favorite. All image preview features except GSS have been rejected.

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Table 4 Results of regression analysis (effects on favorite) Independent variable

Estimate (β)

Standard error

t-value

0.139

0.038

3.6724(*** )

Supported

−0.031

0.154

−0.1987

Not supported

0.008

0.001

12.5062(*** )

Supported Supported

Sale price Mean of star-points Rating volume

0.250

0.124

2.0154(* )

−0.119

0.145

−0.8224

Not supported

0.229

0.106

2.1696(* )

Supported Supported

Material Size

Supported/Not supported

Price Delivery

0.594

0.193

3.0791(** )

GSS

0.714

0.134

5.3076(*** )

Supported

Color harmony

0.894

0.357

2.5030(* )

Supported Supported

Vivid color

−0.387

0.189

−2.0523(* )

Depth of field

−0.246

0.344

−0.7159

Not supported

Object emphasis

−0.885

0.257

−3.4417(*** )

Supported

0.032

−3.0574(** )

Supported

Model number *p

< 0.05,

** p

< 0.01,

−0.097 *** p

< 0.001

This means that consumers review the evaluation, costs, and promotions of others when it comes to actual purchasing behavior. Although the social presence, emotion, and aesthetic of the image did not directly affect the customer’s final behavior, purchase, this also explains the need for further analysis of whether the image was indirectly affected by the intrinsic evaluation of the image. The analysis results can be found in (Table 5).

4.5.3

Indirect Effects of Preview and Review as Stimulus on Sale

Finally, this paper analyzed the effect of preview and review characteristics as stimuli on customer behavior through internalization. To measure the indirect effect, the 3-step method claimed by Baron and Kenny [42] was used. Also, the method claimed by Sobel [43] was used to evaluate the significance of the effect. Indirect effects and significance were analyzed according to Eqs. 1 [42] and 2 [43]. As a result of analyzing the indirect effect, it was confirmed that the attributes of all images except the Depth of Field, which did not directly affect the sales, have a significant indirect effect. This means that the characteristics of the image affect the sales through the positive evaluation of internalized customers. Step 1: Independent → Dependent Y = β10 + β11 X + ε1 Step 2: Independent → Mediator Me = β20 + β21 X + ε2

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Table 5 Results of hypotheses tests (effects on sales) Independent variable

Estimate (β)

Standard error

t-value

Supported/Not supported

Sale price

−0.168

0.034

−4.890(*** )

Supported Supported

Mean of star-points

0.409

0.140

2.917(** )

Rating volume

0.009

0.001

15.656(*** )

Supported

Material

0.245

0.113

2.167(* )

Supported

Size

−0.193

0.132

−1.467

Not supported

Price

0.223

0.096

2.317(* )

Supported

Delivery

0.379

0.176

2.153(* )

Supported Supported

GSS

0.430

0.123

3.513(*** )

Color harmony

0.629

0.326

1.932

Not supported

Vivid color

−0.202

0.172

−1.176

Not supported

Depth of field

0.098

0.314

0.312

Not supported

Object emphasis

−0.256

0.234

−1.091

Not supported

−0.033

0.029

−1.155

Not supported

Model number *

p < 0.05,

** p

< 0.01,

*** p

< 0.001

Step 3: Independent + Mediator → Dependent Y = β30 + β31 X + β32 Me + ε3 Indirecteffect = β 21 × β32 (= β11 − β31 )

(1)

Equation 1. Baron and Kenny’s Approach for measuring indirect effect [42] zβ21 β32 = 

β21 × β32 β21 × Seβ32 2 + β32 2 × Seβ21 2 2

Se: Standard error

(2)

Equation 2. Sobel’s test for measuring indirect effect’s significance [43]. For the analysis of the mediation effects, the direct effect of the independent variable on the dependent variable (Step 2) and the direct effect of the independent variable on the parameter (Step 1) can be found in Tables 4 and 5. Therefore, we conducted a regression analysis on the effects of independent variables and parameters on the dependent variables (Step 3), and the results are shown in Table 6. The final results of the mediated effects analysis are shown in Table 7.

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Table 6 Result of 3-step regression analysis for mediating effect verification Step

Independent variable

Estimate (β)

Standard error

t-value

3

Sale price

−0.272

0.020

−13.610

Mean of star-points

0.432

0.080

5.371

Rating volume

0.003

0.000

8.308

Material

0.058

0.065

0.895

Size

−0.104

0.076

−1.382

Price

0.052

0.055

0.935

Delivery

−0.065

0.102

−0.641

GSS

−0.103

0.072

−1.424

Color harmony

−0.039

0.188

−0.207

Vivid color

0.087

0.099

0.879

Depth of field

0.282

0.180

1.567

Object emphasis

0.405

0.136

2.984

Model number

0.039

0.017

2.336

Favorite

0.747

0.022

33.341

Table 7 Result of indirect effect and significance

Independent variable Sale price Mean of star-points

Indirect effect 0.1037 −0.0228

t-value 3.6487(*** ) −0.1986

Rating volume

0.0060

11.7049(*** )

Material

0.1865

2.0109(* )

Size

−0.0888

−0.8217

Price

0.1711

2.1640(* )

Delivery

0.4439

3.0647(** )

GSS

0.5331

5.2393(*** )

Color harmony

0.6679

2.4949(* )

Vivid color

−0.2891

−2.0475(* )

Depth of field

−0.1840

−0.7154

Object emphasis

−0.6611

−3.4220(*** )

Model number

−0.0726

−3.0433(** )

*p

< 0.05, ** p < 0.01, *** p < 0.001

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5 Conclusions 5.1 Research Summary and Implication This paper was a study based on the S-O-R model to confirm that the attributes of image preview and verbal review lead to the final action through the customer’s intrinsic evaluation. Image attributes were extracted using API and Deep-CNNs [9], and verbal review characteristics were extracted using LDA [16, 17, 36] and sentiment API. Regression analysis was conducted to analyze the direct effects of the extracted attributes on the favorite to measure the customer’s internalized organization and the sale to measure the customer’s response. Finally, through internalized evaluation, this paper conducted mediating effect analysis on whether preview and review indirectly affect customer behavior. Most of the characteristics of preview and review on the favorite had a significant effect. However, the attributes of all reviews except GSS did not significantly affect the sale. In the mediation effect analysis, the attributes of all images except Depth of Field significantly affected the sale. This means that, theoretically based on the S-O-R model, the attributes of the image have a significance indirect effect to the behavior through the customer’s intrinsic evaluation (favorite). In addition, this paper has confirmed that the evaluation of consumers who have already purchased is a powerful factor that directly influences behavior for customers who purchase later. This paper has the following implications. First, the research the effect of product image characteristics on m-commerce sales performance. Second, through the S-OR model, this research examined the direct, indirect and indirect effects to explain customer behavior in m-commerce. Lastly, by analyzing the preview provided by the seller to the product explorer and the review evaluated by the customer who has already purchased, the customer behavior was explained by using the complex information that the consumer will encounter when purchasing the product.

5.2 Limitations Although this paper has tried to explain customer behavior in terms of the nature of information, it has the following limitations. First of all, this paper was studied only on men’s t-shirts. This means that the analysis results are limited in explaining the whole m-commerce. Therefore, there is a need for analysis along with other products sold in m-commerce. Second, there is a limit to the control of variables. The collected data was not controlled by temporal factors. Products that are uploaded quickly may be exposed

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to consumers relatively more. Therefore, it shows high favorite and sale performance regardless of the attributes of preview and review. Thus, in future research, control is needed when product pages are uploaded or when reviews are written. Finally, the data is collected through HTML parsing and cannot describe the continuous behavior of a customer. This has the limitation of not being able to verify whether the characteristics of the information used were exposed by customers. In future research, customer decision making continuous process from customer exposed to information to actual behavior using customer’s log data or eye-gaze processing data is needed to explain the continuous relationship of the effect of information on customer behavior.

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A Study on Factors Affecting the Intention to Use Library Information System Ju Hyung Kim, Sang Bin Jeong, Kyoung Jin Lee, and Gwang Yong Gim

Abstract The new virus COVID-19 outbreak in Wuhan, China on Dec. 08, 2019 has had a huge impact on all sectors of society including economy, politics, and science sector, around the world. As a result, the government is continuing its contactless lifestyle by implementing life prevention guidelines which prevent the spread of COVID-19 by minimizing human-to-human contact. Several public user services are also being converted to non-contact way, changing the overall environment of our society. The Library Information System, one of the user services, provides various services through online and offline, including lending and reading books, non-books lending and reading, and operating cultural spaces. However, due to the influence of COVID-19, online user services are required to be expanded, making it important for the Library Information System to improve stability and reliability. This study defined the user service of the Library Information System and examined the variables that are needed for the construction of next generation Library Information System through a group of experts. Based on these selected factors, the research contributes to the construction of the next generation Library Information System with the correlation results of the empirical analysis. Keywords Library information system · COVID-19 · User service · Construction · IS success model · AHP · Delphi

J. H. Kim · S. B. Jeong · K. J. Lee Department of IT Policy and Management, Graduate School of Soongsil Univ, Seoul, Korea e-mail: [email protected] S. B. Jeong e-mail: [email protected] K. J. Lee e-mail: [email protected] G. Y. Gim (B) Department of Business Administration, Soongsil Univ, Seoul, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5_18

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1 Introduction Library Information System is a user service provided in the library via online and offline, and it can provide the service of library guidance, search, cultural service and open space. Classical Library Information System was a system that provided simple information such as the status of facilities, data status, and organization status of the library. Currently, due to the development of technology, it is possible to search online and receive e-book service through mobile or PC, and a system that can receive data remotely by applying for delivery service through library information system. In addition, the function to find the desired data quickly and accurately is provided by recommending the data using the big data-based Curation service in the library information system. These Library Information Systems are being expanded and converted into contactless online user services and the user services are also diversified due to COVID-19, which occurred on December 8, 2019 in Wuhan, China. With the development of changing technologies and demand for contactless services, the need for stable system infrastructure and various information services is increasing. Therefore, it is necessary to overcome the limitations of library information system quality from the perspective of providers and users for stable systems and various information services. In this research, to identify the factors affecting the intention to use library information system, the priority of the factors selected in the expert group is examined, and the empirical analysis for finding the impact relation is analyzed using the information system success model (D&M model). The goal of the research is to ensure that these analyzed impact relations contribute to the improvement of the Library Information System.

2 Theoretical Background 2.1 Status and Concepts Definition Recently, its functions have been expanded to include the right to use electronic resources such as e-books, big data, and original database. In addition, due to the spread of smartphones, SNS and tablet PCs, library services are becoming a smart cultural space beyond digital information libraries, including the participation of general users, sharing and integrating information resources, and expanding the base of Ubiquitous computing are required. The rapidly developing library information system environment puts a heavy burden on the operation of library information system in terms of operation of library. Moreover, it will be subject to technical subordination to system supplier as well as the heavy work of computer personnel who operate system in terms of management. In response, advanced foreign libraries began to apply to library information systems

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with great interest in Cloud Computing and Open Source Software-based systems as an alternative to significantly reduce costs, respond quickly to new services or systems, and reduce risk factors that may arise from reliance on certain products.

2.2 Literature Review More specifically, Lee [1] found that the content and features offered had a positive impact on the user’s perceived usefulness. Kwak and Shin [2] suggested that a library information system with excellent system performance is essential to the efficiency of the tasks in library. Noh [3] conducted a study comparing librarians with the perception of general users. Choi [4] suggested that the quality of internal service of university education services would lead to customer satisfaction if internal members were satisfied. In addition, the satisfaction and motivation of service users in the hospital’s service perspective were examined as key factors in determining the quality of service provided to customers [5]. Yi [6] identified the differences between the self-service quality assessment and the consumer service quality assessment in the service sector. In the research on the quality, satisfaction, and utilization of the library information system, Gang [7] examined the difference between librarians and users’ perception of the library’s response to environmental changes. Quality means the factors that can evaluate the information system [8]. Typical information system quality is known as system quality, information quality and service quality, but in the case of service quality, it is excluded and studied in some studies because it is not related to the system [9]. Recently, interface design quality has been proposed as information system quality [10]. System quality refers to the quality of the system itself appearing in the information system, including the stability, accuracy, and flexibility of the system [11]. Information quality indicates the quality of information provided by the system and typically includes accuracy, timeliness, and completeness [12]. Interface design quality being studied recently means the aesthetic quality of information systems themselves, such as websites and mobile devices. Users will stop using the information system when they feel that the quality of the information system is not good. In non-face-to-face situations, the discontinuance of use is more pronounced than in face-to-face situations [13]. Without trust, users will feel unpleasant emotions, which will soon lead to discontinuance of use [14]. System use is an important factor in the quality measurement of information systems [15]. Alzahrani [12] studied the relationship between digital library information system quality and user behavior, and system quality and information quality had a significant impact on the intention to use. The quality users evaluate is relatively lower for smaller companies than for larger ones [16].

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3 Research Design First, this study conducted the extraction and validity verification of factors through open primary surveys and closed secondary surveys to expert groups. Next, this study designed the extracted variables with a hierarchical model and derived priority through pairwise comparison between them. Finally, Structural Equation Modeling (SEM) was conducted for the path analysis using Delone & Mclean model (D&M model). In the SEM, the three factors which were the highest priority in AHP were used for independent variables.

3.1 Extracting Factors Using Delphi Delphi was used for reflecting expert opinions, and Analytic Hierarchy Process (AHP) was implemented to derive priorities of variables extracted from Delphi. The procedure of Delphi method consisted of 20 experts from the Library Information System in the first phase to collect the survey data to answer the open questions needed to build the next-generation Library Information System. Factors extracted from first Delphi survey are shown in Table 1. The factors from the first survey were arranged and analyzed. Then, in the second phase, the survey data were collected with a closed type survey and the Rekert 5-point scale, and the validity test was conducted on nine factors. In the Analysis, the stability of the response was evaluated using the coefficient of variation. The summary of the result for the second Delphi analysis is shown in Table 2. If the agreements value is closer to 1, the item is more reasonable and if it is greater than 0.75, it can be positive index. The convergence value is considered a very positive value between 0 and 0.5, and the zero means the opinion is all gathered at one point. To evaluate the stability of the response, the variable coefficient from the standard deviation divided by the arithmetic mean was used. No further survey is required if the coefficient of variation is less than 0.5.

3.2 Analytic Hierarchy Process The research model was constructed with the quality factors of the library information system collected through Delphi and shown in Fig. 1 the quality factors were classified as Information Quality, System Quality, and Service Quality and listed the sub-items of each factor. For AHP analysis, a nine-point scale survey of three factors and nine sub-factors was also conducted. The summary of the relative weights and priority is shown in Table 3.

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Table 1 Variables collected through 1st delphi Requirement

Description

The value of the information provided

Content satisfaction with visitor needs Continuous delivery of content information

Understanding of the information provided Visual representation of information Offering access to information for the disabled Accuracy of information provided

Gain accuracy in copyright, source, and grammar of content information The extent to which content information meets the purpose of the site

System performance

Providing system performance and extensibility Gaining a wideband network for fast communications

System security

Applying encryption for personal information communication Applying security policies to hardware and software

System stability

Configuring system dualization for stable operations Necessity for system failover and backup

Quickness of customer support

Rapid response to customer support Establishing a measure for response to customer requirements

Kindness of customer support

Necessity for systematic customer response training Establishing a feedback plan for customer needs

Variety of customer support methods

Providing online customer service methods Providing offline customer service methods

The AHP technique was applied to calculate importance after performing the pairwise comparison for each factor. According to a survey of 20 experts, the importance value of Information Quality was the highest in the three factors with 0.4212, followed by Service Quality with 0.2944 and System Quality with 0.2844. In subfactor, ‘The Value of the Information Provided’ of Information Quality was derived as the most important variable, and ‘System Performance’ of system quality was derived as the second most important variable. Lastly, the third most important variable was ‘Quickness of Customer Support’ for Service Quality.

3.3 Structural Equation Model and Hypothesis This study constructed a research model to identify factors that affect actual intention to use for general users and library workers who actually use the information systems offered by libraries. The research model is shown in Fig. 2.

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Table 2 2nd delphi analysis result Mean

S. D

Agreement

Convergence

Coefficient of variation

Validity

The value of the information provided

4.45

0.514

1

0.5

0.11

1

Understanding of the information provided

4.60

0.604

0.8

0.5

0.12

0.8

Accuracy of information provided

4.09

0.488

0.8

0

0.14

0.7

System performance

4.67

0.640

0.9

0.5

0.12

1

System security

4.42

0.655

0.8

0.5

0.16

0.7

System stability

4.35

0.424

0.8

0.5

0.18

0.6

Quickness of customer support

4.80

0.712

0.9

0.5

0.12

1

Kindness of customer support

4.20

0.596

0.8

0

0.14

0.8

Variety of customer support methods

4.53

0.621

0.8

0.5

0.12

0.8

Fig. 1 AHP research model

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Table 3 AHP analysis result table Factor

Weights

Sub-factor

Weights

Importance

Priority

Information quality

0.421

Value of the information provided

0.362

0.184

1

Understanding of the information offered

0.312

0.108

5

Accuracy of information provided

0.325

0.128

4

System performance

0.421

0.152

2

System security

0.343

0.062

9

System stability

0.235

0.084

6

Quickness of customer 0.486 support

0.131

3

Kindness of customer support

0.279

0.078

7

Variety of customer support methods

0.234

0.069

8

System quality

Service quality

0.284

0.294

Fig. 2 Research model

The summary of hypotheses is shown in Table 4. For independent variables, Value of information provided, system performance, Quickness of Customer support selected through AHP analysis were used, and user satisfaction and intention to use in the D&M model were used to determine the success factors of the library information system. Through the analysis of moderating effect, the influence of the success factors of the library information system between the general users and the library staffs was compared to identify the success factors from different perspectives. The operational definitions and reference for each variable are summarized in Table 5.

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Table 4 Research hypothesis H1

System performance of system quality has a positive effect on user satisfaction with library information system

H2

The value of the information provided of Information Quality has a positive effect on user satisfaction with library information system

H3

Quickness of customer support of Systems Quality has a positive impact on user satisfaction with library information system

H4

The value of the information provided of Information Quality has a positive effect on intention to use library information system

H5

Quickness of customer support of Systems Quality has a positive impact on intention to use library information system

H6

User satisfaction has a positive effect on intention to use library information system

H7

The factors affecting intention to use library information system are moderated by library staffs and general users

Table 5 Definition and reference of variable Variable

Definition

References

System performance

The degree of performance of the library information system

[17]

Value of the information provided

The value of information provided by the library information system

[10]

Quickness of customer support

The degree of rapid customer support in using the library information system

[18]

User satisfaction

The overall satisfaction level of users with [17, 19] the library information system

Intention to use

The degree of intention to use the library information system

[17, 19]

4 Empirical Data Analysis 4.1 Data Collection For data collection, surveys were conducted on general users and library workers at more than 10 libraries. A total of 243 responses were collected and 53 insincere responses were excluded. Finally, 190 data were analyzed with SPSS and AMOS. The demographics of survey respondents was analyzed by a frequency analysis. In the results, 72% were men and 28% were women. Regarding the age, 42.9% were in their 20 s, 16.4% were in their 30 s, 28.6% were in their 40 s, and 11.6% were in their over 50 s. In the jobs, 47.9% were office workers and 25.8% were professionals. The number of times to use the library was 37.4% once a week, 34.2% twice a week and 20.5% more than three times a week. Finally, library workers account for 21% and general users for 79%.

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4.2 Reliability and Convergence Validity Reliability and validity tests were conducted to verify the research model by Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). First, All the factor loading was found to be reliable as the values were higher than 0.9. The Cronbach’s alpha and Composite Reliability (CR) were 0.7 or higher and this address the reliability of the variables was secured. The Average Variance Extracted (AVE) was higher than the threshold 0.5, the measurement in this study was determined to have a convergence validity. The summary of the indexes is shown in Table 6 [20, 21]. Table 6 Exploratory factor analysis result Intention to use

System performance

Value of information provided

Quickness of customer support

User satisfaction

Measurement

Factor Loading

ronbach’s α

CR

AVE

IU3

0.889

0.955

0.922

0.747

IU4

0.851

IU1

0.825

IU2

0.820

IU5

0.703

SP3

0.703

0.928

0.897

0.744

SP5

0.689

SP4

0.675

SP1

0.664 0.954

0.91

0.772

0.92

0.916

0.732

0.941

0.916

0.732

SP2

0.631

VIP2

0.767

VIP3

0.753

VIP4

0.752

VIP1

0.705

QCS3

0.721

QCS4

0.705

QCS1

0.699

QCS2

0.641

US4

0.634

US3

0.628

US5

0.543

US2

0.506

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Table 7 The summary of model fit index Index

Value

Recommend

Chi-square/df

2.175

1.0  Chi-square/df  3.0

RMSEA

0.079

0.05~0.08

GFI

0.871

0.8~0.9

AGFI

0.815

0.8~0.9

PGFI

0.606

0.5~0.6

NFI

0.921

0.8~0.9

CFI

0.955

0.8~0.9

Table 8 Discriminative validity of confirmatory factor analysis SP

QCS

VIP

US

SP

0.747

QCS

0.825

0.744

VIP

0.764

0.735

0.772

US

0.820

0.739

0.670

0.732

IU

0.765

0.615

0.567

0.641

IU

0.732

4.3 Model Fit and Discriminant Validity CFA was performed to identify goodness of fit using AMOS 22. The summary of the model fit indexes was shown as Table 7. All the indices were shown to meet the threshold criteria, so the study model was judged to be suitable. [22]. Assessment of the discriminant validity results of the CFA is shown in Table 8. The correlation between System Quality and Service Quality is 0.825. The squared correlation, in other words, the coefficient of determination is 0.680, and it is lower than all the AVE, ensuring the discriminative validity [21].

4.4 Hypothesis Test The hypothesis was tested by using a structural equation modeling to identify factors affecting the intended use of users using the library information system. Path analysis was performed using AMOS 22 program to verify each hypothesis of the research model, and the analysis results were shown in Table 9. All the path from the three quality to user satisfaction was estimated to positive effects, and the user satisfaction had a positive impact on intention to use, thus the hypotheses H1, H2, H3 and H6 were supported but the two were rejected. To examine the difference in the path of the model, a moderating effect was analyzed. The response data was divided into 150 general users and 40 library workers

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Table 9 Hypothesis test result β

Hypothesis

S. E

t

p

H1

System performance → user satisfaction

0.394

0.093

4.233

***

H2

Value of information provided → user satisfaction

0.443

0.086

5.12

***

H3

Quickness of customer support → user satisfaction

0.225

0.069

3.265

***

H4

Value of information provided → intention to use

0.054

0.054

0.435

0.663

H5

Quickness of customer support → intention to use

−0.043

0.086

−0.498

0.618

H6

User satisfaction → intention to use

0.713

0.136

5.237

***

of the library information system. Multiple-sample Confirmatory Factor Analysis (MCFA) was conducted to find moderating effect of the groups, and the analysis results were shown in Table 10. In the results of the Multi-group Confirmatory Factor Analysis, χ2 of the unconstrained model was 461.616, and χ2 of the structural-weights model was 467.836. As a result of the difference analysis of χ2 , χ2 = 6.22 and p = 0.938 were obtained, making it statistically significant for the two groups to have the same distribution. Next, the Multi-group Structural Equation Modeling (MSEM) was conducted but the p-value on chi-square difference was estimated to 0.902 (>0.05) which means it is meaningless as a moderate variable between service users (general users/library staffs) as shown in Table 11. Table 10 Multi-group confirmation factor analysis results (general users/library workers)

Index

Unconstrained model

Structural-weights model

χ2

461.616

467.836

χ2

Table 11 Multi-group analysis results (general users/library workers)

difference

6.22

p-value

0.938

Index

Un-constrained model

Structural-weights model

χ2

470.296

480.333

χ2 difference

10.037

p-value

0.902

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5 Conclusions The empirical analysis has tested each hypothesis in the study model, and based on the results, the following conclusions can be addressed: Various technologies and services for the Fourth Industrial Revolution will be reflected in the library information system and various information services will be provided. Thus, this study aims to contribute to the establishment of a systematic and efficient system of library information systems and the establishment of a system that reflects the requirements of general users and library staffs. In the empirical analysis, all hypotheses have been adopted that the characteristics of library information system technology variables affect user satisfaction and intent of use. As the library information system developed into various information services, it was derived that the library information system should be established to provide stable, convenient, and valuable services. However, in the moderating effect analysis, there was no difference between the two users group. Further research is needed because it can be controlled by the number of data from multi-group. It is expected to be used as valuable source for similar studies in the future. In addition, we hope that this study will be used as a systematic and basic data on factors affecting the intention to use the systematic library information system, and that various services suitable for the 4th Industrial Revolution era will be applied to help operate a library information system that can achieve each goal by providing users with higher service and satisfaction.

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Author Index

A Ahn, Jeongil, 155 Ahn, Taeho, 155 An, Yong Jun, 87

C Choi, Hye Soo, 105 Choi, Sun-O, 51, 119

G Gim, Gwang Yong, 141, 193, 229 Gim, Simon, 209

H Han, Sung-Hwa, 63 Heo, Jae Hyuk, 141 Hur, Seong Il, 25, 87 Hwang, Chanwoong, 181

K Kang, Heeyong, 169 Kang, Yoonkyu, 169 Kim, Doyeon, 181 Kim, Duk Jin, 193 Kim, Jongbae, 51, 119, 169 Kim, Ju Hyung, 229 Kim, MinSu, 1 Kim, Yongmuk, 39

L Lee, Hoo-Ki, 63 Lee, Jun Yeob, 209 Lee, Kyoung Jin, 229 Lee, Sang Wook, 141 Lee, Sung Taek, 141 Lee, Taejin, 181 Lee, Woo Young, 193

N Nguyen, Thi-Kieu-Trang, 73 I Im, Eun Tack, 209 O Oh, Myung Suk, 209 J Jang, Jin Won, 25, 87 Jeon, Byeong Ryun, 193 Jeong, Sang Bin, 229 Jo, Dong Hyuk, 105

P Park, Jongwoo, 39 Park, Yong Gi, 25, 87

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 R. Lee and J. B. Kim (eds.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, Studies in Computational Intelligence 951, https://doi.org/10.1007/978-3-030-67008-5

243

244

Author Index

Phuong, Huy Tung, 209

Vu, Thi-Quynh-Anh, 73

V Van, Hung-Trong, 13, 73 Vo, Thi-Thanh-Thao, 13

Y Yang, Hwan-Seok, 131