Big Data and Data Science Engineering: Volume 6 (Studies in Computational Intelligence, 1139) [2024 ed.] 3031533844, 9783031533846

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Studies in Computational Intelligence 1139

Roger Lee   Editor

Big Data and Data Science Engineering Volume 6

Studies in Computational Intelligence Volume 1139

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, selforganizing 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.

Roger Lee Editor

Big Data and Data Science Engineering Volume 6

Editor Roger Lee Software Engineering and Information Central Michigan University Mount Pleasant, MI, USA

ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-031-53384-6 ISBN 978-3-031-53385-3 (eBook) https://doi.org/10.1007/978-3-031-53385-3 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 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 Paper in this product is recyclable.

Foreword

The main purpose of this book is to seek peer-reviewed original research papers on the foundations and new developments in Big Data, Cloud Computing and Data Science Engineering. The focus will also be on publishing in a timely manner, the results of applying new and emerging technologies originating from research in Big Data, Cloud Computing and Data Science Engineering. The finding of this book can be applied to a variety of areas, and applications can range across many fields. The papers in this book were chosen based on review scores submitted by members of the editorial review board and underwent rigorous rounds of review. We would like to thank all contributors including all reviewers, and all editorial board members of this book for their cooperation in helping to publish this book. It is our sincere hope that this book provides stimulation and inspiration, and that it will be used as a foundation for works to come. December 2023

Prof. Jongwoo Park Soongsil University Seoul, South Korea Prof. Thi Phuong Lan Ngo University of Social Sciences and Humanities—VNUHCM Ho Chi Minh City, Vietnam

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Editorial Review Board

Kiumi Akingbehin, University of Michigan, United States Yasmine Arafa, University of Greenwich, United Kingdom Jongmoon Baik, Korea Advanced Institute of Science and Technology, South Korea Ala Barzinji, University of Greenwich, United Kingdom Radhakrishna Bhat, Manipal Institute of Technology, India Victor Chan, Macao Polytechnic Institute, Macao Morshed Chowdhury, Deakin University, Australia Alfredo Cuzzocrea, University of Calabria, Italy Hongbin Dong, Harbin Engineering University, China Yucong Duan Hainan, University, China Zongming Fei, University of Kentucky, United States Honghao Gao, Shanghai University, China Cigdem Gencel Ambrosini, Ankara Medipol University, Italy Gwangyong Gim, Soongsil University, South Korea Takaaki Goto, Toyo University, Japan Gongzhu Hu, Central Michigan University, United States Wen-Chen Hu, University of North Dakota, United States Naohiro Ishii, Advanced Institute of Industrial Technology, Japan Motoi Iwashita, Chiba Institute of Technology, Japan Kazunori Iwata, Aichi University, Japan Keiichi Kaneko, Tokyo University of Agriculture and Technology, Japan Jong-Bae Kim, Soongsil University, South Korea Jongyeop Kim, Georgia Southern University, United States Hidetsugu Kohzaki, Kyoto University, Japan Cyril S. Ku, William Paterson University, United States Joonhee Kwon, Kyonggi University, South Korea Sungtaek Lee, Yong In University, South Korea Weimin Li, Shanghai university, China Jay Ligatti, University of South Florida, United States Chuan-Ming Liu, National Taipei University of Technology, Taiwan Man Fung Lo, The University of Hong Kong, Hong Kong vii

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Chaoying Ma, Greenwich University, United Kingdom Prabhat Mahanti, University of New Brunswick, Canada Tokuro Matsuo, Advanced Institute of Industrial Technology, Japan Mohamed Arezki Mellal, M’Hamed Bougara University, Algeria Jose M.Molina, Universidad Carlos III de Madrid, Spain Kazuya Odagiri Sugiyama, Jogakuen University, Japan Takanobu Otsuka, Nagoya Institute of Technology, Japan Anupam Panwar, Apple Inc., United States Kyungeun Park, Towson University, United States Chang-Shyh Peng, California Lutheran University, United States Taoxin Peng, Edinburgh Napier University, United Kingdom Isidoros Perikos, University of Patras, Greece Laxmisha Rai, Shandong University of Science and Technology, China Fenghui Ren, University of Wollongong, Australia Kyung-Hyune Rhee, Pukyong National University, South Korea Abdel-Badeeh Salem, Ain Shams University, Egypt Toramatsu Shintani, Nagoya Insutitute of Technology, Japan Junping Sun, Nova Southeastern University, United States Haruaki Tamada, Kyoto Sangyo University, Japan Takao Terano, Tokyo Institute of Technology, Japan Kar-Ann Toh, Yonsei University, South Korea Masateru Tsunoda, Kindai University, Japan Trong Van Hung, Vietnam Korea University of Information and Communications Tech., Viet Nam Shang Wenqian, Communication University of China, China John Z. Zhang, University of Lethbridge, Canada Rei Zhg, Tongji University, China

Contents

A Study on the Factors Influencing the Intention to Use MyData Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Seockho Jang, Seungbok Ahn, Jongmo Hwang, and Gwangyong Gim

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A Study on the Intention to Use the Urban Air Mobility Passenger Transportation Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soohong Min

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A Study on the Integration of Endpoint Security Service Operations Management: Focusing on Cloud Services . . . . . . . . . . . . . . . . Seungbok Ahn, Gwangyong Gim, Seockho Jang, and Jongmo Hwang

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A Study on the Factors Affecting the Korean Financial Institution’s Switching Intention to Open Source Software: Focused on System Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heeyoung Kim, Gwangyong Gim, Hyongyong Lee, and Hyuna Kim A Study on the Intention to Utilize Overseas Developers Through Offshoring—Using the Value-Acceptance Model (VAM) . . . . . . . . . . . . . . . Hyongyong Lee, Euntack Im, Myeongseok Oh, and Gwangyong Gim The Role of Co-Creation Experience and Switching Cost in the Relationship Between Service Recovery and Customer Loyalty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bum Seok Kim, Jin-Han Kim, and Woosub Kim

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A Comprehensive Analysis of Security Measures for MyData in South Korea Based on AHP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jongmo Hwang, Seungbok Ahn, Seockho Jang, and Gwangyong Gim

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A Study on the Factors Influencing the Performance of Korea Venture Capital Funds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . In Mo Yeo, Gwangyong Gim, Youngkun Yang, and Youngsu Kim

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Contents

A Research on Factors Influencing the Survival of Small Businesses: Focusing on Franchise Convenience Stores . . . . . . . . . . . . . . . . 107 Youngsu Kim, Gwangyong Gim, Youngkun Yang, and In Mo Yeo A Study on Purchase Intention for Innovative Products: Focusing on Oxygen-Generating Air Purifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Chae youl Leem, Sang soo Ha, Gwang yong Gim, and Chung ku Han The Impact of Company’s ESG Activities on Corporate Reputation . . . . 131 Jang-woo Kim, Gwang-yong Gim, Hyong-yong Lee, and Dorjtsembe Zul-Erdene The Impact of ESG Activities in Midsize Manufacturing Companies on Purchase Intentions: Focusing on the Mediating Roles of Corporate Reputation, Brand Image, and Perceived Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 Hui-ryang Eom, Hyun-a Kim, Hee-young Kim, and Gwang-yong Gim A Study on the Intention to Utilize Overseas Developers Through Offshoring Approach and Strategy for Economic Cooperation Between South and North Korea Using the Value-Acceptance Model (VAM)—Based on the Case Study of the Kaesong Industrial Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Insun Kang A Study on the Intention to Use Home Network Services . . . . . . . . . . . . . . 169 Seong-byeong An, Gwang-yong Gim, Yoon-je Sung, and Sae-yeon Lee A Study on the Impact of Green Patent Data on ESG Environment Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Hyunyoung Kwak and Sungtaek Lee Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

A Study on the Factors Influencing the Intention to Use MyData Service Seockho Jang, Seungbok Ahn, Jongmo Hwang, and Gwangyong Gim

Abstract MyData service involves a vast amount of personal information data created by individuals, and these services that involve personal information are increasingly playing a bigger and more important role in our daily lives. However, despite the critical importance of data quality in such services, issues related to data quality degradation persist over time due to the exponential growth of data and increased management factors associated with the adoption of new technologies. This study aims to identify the factors influencing the intention to use MyData service and, specifically, to determine which factors related to data quality have a more substantial impact on usage intention. To achieve this, four relevant prior studies were examined. These studies encompassed the analysis of factors related to the characteristics of MyData service and the relevant legal framework, an analysis of prior research on information system success models that include MyData service, and an examination of the impact of perceived ease of use and perceived usefulness as mediating factors on usage intention within the context of the Technology Acceptance Model (TAM) theory. Based on the findings from the prior studies, nine independent factors related to data were identified and categorized into three main areas that constitute an information system: information quality, system quality, and service quality. The first set of factors includes accuracy, reliability, and appropriateness, the second set includes convenience, accessibility, and security, and the final set includes consistency, functionality, and value. This study investigated the relationship between these nine derived factors and the mediating variables of perceived ease of use and usefulness. S. Jang · S. Ahn · J. Hwang Graduate School of IT Policy and Management, Soongsil University, Seoul, South Korea e-mail: [email protected] S. Ahn e-mail: [email protected] J. Hwang e-mail: [email protected] G. Gim (B) Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_1

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The research results obtained in this study have demonstrated reliability and validity. It was found that independent factors such as reliability, accuracy, and convenience have an impact on the intention to use MyData information systems. The conclusions of this study are expected to contribute to the establishment of criteria for the intention to use MyData service by considering information quality, system quality, and service quality when developing or designing MyData service. Keywords MyData · Technology Acceptance Model · Information System Success Model · Service Quality · Information Quality · System Quality

1 Introduction MyData is a paradigm in which individuals have control over their personal data and can use it as they wish [1]. Even before, personal data was proliferating, but after the COVID-19 pandemic, the growth of data has expanded even more, with personal data generated and utilized by individuals accounting for up to 75% of the total data. Within this trend, as businesses and public institutions digitize their operations, issues of redundancy and inconsistency have arisen among information system data, and the importance of data standards and data consistency for external agencies’ integration has become paramount [2]. As the scope and complexity of information systems have expanded, the quality of MyData service has become a significant concern. Thus, the purpose of this study is to pay attention to the quality characteristics of MyData among the gradually exploding personal data and to find out the relationship between the characteristic factors on the intention to use through comprehensive research and consideration of MyData service. The research question, or the purpose of this study, can be broadly summarized into three main points: What are the quality factors of MyData? Do the quality factors of MyData have associations with perceived ease of use and perceived usefulness? How do perceived ease of use and perceived usefulness relate to usage intention? While existing studies have examined the impact of perceived ease of use and perceived usefulness on usage intention, research approaching this from the perspective of MyData service is scarce. Hence, this study will utilize structural equation modeling to investigate the relationships between perceived ease of use, perceived usefulness, and usage intention. This study is expected to serve as the foundation for various research on MyData service usage satisfaction, continuity, user loyalty, and policy utilization effects. The value of this study is that it presents a direction from the perspective of quality inspection and operation management to be pursued in the MyData service information system through factors that were shown to be highly related to the intention to use the service.

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2 Theoretical Background 2.1 MyData The principles of MyData revolve around individuals, who are the producers of information, having the authority to choose when to provide data, and creating an environment where data access is convenient. When utilizing data, individuals have the power to move data based on their requests and approvals, allowing third parties to access it [1]. Personal information refers to all information that can identify an individual to the extent that it is related to that specific person [3]. In other words, personal information includes direct identifiers like names and resident registration numbers, as well as information that, even if not sufficient to identify an individual on its own, can be used in combination with other information to make such identification possible [4]. Ultimately, MyData can be considered as personal information encompassing the three principles of data authorization, provision, and utilization. In the past, data was constantly provided to various organizations when borrowing money, shopping, traveling, or visiting hospitals. The accumulated data resulting from these transactions has been used by businesses and organizations to provide their services. This data has also been subject to potential breaches or implicit utilization for other purposes, such as in the case of hacking incidents. Responsibility for data utilization and management has traditionally rested with institutions and companies. MyData, on the other hand, is about seeking rights and control over data that has been used without the consent or permission of individuals [5]. The characteristics of such MyData service include transparency, reliability, control, and value [1].

2.2 Data Quality The term data quality, as shown in the word itself, implies the management of data quality, and the reason for conducting data quality management can be described as ensuring the availability of the most accurate and up-to-date data when needed [6]. In other words, data quality pertains to the situation where data is used in accordance with the user’s intended purposes, signifying that the data storage structure, data values, and data management processes meet the requirements and satisfaction of the stakeholders using the data [7]. Both domestically and internationally, several standards have been established for data quality. Internationally, there are standards such as ISO 9000, which covers quality management comprehensively, as well as ISO/IEC 14598 for product evaluation and ISO/IEC 12207 for the characteristics of process quality. In addition, guides like DMBOK and ISO/IEC 25012 are used to define standards related to data quality.

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2.3 Technology Acceptance Model (TAM) The Technology Acceptance Model (TAM) theory is a model designed for studying how the acceptance of information technology influences organizational performance and improvement [8]. It is based on the Theory of Reasoned Action (TRA) and aims to explain technology acceptance [8, 9]. The TAM consists of four components: perceived usefulness, perceived ease of use, the intention to act, and attitude. Perceived usefulness refers to the assessment of whether new technology adoption will be beneficial in terms of efficiency, work productivity, or overall quality of life for the user. Perceived ease of use, on the other hand, is the perception that the effort required to learn and use a new product or information technology will be reduced. In other words, the individual’s attitude toward adopting new technology or a novel system is determined by whether the technology is perceived as easy to use or as a necessary consideration for their work or daily life [10]. Such formed attitudes manifest emotions and beliefs related to an individual’s behavior, directly impacting their intention to act. These attitudes can also be influenced by perceived usefulness and ease of use, which in turn affect the intention to act [11].

2.4 Information System Success Model Delone and McLean (1992) conducted research related to measuring information system success and categorized the variables influencing the impact of information systems into individual impact, system use, organizational impact, system quality, user satisfaction, and information quality [12]. They also presented a model to examine the causal relationships among these variables. In the DeLone and McLean Information System Success Model, information quality measures success from a semantic perspective, while system quality represents technical success, and user usage, satisfaction, and individual and organizational impact are used to measure effectiveness [13]. They researched the essential conditions for information system success and related indicators, categorized them into six types, and developed an interrelated model. Subsequently, Seddon (1997) addressed the ambiguity in the concept of system use in the DeLone & McLean model by introducing perceived usefulness in the user involvement section of the modified model [14].

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Fig. 1 Research method

3 Research Model 3.1 Research Model and Hypotheses Establishment The research model for this study is presented in Fig. 1. This research aims to empirically validate the impact of the independent factors that constitute MyData service on the intention to use MyData information systems through the mediating effects of perceived ease of use and perceived usefulness. To achieve this, the MyData service is divided into three main characteristics: information quality, system quality, and service quality. Information quality consists of accuracy, reliability, and suitability, while system quality encompasses convenience, accessibility, and security, and service quality is characterized by consistency, functionality, and value. These characteristics are designated as independent variables.

3.2 Operational Definition of Variables Operational definitions of independent variables, parameters, and dependent variables included in the research model and research hypothesis were defined and summarized. The definitions are shown in Table 1.

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Table 1 Operational definition of variables Factor

Operational definition of each factor

References

Accuracy

The degree to which the content of the data in the MyData service is accurately represented

[15, 16]

Reliability

The degree of trust that can be trusted and relied on for the contents [17, 18] and procedures of the MyData service provided

Suitability

The content of the MyData service provided is complete and accurate, the degree to which one satisfies the range

[19, 20]

Convenience

The degree of convenience for users of the provided MyData service to access the system

[12, 21]

Accessibility

The degree to which you feel that the MyData service provided is readily available

[22, 23]

Security

The degree of protection for users of the MyData service provided

[24, 25]

Consistency

The degree to which the data in the MyData service provided is consistently represented

[26, 27]

Functionality

The function of Mydata services to meet the needs and needs of the [28, 29] user

Value

The degree to which you think the use of the MyData service provided will be useful

The perceived The degree to which it is believed to be easy and convenient to use ease of use when using MyData

[30, 31] [8, 15]

The perceived The degree to which the MyData service is fit for purpose or meets [8, 32] usefulness the needs The intention to use

The degree of intention to use the provided MyData service

[8, 12]

3.3 Hypotheses In this study, the dependent variable refers to the intention to use the MyData service information system. Through the construction of the research model, we have derived the hypotheses, as shown in Table 2. There are a total of 21 hypotheses. Among the nine independent factors, it is hypothesized that each one will have a positive effect on both perceived usefulness and perceived ease of use. Additionally, it is hypothesized that perceived ease of use has a positive influence on perceived usefulness, and both perceived ease of use and perceived usefulness mediate a positive impact on usage intention.

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Table 2 Research hypothesis H1

Accuracy will have a positive(+) impact on perceived ease of use

H12

Security will have a positive(+) impact on perceived usefulness

H2

Accuracy will have a positive(+) impact on perceived usefulness

H13

Consistency will have a positive(+) impact on perceived ease of use

H3

Reliability will have a positive(+) impact on perceived ease of use

H14

Consistency will have a positive(+) impact on perceived usefulness

H4

Reliability will have a positive(+) impact on perceived usefulness

H15

Functionality will have a positive(+) impact on perceived ease of use

H5

Suitability will have a positive(+) impact on perceived ease of use

H16

Functionality will have a positive(+) impact on perceived usefulness

H6

Suitability will have a positive(+) impact on perceived usefulness

H17

Value will have a positive(+) impact on perceived ease of use

H7

Convenience will have a positive(+) impact on perceived ease of use

H18

Value will have a positive(+) impact on perceived usefulness

H8

Convenience will have a positive(+) impact on perceived usefulness

H19

Perceived ease of use will have a positive(+) impact on perceived usefulness

H9

Accessibility will have a positive(+) impact on perceived ease of use

H20

Perceived ease of use will have a positive(+) impact on intention to use

H10

Accessibility will have a positive(+) impact on perceived usefulness

H21

Perceived usefulness will have a positive(+) impact on intention to use

H11

Security will have a positive(+) impact on perceived ease of use

4 Empirical Analysis 4.1 Data Collection and Analysis In this study, the collected data were analyzed using SPSS 22.0 and smartPLS 4.0 as follows. First, a frequency analysis was conducted to understand the demographic characteristics of the sample and the current status of MyData service usage. Descriptive statistics were also used to understand the main characteristics of the research variables. Secondly, confirmatory factor analysis and reliability analysis were performed to assess the validity and reliability of the measurement tools. Furthermore, a correlation analysis was conducted to gauge the degree of relationships between variables. Finally, hypothesis testing was performed through goodness-of-fit analysis and structural equation modeling.

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4.2 Reliability Evaluation of the Research Model The evaluation of the measurement model is typically conducted in the initial stages of model assessment, focusing on convergent validity, discriminant validity, and internal consistency validation [33]. The results of the reliability assessment are shown in Table 3. As evident from the evaluation results, Cronbach’s Alpha values for all factors should exceed 0.6, while rho_A values should exceed 0.7, and CR values should also surpass 0.7 to confirm internal consistency reliability [34–36]. Discriminant validity analysis, a method for assessing whether reflective latent variables have stronger relationships with their respective factors compared to other factors, was conducted using the Fornell-Larcker criterion [36]. Table 3 presents the results of discriminant validity analysis based on the Fornell-Larcker criterion. Discriminant validity analysis based on the Fornell-Larcker criterion involves comparing the Average Variance Extracted (AVE) values of each factor with the squared Pearson correlation coefficients between latent variables. When the AVE value is greater than the squared correlation coefficient, discriminant validity is considered to be established [37, 38]. Among all the correlation coefficients between latent variables in this study, the highest value was 0.868, which is greater than the lowest AVE value of 0.906, confirming the establishment of discriminant validity (Table 4).

4.3 Hypotheses Verification Results In summary, considering the relationships between the mediating variables and the dependent variable, the ease of use and usefulness of the MyData information system were found to have a significant impact on usage intention. None of the attributes of MyData’s information quality, system quality, and service quality were found to significantly satisfy both ease of use and usefulness. Among the information quality attributes, accuracy was found to satisfy both ease of use and usefulness, while reliability was only accepted for ease of use, and suitability was only adopted for usefulness. There were three attributes within system quality: convenience, security, and accessibility. Among these, security showed significant results for usefulness, while accessibility and convenience all yielded research results supporting the hypotheses for ease of use and usefulness. Lastly, service quality consists of three attributes: value, consistency, and functionality. Value was found to be significant for usefulness, while functionality and consistency were significant for ease of use (Fig. 2).

Perceived usefulness (PUF)

Convenience (CONV)

Suitability (SU)

Consistency (CONS)

Security (SEC)

VU2

Value (VU)

CONV1

PUF4

PUF2

PUF1

CONV4

CONV2

0.938

0.938

0.96

The Intention to use (IU)

Perceived ease of use (PEU)

Accuracy (AC)

Accessibility (ACS)

AC2

AC1

ACS5

ACS4

ACS1

IU5

IU4

IU3

PEU4

PEU3

PEU1

AC5 0.947

0.957

0.943

SU5 0.917

0.941

0.91

AC4

0.917

0.94

0.909

RE3 RE5

SU3

SU2

SU1

CONS5

CONS4

CONS1

SEC5

SEC3

RE1

Reliability (RE)

FUNC1 FUNC2

SEC2

Functionality (FUNC) FUNC3

0.945

0.952

FUNC4 0.918

0.933

Latent variables Measuring variables

VU4

0.912

0.932

Threshold: 0.70 above

Threshold: 0.60 above

Threshold: 0.70 above

CR

Cronbach’s α rho_A

Internal consistency reliability

VU5

VU3

Measuring variables

Latent variables

Table 3 Result of reliability evaluation Internal consistency reliability

0.923

0.92

0.94

0.935

0.891

0.95

Threshold: 0.60 above

0.926

0.92

0.94

0.936

0.892

0.95

Threshold: 0.70 above

Cronbach’s α rho_A

0.951

0.949

0.957

0.958

0.932

0.964

Threshold: 0.70 above

CR

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Table 4 Result of convergent validity and discriminant validity analysis 1 VU

2

3

4

5

6

7

8

9

10

11

12

0.912

FUNC 0.83

0.933

SEC

0.673 0.694 0.922

RE

0.692 0.748 0.664 0.906

PUF

0.808 0.821 0.799 0.798 0.943

PEU

0.767 0.848 0.708 0.817 0.849 0.928

IU

0.705 0.683 0.634 0.588 0.731 0.721 0.931

CONS 0.715 0.786 0.665 0.783 0.751 0.811 0.65

0.92

SU

0.76

0.763 0.764 0.795 0.868 0.769 0.645 0.744 0.92

ACS

0.73

0.787 0.714 0.735 0.85

AC

0.751 0.827 0.716 0.838 0.854 0.884 0.657 0.786 0.788 0.828 0.92

0.816 0.67

0.714 0.795 0.94

CONV 0.743 0.781 0.718 0.758 0.847 0.801 0.696 0.718 0.797 0.832 0.774 0.926 (a): AVE value a is [Reliable,4], (b): the b value is [Suitability,5]. square of the correlation coefficient, Ringle, C. M., Wende, S., and Becker, J.-M. 2022. “SmartPLS 4.” Oststeinbek: SmartPLS GmbH, http://www.smartpls.com

Fig. 2 Result of path analysis

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5 Conclusion 5.1 Research Results and Implications This study aimed to investigate the factors influencing the intention to use MyData service among IT professionals and the general public. The research was conducted through a survey of 257 respondents, and the data obtained were analyzed using PLS structural equation modeling. While previous MyData-related studies have mainly focused on concepts, prospects, policy comparisons between countries, sector-specific use cases, and current status, this study explored the research from the perspective of data elements to be used on MyData-based platforms. The analysis results revealed that accuracy, reliability, and suitability had a significant impact on both ease of use and usefulness from the perspective of information quality. Concerning system quality, security and accessibility were relatively more important than convenience. In terms of service quality, data attributes such as consistency, value, and functionality were identified as crucial indicators. The research model, composed of 21 hypotheses, adopted 15 hypotheses and rejected 6 hypotheses. Accuracy and convenience were adopted for both perceived ease of use and usefulness. Reliability, consistency, and functionality were only adopted for perceived ease of use, while security, suitability, and value were only adopted for perceived usefulness. Among the independent factors, accuracy and functionality showed a strong association with perceived ease of use, and a third association was observed between security and perceived usefulness. The hypothesis regarding the relationship between perceived ease of use and perceived usefulness was adopted, highlighting that perceived usefulness has a higher relevance to usage intention than perceived ease of use. Since the system quality has been confirmed to be adopted in all the usefulness of the MyData system, it is judged that it will be worthwhile for future research or system construction and use.

5.2 Research Implications First, in contrast to previous research that primarily focused on the characteristics and policy aspects of MyData service, this study differentiates itself by classifying the quality of MyData’s data itself into various quality factors and conducting an in-depth analysis. This distinction adds academic significance to the research. Second, drawing insights from the research conclusions, it can be inferred that accuracy, convenience, and accessibility, among the three factors identified, play a relatively more critical role as quality factors in determining the intention to use MyData information systems compared to other factors like security, suitability, and functionality. This insight can be valuable for considering the importance of specific quality attributes in MyData service utilization.

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23. Jeon, S. C. (2014). A study on the impact of the level of acceptance intention of information system audit technology using the TAM model on utilization. Journal of the Korean Navigation and Port Research Society, 18(6), 609–618. 24. Song, S. J., & Lee, H. Y. (2021). A study on the impact of perceived characteristics of RPA on intention to use. Knowledge Management Research, 22(4), 283–301. 25. Jung, B. C., & Hong, S. K. (2021). Analysis of the impact of domestic open banking quality factors on user intention to use. Journal of the Korea Internet Information Society, 22(5), 69–77. 26. Lim, H. J. (2020). A study on the factors influencing consumers’ intention to use chatbot services in online shopping malls: Focusing on the Extended Technology Acceptance Model (ETAM). (Master’s thesis). Hongik University. 27. Lim, M. R. (2020). A study on the variables affecting the acceptance intention of chatbots in small and medium-sized enterprises: Focusing on the aspect of enhancing global competitiveness. (Master’s thesis). Inha University. 28. Park, J. M. (2015). The influence of tablet PC selection attributes on beliefs and learning acceptance intentions in smart learning environments. (Master’s thesis). Catholic Kwandong University. 29. Lee, J. W., Choi, J. H., & Park, J. W. (2014). An empirical study on the impact of personal and device characteristics on smartwatch usage intention. Journal of the Korean Society for Information Technology, 12(11), 201–214. 30. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. 31. Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137–155. 32. Lee, S. H. (2022). A study on the security factors influencing the intention to adopt MyData services in the financial sector: Research framework. (Master’s thesis). Chung-Ang University. 33. Kim, Y. T., & Lee, S. J. (2015). Utilization of R program for partial least squares model: A comparison between SmartPLS and R. Journal of Digital Convergence, 13(12), 117–124. 34. Buzeta, C., De Pelsmacker, P., & Dens, N. (2020). Motivations to use different social media types and their impact on consumers’ online brand-related activities (COBRAs). Journal of Interactive Marketing, 52, 79–98. 35. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. 36. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. 37. Bagozzi, R. P. & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. 38. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.

A Study on the Intention to Use the Urban Air Mobility Passenger Transportation Service Soohong Min

Abstract Many countries and companies around the world are preparing to introduce Urban Air Mobility (UAM) Passenger Transportation Service (PTS) as a means of transportation to solve the increasingly serious traffic congestion in the city center and environmental pollution caused by cars. This study empirically analyzed the factors affecting future users’ Intention to Use UAM PTS as an essential condition for the successful introduction of PTS. This study designed a research model of the TAM and UTAUT methods and analyzed them with a structural equation Model. As a result, Perceived Usefulness, Perceived Ease of Use, Social Influence, and Facilitating Conditions were found to have a statistically significant impact. Particularly, Social Influence demonstrated a strong influence on Intention to Use UAM PTS. Policy authorities, manufacturers, operators, and boarding facility operators preparing for UAM PTS should reflect on these outcomes from the design stage for the early establishment of UAM PTS. Keywords Urban Air Mobility · UAM · eVTOL · PAV · Drone Taxi · Air Taxi

1 Introduction Due to the increasing concentration of the population in major metropolitan and capital areas, the total cost of traffic congestion in South Korea exceeded 44.64 billion dollars as of 2020 [1]. In 2019, Americans wasted an average of 99 h per year due to traffic congestion, resulting in an annual direct and indirect cost of around 88 billion dollars [2]. To address the social costs of traffic congestion and environmental issues caused by car emissions and noise, preparations are underway for Urban Air Mobility Passenger Transportation Service (UAM PTS) as the next-generation transportation method. UAM PTS utilizes the urban sky, avoiding ground congestion, and, being electrically powered, eliminates emissions. Using electric Vertical Take-Off S. Min (B) Department of Drone, Sejong Cyber University, Seoul, South Korea e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_2

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and Landing (eVTOL) technology allows for vertical take-off and landing without the need for runways, providing advantages in terms of space, time, and environmental considerations. Major advanced countries worldwide are competitively developing aircraft, software for air traffic control, and systems for boarding facilities and procedures to operate UAM PTS. For the successful adoption of UAM PTS in the future, many people must use it. The essential prerequisite for this is the reliability and safety of UAM-related technologies and services. Additionally, the service should provide economic feasibilities in terms of time and cost for the general public, and it should be convenient and user-friendly. Given that this is a new and unfamiliar service, incorporating entertainment and joy into the user experience could accelerate its adoption. In addition to personal satisfaction, UAM PTS requires significant capital and facilities, necessitating active government support and societal consensus. Therefore, considerations regarding facilitating conditions and social influences, such as environmental characteristics, are essential.

2 Conceptual Background and Research Model 2.1 Definition of UAM The UAM Law of Korea related to the promotion and support of urban air transportation defines it as a system that includes individual or interconnected urban aircraft, vertiports, and urban air transportation networks used for activities related to the transport of people or goods [3]. NASA defines UAM as a safe and efficient air transportation system for manned and unmanned aircraft in major metropolitan areas [4]. UAM is not limited to just the aircraft; it involves various components such as transportation operators utilizing the aircraft, aviation control agencies managing airspace, transportation management operators overseeing operations, and the societal foundation supporting the entire system [5]. Recent trends in UAM terminology include the use of the term Regional Air Mobility (RAM) to represent inter-city air transportation concepts. Furthermore, the term Advanced Air Mobility (AAM) is used to represent the future advanced air transportation system. Passengers would use eVTOL aircraft capable of vertical takeoff and landing, traveling through urban corridors using dedicated eVTOL routes and vertiports for landing. Figure 1 provides a conceptual illustration of eVTOL, vertiports, and dedicated corridors [4, 6].

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Fig. 1 Vertiport & Corridor

Table 1 Characteristics of UAM aircraftt by type [7] Category

Vectored thrust

Lift & cruise

Multicopter

• Independent fixed propulsion system • Three flight modes (Fixed-Wing, Rotary-Wing, Transition Flight) • Easier vertical takeoff and landing than vectored thrust type • High efficiency in forward flight

• Configured with rotary wings • Single flight mode (Rotary-Wing) • High efficiency in hover • High safety • Lower efficiency in forward flight

Imagery

Characteristics • Tilt system • Three flight modes (Fixed Wing, Rotary-Wing, Transition Flight) • High forward flight • Low hover efficiency

2.2 Characteristics and Development Status of UAM Aircraft by Type As shown in Table 1, UAM aircraft are generally classified into three types based on the propulsion type of lift and thrust. It is classified into a Vector Thrust type in which the propeller is tilted upward and forward, a lift & cruise type with both fixed and rotational blades, and a Multicopter type composed purely of rotor blades without fixed blades.

2.3 Research Model and Hypothesis This study is an empirical investigation of factors influencing the intention to use UAM PTS. The research model is based on the TAM, commonly used in studies

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Fig. 2 Research model

related to the introduction of new technologies. However, TAM has limitations as it does not specify external variables that can influence the technology adoption process, aside from perceived usefulness, perceived ease of use, and behavioral intention [8]. To address this limitation, we incorporated some components of the UTAUT model. The selection of variables for this study involved extracting and applying various factors from prior researches. Convenience, economic feasibility, reliability and hedonic motivation were chosen as variables. And we introduced social influence and facilitating conditions like government policies. The research model to explore these relationships is shown in Fig. 2.

3 Methodology and Results 3.1 Dataset and Methodology To understand the influence relationships among variables regarding the intention to use UAM PTS, a survey was conducted with a total of 560 individuals. Among the 421 responses received, 372 responses were analyzed after excluding dishonest responses. SPSS 22.0 and Smart PLS 3.0 software were utilized to validate the reliability and validity of the measurement model. Furthermore, the hypotheses set in the research model were tested, and a mediating effect analysis through perceived usefulness and perceived ease of use was conducted. For this study, a review of previous research was conducted to establish operational definitions for each variable, as presented in Table 2.

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Table 2 Operational definitions of variables Variables

Operational definition

Convenience

The degree of ease and freedom in accessing and using UAM PTS

Economic Feasibility

The extent to which one can save time and costs by using UAM PTS

Reliability

The level of trust in the overall system stability of UAM PTS

Hedonic Motivation

The extent of joy and pleasure experienced when using UAM PTS

Social Influence

The influence of people around on an individual’s decision to use UAM PTS

Facilitatinng Condition

The degree to which an individual believes there will be organizational and systematic support for UAM PTS

Perceived Usefulness

The extent to which an individual believes that using UAM PTS will enhance their quality of life and work efficiency

Perceived Ease of Use

The degree to which an individual believes that using UAM PTS will be easy without significant effort

Intention to Use

The level of personal intention to recommend and promote UAM PTS usage to others

3.2 Results 3.2.1

Measurement Model Testing

In this study, for the validation of the measurement model, Internal Consistency Reliability, Convergent Validity, and Discriminant Validity were assessed. First, to evaluate Internal Consistency Reliability, Cronbach’s α coefficient, D-H rho_A coefficient, and Composite Reliability (CR) were used. Cronbach’s α coefficient exceeding 0.7 is considered to ensure desirable reliability, and D-H rho_A values exceeding 0.7 are also considered to indicate desirable reliability [9]. Additionally, CR values exceeding 0.7 are considered to provide satisfactory reliability [10, 11]. As shown in Table 3, the Cronbach’s α coefficients for all latent variables in this study ranged from 0.740 to 0.933, surpassing 0.7. The D-H rho_A values also exceeded 0.7 for all variables, and the Composite Reliability (CR) values were all above 0.7, confirming the reliability of the measurement model in this study. Furthermore, a validity check was conducted for the measurement model, which includes Convergent Validity and Discriminant Validity assessments. Convergent Validity is typically considered to be achieved when the Average Variance Extracted (AVE) value exceeds 0.5 [12]. Additionally, if the factor loadings of measurement variables are above 0.7, Convergent Validity is considered to be present [13]. As shown in Table 4, most of the factor loading values exceeded 0.7, and all AVE values were above 0.5. Thus, it can be concluded that Convergent Validity for the measurement model in this study has been established.

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Table 3 Internal consistency reliability Variables

Cronbach’s alpha

D-H rho_A

Composite reliability

EF

0.740

0.779

0.830

IU

0.933

0.934

0.949

SI

0.895

0.903

0.923

RE

0.906

0.909

0.931

HM

0.925

0.925

0.943

PEU

0.902

0.904

0.928

PU

0.928

0.930

0.946

FC

0.866

0.867

0.909

CO

0.867

0.880

0.904

Notes EF = Economic Feasibility, IU = Intention to Use, SI = Social Influence, RE = Relaibility, HM = Hedonic Motivation, PEU = Perceived Ease of Use, PU = Perceived Usefulness, FC = Faciliatating Condition, CO = Convenience

Table 4 Convergent validity

Variables

Measure item loadings

AVE

EF

0.558, 0.797, 0.833, 0.763

0.556

IU

0.862, 0.885, 0.875, 0.914, 0.904

0.789

SI

0.862, 0.890, 0.749, 0.880, 0.810

0.705

RE

0.885, 0.901, 0.855, 0.773, 0.850

0.729

HM

0.887, 0.883, 0.894, 0.840, 0.878

0.769

PEU

0.883, 0.874, 0.887, 0.847, 0.799

0.720

PU

0.862, 0.893, 0.880, 0.891, 0.882

0.777

FC

0.830, 0.864, 0.850, 0.836

0.714

CO

0.783, 0.832, 0.808, 0.737, 0.875

0.654

According to the Fornell-Larcker Criterion, Discriminant Validity is considered to be established when the square root of the AVE of each latent variable is greater than the correlation between the latent variables [12]. As shown in Table 5, using the Fornell-Larcker Criterion, it is observed that the square root of the AVE for all latent variables, except Economic Feasibility, is greater than the correlation values between different latent variables. Thus, it can be concluded that Discriminant Validity for the measurement model in this study is partially established.

3.2.2

Structural Model Fit Testing

The measurement model has been confirmed to achieve reliability and validity, and now the next step involves evaluating the goodness of fit for the structural model.

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Table 5 Discriminant validity (Fornell-Larcker Criterion) EF EF

IU

SI

RE

HM

PEU

PU

FC

CO

0.745

IU

0.596

0.888

SI

0.579

0.788

0.840

RE

0.556

0.590

0.620

0.854

HM

0.384

0.597

0.600

0.518

0.877

PEU

0.499

0.655

0.584

0.598

0.438

0.848

PU

0.528

0.655

0.630

0.546

0.685

0.555

0.882

FC

0.472

0.650

0.608

0.509

0.312

0.608

0.388

0.845

CO

0.584

0.634

0.621

0.477

0.468

0.472

0.633

0.404

0.809

The assessment of the structural model’s fit includes considerations such as multicollinearity, determination coefficient (R2 ), effect size (f2 ), and predictive relevance (Q2 ). To begin with, the evaluation of multicollinearity is based on the Inner Variance Inflation Factor (VIF) values, with values below 5 considered indicative of the absence of multicollinearity [13]. As shown in Table 6, all Inner VIF values are below 5, indicating the absence of multicollinearity in the structural model. Next, the determination coefficient (R2 ) represents the explanatory power of the exogenous latent variables on the endogenous latent variables. An R2 of 0.25 indicates weak explanatory power, 0.50 indicates moderate explanatory power, and 0.75 indicates strong explanatory power [14]. As shown in Table 7, the overall average is 0.592, signifying above-moderate explanatory power. Thus, it can be concluded that the structural model is deemed appropriate. The effect size (f2 ), which indicates the relative impact of exogenous latent variables on endogenous latent variables, is classified as a small effect size at 0.02, a medium effect size at 0.15, and a large effect size at 0.35 [14]. As shown in Table 8, Table 6 Inner VIF EF

IU

EF

SI

RE

HM

PEU

PU

1.788

1.832

1.783

1.995

IU SI

2.281

RE HM

1.497

PEU

1.999

PU

1.848

FC

1.905

CO

1.518 1.740

1.720

1.756

FC

CO

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Table 7 Determination coefficients (R2 )

R square

R square adjusted

IU

0.720

0.717

PEU

0.425

0.419

PU

0.632

0.627

Average

0.592

0.588

the relative impact of economic feasibility and reliability on perceived usefulness and the relative impact of hedonic motivation on perceived ease of use factors may be somewhat low. However, in all other relationships, the values are above 0.02, indicating an overall satisfactory fit of the structural model. Moreover, the Predictive Relevance (Q2 ) values, which indicate the predictive power of the structural model for specific endogenous latent variables, are considered to have predictive adequacy when they are greater than 0 [13]. As shown in Table 9, all Q2 values for the endogenous latent variables are greater than 0, indicating that the structural model has predictive adequacy. Table 8 The effect size (f2 ) EF

IU

SI

RE

EF

HM

PEU

PU

0.024

0.014

FC

IU SI

0.297

RE

0.147

0.002

HM

0.014

0.321

PEU

0.043

PU

0.089

FC

0.081

0.043

CO

Table 9 Predictive relevance (Q2 )

0.021

Variables

0.122

Predictive relevance (Q2 )

EF

0.286

IU

0.673

SI

0.556

RE

0.588

HM

0.644

PEU

0.573

PU

0.656

FC

0.512

CO

0.477

CO

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Hypotheses Testing

Through the analysis, the reliability and validity of the measurement model have been verified, and the goodness of fit of the structural model has been examined. The results of hypothesis testing based on the research model are presented in Table 10. The path coefficients, t-values, and p-values were derived through the execution of the PLS-SEM algorithm and bootstrapping. Path coefficients represent the strength of the relationship between latent variables in the structural model, and a t-value absolute greater than 1.96 indicates that the research hypothesis is accepted. Similarly, if a pvalue is less than 0.05, the research hypothesis is considered statistically significant. In this study, two hypotheses concerning the relationship between economic feasibility and perceived usefulness and the relationship between reliability and perceived usefulness did not meet these conditions and were rejected. However, the remaining 11 hypotheses all had t-values with an absolute value greater than 1.96 and p-values less than 0.05, indicating that they have a statistically significant impact. Furthermore, concerning the coefficient representing the predictive power of the research model, the determination coefficients (R2 ) for Perceived Ease of Use (0.425), Perceived Usefulness (0.632), and Intention to Use (0.720) all significantly exceeded the threshold of 0.10 [10]. The average R2 value of 0.592 indicates moderate to high explanatory power. Convenience and Hedonic Motivation were both found to have a significant impact on Perceived Usefulness and Perceived Ease of Use. Economic Feasibility and Reliability showed a significant influence on Perceived Ease of Use but did not have a statistically significant impact on Perceived Usefulness. Regarding Intention to Use, all factors including Perceived Usefulness, Perceived Ease of Use, Social Influence, and Facilitating Conditions were found to have a Table 10 Results of the validation based on the path analysis Hypothesis

Path

1

CO → PU

2

CO → PEU

Path coefficient

t-value

p-value

0.282

5.096

0.000***

Acceptance Accepted

0.148

2.607

0.009**

Accepted

3

EF → PU

0.098

1.930

0.054

Rejected

4

EF → PEU

0.159

2.632

0.009***

Accepted

5

RE → PU

0.040

0.794

0.428

Rejected Accepted

6

RE → PEU

0.386

6.641

0.000***

7

HM → PU

0.422

10.157

0.000***

Accepted

8

HM → PEU

0.111

2.111

0.035*

Accepted Accepted

9

PEU → PU

0.165

3.927

0.000***

10

PU → IU

0.218

4.305

0.000***

Accepted

11

PEU → IU

0.155

3.216

0.001**

Accepted

12

SI → IU

0.432

8.397

0.000***

Accepted

13

FC → IU

0.209

5.281

0.000***

Accepted

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

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statistically significant impact. Particularly, Social Influence demonstrated a strong influence on Intention to Use, as did Hedonic Motivation on Perceived Usefulness, and Reliability on Perceived Ease of Use.

4 Conclusion This study analyzes factors influencing the intention to use UAM PTS. Overall, it was found that various factors positively impact the intention to use UAM PTS. The results indicate that people are most influenced by the opinions of those around them when deciding to use UAM PTS. In order for the successful establishment of UAM PTS in the future, it will be crucial for policy authorities and relevant companies to actively foster a positive societal atmosphere. The factor of usefulness demonstrated the second-largest impact on determining the intention to use. The anticipation of enhanced productivity through improved work efficiency, stemming from the utility of time, shortened commute durations, and related benefits, is a highly significant factor influencing the intention to use. Therefore, Policy authorities involved in the development of UAM systems, as well as aircraft manufacturers, operators, vertiport operators, and air traffic control entities, should prioritize efficient boarding procedures and control measures to reduce travel time. The hedonic factors such as enjoyment and pleasure exert a significant influence on the perceived usefulness. In other words, individuals associate a sense of happiness with UAM boarding itself and believe that it enhances their overall quality of life. Therefore, Policy authorities and UAM-related companies need to strongly promote the concept that UAM boarding is not merely a means of transportation but a new concept that offers both enjoyment and tourism simultaneously. The factor exerting the greatest influence on the perceived ease of use variable is reliability. The perceived ease of use, characterized by the accessibility and simple procedures for UAM PTS, is heavily influenced by the trust in the safety of aircraft and air traffic control systems. This, in turn, directly impacts the intention to use UAM PTS. Therefore, aircraft manufacturers and air traffic control authorities should focus on manufacturing aircraft that the public can trust and establishing robust air traffic control systems.

References 1. S. Korea, “Korean statistical information service,” Statistics Korea, https://kosis.kr/index/index. do (accessed 15, August, 2023). 2. J. H. Choi, Y. H. Park, and I. S. Jeon, “A study on the cost of fare for UAM (Urban Air Mobility) airport shuttle service,” (in Korean), Journal of Korean Society of Transportation, vol. 39, no. 5, pp. 593–605, 2021. [Online]. Available: https://www.riss.kr/link?id=A107901569.

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3. Korea Ministry of Government Legislation, “Act on the promotion and support of the utilization of urban air mobility,” https://www.moleg.go.kr/ (accessed November 10, 2023). 4. D. P. Thipphavong et al., “Urban air mobility airspace integration concepts and considerations,” in 2018 aviation technology, integration, and operations conference, 2018, p. 3676. 5. eVTOL News, “The electric VTOL news,” https://evtol.news (accessed November 10, 2023). 6. (2022). Urban air mobility overview. 7. (2021). K-UAM concept of operations 1.0. 8. V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly, pp. 425–478, 2003. 9. L. J. Cronbach, “Coefficient alpha and the internal structure of tests,” Psychometrika, vol. 16, no. 3, pp. 297–334, 1951. 10. G. K. Shin, “Partial least squares structural equation modeling with smart PLS 3.0,” 2nd ed. Seoul: Chung Ram Publishing, 2022, pp. 195–196. 11. C. E. Werts, R. L. Linn, and K. G. Jöreskog, “Intraclass reliability estimates: Testing structural assumptions,” Educational and Psychological Measurement, vol. 34, no. 1, pp. 25–33, 1974. 12. C. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” Journal of Marketing Research, vol. 18, no. 1, pp. 39–50, 1981. 13. J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson, W. Black, and R. Anderson, “Multivariate data analysis (Eighth),” Cengage Learning EMEA, 2019. 14. G. K. Shin, “Partial least squares structural equation modeling with smart PLS 3.0,” 2nd ed. Seoul: Chung Ram Publishing, 2022, p. 248.

A Study on the Integration of Endpoint Security Service Operations Management: Focusing on Cloud Services Seungbok Ahn, Gwangyong Gim, Seockho Jang, and Jongmo Hwang

Abstract Enterprises are increasingly adopting SECaaS (Security as a Service) to address external threats and safeguard information assets of endpoints, owing to its cost-effectiveness compared to on-premises solutions. However, the proliferation of security products and service types, coupled with a shortage of operational personnel, has led to reduced operational efficiency and suboptimal maintenance of the optimal operational state. This study aims to investigate operational issues and inefficiencies encountered by small and medium-sized enterprises (SMEs) that implement multiple cloud Security as a service (SECaaS). This paper proposes three strategies for integration. First, the enhancement of the SaaS Maturity Model, specifically the four-Level model, focusing on multi-tenancy, to facilitate the configuration of integrated operational management systems. Second, addressing the technical limitations that arise during the adoption of services (stability and security concerns) through prior verification conducted by security service providers. Third, enhancing operational efficiency through integrated management of operations and agents. This study is expected to lead to improved operational efficiency for small and medium-sized enterprises (SMEs) that have adopted numerous endpoint security services. Furthermore, it is anticipated that companies providing SECaaS will benefit from the development of platform services that support integrated operational management for various enterprises and heterogeneous cloud information Security as a service (SECaaS).

S. Ahn (B) · S. Jang · J. Hwang Department of IT Policy and Management, Soongsil University, Seoul, South Korea e-mail: [email protected] S. Jang e-mail: [email protected] J. Hwang e-mail: [email protected] G. Gim Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_3

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Keywords Endpoint Security · SECaaS · Security Platform

1 Introduction In the contemporary business environment, corporate information is a critical asset and an essential element that directly impacts competitiveness. Efforts to protect this information are not merely an option but a necessity. Modern businesses are transitioning towards a data-centric information security paradigm as they actively incorporate smart devices like PCs, laptops, and smartphones into their operations [1]. Small and medium-sized enterprises (SMEs) have been increasingly adopting a relatively cost-effective approach by utilizing multiple cloud information Security as a service (SECaaS) to address both external and internal threats [2–4]. However, as the number of managed products grows, challenges related to operational management and decreasing usability have emerged, posing additional security threats [5]. Thus, the purpose of this research is to establish an integrated management approach to enhance the operational stability and efficiency of cloud-based endpoint information security services. Given the extensive nature of the information security domain, this study focuses on SMEs that operate multiple cloud information security services and the endpoint area, which has become increasingly vital in information security [6]. The research methodology involved assessing the forms of information security services. A detailed analysis of the operational methods was conducted through the examination of manuals for administrators and users, phone inquiries, and face-toface interviews regarding ten different distributed information security services in South Korea. Based on this analysis, an integrated system configuration proposal was presented, and operational efficiency enhancement possibilities were verified through a comparison of pre-and post-adoption scenarios and a survey.

2 Endpoint Information Security and Cloud Services 2.1 Information Security Services and Endpoint Area Information Security Services Information security services and products are classified into two major categories in the “2023 Domestic Information Protection Industry Survey”: information security system development and supply, and information security-related services. Among these, the subcategories include network security, system security, data loss prevention, encryption/authentication, and security management, all of which fall under the Endpoint area [7, 8]. The security products for PCs and laptops, which are the focus of this research, are listed in Table 1.

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Table 1 Endpoint area security products and systems Security products and systems

Security area

• Malware Response, ransomware Response, • Digital Rights Management (DRM), • DLP(Endpoint), endpoint detection and response (EDR), etc

Endpoint (PC etc.)

Source Reclassified based on the “2023 Information Protection Industry Survey” (Ministry of Science and ICT, KISIA)

2.2 Cloud Computing and Security as a Service (SECaaS) Cloud computing is defined as an information processing system that enables the flexible use of information and communication resources, such as integrated shared information and communication devices, communication facilities, software, etc., through information networks in response to user demands and changes in demand [9]. Cloud services are categorized into three main types: SaaS (Software as a Service) which provides application software services, PaaS (Platform as a Service) which provides software development environments (platforms), and IaaS (Infrastructure as a Service) which offers IT infrastructure services (servers, storage, etc.). Additionally, there is SECaaS (Security as a Service) that leverages cloud computing to provide security functions as services [10–12]. SECaaS is based on cloud computing technology and offers security solutions as services, allowing organizations to use these services without the need for in-house infrastructure distribution. This results in cost efficiency, as users pay for the services they utilize [13, 14]. Furthermore, SECaaS offers a diverse range of security functions, making it more affordable for small and medium-sized enterprises to establish information security without significant infrastructure investment [11].

2.3 Software as a Service (SaaS) The maturity model of SaaS, a cloud service, is classified into four levels [15]. SaaS maturity models and domestic security products are shown in Table 2. The first level is Ad Hoc/Custom, which involves providing services tailored to individual instances. This is similar to traditional Application Service Providers (ASPs). The second level is Configurable, Single Tenant, where each user has a dedicated server operated from a single source. The third level is Configurable, Multi-Tenant-Efficient, where multiple users are served from a single instance, enabling cost-effective service delivery to each user. Typically, SaaS is considered to begin from the third level. The fourth level is Scalable, Configurable, Multi-TenantEfficient, which utilizes Load Balancers to address resource shortages and ensure scalability, allowing flexible adaptation to increasing user demands [15]. Most of the

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Table 2 SaaS maturity model and domestic security products 1

2

– Cloud Users Build on infrastructure (ASP) – Document Centralization (JIRANSECURITY), PC-OFF (Janice) 3

4

– DLP (JIRANSOFT), DRM (MarkAny, Softcamp), Anti Ransomware (CheckMAL), antivirus (AhnLab, etc.) Source MSDN “Architecture Strategies for Cat ching the Long Tail”, 2006

cloud information security services in South Korea are at maturity levels 3 and 4 in terms of SaaS services.

3 Issues with Individual Operational Management of Endpoint Security Services 3.1 Technical Limitations Since 2017, ransomware attacks have been on the rise, leading to a significant increase in the prominence of endpoint security in the list of concerns [16]. Consequently, many domestic companies and organizations have been adopting various endpoint security services, which has exposed them to several technical and operational challenges. These issues and problems were identified through research on the decision-making processes for adopting enterprise-level endpoint security solutions and interviews with information security service providers. One major issue arises from the installation of multiple security agents, leading to performance and stability degradation. Security agents installed on users’ (employees) PCs monitor all processes running on the PC to detect and control unauthorized activities. This can indirectly affect the PC’s performance. Moreover, there is a potential for conflicts with other software, making thorough checks necessary before implementation [17]. Security products must be adapted to the type and version of the PC’s operating system, just like regular software. Thus, it’s essential to assess how quickly they

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can adapt to changes in the OS and its versions during implementation. Additionally, verifying and testing for responses to new security vulnerabilities, compatibility with existing solutions, and other factors are necessary when adopting security products [17].

3.2 Limitations of Operations Management In the enterprise sector, there are 79,818 information security personnel. The ratio of information security personnel to businesses with five or more employees is 22% among 350,368 enterprises. Additionally, it has been noted that information security personnel in small and medium-sized enterprises (SMEs) often have multiple job responsibilities, indicating that they juggle other tasks alongside their security duties [18]. Even in situations where there is a shortage of security personnel, it is crucial to maintain a certain level of organizational information security. An evaluation of the operational management and identified issues for 10 different domestically distributed cloud products is presented in Table 3. First, there is an issue related to operational aspects. Security personnel are required to perform repetitive tasks listed in Table 4 and manage numerous information security products, as illustrated in Fig. 1. This leads to an increase in redundant tasks and subsequently results in a decline in operational efficiency. The second issue pertains to usability. User (employee) engagement is a critical factor in determining the organizational security level [17]. Each user needs to be aware of the company’s security policies, comply with them, and check whether security products are reinstalled and functioning correctly when replacing PCs and other devices. However, it is challenging for each individual to perform these tasks. The factors that reduce usability are summarized in Table 5.

Table 3 Domestic cloud endpoint products Security products

Company/product & service

Privacy

JIRANDATA/PC Filter

DLP

JIRANSOFT/OfficeKeeper, dsntech/OFFICESAFER

Document Centralization (backup)

JIRANSECURITY/DocuONE cloud

DRM

MarkAny/Document SAFER cloud, SOFTCAMP/Document Security V5.0

AntiRansomware

CHECKMAL/AppCheck

Antivirus

AhnLab/V3 MSS, Avast/avast business, ESET/ ESET INTERNET SECURITY

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Table 4 Information security officer operations management items Item

View

Different operations

1. Agent Management

Initial installation and addition/change

– Distribution: Email, files, URLs, etc – ID: E-mail, resident number, nickname, company number

2. Management Screen

Login and Authentication

– Authentication: ID, PW, 2 channels – UI: WEB, APP, Console

3. User Operations

Not installed, reinstalling, – Unused: Request, Period changing users Verification, Security Guidelines (Resignation, Organizational Change, PC Replacement, etc.) – Delete: automatic, manual, automatic + manual User Usage Status Check – Reinstallation: Automatic, manual, installation guidance Urgent and regular notice – Method: email, pop-up, SMS, etc – Items: Security threats, changes, payment status

4. Announcements and Issues

Source 10 types of research, including document security service manual (Mark Annie) and office Safer instruction manual (DSNTech)

Fig. 1 Endpoint security service operations management configuration chart

Operational and usability aspects of operational items naturally increase as more security products are added. This leads to the issue of increased redundant tasks, and organizational changes within the company also contribute to an increase in workload.

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Table 5 User-side security product usability items Item

View

Different ways of using

5. Agent Distribution (Check status)

Check installation status

– Status: Icon, pop-up, not displayed

Version information

– Info: Support, Unsupported

6. Agent Status

Device Operational Status

– Status notation: marked, not marked

7. Agent Authentication

Product/User Authentication – Authentication time: PC interlocking, automatic, unauthenticated

Source 10 types of research including Officekeeper Manager Manual (Jiranjigyo Soft), AppCheck Cms cloud manual (Checkmull), etc

4 A Study on the Integration of Endpoint Security Service Operations Management 4.1 Integrated System Design and Implementation In this study, the integrated system aims to provide multiple cloud Endpoint security services as a single unified service. The technical limitations discussed in the Sect. 3. Issues with Individual Operation Management of Endpoint Security Services are addressed through the configuration of the service site and test server. Operational issues are tackled through the administrator site (integrated console), while usability issues are addressed through the integrated management agent, as shown in Table 6. The direction for the integrated system configuration is based on the SaaS maturity model’s 4th level of multi-sharing (Scalable, Configurable, Multi-Tenant-Efficient), as the foundation, and adding the FrontEnd (UI/UX) for integrated management (system), as illustrated in Fig. 2. Furthermore, the security product agents installed on PCs are also covered by the research on ‘Efficiency Improvement Approaches Using a Common Agent for Security Service Programs’ [19]. They propose a common agent for each security function. Thus, this paper aim to present a modified version of this as the integrated management agent method, as illustrated in Fig. 3. Through the integrated management agent, each security product agent is capable of managing agent installation, removal, and re-execution based on security policies set in the administrator site. It also provides agent installation control, operational status monitoring, and authentication of individual security agents through integrated management, addressing usability issues. The security product’s security functions (detection, response, control, etc.) are executed directly by the security product agent, ensuring that security performance remains intact. The operational and functional components of the integrated system, including the service site, test server, administrator site, and integrated management agent, are illustrated in Fig. 4. Cloud service providers interested in providing information

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Table 6 Solutions to operational management problems Sortation

Operational management problems

Response

Expected effects

Technical Limitations

– Performance and reliability – Low security

Service site, test server

– Ensure agent reliability – Ensure operational performance and security

Operations Management Limits

Operational Problems

– Agent Operations Administrator Site (Integrated – Operation of Console) management screen – User Operations – Announcement/ Issue

– Integrating Administrator Authentication – Simplify distribution tasks – Increased operational management efficiency

Usability Problems

– Agent Distribution – Agent Status – Agent Authentication

– Automate agent installation – Automatic Agent Authentication – Understand local operational status

Integrated Management Agent

Fig. 2 SaaS maturity model 4-Level integrated management proposal (reconfiguration)

security services register their service information (services, agents, site configuration, etc.) through the service site. The security product agent undergoes basic functionality testing, performance evaluation, and software compatibility checks on the test server before being transmitted to the administrator site. Once this process is complete, the information security service becomes operational. The enterprise’s security administrators perform administrative registration and policy configuration

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Fig. 3 Integrated management agent conceptual chart

Fig. 4 Operations and functional configuration diagram

on user devices (Devices such as PCs) within the administrator site, which then initiates automated installation through the integrated management agent. Subsequently, the integrated management agent operates according to the security policies configured in the administrator site. The infrastructure for the integrated system configuration is structured as depicted in Fig. 5, and the security services from the perspectives of the final administrators (security personnel) and users are illustrated in Fig. 6.

4.2 Confirmation of Integrated System Improvements To verify the effectiveness of the proposed integrated system, the operational tasks of security personnel were prepared and compared for each scenario (Case) before and after the introduction of integrated management. 1. The scenario and verification of technical limitations are shown in Table 7. The tasks before the new introduction are security service review and test tasks, and

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Fig. 5 Integrated system infrastructure configuration chart

Fig. 6 Security manager/user operations management configuration char

the tasks increase according to the number of tasks repeated each time the service is introduced. During the review and testing of scenario Level 1, the test case should be repeated [Rn + (Rn × Tn)] by the number of security service reviews and the number of operating environments. After the implementation, direct testing cases [Rn + 0] are no longer necessary because the security service provider has completed the pre-testing. Therefore, this approach improves efficiency regarding the technical limitations. 2. The scenarios and verification for operational limitations (operational efficiency and usability issues) are as shown in Table 8. Scenario Level 1 for operational limitation verification involves repeating all steps from authentication to the current status. As common functionalities of the security services in operation, the workload increases as you introduce

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Table 7 Technical limit verification scenario and verification Management scenario

Validation

Case ID

Level-1

Level-2

Before implementation

After implementation (integration)

T1-1

Review

Security service scanning

Rn

Rn

Test

Establishing a test operating environment

Rn × Tn

0 × Tn

Rn + (Rn × Tn)

Rn + 0

Review key features

T1-2 T2-1 T2-2

Setting the Operational Policy

T2-3

Control Policy Settings

T2-4

Agent Distribution

T2-5

Test Service Functionality test

Effect

Rn = Number of reviews to introduce new security services Tn = Number of test operating environments for new security services

as many security services as possible [9 cases × n]. After the introduction, the redundancy in operational and usability tasks is expected to be integrated, leading to improved efficiency. For the 9 cases in Scenario Level 1, in the conventional operating method, all cases must be repeated. However, after introducing the integrated system, only the policy setting for the control function (MO2-2) case is repeated, and the other 8 cases are eliminated [8 cases + (1 case × n)], which can enhance efficiency. Regarding operational limitations, the introduction of an integrated operational management system is expected to improve them. A survey was conducted among 22 sales representatives selling cloud information security services. The survey used a 5-point scale, and the responses indicating the expectation of improvement (very likely, likely) were 95.5% for operational efficiency and 86.4% for usability.

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Table 8 Operations management limit verification scenario and verification Management scenario Case ID

Level-1

Verification Level-2

Before After implementation implementation (Integration)

Operational MO1-1 Certification

Accessing the Administrator Console

n

1

MO1-2

Administrator authentication

MO2-1 Policy Setting

Establish n operational policies, such as organization, administrator configuration, etc

1

MO2-2

Configuring the n Offering Function Control Policy

n

MO3-1 Distribution

Establish distribution procedures and plans

n

1

MO3-2

Notification of installation plan

MO4-1 Agent Management

Checking agent behavior status

n

1

MO4-2

Announce agent deletion or reinstallation n

1

n

1

n

1

MO5-1 Announcement Registration of notices (installation, security issues, etc.)

Usability

MO5-2

Confirmation of the announcement

MU1-1 Installation

Downloading the installation file

MU1-2

Average 4 steps of installation process

MU2-1 Certification

Confirmation of the announced plan

(continued)

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Table 8 (continued) Management scenario Case ID

Level-1

Verification Level-2

Before After implementation implementation (Integration)

MU2-2

User authentication

MU3-1 Current Situation

Checking the n operational status of the agent

MU3-2

Administrator inquiries in case of abnormal behavior

Effect

9 Case × n

1

8 Case + (1 Case × n)

n = Number of security services in operation

5 Conclusion Small and medium-sized enterprises (SMEs) have been adopting various Endpoint security products to counteract industrial technology leakage and respond to information security breaches. However, the increasing workload of security management and a lack of personnel have led to technical limitations and operational management issues (operational and usability limits) in the distribution and operation of Endpoint security services. In this regard, this study conducted research on operational management integration strategies to enhance efficiency. First, the integrated management system was designed to improve the capability to accommodate multiple security services by refining the SaaS maturity model to a 4-level model (multi-sharing). Second, it minimized technical limitations (stability and security) arising during distribution through the use of a test server and reduced redundant tasks. Third, it proposed improving operational efficiency through the integrated management of the administration site and agents. To validate these improvements, operational management scenarios (cases) were developed, and the before-and-after results were assessed. As a result, the introduction of a test server for service testing reduced tasks by Rn × Tn, and integrated operational management reduced operational tasks by n × operational case tasks, minimizing redundancy in operational tasks. Through this study, it is anticipated that the operational efficiency of SMEs adopting multiple security as a service (SECaaS) will be improved, and SECaaS providers will benefit from being able to offer services with integrated operational management and develop platform services for various security as a service (SECaaS).

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However, this study has limitations regarding the verification of improvement effects in various operational environments in enterprises, and the development and testing of integrated systems require the participation of service providers. Securing participation from service providers has proven to be challenging. These limitations are considered potential future research tasks.

References 1. IDC Japan.: IT Service Taxonomy: Solutions, Competitive Services Categories and DemandSide Methodology, Analysts: Philip Winthrop and Christopher Hoffman (2001). 2. Plá, L. F., Shashidhar, N., Varol, C.: On-Premises Versus SECaaS Security Models. In 2020 8th International Symposium on Digital Forensics and Security (ISDFS), 1–6. IEEE (2020). 3. Gartner Report.: Key Issues for Network, Messaging, Mobile Security and Security Services Infrastructure Protection, G00166136 (2009). 4. Jon Oberheide, Evan Cooke, Farnam Jahanian, CloudAV.: N-Version Antivirus in the Network Cloud, 17th USENIX Security Symposium (2008). 5. Security World.: 2020 Information Security Survey (2020). 6. IDC.: Worldwide and U.S Security Services 2006–2011 Forecast and Analysis (2008). 7. KWAK, S. K.: A Study on Effective APT Attack Defence of Endpoint Level at Enterprise, Dongguk University Department of Information Security Graduate School of International Affairs & Information Security, Master’s Thesis (2019). 8. KISIA.: Survey for Information Security Industry in Korea: Year 2023 (2023). 9. KISA.: Cloud Information Protection Guide for Enterprise IT Personnel Using Cloud Services (2017). 10. Mohammed Hussain, Hanady Abdulsalam.: SECaaS: Security as a Service for Cloud Based Application, ACM Proceeding (2011). 11. Lee H. J., Song K. H., Lee J. I.: Measures to Strengthen Cloud Service-Based Corporate Information Protection, Korean Society for Information Protection, 23(4) (2013). 12. Furfaro, A., Garro, A., Tundis, A.: Towards Security as a Service (SECaaS): On the Modeling of Security Services for Cloud Computing. In 2014 International Carnahan Conference on Security Technology (ICCST), 1–6. IEEE (2014). 13. Frederick Chong.: Architecture Strategies for Cat Ching the Long Tail, MSDN (2006) 2020.08.30. http://pamungkaswave.blogspot.com/2015/06/architecture-strategies-forcatching.html. 14. Roh Y. H.: A Technique of Extracting Potential Security Violations by Using Exponentially Weighted Moving Average and Sliding Window, Sogang University’s Master’s Degree (2013). 15. Moon, H., Roh, Y., Park, S.: A Study on the Decision Process for Adoption of Enterprise Endpoint Security Solutions. Journal of Information Technology and Architecture, 11(2), 143– 155 (2014). 16. KISA.: 2016 Information Security Manpower Recruitment Survey (2017). 17. Kim, J. H., Kang, T. S., Lee, J. K., Kim, K.J., Shin, Y.: Improvement of the Efficiency of Utilization with Security Service Program Common Agent. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, 7(3), 351–359 (2017). 18. Lee J. M.: Study on How to Utilize SECaaS to Strengthen Security in Small and Medium-Sized Manufacturing Industries, Chung-Ang University’s Master’s Degree (2018). 19. Han J. I. and 7 others: Designing a User Rights Management System for the SaaS Platform, Journal of the Spring Conference of the Korean Society for Information Processing, 18(1) (2011).

A Study on the Factors Affecting the Korean Financial Institution’s Switching Intention to Open Source Software: Focused on System Software Heeyoung Kim, Gwangyong Gim, Hyongyong Lee, and Hyuna Kim

Abstract This study aims to identify the factors influencing the intention of domestic financial institutions to switch from using proprietary system software to open source software (OSS). Based on the Technology, Organization, and Environment (TOE) Framework, this study categorizes technological, organizational, and environmental considerations as primary factors. Subsequently, subordinate factors were derived for each primary category, culminating in the construction of an Analytic Hierarchy Process (AHP) model to systematically evaluate their relative importance. Surveys were conducted among groups of internal and external IT experts within financial institutions, comparing and analyzing the results of factor weights between the two groups. It was found that internal IT experts placed higher weights on technological factors such as security, job suitability, and functional indifference, while external IT experts assigned higher weights to organizational factors like technological competency and cost advantage, along with job suitability of technological factors. This research highlights the differences in perception of the importance of factors between internal and external parties regarding transitioning to open source in the system software sector. Based on these findings, it is hoped that financial institutions will be provided with the environmental and institutional foundation to more actively participate in and contribute to open source projects. Keywords Open Source Software · System Software · Switching Intention · Financial Institution · TOE · AHP H. Kim (B) · H. Lee · H. Kim Department of IT Policy and Management, Soongsil University, Seoul, South Korea e-mail: [email protected] H. Lee e-mail: [email protected] H. Kim e-mail: [email protected] G. Gim Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_4

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1 Introduction Traditionally, the financial industry has been categorized as a regulated sector due to its stringent regulatory framework, fostering conservatism towards change. However, the emergence of new technologies based on open source, like blockchain and artificial intelligence, has significantly expanded the fintech industry, piquing the interest of financial institutions in open source technology. In South Korea, major tech companies such as Naver and Kakao have not only invested in specialized companies but also extended their presence into the financial industry, competing with traditional institutions based on services like Naver Pay and Kakao Bank. This evolving landscape has prompted financial institutions to transform their IT organizations, embracing new technologies and methodologies like Cloud, Microservice, and Open API, with an increasing uptake of open source software. The core platforms of the domestic financial sector were predominantly Mainframes until the early 2000s, but after transitioning through Unix to ×86 servers, which now, amidst a changing paradigm toward cloud computing and artificial intelligence, are increasingly adopting the flexible and compatible Linux operating system. According to data from the Bank of Korea in 2021, the use of Linux operating systems for server-class computing devices in financial institutions increased by 12%p in two years, from 37.8% in 2019 to 49.9% in 2021, followed by Windows at 23.2%, and Unix at 18.3% [1]. In South Korea, the acceleration of collaboration and competition between financial institutions and fintech companies was further propelled by the launch of Open Banking in December 2019 and MyData services in January 2022. Financial institutions are considering a shift from conventional proprietary software to open source software in various application development foundations such as web servers, web application servers, and frameworks, in addition to operating systems, to support customer-centric and rapid service development and operations. Despite various studies and analyses being conducted on the adoption and utilization of open source software, there is a notable paucity of research regarding the status of its implementation across different industries domestically, as well as the types of software adopted. This study aims to examine the factors influencing domestic financial institutions’ intention to migrate their system software to open source software, categorizing them into technological, organizational, and environmental contexts, through interviews with both internal and external IT experts in the financial sector. The findings of this study are anticipated to provide strategic implications to financial institutions aiming to leverage open source software and to enterprises developing financial IT services.

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2 Theoretical Background 2.1 Study on the Adoption of Open Source Software in South Korea Study on the adoption of open source software in South Korea has highlighted policy factor such as the central government’s adoption decision and software quality as critical factors for adoption in public institutions. Job suitability and cost advantage do not have a positive effect on the intention to adopt. However, it has been confirmed that policy factor, job suitability, cost advantage, and software quality have a positive moderating effect on the adoption of open source software if there is external open source participant support [2]. Empirical analysis has confirmed that domestic companies are influenced to adopt open source software due to the negative ‘push’ factors associated with proprietary software, such as ongoing maintenance costs, vendor dependence, functional indifference, and inefficiency of software resources, and also due to positive ‘pull’ factors for open source software like network-oriented support, testability, and strategic flexibility [3]. Influential factors in a company’s adoption of open source software include organizational necessities such as transformative leadership, adaptive performance, readiness for change, as well as technological necessities like cost benefits and software quality. It has been revealed that if the government provides diverse support in terms of policy, finance and technology, the acceptance of open source software by companies is further reinforced [4].

2.2 Concept of System Software and Its Usage Status in Korea System software, as depicted in Fig. 1, refers to a collection of programs that create a user-friendly environment, facilitating the easy use of computers. It efficiently operates the computer system and controls the process of information handling [5]. System software is a general term for software that exists between application software and hardware devices, encompassing operating systems (OS), compilers, assemblers, middleware, software platforms, and runtime systems. Middleware, such as Web Application Servers (WAS) and Frameworks, refers to software that enables developers and operators to construct and deploy applications more efficiently by providing common services and functionalities to applications, acting as an intermediary between the operating system and application software [6]. Table 1 shows the types of system software commonly used in Korea. The process of transitioning system software like Web Application Servers (WAS) to open source software is more complex and challenging compared to switching operating systems or DBMS, since it involves environmental analysis, application

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Fig. 1 The hierarchical structure of software

Table 1 System software types Classification

Proprietary software

Open source software

Operating System

Unix, Windows

Linux, HanCom Gooroom

Web Server

IIS, WebtoB

Apache, Nginx

Web Application Server (WAS)

JEUS, WebLogic, WebSphere

JBoss, Tomcat, WildFly

Framework

DevOn, Nexcore

Spring, SpringBoot

analysis, application transition, and compatibility verification [7]. This is particularly true for financial institutions that must process transactions, such as customer deposits and withdrawals, in real time without disruptions. Therefore, decisions to migrate system software, including operating systems, from proprietary to open source solutions within such institutions must be made with utmost caution. Financial organizations typically do not consider migrating to open source system software unless in situations where substantial budget and adequate development resources are available such as developing next-generation systems or transitioning unit business systems to the cloud. However, Korean financial institutions, which have been hesitant to adopt open source software, are actively considering converting their overall system software to open source software after experiencing directly or indirectly the successful transition to the Linux operating system.

3 Research Method 3.1 TOE Framework & Analytic Hierarchy Process Tornatzky and Fleischer elaborated on the factors influencing an organization’s adoption of technological innovations from three perspectives: technological, organizational, and environmental, thereby constructing the Technology—Organization— Environment (TOE) Framework [8]. Technological factors encompass both the

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internal and external technologies related to the organization, including the procured equipment and the processes of organizational members. Organizational factors refer to the characteristics and resources of an organization, such as company size, human and material resources, the scale of funding, costs, and the interconnectedness of employees. Environmental factors pertain to the space in which a business operates, including industry structure, competitors, government regulations, and business partners. These three sets of factors represent constraints and opportunities for technological innovation and influence how an organization identifies the need for, evaluates, and adopts new technologies [9]. In order to analyze the factors affecting the switching intention of Korean financial institutions to open source system software, this study reorganized the constituent concepts of previous literature on open source software to form a TOE Framework by classifying them into technological, organizational, and environmental factors. In addition, AHP, a hierarchical decision-making method, was used to analyze the relative importance and priority of each factor. AHP is a decision-making methodology developed by Thomas Saaty to improve the inefficiency of the decision-making process. Effective human thinking is based on the principle of hierarchy structure, relative importance weighting, and logical consistency [10]. AHP is an analytical technique that supports rational decision-making based on insights and systematic analysis by experts with expertise from field experience. This study aims to extract the factors that have the greatest impact on the intention to switch to open source system software and to identify the relative weights between factors through pairwise comparisons based on AHP technique.

3.2 Research Model This study designed a three-level hierarchy of factors that influencing the intention of Korean financial institutions to migrate to open source system software, as depicted in Fig. 2. It is based on the TOE Framework, encompassing technological, organizational, and environmental factors as primary factors along with nine subfactors derived from analysis of previous literature and interviews with IT experts from financial institutions. The technological factors include job suitability, functional indifference, and security. Organizational factors encompass top management support, cost advantage, and technological competency. Lastly, the environmental factors consist of policy factor, competitive pressure, and network-oriented support. The construction of evaluation criteria and the operational definitions for each subfactor are detailed in Table 2.

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Fig. 2 AHP hierarchy

4 Empirical Data Analysis 4.1 Expert Opinion Consultation and Data Collection In this study, internal and external IT professionals of domestic financial institutions were divided into two groups as shown in Table 3, and in-person survey was conducted with 12 professionals in each group. The questionnaire was designed to allow for pairwise comparisons between factors in order to evaluate the relative importance of factors in the immediate lower level from the perspective of a single factor in a certain level. In order to capture the intention to switch to open source system software and to help respondents understand the survey, the participants were given an explanation of what system software is and were provided with operational definitions for each of the evaluation items. The survey was conducted over nine days from October 12, 2023 to October 20, 2023. In order to maintain the Consistency Ratio (CR), the most important aspect in expert knowledge extraction based on AHP, the in-person survey was conducted in a way that the extraction was repeatedly performed until the CR improved if the CR from the extracted expert knowledge was low. The 9-point scale proposed by Saaty was used as the evaluation scale for the survey.

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Table 2 Construction of evaluation criteria and operational definition of subfactors Primary factor

Subfactor

Operational definition

Technological Factors

Job Suitability

Whether the open source [2, 11] system software is appropriate for developing the financial institution’s business and services and can contribute to achieve the financial institution’s business performance and objectives

Functional Indifference

Whether the range of differentiation in the features provided by proprietary system software compared to those offered by open source system software is negligible

[3, 12]

Security

Whether the open source system software supports the ability to protect the financial institution’s systems and services from external penetration threats

[13, 14]

Top Management Support

The level of interest and [12] support from the financial institution’s management regarding the adoption of open source system software

Cost Advantage

Whether the anticipated implementation and maintenance costs of switching to open source system software outweigh the ongoing maintenance costs of proprietary system software

Technological Competency

Whether the financial [12, 15] institution IT development/ operations staff possess technological competency in open source system software in terms of knowledge, development and operational proficiency, and troubleshooting abilities

Organizational Factors

Reference

[2]

(continued)

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Table 2 (continued) Primary factor

Subfactor

Operational definition

Reference

Environmental Factors

Policy Factor

Availability of policy support from the Financial Supervisory Service, Financial Security Service, etc. such as systems and guides to help financial institutions adopt and operate open source system software

[2]

Competitive Pressures

Whether financial institutions are feeling pressure to move to open source system software due to competition among financial institutions as digital transformation accelerates

[12]

Network-Oriented Support

Availability of support on [16] system software related useful information and services by communities of voluntary open source developers via network such as the internet

Table 3 Qualifications of expert by group Group

Qualifications of expert

A

CIO in Financial Institution with 20+ years of experience

B

At least Department Head of Financial solutions IT company (System Integration or Software) with 20+ years of experience

4.2 AHP Results: Comparison Between Groups AHP analysis was done using Super Decisions software and Excel with responses from 20 participants that were selected from the survey conducted among two groups, labeled A and B, each consisting of 12 experts after excluding the responses of two participants each from Group A and B whose Consistency Ratio exceeded 0.2. Using the pairwise comparison calculation results for each survey response on the evaluation factor, comparative judgment matrix was built for each respondent to calculate the relative importance of each factor by respondent [17].

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Table 4 Weight and priority of primary factors Primary factors

Group A

Group B

Weight

Priority

Weight

Priority

Technological Factors

0.602

1

0.299

2

Organizational factors

0.172

3

0.487

1

Environmental factors

0.225

2

0.235

3

4.2.1

Weights and Priorities of Primary Factors

The results for the analysis on the weights of the three primary factors are shown in Table 4. According to the survey results on factors affecting the intention of domestic financial institutions to migrate to open source system software, Group A showed 0.602 for technological factors, 0.172 for organizational factors, and 0.225 for environmental factors, thus in the order of technological factors, environmental factors, and organizational factors. Group B, on the other hand, results showed 0.299 for technological factors, 0.487 for organizational factors, and 0.235 for environmental factors, in the order of organizational factors, technological factors, and environmental factors. While Group A rated technological factors very highly, Group B rated organizational factors, which Group A gave the lowest weight, as having the greatest impact.

4.2.2

Weight and Priority of Subfactors

The weights of the subfactors were divided into weights within the primary factor and weights across the subfactors as shown in Table 5. A comparison of the weights of the subfactors between the groups are shown in Fig. 3. In Group A, security was the most important factor for their intention to switch to open source system software, with a significant difference of 4.3 times the weight of security in Group B. Group A considered network-oriented support, such as assistance from the open source developer community, to be more important for migration than technological competency. In contrast, Group B found the technological competency of financial institutions to have the greatest impact on the intention to switch, while network-oriented support had the least impact. Cost advantage, one of the significant advantages of open source software, was evaluated as a higher weight by Group B than Group A. The impact of top management support and policy factor on the intention to migrate to an open source system software also appeared to be greater in Group B than in Group A. With the exception of job suitability, which appeared as the second highest migration factor in both groups, there were differences in the weights and priority rankings of each subfactor between the groups.

Environmental Factors 0.367 0.228 0.405

Competitive Pressure

Network-oriented Support

0.352

Technological Competency

Policy Factor

0.495

Cost Advantage

0.568

Security 0.154

0.162

Functional Indifference

Top Management Support

0.270

Job Suitability

Technological Factors

Organizational Factors

Weight

1

3

2

2

1

3

1

3

2

0.091

0.051

0.083

0.061

0.085

0.026

0.342

0.098

0.162

Weight

Within the primary factors

Primary factors Priority

Overall

Group A

Subfactors

Factors

Table 5 Weight and priority of subfactors

4

8

6

7

5

9

1

3

2

Priority

0.132

0.369

0.499

0.420

0.295

0.285

0.264

0.228

0.508

Weight

3

2

1

1

2

3

2

3

1

Priority

Within the primary factors

Group B

0.031

0.087

0.117

0.196

0.138

0.133

0.079

0.068

0.152

Weight

Overall

9

6

5

1

3

4

7

8

2

Priority

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Fig. 3 Subfactor weight comparison by group

5 Conclusions This study identified the factors influencing financial institutions’ intentions to switch to open source system software by deriving three primary factors and nine subfactors based on the TOE Framework through previous literature and interviews with relevant expert groups. After designing the research model as a three-level hierarchy with the AHP, a face-to-face survey was conducted with groups of internal and external IT experts within financial sectors. Among the internal IT experts within financial institutions, technological factors such as security, job suitability, and functional indifference emerged as the most critical core factors influencing the intention to transition to open source system software. Organizational factors like top management support and technological competency, as well as environmental factors such as competitive pressure, showed lower priority. Conversely, among the external IT expert group of financial institutions, organizational factors like technological competency and cost advantage, and technological factors such as job suitability, were identified as high-priority core factors. Meanwhile, environmental factors like network-oriented support and technological factors such as functional indifference and security showed lower priority. The factors identified and their prioritization influencing the intention to switch to open source system software in domestic financial institutions cannot be regarded as absolute references. However, comparing and analyzing the intent of migration to open source software among senior experts from domestic financial institutions and finance-related IT companies provides significant implications. The differences in the prioritization of factors between internal and external experts of financial institutions could serve as a basis for creating an internal evaluation sheet for decisionmaking regarding the adoption of open source system software for financial institutions. Additionally, for financial IT companies, it could offer insights into strategies

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for developing solutions based on proprietary or open source software and provide direction for marketing initiatives. One limitation of this study was the sample size, which consisted of only ten valid survey respondents per group. A larger research sample encompassing various age groups, years of experience and gender could yield more valuable insights. Future research will focus on enhancing the regulatory framework related to open source within the financial sector. This aims to facilitate Korean financial institutions in increasing migration to open source software and leading the development of the open source ecosystem through the use, contribution, and development of open source technology.

References 1. Bank of Korea.: 2021 Financial Informatization Progress. Financial Informatization Council, 20 (2022). 2. Yoon, S. J., Kim, J. B.: A Study on the Critical Factors Affecting the Adoption of Open Source Software by Public Institutions. Journal of KIIT 15(11), 9–11 (2017). 3. Kim, S., Park, H.: Determinants Affecting Organizational Open Source Software Switch and the Moderating Effects of Managers’ Willingness to Secure SW Competitiveness. Information Systems Review 21(4), 99–123 (2019). 4. Kim, S., Song, Y.: An Empirical Study of Factors Influencing Diffusion of Open Source Software and the Moderating Effect of Government Supports. Information Systems Review 12(3), 90–116 (2010). 5. Ko, E.: Understanding of ICT. Hanvit Academy, pp. 76–78 (2020). 6. RedHat: What is middleware?. www.redhat.com/ko/topics/middleware/what-is-middleware. 7. Cho, E. S.: A Study on Migration Process of Open Source Software WAS. Master’s Thesis, Soongsil University (2013). 8. Tornatzky, L. G., Fleischer, M.: The Processes of Technological Innovation. Lexington Books (1990). 9. Depietro, R., Wiarda, E., Fleischer, M.: The Context for Change: Organization, Technology and Environment, The Processes of Technological Innovation. Lexington Books, pp. 151–175 (1990). 10. Saaty, T. L., Luis, G. V.: Diagnosis with Dependent Symptoms: Bayes Theorem and the Analytic Hierarchy Process. Operations Research 46(4), 491–502 (1998). 11. Kim, Y. S.: An impact of the Open Source Movement on the Government Operating Paradigm. Public Policy Research 27(2), 121–155 (2010). 12. Kim, Y. W., Chae, M.: The Effect on the Job Performance of Open Source Software Usage in Software Development. Journal of the Korea Academia-Industrial cooperation Society 17(4), 74–84 (2016). 13. Kwon, M., Kim, T., Kim, M. H.: An Exploratory Study into Open Source Software Adoption and Resistance Factors. Informatization Policy 15(4), 3–21 (2008). 14. Ko, D., Go, B.: Study on the Barrier of Introduction and Use of Open Source Software. Sustainable ICT Conference 2012 (2012).

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15. Low, C., Chen, Y., Wu, M.: Understanding the Determinants of Cloud Computing Adoption. Industrial Management & Data Systems 111(7), 1006–1023 (2013). 16. Joia, L. A., Vinhais, J. C. S.: From Closed Source to Open Source Software: Analysis of the Migration Process to Open Office. Journal of High Technology Management Research 28(2), 261–272 (2017). 17. Kim, S. H., Choi, J. K., Hong, P.: Big Data Capability Model Development through AHP. Journal of Information Technology and Architecture 15 (3), 297–306 (2018).

A Study on the Intention to Utilize Overseas Developers Through Offshoring—Using the Value-Acceptance Model (VAM) Hyongyong Lee, Euntack Im, Myeongseok Oh, and Gwangyong Gim

Abstract IT and technology-based companies leverage developers from different countries to gain advantages such as cost-effective development efficiency in the global developer pool. This study focuses on analyzing various independent factors and mediators in utilizing overseas developers through offshoring, with significance placed on analyzing the resulting intention of use. The research model is based on the Value-Acceptance Model (VAM), setting the independent variables as the IT skills, job understanding abilities, and relationship management abilities of overseas developers. Perceived benefits of VAM include abundant utilization of development resources and cost savings, while perceived sacrifices are designed to include communication costs and quality management. To empirically analyze the model, a survey was conducted targeting domestic IT developers. Using 335 survey responses as the basis, statistical analysis was carried out utilizing SPSS 22. The results of the analysis indicate that IT skills influence the abundant utilization of development resources, while job understanding abilities affect development resource utilization, cost savings, and quality management. Relationship management abilities influence development resource utilization, cost savings, and communication costs. It was verified that perceived benefits such as development resource utilization and cost savings, and perceived sacrifices such as quality management, impact perceived value. On the other hand, communication costs were found not to affect perceived value. Keywords Offshoring · IT outsourcing · IT development H. Lee (B) · E. Im · M. Oh · G. Gim Department of IT Policy and Management, Soongsil University, Seoul, South Korea e-mail: [email protected] E. Im e-mail: [email protected] M. Oh e-mail: [email protected] G. Gim e-mail: [email protected] G. Gim Department of Business Adminisatration, Soongsil University, Seoul, South Korea © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_5

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1 Introduction Until the early 1990s, IT outsourcing was predominantly driven by cost-cutting measures. However, in the late 1990s, there was a shift towards strategic factors such as acquiring new technologies and enhancing competitiveness [1]. In recent times, with the advancement of information technology and business processes, as well as the shortened product life cycle, there has been a transformation away from IT-centric outsourcing towards improving core competencies by enhancing business processes. One of the delivery models of IT outsourcing, Offshore Outsourcing, gained significant attention when U.S. companies began sourcing IT services from India in the late 1990s, especially during the Y2K project. Recently, its scope has expanded beyond Business Process Outsourcing (BPO) to include ASP maintenance and support, new application development, R&D, market research, engineering services, and more [1]. Within the realm of global outsourcing, particularly in the IT services sector, countries like India and China, boasting a pool of talented professionals and advancements in both wired and wireless IT infrastructure, have become preferred destinations for many multinational companies seeking cost savings and operational efficiency. Numerous successful cases attest to the effectiveness of this strategy. However, it is a known fact that even in Korea, multinational companies with extensive business experience struggle to establish successful local branches [2]. Efforts persist to address the challenges of rising IT development costs and the difficulty in securing skilled development personnel, aiming to secure a cost-effective and stable development workforce pool. As Offshoring IT services continue to expand in the IT development landscape, understanding the acceptance intentions of Offshoring development practices, especially in the more conservative financial sector IT development environments, and conducting an analysis of the acceptance intentions, will be a meaningful research endeavor. This research aims to provide various perspectives on the diversity of development environments, the expansion of development convenience, and the cost-effectiveness of development through the analysis of Offshoring acceptance intentions.

2 Theoretical Background 2.1 Definition of Overseas Development Through Off Shoring Offshoring refers to the reallocation or dispersion of a company’s value chain activities across different countries [3]. This can take the form of internalization (captive offshoring) or be performed by independent companies located abroad (offshore outsourcing) [4]. It is most active in the IT and software industries, and recently, offshoring has extended to areas such as design and accounting. In the field of software development, Southeast Asia, especially Vietnam, stands out as the most active

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region in the offshoring market. With tens of thousands, even hundreds of thousands of IT-related developers, many are engaged in offshoring tasks. The approach to offshoring varies slightly depending on the target country. According to ThinkForBabel, a company specializing in software development and related consulting, it is common to directly visit offshore-specialized companies, assess the work environment, and verify the capabilities of personnel on-site before making a decision [2]. By relocating some or all tasks to countries with lower labor costs, companies can create more value. This allows companies to maintain competitive product or service prices or achieve higher profits. Furthermore, offshoring can be used to secure globally excellent technical personnel. Especially for companies requiring advanced technology or specialized skills in specific areas, offshoring development and operations to locations with lower labor costs can be a cost-effective strategy. Additionally, offshoring sometimes supports entry into new markets. Performing tasks overseas can enhance understanding and accessibility to local markets, contributing to competitiveness in the global market. Despite these advantages, offshoring can be challenging when dealing with cultural differences and language barriers. To overcome these challenges, companies need to provide cultural and language education and improve collaboration tools and processes to facilitate smooth communication.

2.2 IT Developer Competencies Offshoring is a type of IT service that involves leveraging external developers. To realize value through an offshoring strategy, companies will fundamentally consider the competencies of the developers providing the offshoring services. In this regard, this study emphasizes the competencies of IT developers, including their technical skills related to the provided services, understanding of the offshoring tasks, and how the tasks provided by developers can bring significance to the client company. Understanding the development tasks being offered is crucial. Finally, the ability of managers involved in providing offshoring services to manage relationships with client companies is essential in the offshoring service delivery process.

2.2.1

IT Technical Competence

Offshoring is an IT service that utilizes the competencies of overseas developers. Thus, IT technical competence is considered an essential factor in offshoring, being a core element for companies using offshoring to feel satisfied and perceive value. Offshoring encompasses various technological domains in the IT field and has been defined and studied in terms of the ability of organizational members to directly handle knowledge and data [5]. It is defined from the perspective of participants from the client company and service provider, and factors are organized separately, including knowledge of specific technologies, tools, systems, and programming languages, to the extent that they can propose technical solutions [6, 7]. This can

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include a wide range of areas such as hardware, software, networking, databases, security, cloud computing, and more. IT technical competence not only involves knowledge but also the ability to practically apply that knowledge.

2.2.2

IT Business Acumen

IT Business Acumen refers to the capability of IT professionals or individuals performing IT-related roles to effectively understand and utilize IT in a business environment [5]. This encompasses not only technical knowledge but also knowledge in business strategy, economics, finance, and other business areas [5]. IT task understanding involves IT professionals comprehending business goals, providing support, and playing a role as a link between IT projects and business strategies [7]. IT business acumen includes the following aspects. Understanding business objectives requires IT professionals to understand the organization’s business objectives and strategies, and to understand how IT projects or systems can contribute to the business. Next is financial understanding, and IT professionals need to understand the financial aspects of IT projects and systems. Knowledge related to budgeting, cost management, and investment analysis is required. Additionally, customer requirements must be understood and IT professionals must be able to understand and implement them technically [9].

2.2.3

IT Relationship Management Skills

IT Relationship Management Skills refer to the ability of IT professionals or IT managers to effectively manage and collaborate with internal and external stakeholders within an organization [10]. This includes several factors. The first factor is communication skills, where IT professionals need to communicate effectively with a variety of stakeholders. This involves the ability to convey technical content to non-experts. The second factor is stakeholder management, where IT professionals need to establish and maintain relationships with stakeholders both inside and outside the organization. This includes relationships with customers, partners, vendors, and employees. Additionally, problem-solving and negotiation skills are crucial. IT Relationship Management Skills involve the ability to manage conflicts with stakeholders and solve problems. Negotiation skills are also required to reach agreements with stakeholders. Fourth, understanding stakeholder requirements is essential. IT professionals need to accurately understand the requirements of stakeholders and incorporate them into IT projects or service development. Lastly, strategic relationship building is a key aspect of IT Relationship Management Skills. This involves building and maintaining strategic relationships that help achieve organizational goals and improve business performance. IT Relationship Management Skills have a significant impact on the efficiency of IT professionals’ work and business outcomes. They also play a crucial role in the success of IT projects and services.

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2.3 VAM Model Value is described in various fields under the basic context, subdivided into consumption value, acquisition and transaction value, service value, consumer value, and perceived value, among others [5]. Perceived value is defined as an overall evaluation of consumer product usage, incorporating elements of benefits and sacrifices [12, 13]. The Value-based Adoption Model (VAM) was proposed to explain the intention to adopt mobile commerce [14]. VAM suggests that the Technology Acceptance Model (TAM) has limitations in explaining the adoption of new Information Technology (IT) and emphasizes that new IT users should be perceived not merely as technical users but as “consumers” [11]. While TAM assumes the primary concerns of technology users in organizations are usefulness and ease of use, VAM assumes that individual consumers prioritize maximizing value [11]. VAM conceptualizes the benefits and sacrifices that consumers can derive from using new technology, using the paradigm of costs and benefits. It argues that acceptance is determined by comparing the benefits, such as utility and enjoyment, and perceived costs, including sacrifices like the perception of cost and expertise in new information and communication technologies [7].

3 Research Model and Hypothesis To conduct a study on the value and intention to use offshoring, six multiple regression models with a total of 17 research hypotheses were formulated. Multiple Regression Model 1 (h1ac) corresponds to the positive effect (+) of IT developer competencies on the latent variable of an abundant development workforce, which represents perceived benefits. Multiple Regression Model 2 (h2ac) represents the positive effect (+) of IT developer competencies on the perceived benefits related to cost savings. Multiple Regression Models 3 (h3ac) and 4 (h4ac) hypothesize the negative effect (–) of IT developer competencies on the latent variables of perceived sacrifices, specifically communication costs and quality management efforts. Multiple Regression Model 5 (h5a ~ d) sets hypotheses regarding the positive effects (+) of an abundant development workforce and cost savings, both latent variables of perceived benefits in the Value-based Adoption Model (VAM), on the perceived value. It also sets hypotheses regarding the negative effects (–) of communication costs and quality management efforts, latent variables of perceived sacrifices, on the perceived value. Finally, Simple Regression Model 6 (h6) sets a hypothesis about the positive effect (+) of perceived value on the intention to use offshoring. H1b: Business understanding skills will positively influence an abundant development workforce. H1c: Relationship management skills will positively influence an abundant development workforce. H2a: IT technical knowledge and skills will positively influence cost savings.

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H2b: Business understanding skills will positively influence cost savings. H2c: Relationship management skills will positively influence cost savings. H3a: IT technical knowledge and skills will negatively influence communication costs. H3b: Business understanding skills will negatively influence communication costs. H3c: Relationship management skills will negatively influence communication costs. H4a: IT technical knowledge and skills will negatively influence quality management efforts. H4b: Business understanding skills will negatively influence quality management efforts. H4c: Relationship management skills will negatively influence quality management efforts. H5a: Abundant development workforce will positively influence perceived value. H5b: Cost savings will positively influence perceived value. H5c: Communication costs will negatively influence perceived value. H5d: Quality management efforts will negatively influence perceived value. H6: Perceived value will positively influence the intention to use.

4 Empirical Analysis 4.1 Data Collection This study conducted a survey targeting IT practitioners aged 20 and above to analyze the relationships among IT developer competencies, service utilization awareness, perceived value, and intention to use offshore developers. A total of 335 survey responses were collected and utilized for analysis. Examining the demographic characteristics of the survey respondents, the gender distribution consisted of 196 males (58.5%) and 139 females (41.5%). In terms of age, there were 38 respondents in their 20 s (11.3%), 124 in their 30 s (37.0%), 114 in their 40 s (34.0%), and 59 in their 50 s (17.6%). Regarding the IT professional fields respondents were engaged in, 123 were IT developers (36.7%), 61 were IT planners (18.2%), 48 were IT operators (14.3%), and 96 were IT managers (28.7%). Additionally, 7 respondents (2.1%) belonged to other categories. In terms of the industry sectors where respondents worked, 51 were in the public sector (15.2%), 37 in the financial sector (11.0%), 245 in general corporate sectors (73.1%), and 2 in other sectors (0.6%). Finally, among the respondents, 245 were in the process of implementing IT outsourcing (73.1%), while 90 respondents had not implemented it yet (26.9%).

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4.2 Factor Analysis and Reliability Analysis The evaluation of the measurement items in this study was based on factors derived from previous research. An attempt was made to operationally define abstract concepts more concretely for measurement and assessment. To assess the validity and reliability of these measurement items, exploratory factor analysis was conducted, followed by reliability analysis using Cronbach’s alpha. If the factor loading of a measurement item in the exploratory factor analysis is 0.5 or higher, it is considered valid [16]. Items with factor loadings below 0.5 (IT5, JOB1, JOB2, RELA5) were removed, and exploratory factor analysis was conducted again. This study performed exploratory factor analysis based on the independent variables that would be used in the upcoming multiple regression analysis. Factor scores were extracted for each independent variable and utilized as independent variables in the multiple regression analysis model. Additionally, reliability analysis of the measurement items was conducted using Cronbach’s alpha. If the Cronbach’s alpha value is 0.7 or higher, the reliability can be considered at an acceptable level [17]. As shown in Tables 1 and 3, by removing items with low factor loadings, validity was ensured with factor loadings of 0.5 or higher for all items (Table 2). Furthermore, the Cronbach’s alpha values for each latent variable were above 0.7, indicating reliability. Table 1 Results of factor analysis and reliability analysis (1st Level) Factor 1

Factor 2

Factor 3

Cronbach’s alpha

IT1

0.722

0.371

0.108

0.785

IT2

0.603

0.382

0.234

IT3

0.808

0.101

0.116

IT4

0.701

0.171

0.265

JOB3

0.105

0.328

0.807

JOB4

0.257

0.113

0.876

JOB5

0.225

0.307

0.772

RELA1

0.381

0.569

0.330

RELA2

0.244

0.752

0.168

RELA3

0.117

0.791

0.204

RELA4

0.322

0.620

0.279

0.855

0.790

IT IT technical knowledge ability, JOB job understanding ability, RELA relationship management ability

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Table 2 Results of factor analysis and reliability analysis (2nd Level) Factor 1

Factor 2

Factor 3

Factor 4

Cronbach’s alpha 0.899

COST2

0.863

0.040

0.148

0.021

COST3

0.825

0.071

0.230

0.066

COST5

0.805

0.117

0.235

–0.049

COST4

0.796

0.095

0.231

–0.060

COST1

0.759

0.106

0.114

0.079

QUAL3

0.083

0.861

–0.112

0.168

Q UAL4

0.102

0.818

–0.129

0.153

QUAL2

0.007

0.817

0.012

0.242

QUAL5

0.069

0.798

–0.049

0.174

QUAL1

0.171

0.765

–0.006

0.154

LAB2

0.220

–0.076

0.764

–0.051

LAB1

0.054

0.048

0.764

0.058

LAB4

0.219

–0.086

0.729

–0.040

LAB3

0.178

0.007

0.725

–0.098

LAB5

0.249

–0.248

0.545

0.072

COMM3

0.117

0.075

–0.043

0.771

COMM2

–0.141

0.256

0.105

0.763

COMM1

–0.188

0.259

0.104

0.699

COMM4

0.228

0.182

–0.112

0.681

COMM5

0.015

0.119

–0.084

0.677

0.897

0.792

0.796

LAB abundant development workforce, COST cost savings, COMM communication costs, QUAL quality management Table 3 Results of factor analysis and reliability analysis (3rd Level)

Factor 1

Factor 2

Cronbach’s alpha 0.893

INT4

0.831

0.253

INT2

0.779

0.366

INT1

0.767

0.419

INT3

0.722

0.445

INT5

0.584

0.475

VAL2

0.309

0.787

VAL3

0.315

0.782

VAL5

0.300

0.689

VAL4

0.446

0.673

VAL1

0.412

0.658

VAL perceived value, ITN intention to use

0.863

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Table 4 Results of linear regression analysis (Model 1)

(Model 2)

(Model 3)

(Model 4)

(Model 5)

(Model 6)

LAB

COST

COMM

QUAL

VAL

INT

IT

0.365*** (0.000)a

–0.024 (0.648)

0.127* (0.018)

–0.078 (0.151)

JOB

0.385*** (0.000)

0.141** (0.007)

0.021 (0.698)

–0.138* (0.011)

RELA

0.164*** (0.000)

0.303*** (0.000)

–0.190*** (0.000)

–0.102 (0.060)

Variable

LAB

0.621*** (0.000)

COST

0.470*** (0.000)

COMM

–0.042 (0.382)

QUAL

–0.244*** (0.000)

VAL

0.867*** (0.000)

Observations

335

335

335

335

335

335

R-square

0.308

0.112

0.053

0.035

0.471

0.637

***p < 0.001, ** p < 0.01, * p < 0.05 a p-value

4.3 Linear Regression Analysis In this study, linear regression models can be categorized into three main groups. First, there are multiple regression models analyzing the relationships between IT developer capabilities (IT, JOB, RELA) and service usage perceptions (LAB, COST, COMM, QUAL). Second, there is a multiple regression model examining the relationship between service usage perceptions and perceived value (VAL). Finally, there is a model analyzing the relationship between perceived value and intention to use (INT). The results of each model analysis are shown in Table 4.

4.3.1

Relationship Between IT Developer Capabilities and Service Usage Perceptions

Regarding the perceived benefits, the analysis of the relationship between the three factors of IT developer capabilities and the abundance of development resources (1a) showed that IT technical knowledge ability (β = 0.365, p < 0.001), job understanding ability (β = 0.385, p < 0.001), and relationship management ability (β = 0.164, p < 0.001) all exhibited significant positive effects on the abundance of

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development resources. The R2 of the regression model is 0.308, indicating a reasonable explanatory power [18]. Additionally, in the relationship between IT developer capabilities and cost savings (1b), job understanding ability (β = 0.141, p < 0.01) and relationship management ability (β = 0.303, p < 0.001) showed significant positive effects on cost savings, while IT technical knowledge ability was rejected as the significance level exceeded 0.05. The R2 is 0.112, indicating a weak explanatory power [18]. Concerning perceived sacrifices, the analysis of the relationship between IT developer capabilities and communication costs (1c) revealed that relationship management ability (β = –0.190, p < 0.001) had a significant negative impact on communication costs, IT technical knowledge ability (β = 0.127, p < 0.05) exhibited a significant positive relationship, and job understanding ability did not have a significant impact. The R2 is 0.053, indicating weak explanatory power [18]. Finally, in the relationship between IT developer capabilities and quality management (1d), job understanding ability (β = –0.138, p < 0.05) showed a significant negative relationship, but IT technical knowledge ability and relationship management ability were rejected as their significance levels exceeded 0.05. The R2 of the regression model is 0.035, indicating weak explanatory power [18].

4.3.2

Relationship Between Service Usage Perception and Perceived Value

Analyzing the impact of four factors related to service usage perception on perceived value (2), the results showed that an abundance of development resources (β = 0.621, p < 0.001) and cost savings (β = 0.470, p < 0.001) had a significant positive impact on perceived value, while quality management (β = –0.244, p < 0.001) had a significant negative impact. For communication costs, the p-value was higher than 0.05, indicating no significant impact. The R2 of the regression model is 0.471, suggesting a reasonable explanatory power [18].

4.3.3

Relationship Between Perceived Value and Usage Intention

Analyzing the relationship between perceived value and usage intention (3), the results showed that perceived value (β = 0.867, p < 0.001) has a significant positive impact on usage intention. The R2 value of 0.637 indicates that the regression model has sufficient explanatory power [18].

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5 Conclusion This study conducted a survey on the intention to use offshoring in the Korean IT development field. Building on previous research, the study focused on the perspectives of convenience and sacrifices in the yet-to-be-universalized Korean IT development environment. To achieve this, the research model of the Value-based Adoption Model (VAM) was used to analyze the impact of various independent variables on the mediating variables and ultimately identify factors influencing the positive and negative effects on perceived value and usage intention, the dependent variables. Based on the results of regression analysis, the following findings were obtained. First, the independent factor of IT technical knowledge and skills validated the hypothesis that it expands the development workforce utilization pool. However, the hypothesis that it affects perceived benefits in terms of cost savings and perceived sacrifices in communication costs and quality management was rejected. This suggests that individuals with excellent IT technical knowledge may not significantly influence cost savings, especially for the latest IT technologies such as JAVA and Cloud, where the demand for skilled personnel is high. Additionally, IT technical knowledge appears to be a separate domain from communication costs and quality management, showing no mutual impact. Second, the independent factor of business understanding skills was confirmed to have a positive impact on the mediating variables of development workforce utilization, cost savings, and quality management. However, it did not have an impact on communication costs, possibly because even with high business understanding skills, communication costs arise independently when communicating in different languages or using a common language like English. Third, the independent factor of relationship management skills was found to have a positive impact on development workforce utilization, cost savings, and communication costs. However, it had a negative impact on the mediating variable of quality management. This suggests that while relationship management is crucial for development and project execution, it may not significantly influence quality management, as indicated by the survey results. Out of the total 17 hypotheses, 11 were accepted, and 6 were rejected. Some rejected hypotheses diverged from common industry knowledge, highlighting the need for more nuanced designs of independent and mediating variables, as well as further industry-specific analyses of IT developers.

References 1. Yoo, J.H., Kwon, Y.M., & Lee, Y.S. (2005). The feasibility study of offshore outsourcing in Korea SI industry: Comparison between India and China case. Journal of Korea Society of IT Service, 4(2), 135–144 2. Lee, J.N. (2016). The case studies and improvement plan for the offshore IT service of financial institutions. Master Thesis of Kroea Advanced Institue of Science and Technology. 3. Doh et al., 2009; Kedia and Mukherjee, 2009; Kotabe, 1990. 4. Contractor et al., 2010; Doh, 2005; Mukherjee et al., 2013; Mukherjee et al., 2019.

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5. Spenser, L. M., & Spenser, S. M. (1993). Competence at work: Models for superior performance. John Wiley & Sons. 6. Terry Anthony Byrd, D. E. T. (2000). Measuring the flexibility of information technology infrastructure: Exploratory analysis of a construct. Journal of management information systems, 17(1), 167–208. 7. Kang, J.H. & Heong, D.Y. (2012). What factors affect on the job involvement of S/W development workers in the small and medium IT enterprises. Journal of the Korea Academia-Industrial cooperation Society, 13(8), 3460–3469. 8. Hekman, D.R., Steensma, H.K., Bigley, G.A., & Hereford, J.F. (2009). Effects of organizational and professional identification on the relationship between administrators’ social influence and professional employees’ adoption of new work behavior. Journal of Applied Psychology, 94(5), 1325. 9. Zimmerman, R.D., Triana, M.D.C., & Barrick, M.R. (2010). Predictive criterion-related validity of observer ratings of personality and job-related competencies using multiple raters and multiple performance criteria. Human Performance, 23(4), 361–378. 10. Bonder, A., Bouchard, C.D., & Bellemare, G. (2011). Competency-based management—An integrated approach to human resource management in the canadian public sector. Public Personnel Management, 40(1), 1–10. 11. Kim, Y.H. (2016). A study on adoption of IoT smart home service: Based on contingent valuation method and value-based adoption model. PhD Thesis of Soongsil University. 12. Zeithaml, V.A. (1988). Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. Journal of marketing, 52(3), 2–22. 13. Lovelock, C., & Patterson, P. (2015). Services marketing. Pearson Australia. 14. Kim, H.W., Chan, H.C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111–126. 15. Davis, F.D. (1987). User acceptance of information systems: The technology acceptance model (TAM). 16. Yong, A.G., & Pearce, S. (2013). A beginner’s guide to factor analysis: Focusing on exploratory factor analysis. Tutorials in Quantitative Methods for Psychology, 9(2), 79–94. 17. Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psy-chometrika, 16(3), 297–334. 18. Newbold, P., Carlson, W.L., & Thorne, B.M. (2013). Statistics for business and economics. Pearson Education.

The Role of Co-Creation Experience and Switching Cost in the Relationship Between Service Recovery and Customer Loyalty Bum Seok Kim, Jin-Han Kim, and Woosub Kim

Abstract The purpose of this study is to hypothesize and test the co-creation experience and switching costs of service recovery as important mediators of the relationship between service recovery and customer loyalty. Using Hayes (Hayes in Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. The Guilford Press, 2018) process macros such as parallel multiple mediation model and serial multiple mediation model, a model based on the literature review is systematically validated. An important finding is that service recovery may not be sufficient to directly influence customer loyalty; rather, empirical evidence clearly demonstrates the mediating effects of the service recovery co-creation experience, satisfaction with the experience, service recovery satisfaction, and switching costs. Finally, the results and implications of the study are discussed, followed by limitations and future research directions. Keywords Service recovery · Co-creation experiences · Switching costs · Serial multiple mediation · Process macros

B. S. Kim Department of Business Administration, Yong In University, Yongin, South Korea e-mail: [email protected] J.-H. Kim Department of Business Administration, Kumoh National Institute of Technology, Gumi, South Korea e-mail: [email protected] W. Kim (B) Management Evaluation Office, Evaluation Institute of Regional Public Corporation, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_6

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1 Introduction Customer satisfaction through service delivery is the desire of every service provider. However, contrary to their wishes, there is a possibility of service failure, where any service fails to reach customer satisfaction [22], and such service failures occur frequently. Therefore, in addition to textbook topics such as how to provide a successful service, the topic of recovery after service failure continues to be of interest in the field of practice. In addition, the topic of service recovery has received a lot of attention in academia, with various studies [7] related to the service failure paradox, which suggests that a good service recovery process after a service failure can actually increase customer satisfaction and loyalty. However, since the desirable service recovery process, i.e., service recovery fairness, can only explain a limited amount of service recovery outcomes, such as 43% of customer satisfaction and 63% of behavioral intention [16], it has been argued that it is necessary to understand the effect of service recovery on customers more comprehensively by considering mediating factors other than fairness [19]. In order to support this argument, it is necessary to clarify which additional mediators exist and their role in strengthening the relationship between service recovery and customer satisfaction and loyalty beyond the relationship between fairness and customer satisfaction. Therefore, this study exploratively attepmts to identifies the key mediators that can be considered in linking service recovery to customer loyalty and demonstrate them. Specifically, this study establishes a model that adds the variables of service recovery co-creation experience and switching cost to the relationship between service recovery and customer loyalty proposed by many existing studies. This study is significant in that it does not discuss the cross-sectional content of service recovery by focusing on the step-by-step process of executing service failure recovery, but rather examine the process aspect [19] in order to contribute to the development of a service recovery framework.

2 Research Models and Hypotheses 2.1 Service Recovery and Customer Loyalty Customer loyalty can be defined as a deep and sustained commitment to repurchase or reuse a preferred product or service in the future [29], and based on this definition, repeated purchase intentions and behaviors can be attributed to customer loyalty. A number of studies have clearly shown that successful service recovery strengthens customer loyalty [8, 33]. To summarize, this study first hypothesizes that service recovery will have a positive relationship with customer loyalty because successful service recovery can improve customers’ perceptions of service quality and the service organization, which can lead to positive word-of-mouth, which can improve customer satisfaction and ultimately build customer loyalty [28].

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H1: Service recovery will have a positive relationship with customer loyalty.

2.2 The Role of Switching Costs in Service Recovery Post-service recovery satisfaction is one of the most important topics in the service field, but the impact of service recovery on satisfaction and dissatisfaction has been pointed out as one of the under-researched areas [35]. Satisfaction with service recovery, as proposed by Oliver [29], refers to an individual customer’s satisfaction status based on a single service transaction. This service recovery satisfaction is therefore different from overall customer satisfaction, which refers to a customer’s overall evaluation based on all experiences or service encounters [32]. As a result, proper service recovery will mitigate negative effects and increase service recovery satisfaction [35], this study therefore hypothesizes the following. H2: Service recovery will have a positive relationship with service recovery satisfaction. Although there are also cases where dissatisfied customers do not leave their current service provider after experiencing a service failure, dissatisfied customers will have higher switching intentions than satisfied customers [5]. Thus proposed model hypothesizes a positive effect of the traditional relationship between service recovery and switching costs. H3: Service recovery and switching costs will have a positive relationship. Although customers with high levels of service recovery satisfaction may make irrational decisions to switch to other services, it is generally accepted that when customers have high levels of service recovery satisfaction, they will form high switching barriers, such as reducing their intention to switch to other services [27], and one of these barriers is related to customer perceived switching costs. Since the effect of service recovery satisfaction on customer loyalty is expected to be higher at low switching costs than at high switching costs [12], the moderating effect of switching costs on the relationship between service recovery satisfaction and customer loyalty can also be considered, with Aydin and Özer [3] arguing that switching costs partially or fully mediate the customer satisfaction-customer loyalty relationship. Although the moderating effect of switching costs can also be examined, the primary interest of this study is in the mediating role of switching costs, therefore this study hypothesizes the following in the proposed model. H4: Service recovery satisfaction will be positively related to switching costs. Negative experiences due to service failure can affect future behavioral intentions, such as customer loyalty [20]. Once a customer experiences a service failure and its recovery, he or she will form satisfaction (or dissatisfaction) with the positive (or negative) service recovery experience [12]. Consequently, based on the service failure

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and the organization’s response to resolve the failure, service recovery satisfaction can influence customers’ behavioral intentions such as loyalty and recommendation. A meta-analysis by Orsingher et al. [30] shows that service recovery satisfaction is strongly related to repurchase intention, a component of customer loyalty. Therefore, the fifth hypothesis is stated as follows. H5: Service recovery satisfaction will be positively related to customer loyalty. Colgate and Lang [11] studied the relationship between switching costs and customer loyalty and argued that customers tend to remain loyal to their original provider when the cost of switching from the original provider is greater than the cost of creating a relationship with another provider. Lam et al. [24] also argued that customers will stay with their original service provider regardless of their satisfaction level under high switching costs, while dissatisfied consumers will switch frequently to other service providers under low switching costs. So in the basic proposed model this study hypothesizes the following. H6: Switching costs will be positively related to customer loyalty.

2.3 The Role of Recovery Co-Creation Experiences in Service Recovery The effectiveness of customer engagement in service recovery has received increasing attention since the emergence of the service dominance logic. One of the reasons for this is that fairness is important to induce customers’ trust and commitment to the service provider after a service failure [35], and this fairness can be enhanced by co-creation of service recoveryService recovery will be positively related to recovery co-creation experience because customers will be able to utilize their capabilities and resources to participate in recovery co-creation when a service failure occurs, and even when a co-created service fails, customers will be able to contribute to new value creation as co-producers of the service. H7: Service recovery will be positively related to recovery co-creation experience. The literature on customer engagement generally hypothesizes that as the level of customer engagement increases, customers become more motivated and engaged by co-creation [40] and perceive higher service quality. As customers’ level of involvement in the service process increases, they will evaluate their work more positively and be more satisfied with the recovery outcome. Moreover, the more empowered and experienced they become through their involvement in the service recovery process, the greater the utility of their service consumption. In other words, improved selffulfillment through a successful service recovery co-creation will result in greater customer satisfaction and positive customer reviews. H8: Service recovery will be positively related to satisfaction with recovery cocreation.

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Studies by Karande et al. [21] and Vaerenbergh et al. [36] show that customer involvement affects satisfaction with service recovery through distributive and procedural fairness, and overall satisfaction through distributive and interactional fairness. These results indicate that, as expected by Karande et al. [21], customers consider service recovery co-creation not only as a fair process (procedural fairness), but also as a symbol of equitable interpersonal treatment (interactive fairness) and even equitable compensation (distributive fairness). However, these do not lead directly to customer satisfaction, but rather to satisfaction as a result of the overall assessment of recovery co-participation. Thus, in line with cognitive behavioral theory, recovery co-creation goes through satisfaction, which represents the overall evaluation of recovery co-participation, before customers’ behavioral and cognitive outcome of loyalty occurs. H9: Co-creation experience will be positively related to co-creation satisfaction. In general, creating a shared service experience influences word-of-mouth and customer loyalty [38]. Customers are perceived as active participants in the creation of their experience, and the co-created experience becomes a platform for a relationship, which creates an emotional attachment to the service provider. In turn, this emotion has a lasting impact on customers’ decisions and behaviors, and ultimately influences their loyalty [38]. In summary, recovery co-creation experience can provide a platform for relationship enhancement that improves customer loyalty [14]. H10: Co-creation experiences will be positively related to customer loyalty. The experience of co-creation in service recovery provides customers with a variety of experiences, as categorized in the literature [39] as multidimensional. These experiences will determine their overall level of satisfaction with the recovery co-creation, which in turn will determine their perceptions and behaviors toward the service provider. Furthermore, as perceived fairness can influence customer loyalty [35], it is logical that the perceived fairness generated by the service recovery cocreation effort will increase their overall level of satisfaction with the engagement, which in turn will influence customer loyalty. H11: Co-creation satisfaction will be positively related to customer loyalty.

2.4 Mediating Effects The relationship consisting of H2, H4, and H6 implies a serial mediation effect of recovery satisfaction and switching costs on the relationship between service recovery and customer loyalty. As mentioned earlier, customer satisfaction leads to customer loyalty. However, a meta-analysis by Szymanski and Henard [34] found that customer satisfaction explains less than 25% of the variation in repurchases. Therefore, switching costs can be considered as either a mediator or moderator in the customer satisfaction-customer loyalty relationship [23].

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Furthermore, the relationship comprising H7, H9, and H11 implies a serial mediating effect of recovery co-creation experience and co-creation satisfaction in the relationship between service recovery and customer loyalty. These relationships reflect the logic that service recovery efforts are more likely to be successful when customers experience active participation in co-creation and overall satisfaction with this co-creation. It can also be argued that overall satisfaction with co-creation reflects perceptions of the recovery co-creation experience and that customer loyalty results from behavior based on those perceptions.

3 Research Methodology 3.1 Measurement and Data Collection As many studies have measured service recovery, the independent variable of this study, with apology and compensation factors in general, this study also measures apology and compensation factors based on the items used by Smith et al. [33] and Mattila and Cranage [26]. On the other hand, the dependent variable, customer loyalty, was measured by two core components, attitudinal and behavioral factors, based on Oliver’s [29] concept of customer loyalty, which has also been used in studies such as Chaudhuri and Holbrook [10] and Parasuraman et al. [31], and the measures used in Dick and Basu [13] and Yang and Peterson [41] were applied. The measure of switching costs considered as a mediator in this study is based on the study of Matzler et al. [27], and the measure of recovery satisfaction is modified from the items of Vazquez-Casielles et al. [37] and Hazée et al. [19]. In addition, based on the gap model of Zeithaml et al. [42], this study uses the measure of cocreation experience adapted by Verleye [39], given the need to assess the overall co-creation experience. The satisfaction of the co-creation experience also utilises the overall evaluation scale for service recovery co-creation applied by Verleye [39] for consistency. The control variables used to partially control for collateral effects and confounding are the respondent’s gender, age, income level, and franchise status, which have already been shown to be effective in studies of service recovery [1]. Using these variables, this study does not apply the structural equation model, but applies Hayes’ [18] PROCESS Macro Ver3.4 to SPSS to analyze the model. This method is effectively applied to mediation analysis, moderation analysis, and controlled mediation analysis. It overcomes the shortcomings of Baron and Kenny’s [4] method in that it allows quantification of indirect effects in mediation analysis, simultaneous inferential testing of indirect effects to improve power, effective separation of total and indirect effects, and identification of differences in indirect effects. In the analysis of this study, the number of bootstrap samples is 10,000, and the direct and indirect effects of the model are analyzed simultaneously, including multiple mediators.

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Data for this study was collected online through a survey firm from consumers who have experienced service failures, asking them about their most recent memorable service failure, the type of failure, service recovery, and their experience co-creating service recovery. Of the 295 total items collected, 258 were used in the analysis after excluding insincere responses, including more than 90% of the same responses, non-specific service sector and manufacturing responses, and some extreme values and discrepancies in income and age. Prior to analysis, it was conducted preliminary validation for non-response and same-method bias, two common problems in the sample of surveys such as this one. Following the method used in Armstrong and Overton [2], it is compared early respondents with late respondents to test for non-response bias. Specifically, the final sample of 258 was categorized into two groups of 129 respondents each according to the order in which they responded, and t-tests were performed including demographic variables as well as one randomly selected indicator for each construct, and Mann– Whitney U-tests were performed to account for violations of normality. As a result, there is no statistically significant differences between the two groups for any of the variables except for one construct (service compensation).

3.2 Reliability and Validity Analysis Exploratory and confirmatory factor analyses were conducted to check the reliability and validity of the constructs, and unidimensionality of the scale. The conceptual reliability (CR) of the measures related to internal reliability was found to be above the general standard of 0.7 [17]. Cronbach’s alpha also showed the same results, indicating that the conceptual reliability of the constructs was secured. Next, an exploratory factor analysis was conducted to ensure the unidimensionality of the scale for each construct. The results of the exploratory factor analysis indicate that the items are unidimensional because they all have strong loadings on the constructs they are expected to measure. In addition, the results of the exploratory factor analysis show that Bartlett’s test of sphericity is significant, and the Kaiser– Meyer–Olkin values are all above 0.7, confirming that an appropriate sample was used for the factor analysis. On the other hand, the results of the confirmatory factor analysis show that the smallest factor loadings were 0.804 and the smallest t-value was 4.147, which met the minimum factor loadings criterion (0.5) and the significant t-value criterion (t > 2.0), and the AVE values were all above 0.5 for the constructs, so it can be concluded that a satisfactory level of convergent validity was secured [15]. Finally, it was compared the squared values of the correlation coefficients between the constructs with the AVE values to check the discriminant validity. According to the results, it can be concluded that discriminant validity is also secured because the AVE value is greater than the square value of the correlation coefficient between the constructs [15].

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4 Results The effects of a parallel multiple mediation model and a serial multiple mediation model to test the hypotheses in this study are summarized in Fig. 1.

4.1 Parallel Multiple Mediation Model Results First, a parallel multiple mediation model was analyzed to determine whether the variables considered in this study have mediating potential in the relationship between service recovery and customer loyalty. The results of the analysis show that all mediators have a statistically significant relationship with service recovery at the 1% level of significance, co-creation experience has a statistically significant relationship with customer loyalty at the 5% level of significance, and the remaining variables except co-creation experience have a statistically significant relationship with customer loyalty at the 1% level of significance. In addition, the total effect of service recovery on customer loyalty in the full model where all mediators are considered is 30.65%, with a coefficient of 0.5225 (p < 0.001), indicating service recovery has a significant effect on loyalty. Looking at each effect separately, first, it can be concluded that the indirect effects through the mediators are all effective because the magnitudes of recovery satisfaction, switching cost, co-creation experience, and co-creation satisfaction are 0.2323, 0.1149, 0.0719, and 0.0970, respectively, and the bootstrap confidence intervals do not include zero. The effect size of each mediating effect is not statistically significant, except that recovery satisfaction is statistically significantly larger than co-creation experience (coefficient = 0.1604, bootstrap lower bound = 0.0244, bootstrap upper bound = 0.3030). On the other hand, the magnitude of the direct effect of service recovery on customer loyalty is 0.0064 (p = 0.9057, bootstrap lower bound –0.1000 and upper bound 0.1128, including zero), which is not statistically significant. In other words,

Fig. 1 Results of a parallel and serial multiple mediation analysis (Notes *, ** means statistically significant at 5% and 1% significance level, respectively)

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there is no direct effect, but only a total indirect effect, and the indirect effects of individual mediators are also statistically significant, so it can be concluded that all parallel multiple mediation models in this study are fully mediated.

4.2 Serial Multiple Mediation Model Results According to the results in right side of Fig. 1, the direct effect of service recovery on customer loyalty is 0.0064 (p = 0.9057), which is not statistically significant. The specific indirect effects of the independent variables on the dependent variable that are statistically significant are: service recovery → recovery satisfaction → customer loyalty (effect size = 0.2451), service recovery → recovery satisfaction → switching cost → customer loyalty (effect size = 0.0718), service recovery → co-creation experience → customer loyalty (effect size = 0.0719), service recovery → co-creation satisfaction → customer loyalty (effect size = 0.0551), and service recovery → co-creation experience → co-creation satisfaction → customer loyalty (effect size = 0.0419). On the other hand, the indirect effect of service recovery → switching cost → customer loyalty is not statistically significant with a bootstrap confidence interval of zero. These results suggest that service recovery efforts itself are not directly effective enough to drive customer loyalty, so it is necessary to provide satisfaction through service recovery or increase switching costs by increasing recovery satisfaction. It can also be concluded that, in addition to service recovery efforts, increasing the service co-creation experience or increasing the level of co-creation satisfaction through that experience is absolutely necessary to increase customer loyalty. On the other hand, when statistically comparing the magnitude of specific indirect effects presented, the coefficient of the path from service recovery → recovery satisfaction → customer loyalty is significantly larger than the magnitude of indirect effects of other paths. This suggests that it is necessary to increase recovery satisfaction through service recovery and improve customer loyalty based on it in order to increase customer loyalty through service recovery most effectively.

5 Conclusion The implications of the research findings can be summarized as follows. First, it is found a new possibility that overturns existing research on the relationship between service recovery and customer loyalty [9]. As noted earlier, much of the literature suggests that service recovery efforts directly affect customer loyalty. However, the results of this study contradict this and show that service recovery can only influence customer loyalty through the mediators considered in this study. Therefore, it can be convincingly argued that service recovery strategies need to actively capitalize on switching costs and co-creation experiences, and that they need to be accompanied

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by efforts to increase recovery satisfaction and satisfaction with service recovery co-creation. Second, it is confirmed the impact of service recovery and recovery satisfaction on switching costs. Switching costs are affected by both service recovery and service recovery satisfaction, but also indirectly through service recovery to recovery satisfaction. Therefore, in order to strengthen switching costs, service organizations should focus not only on service recovery efforts through apologies and compensation, but also on increasing customers’ recovery satisfaction through these efforts. Third, it is verified the effects of recovery satisfaction and switching costs on customer loyalty. However, it is important to note that switching costs do not directly mediate the relationship between service recovery and customer loyalty. This implies that switching costs are only effective after satisfaction with service recovery efforts. Fourth, an important finding of this study is that switching costs do not play either a moderating or mediating role in the relationship between service recovery and customer loyalty. de Matos et al. [12] argued the moderating effect of switching costs on customer satisfaction and customer loyalty after a service failure. In general, the related literature suggests that switching costs may deter customers from switching providers in the event of dissatisfaction. However, customers may react to dissatisfaction in different ways, i.e., switching costs may act as a deterrent to switching providers, but they may also negatively relay their experiences to third parties [25]. Therefore, to maximize the effectiveness of switching costs, it indicates that service providers need to not only engage in efforts to increase switching costs themselves, but also pursue policies to enhance recovery satisfaction to prevent negative relaying. Fifth, it is figured out the importance of service recovery co-creation experiences. While the co-creation experience partially mediates the relationship between service recovery and customer loyalty, it also influences customer loyalty through the co-creation satisfaction of service recovery. Similar to switching costs, this suggests that the overall evaluation of the co-creation experience acts as a form of satisfaction (perception), which in turn leads to customer loyalty (behavior). Therefore, the active application of service recovery co-creation with customers is essential for successful service recovery and customer loyalty in the future. Not all service recovery efforts lead to high customer loyalty, as is often claimed. Despite many service recovery efforts, customers will have perceptions and behaviors called customer loyalty through a variety of measures. In this regard, social exchange theory argues that people who put more effort into an activity, such as co-creating customers, are more motivated by the benefits they expect from the activity [6]. Existing research on customer motivation to co-create value has already noted that customers expect different benefits in return for co-creation. Therefore, much thought and effort should be given to how to enhance the benefits of customers who participate in service recovery. The limitations of this study include the following, which can be addressed and set the direction for future research. First, this study analyzed the relationship based on the premise of expecting only positive effects of each variable. Depending on the situation, however, negative roles for each of these variables can also occur. For

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example, research on the effects of not only the positive but also the negative role of switching costs, the negative experience of co-creating recovery, i.e., the failure of co-recovery of services (double failure), needs to be extended to models that can reflect these realities. Second, this study did not consider the sub-dimensions of each variable. In order to obtain richer results and implications, it is necessary to subdivide the constructs considered in this study into multiple dimensions and conduct detailed tests based on each sub-dimension. For example, based on the existing literature, switching costs can be decomposed into relational, financial, and procedural switching costs; service recovery can be decomposed into apology and compensation; customer loyalty can be decomposed into attitudinal and behavioral loyalty; and co-creation experiences can be decomposed into hedonic, cognitive, social/personal, and practical/economic experiences.

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A Comprehensive Analysis of Security Measures for MyData in South Korea Based on AHP Jongmo Hwang, Seungbok Ahn, Seockho Jang, and Gwangyong Gim

Abstract With the full launch of Korea’s MyData service in January 2022, data subjects as individuals have gained the right to data portability, which allows them to access, transfer, delete, and utilize their personal credit information proactively. Data subjects can demand that financial institutions holding their personal credit information transmit the data to MyData operators. Then, the MyData operators can manage the received data efficiently and provide various services to the data subjects by analyzing the relevant data. In the MyData ecosystem, large amounts of data are transmitted over the Internet; thus, sufficient security is required protect these data. Thus, this paper introduces various security mechanisms applied to South Korea’s MyData services, e.g., secure authentication methods, data transmission via standard APIs, mutual authentication between organizations, and secure communication. However, due to the concentration of data within the MyData operators, ineffective security measures could result in a significant data breach compromising large amounts of personal credit information. Thus, this study also analyzes the priority of security measures to enhance the security of MyData operators through Analytic Hierarchy Process (AHP) empirical analysis. The results indicate that it is critical to prioritize protecting the large volume of data from external exposure and reinforce infrastructure security against external hacking threats. Keywords MyData · Architecture · Security mechanism · API

J. Hwang (B) · S. Ahn · S. Jang Department of IT Policy and Management, Soongsil University, Seoul, South Korea e-mail: [email protected] S. Ahn e-mail: [email protected] S. Jang e-mail: [email protected] G. Gim Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_7

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1 Introduction MyData is a data infrastructure and ecosystem where individuals control their own data management. Within this ecosystem, data subjects can access, transfer, delete, and utilize their personal credit information proactively [1, 2]. Previously, companies would collect individuals’ information with their consent and utilize it for marketing, product development, and other purposes. However, with the introduction of MyData, data are owned by the data subjects rather than being an asset of the collecting company. Thus, the data subjects can manage and utilize these data proactively [3]. To establish the MyData environment, the financial authority in South Korea introduced the right to data portability (RDP) and MyData industry. When data subjects exercise their RDP to request the transfer of their data stored in various financial institutions to MyData operators, the MyData operator manages the collected data on behalf of the data subjects and provides various services by analyzing the data [4]. Through this series of processes, it is possible to accelerate data-driven financial technology innovations and contribute to activation of the finance industry’s data economy based on customer data. This paper explains the process leading up to the full launch of the MyData service in South Korea and the RDP that forms the basis of the MyData service. We then introduce the entire architecture of the MyData ecosystem and various security mechanisms implemented to protect data in an environment where a large volume of data is transmitted over the Internet. Finally, through AHP empirical analysis, we analyze the priorities of the security measures in each stage of the data lifecycle to strengthen the security of MyData operators.

2 Background 2.1 Progress In 2018, the Financial Services Commission of South Korea announced a plan to introduce the MyData service to the financial sector to enhance the competitiveness of the financial data industry. In addition, in November of the same year, it proposed an amendment to the Credit Information Act that included the introduction of RDP and the establishment of the MyData industry. As of April 2019, a MyData working group supervised by the Financial Services Commission has operated to prepare for the MyData service. Approximately 70 financial institutions participated in this working group to develop MyData service procedures, guidelines, and standard API specifications. With the enforcement of the amended Credit Information Act on February 4, 2020, financial institutions and the MyData operators began to develop MyData related systems by adopting standard API specifications. Following interoperability tests in the latter half of 2021, the MyData service was implemented in full starting from January 2022.

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2.2 The Right to Data Portability The RDP is a legitimate right of data subjects that ensures their right to selfdetermination of personal information, which enables data subjects to self-determine demand that data providers holding their personal credit information transmit the data to the MyData operators or other information recipients. When the data subjects request the transfer of data, the data providers must transmit the data to the MyData operators promptly in a format processable by computer. Failure to comply with this obligation may lead to the imposition of fines or penalties on the data providers. When exercising the RDP, the data subjects must specify the requirements for the RDP, e.g., the data provider responsible for transmitting the information, the information recipient, the specific type of data being requested, and the purpose of the request. However, even if the data contain personal credit information, sensitive information, business secrets, and processed information are excluded from the scope of the data to be transmitted.

2.3 MyData Industry The MyData industry refers to an industry where personal credit information collected from multiple data providers through the RDP by data subjects is managed efficiently on behalf of the data subject [5]. For MyData operators to offer various financial services, e.g., integrated asset inquiring services, financial status management to customers, and financial product recommendations, the MyData operators must obtain approval from the financial authority [6, 7]. In addition, the MyData operators should not attempt to coerce the data subjects to submit transfer requests, refuse to delete data upon request by the data subject, or alter the contents of the transfer requests without the data subject’s consent. Violating these policies could lead to administrative sanctions, e.g., business suspension or fines.

3 MyData Architecture The MyData service in the financial sector comprises several components, i.e., the data subject, data provider, MyData operator, integrated authentication institute, intermediary institute, and trusted MyData supporting institute as shown in Fig. 1. The data subject, i.e., the individual who has personal credit information stored by data providers, e.g., financial institutions, can demand that the data providers transmit their data to MyData operators. The data provider refers to the entity that, upon verifying the requesting data subject’s identity, complies with the data subject’s demand to transmit data to the MyData operators. The MyData operator provides customers with the means and methods to request the transfer of their data from the data

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Fig. 1 MyData architecture

providers. The MyData operator can utilize the data received from these providers to offer customers various MyData services. The intermediary institute transmits the customers’ data to the MyData operator on behalf of certain data providers in response to the data subject’s RDP. If small to medium-sized financial institutions find it difficult to develop their own MyData API systems, they can use the systems provided by intermediary institutes to process standard APIs and transmit the data to the MyData operators. The integrated authentication institute issues and manages integrated authentication certificates for the data subject and verifies the certificates to enable data providers to identify the data subject. Finally, a trusted third-party entity known as a MyData supporting institute supports the MyData ecosystem by managing standard API specifications, ensuring financial consumer rights, establishing authentication standards, and supporting security-related tasks. Specifically, the supporting institute operates the MyData portal website, which enables the registration and management of the data providers and the MyData operators, as well as performing various MyData support functions.

4 Security Mechanisms The data subjects can transmit their own data from all financial institutions they use to the MyData operators; thus, insufficient security measures by the MyData operators could result in a massive data leak [8]. Therefore, in the following, we describe the various security mechanisms applied to the MyData ecosystem to enhance security.

4.1 Secure and Convenient Authentication Data providers must authenticate the data subjects in a secure and reliable manner before complying with the data subjects’ RDP. Here, two authentication methods are utilized, i.e., individual authentication and integrated authentication. Individual

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authentication refers to the method where a data subject performs authentication using the authentication means provided by each data provider. Thus, if a data subject exercises their RDP from N financial institutions, the data subject must perform a total of N authentication processes using different methods, which is potentially inconvenient. The integrated authentication method was designed to alleviate this inconvenience, where a single authentication process enables the data subject to authenticate from multiple data providers. Here, the data subject performs authentication only once; however, the integrated authentication module installed on a smartphone distinguishes data provider–specific information and generates N independent digital signatures. Each signature is then sent to the respective data providers, where each data provider verifies the validity of the certificate and signature to authenticate the data subject’s identity.

4.2 Adoption of MyData Standard API Prior to the adoption of standard APIs, screen scraping was widely used to collect customer data visible on a screen after logging in using pre-saved customer credentials. However, this process has many issues, e.g., exposure of authentication information, excessive load on systems, and unnecessary information collection. Thus, the MyData operators are mandated to collect data using the MyData standard APIs. The MyData standard APIs, i.e., the authentication API, data API, and support API, adhere to the OAuth 2.0 specification and are designed to send and receive JSON-formatted data via REST APIs. Here, the authentication API performs individual or integrated authentication by complying with OAuth 2.0. As a result, the data provider issues a JWS access token to the MyData operator. Then, the MyData operator calls the data APIs using this access token as an API key to request and receive data from the data providers. The support API is used to support the data providers and MyData operators, as well as manage the MyData ecosystem, e.g., collecting API statistical data, distributing organization information, and issuing credentials.

4.3 Access Control To participate in the MyData ecosystem, the MyData operators and data providers must register their organization’s information on the MyData portal and undergo an evaluation. Note that the MyData portal issues the organization code and client credentials (i.e., the client ID and password) required for API calls to the MyData operators or data providers that have passed the evaluation; thus, only verified institutions are authorized to make API calls. In addition, the MyData operators and data providers conduct IP access control through firewalls. Thus, all requests to access MyData related systems or API calls from unauthorized external IPs are blocked.

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Table 1 Major security measures in the credit information act Category

Security measures

Administrative Designation of data management/protection officers, inspection of the status of data management and protection, conducting employee training on information protection, collection of data within the scope of consent, preservation of records of data processing, secure storage/deletion of data, separation of duties Physical

Physical access control of computer facilities, record/manage entry and exit logs, access control of removable storage media, duplication of communication lines, installation of CCTV, secure document storage

Technical

Password management, password encryption, encryption of personal credit information, encryption of communication channels, encryption key management, operation of IPS/IDS, installation of antivirus software

4.4 Secure Communication When calling MyData APIs, mutual TLS using an extended validation certificate is required to realize encrypted communication and mutual authentication between organizations. In addition, during the TLS threeway handshake, the Subject:serialNumber (OID: 2.5.4.5) value stored in the peer’s TLS certificate must be verified. Here, the serialNumber field typically contains a business registration number or a corporate registration number. When the MyData operator calls any API, the data provider must validate and compare the serialNumber value within the MyData operator’s TLS certificate against the serialNumber of the MyData operator preregistered in the MyData portal to ensure they match.

4.5 Complying with the Compliance Requirements MyData participants must comply with the administrative, technical, and physical security measures specified in the Credit Information Act. The primary security measures are outlined in Table 1.

4.6 Other Security Measures The MyData operators must undergo functional compliance assessment from the support institute to verify whether their services are developed according to the standard API specifications and comply with consumer protection measures [9]. In addition, they must conduct annual security vulnerability assessments of the MyData related systems. With over one million customers, the MyData operators must monitor for hacking attempts and respond to security breaches to ensure that the customers can utilize the services confidently.

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Fig. 2 AHP research framework

5 AHP Analysis for MyData Operator Security 5.1 Research Framework Design Despite the implementation of various security mechanisms to provide a secure MyData environment, it is expected that threats targeting MyData operators will escalate because large volumes of data are processed and stored in the MyData operators’ systems. Thus, a low level of security of the MyData operators could lead to large-scale leakage of data. The purpose of establishing this AHP research framework is to assist the MyData operators in determining the priority of security measures that should be established for each stage of the data transmission requests, provisioning/utilization, and management/destruction. In addition, this analysis aims to provide a method to enhance the effectiveness of security investments at limited cost (Fig. 2). The first level of the hierarchy is the overall goal, and each stage of the data lifecycle [10] is placed at the second level. The important security measures that the MyData operators should establish for each stage are located in the third level (Tables 2 and 3).

5.2 Data Collection In this study, a survey was conducted with a total of 18 participants in three groups: eight data subjects who have experience with MyData services, five employees working as MyData operators, and five employees working as data providers. Note

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Table 2 Definitions of criteria Criterion

Definition

Security Measures in Data Transmission Request Stage

Security measures required when the data subject demands the data provider to transmit personal credit information to the MyData operator

Security Measures in Data Provision/ Utilization Stage

Security measures required when the MyData operator utilizes the personal credit information obtained from the data provider

Security Measures in Data Management/ Destruction Stage

Security measures required when the MyData operator manages and disposes data

that all 18 responses did not exceed the consistency ratio threshold of 0.2, thereby ensuring the reliability of the responses [11]; however, a single untruthful response was excluded from the analysis.

5.3 Analysis According to the analysis of the three criteria at the second level, all three groups indicated that security measures in the data management/destruction stage are the most critical in terms of enhancing the security of MyData operators. The analysis suggests that security measures in this stage should be a top priority because a massive data breach from the MyData operators could result in significant damages, e.g., customer privacy breaches and harm to corporate reputations (Table 4). The analysis of the importance in the data transmission request stage resulted in all three groups of participants evaluating the order of importance as providing secure authentication procedures > prohibition of formal consent > prevention of excessive data transmission requests. If authentication is not conducted securely, it may result in inappropriate issuance of access tokens, identity theft, and unauthorized system access. Thus, secure authentication procedures are critical to exercise RDP appropriately, which is fundamental to the MyData system (Table 5). The analysis of the importance in the data provision/utilization stages indicated that the group of data subjects and MyData operators chose access authorization verification as the top priority, and the data provider group selected prevention of data misuse or abuse as the primary focus. Errors during access authorization verification could potentially lead to the exposure of others’ information; thus, this was considered a high-priority concern. However, from the perspective of the data providers, it appears that they considered the potential for MyData operators to misuse or abuse the data provided beyond its intended purpose as the most significant threat (Table 6). In the data management/destruction stages, the analysis revealed that the group of data subjects and data providers rated secure data storage as the top priority. This is interpreted as being due to the potential risk of a massive data breach in the event of security vulnerability, e.g., storing important information in plain text. However,

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Table 3 Definitions of sub-criteria Criterion

Sub-criterion

Definition

Security Measures in Data Transmission Request Stage

Prevention of excessive data transmission requests

Establishing security measures to prevent excessive transmission of information due to unnecessary subscriptions to many MyData services

Prohibition of formal consent

Compliance with informed consent to prevent formal consent by individuals who do not fully understand the terms for RDP

Providing secure authentication procedures

Providing secure authentication procedures to verify whether the data subject is a legitimate customer

Access authorization verification

Verification of access authorization to prevent requests/ provision of others’ information

Prevention of information leakage during data transfer

Establishing security measures to prevent information leakage during the data transmission process

Prevention of data misuse or abuse

Establishing security measures to prevent the misuse/abuse of data beyond the agreed upon purposes

Secure data storage

Establishing security measures, e.g., encryption, to store the data collected by MyData operators securely

Infrastructure security

Establishing security measures to ensure that the infrastructure (servers, network systems, etc.) of MyData operators is not exposed to external threats

Secure destruction of expired personal information

Establishing security measures to dispose of expired personal information securely

Security Measures in Data Provision/Utilization Stage

Security Measures in Data Management/Destruction Stage

Table 4 AHP weights and ranks of three criteria Criterion Data Transmission Request Stage Data Provision/Utilization Stage Data Management/Destruction Stage

Data subject Weights Ranks

MyData operator Weights Ranks

Data provider Weights Ranks

0.109

3

0.214

2

0.199

3

0.416

2

0.145

3

0.240

2

0.475

1

0.641

1

0.561

1

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Table 5 AHP weights and ranks of data transmission request stage criterion Criterion Prevention of excessive data transmission requests Prohibition of formal consent Providing secure authentication procedures

Data subject Weights Ranks

MyData operator Weights Ranks

Data provider Weights Ranks

0.273

2

0.291

2

0.437

2

0.128

3

0.203

3

0.124

3

0.599

1

0.506

1

0.439

1

Table 6 AHP weights and ranks of data provision/utilization stage criterion Criterion Access authorization verification Prevention of information leakage during data transfer Prevention of data misuse or abuse

Data subject Weights Ranks 0.405 1

MyData operator Weights Ranks 0.591 1

Data provider Weights Ranks 0.208 2

0.310

2

0.273

2

0.118

3

0.285

3

0.136

3

0.673

1

Table 7 AHP weights and ranks of data management/destruction stage criterion Criterion Secure data storage Infrastructure security Secure destruction of expired personal information

Data subject Weights Ranks 0.431 1 0.311 2 0.257

3

MyData operator Weights Ranks 0.290 2 0.454 1 0.256

3

Data provider Weights Ranks 0.483 1 0.265 2 0.252

3

the MyData operator group evaluated infrastructure security as the primary security measure that must be established because they consider that if their infrastructure is secure, they can prevent breach incidents in advance (Table 7). The overall analysis of the importance ranked secure data storage and infrastructure security as the first and second priorities, respectively. Thus, to strengthen the security of MyData operators, it is crucial to prioritize protecting the large volume of data from external exposure and reinforcing infrastructure security against external hacking threats at the data management/destruction stage (Table 8).

6 Conclusions With the full launch of the MyData ecosystem in the financial sector, Korean financial consumers can access various MyData services, e.g., integrated asset inquiring services and financial product recommendations based on consumption pattern analysis, by exercising their RDP. In this study, we introduced the architecture and security mechanisms applied in the MyData service. In the MyData ecosystem, hundreds of organizations and entities, e.g., MyData operators, financial institutions,

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Table 8 Overall AHP weights and ranks Criterion Prevention of excessive data transmission requests Prohibition of formal consent Providing secure authentication procedures Access authorization verification Prevention of information leakage during data transfer Prevention of data misuse or abuse Secure data storage Infrastructure security Secure destruction of expired personal information

Weights

Data subject Ranks

Weights

MyData operator Ranks

Weights

Data provider Ranks

Total Ranks

0.030

8

0.062

6

0.087

6

8

0.014

9

0.043

7

0.025

9

9

0.065

7

0.108

4

0.087

5

6

0.168

2

0.086

5

0.050

7

4

0.129

4

0.040

8

0.028

8

7

0.119

6

0.020

9

0.162

2

5

0.205 0.148

1 3

0.186 0.291

2 1

0.271 0.149

1 3

1 2

0.122

5

0.164

3

0.141

4

3

the MyData support institute, and integrated authentication institutes, are interconnected to provide RDP to the data subjects safely by utilizing standard API in as secure manner. It is expected that the implementation of the MyData ecosystem in Korea will act as a beneficial reference point for other nations preparing to implement MyData. In addition, an AHP empirical analysis was conducted to derive the priority of security measures to enhance the security of MyData operators. The results indicated that it is crucial to prioritize protecting the large volume of data from external exposure and reinforcing infrastructure security against external hacking threats. In conclusion, this study has identified potential realistic security threats that MyData operators may encounter and presented key security measures to counter these threats. The results of this study are expected to serve as fundamental data to establish effective security measures and policy recommendations to enhance the information security of MyData operators in the MyData industry.

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A Study on the Factors Influencing the Performance of Korea Venture Capital Funds In Mo Yeo, Gwangyong Gim, Youngkun Yang, and Youngsu Kim

Abstract This study conducted empirical analysis on factors influencing the investment performance of 83 domestic venture capital funds (fund formation 2.66 trillion KRW) newly formed from 2007 to the end of 2022, which had completed their liquidation by the end of 2022. This study categorized and investigated the factors affecting venture capital fund performance as external environmental factors and internal factors. For external environmental factors affecting venture capital fund performance, the study analyzed ‘economic cycles,’ ‘stock market,’ ‘venture market,’ and ‘exit market.’ For internal factors, the research focused on the fund management company’s capabilities, including ‘management company track record,’ ‘professional staff,’ and ‘assets under management (AUM),’ as well as fund structure elements like ‘fund size’ and ‘fund duration’. In summary, the analysis yielded the following results: First, as the yield of the 3-year government bonds, which is a good indicator of economic cycles, decreases, venture fund performance tends to improve, indicating a significant influence of government bond yield levels on fund performance. Second, the total capital inflow in the year of formation, which reflects the competitive intensity of the domestic venture market, was found to have a positive (+) impact on venture capital fund investment performance. Third, short-term funds outperformed long-term funds in terms of profitability. Fourth, it was observed that the larger the assets under management (AUM) of the management company, the better the fund’s return. Finally, the study found that the returns of venture capital funds vary by the year of formation, suggesting that when considering venture capital

I. M. Yeo (B) · Y. Yang · Y. Kim Department of IT Policy and Management, Soongsil University, Seoul, South Korea e-mail: [email protected] Y. Yang e-mail: [email protected] Y. Kim e-mail: [email protected] G. Gim Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_8

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fund investments, constructing a portfolio that takes into account ‘external environmental factors,’ ‘internal factors,’ and the year of formation (Vintage) would be an effective investment strategy for individual investors. Keywords Venture capital funds · Investment performance · Private fund · IRR · Vintage

1 Introduction The shift toward an innovation-centric economy, exemplified by the Fourth Industrial Revolution, bestows significant importance upon the role of venture enterprises. Establishing a robust ecosystem for these businesses is recognized as a crucial task with substantial economic implications for each nation. Venture capital holds a pivotal position within this ecosystem, investing to maximize returns by augmenting enterprise value. The number of domestic venture capital firms increased from 121 in 2017 to 197 in 2021, reflecting a 32.5% growth in invested capital. However, a disparity in returns persists due to reduced policy funding and a challenging IPO market. The Federal Reserve’s policy tightening and high-interest rates constrain venture investments, impacting the valuation of unicorns and unlisted companies. Investing in venture funds entails significant risks. Choosing a reliable venture capital firm is vital to manage these risks and enhance investment performance. While institutional investors utilize specific evaluation metrics, individual investors may find these criteria challenging to comprehend. This study aims to analyze factors influencing the returns of domestic venture funds, thereby providing key guidelines and theoretical foundations for limited partners (LPs) to assess and theoretically select suitable venture capitals.

2 Theoretical Background 2.1 Performance Measurement Metrics: IRR Performance Measurement Metrics for private equity, including venture capital funds, commonly include IRR (Internal Rate of Return). Therefore, in this study, we analyzed the factors influencing investment performance using IRR.

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2.2 Factors Affecting Venture Capital Fund Performance 2.2.1

External Environmental Factors (1): Vintage Effect

In venture capital and private equity, the term “vintage” refers to the year in which a fund is formed. Choi Hyunhee (2018) argued that investment performance in private equity exhibits significant variations based on the vintage year, and as a result of the accumulation of venture capital fund exit cases, domestic limited partners (LPs) need to formulate investment strategies based on the vintage year. Song Ingyu (2015) conducted an analysis of the investment performance of 3,561 U.S. private equity funds and 1,171 European private equity funds by vintage year. He observed that returns declined during the 2008 financial crisis but subsequently recovered as the crisis subsided. He attributed this phenomenon to the removal of asset price bubbles triggered by the financial crisis and the improved returns resulting from acquiring assets at lower prices after the crisis [1, 2].

2.2.2

External Environmental Factors (2): Business Cycle

Choi Hyunhee (2020) highlighted GDP growth rate, interest rates, and credit spreads as commonly used indicators to measure the business cycle. High GDP growth rates, low interest rates, and narrow credit spreads are generally considered indicators of economic expansion. Studies on GDP growth rate revealed contrasting findings. Gresch and Wyss (2011) discovered a positive influence of the average GDP growth rate during the fund period on fund performance. However, Diller and Kaserer (2009) suggested a negative relationship between the average GDP growth rate during the fund period and fund performance. The impact of interest rates is commonly negative. Phalippou and Zollo (2005) emphasized that the level of corporate bond rates (BAA) negatively affects fund performance. Aigner et al. (2008) found that the average interest rate during the fund period negatively impacts fund performance. Regarding credit spreads, Phalippou and Zollo (2005) highlighted the negative influence of corporate bond rate levels on fund performance, indicating a detrimental effect on the fundraising ability of new companies [3–7].

2.2.3

External Environmental Factors (3): Stock Market

Gresch and Wyss (2011) found it challenging to identify the impact of stock prices on fund performance, while Diller and Kaserer (2007) argued that stock market performance in the vintage year has a negative impact on overall performance or has no influence on performance over the fund’s entire length. Aigner et al. (2008) stated that the MSCI return in the vintage year has a negative impact on fund performance, while the MSCI return during the fund period has a positive impact. In contrast,

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Diller and Kaserer (2009) suggested that the MSCI return in the vintage year has a negative impact on fund performance, but the MSCI return during the fund period does not have a significant effect [4, 5, 7].

2.2.4

External Environmental Factors (4): Venture Market

Research on the venture market environment has primarily focused on the impact of competitive intensity, utilizing indicators like total capital inflows and the number of new funds. Choi Hyunhee (2020) argued that total capital inflows have a negative impact on fund performance. This could be interpreted as increased competition for limited investment opportunities intensifying valuation pressures. Conversely, Gompers and Lerner (2000) found a positive relationship between total capital inflows and valuation of new investments [3, 8].

2.2.5

External Environmental Factors (5): Exit Market

Phalippou and Zollo (2005) stated that the number of IPO exits at the exit point has a positive impact on fund performance. Oh et al. (2016) found a negative relationship between the number of exit funds at the exit point and fund performance, and they did not find a significant relationship between the stock market at the exit point and fund performance [6, 9].

2.3 Intrinsic Factors of Funds (1): Fund Structure (Fund Size, Fund Length) 2.3.1

Fund Size

Ljungqvist and Richardson (2003), Kaplan and Schoar (2005), Aigner et al. (2008), found a negative impact of fund size on fund performance. In contrast, Robinson and Sensoy (2013) concluded that larger funds tend to have better returns. Harris et al. (2014) discovered a strong positive relationship between fund size and fund performance specifically in the context of venture capital funds [7, 10–13].

2.3.2

Fund Length

Regarding the fund length, Chae et al. (2014) argued that when using risk-adjusted performance measures, short-term (1 year or less than 2 years) funds outperform long-term (3 years or more than 5 years) funds. Aigner et al. (2008) found a positive impact of fund length on fund performance. In domestic studies, Chea et al. (2014)

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identified performance differences between short-term (1 year or less than 2 years) and long-term funds, with short-term funds exhibiting superior performance [7, 14].

2.4 Intrinsic Factors of Funds (2): Venture Capital Attributes (Experience of the GP, AUM) 2.4.1

Experience of the General Partner (GP): Track Record

Aigner et al. (2008) found that as the experience of the General Partner (GP) increases, the performance initially improves but gradually deteriorates. This analysis suggests that as the track record grows, the fund size also increases, leading to poorer performance. Gompers et al. (2008) stated that GPs with more experience are better equipped to handle stock market upswings. Cho et al. (2009) reported that funds managed by GPs with less than two years of experience had relatively lower performance. They attributed the lower performance of early-stage GPs to a lack of specialized management personnel, operational capabilities, and support systems within the management firm [7, 15, 16].

2.4.2

Asset Under Management (AUM)

Kaplan and Schoar (2005) argued that if a fund performs well and achieves good returns, the subsequent fund established by the general partner (GP) benefits from the positive track record, leading to an increase in the asset under management (AUM) managed by the GP [11].

3 Research Method This study conducted an empirical analysis of investment performance based on data from venture funds with a fund size of 2 billion or more that were liquidated from 2013 to the end of 2022. Small-scale funds were excluded from the sample because their returns could be excessively distorted, potentially affecting the results. To examine the factors influencing the investment performance of domestic venture funds and understand the impact of these factors, multiple regression analysis was conducted. The research model is as follows:   Y = F External Environmental Factors∗ , Fund Internal Factors ∗

External Environmental Factors : Business Cycle, Stock Market, Venture Market, Exit Market

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In the above regression equation, the dependent variable Y represents the investment performance of liquidated domestic venture funds, and the performance measurement indicator used is the Internal Rate of Return (IRR) (Table 1).

3.1 Hypothesis Setting This study presents research hypotheses derived from a thorough review of existing studies and interviews conducted with representatives and general maangers in the VC industry. The hypotheses are categorized into two main categories: external environmental factors and fund internal factors. • Hypotheses related to external environmental factors Hypotheses related to external environmental factors H1. The stock market environment at the time of venture fund formation has a positive relationship with fund performance. H2. The stock market capitalization at the time of venture fund formation has a positive impact on fund performance. H3. The KOSDAQ index growth rate at the time of venture fund formation has a positive influence on fund performance. H4. The business cycle at the time of venture fund formation has a negative relationship with fund performance. H5. The intensity of competition in the venture market has a negative impact on fund performance. H6. The total capital inflow at the time of venture fund formation has a positive effect on fund performance. H7. A favorable exit environment at the time of fund exit has a positive relationship with fund performance. H8. The amount of newly issued IPOs at the time of fund exit has a positive impact on fund performance. H9. The KOSDAQ index growth rate at the time of fund exit has a positive influence on fund performance. • Hypotheses related to internal factors of the fund H10. The experience of the general partner (GP) has a positive impact on fund performance. H11. The expertise and capabilities of the general partner (GP) have a positive impact on fund performance. H12. The size of assets under management (AUM) of the general partner (GP) is positively associated with fund performance.

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Table 1 Variable definitions Variable

Attributes of a variable

Classification

Definition of a variable

Dependent variable

Fund performance

IRR

Indicator for measuring the level of investment performance accounting for the fund’s operating period

Independent variable

Business Cycle 3-year government bond yield

Government Bond 3-year (1-year average since fund formation) → Primary Indicator BBB-rated → Secondary Indicator (Credit Spread: 3-year government bond - BBB-Interest rated) → Secondary Indicator

Stock Market

Stock Market Value

KOSPI market capitalization + KOSDAQ market capitalization) ÷ GDP

KOSDAQ_in

1-year post-fund formation KOSDAQ index return (%)

KOSDAQ_in 3 M AVG Return

3-month average post-fund formation KOSDAQ index return

KOSDAQ_in 6 M AVG Return

6-month average post-fund formation KOSDAQ index return

KOSDAQ_in 1Y AVG 1-Year average post-fund formation Return KOSDAQ index return Independent variable

Venture Market

Capital Inflow

(Total capital inflow from new fund formation annually/GDP)

Exit Market

Number of IPOs

Number of IPOs

IPO Amount

IPO Amount

KOSDAQ_exit

KOSDAQ_exit (%) (1-year period return before exit)

KOSDAQ _exit 3-month trailing average return

3-month pre-liquidation KOSDAQ average return (%)

KOSDAQ _exit 6-month trailing average return

6-month pre-liquidation KOSDAQ average return (%)

KOSDAQ _exit 1-Year pre-liquidation KOSDAQ 1-Year trailing average average return (%) return GP Capability

Experience of Asset Manager (years)

GP Experience at Inception (Years since Fund Formation GP Establishment Year)

AUM of Asset Manager

AUM of the Fund Manager at Liquidation Year (continued)

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Table 1 (continued) Variable

Attributes of a variable

Classification

Definition of a variable

Fund manager personnel

Fund manager personnel (number)

Fund Structure Fund size Fund length

Fund’s Fund Capital Commitment Fund’s Duration (Years)

H13. The size of the venture capital fund (Size) has a negative impact on fund performance. H14. Short-term funds outperform long-term funds in terms of fund performance.

4 Empirical Analysis Results 4.1 Descriptive Statistic The overall descriptive statistics for the sample are presented in Table 2. The total capital raised for the entire sample of venture funds liquidated within the past 10 years was set at KRW 2.6567 trillion, with an average capital raised of KRW 3.2 billion. The simple average internal rate of return (IRR) was 24.29%, while the weighted average IRR was 21.95%. The standard deviation was 35.78%, with a minimum return of –20.36% and a maximum return of 276.4%. In Table 2, the annual returns for the sample funds are compared with the average annual return of the KOSPI index (0.70%) during the same period (2007–2022). The sample funds achieved significantly higher returns, with an average annual return of 24.29%. However, funds established after the 2008 financial crisis (2009–2011) showed relatively lower returns, averaging around 10% annually. On the other hand, funds established after 2012 exhibited more favorable returns, ranging from approximately 19% to 29% annually. Overall, there was an increasing trend in returns over time. It should be noted that funds established in 2018 showed lower returns compared to other years, and the year 2020, although with a smaller sample size, demonstrated an exceptional return of 225.59%. This suggests the presence of a vintage effect, indicating that returns vary by the year of establishment. Table 3 presents the descriptive statistics of the independent variables. The median level of the 3-year government bond rate, defined as the business cycle indicator, was 2.72%. The median multiple of stock market capitalization to GDP indicated an undervalued overall stock market at 0.89 times. The median annual growth rate of the KOSDAQ index during the first year of fund formation was a minimal 1.71%. The median growth rate of the KOSDAQ index one year before exit was 7.09%. The median annual amount of newly issued IPOs during the research period was 9.7469 trillion KRW. The median tenure of fund managers indicated substantial experience

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Table 2 Fund formation amount and internal rate of return (IRR) Number Amount Year of of capital 수익률 (IRR) (%) establishment of formation Average Weighted Median Standard Min funds average deviation 2007년

4

1,250.00

15.27

9.37

13.5

13.84

0.47

Max 33.6

2008년

4

2,535.00

17.3

15.61

18.7

7.91

7.3

24.48

2009년

6

1,381.20

10.58

9.33

11.07

12.91

–5.84

28.7

2010년

8

2,560.20

10.77

10.47

10.45

11.76

–4.8

24.97

2011년

6

2,950.80

11

11.22

11.1

1.35

9.5

12.3

2012년

5

995

18.81

19.15

19.24

4.24

12.75

23.56

2013년

7

3,087.00

25.41

28.43

27.4

13.17

0.3

41 52.1

2014년

9

4,458.00

24.78

23.67

20.11

15.67

10.11

2015년

3

720

28.84

68.95

15.98

56.73

–20.36 90.9

2016년

8

3,021.00

25.52

25.75

23.96

17.59

0

55.8

2017년

10

2,172.40

19.1

25.84

19.6

16.94

–7.81

48.43

2018년

6

480.2

9.37

14.09

9.01

6.52

2.1

18.27

2019년

2

664

31.6

35.02

31.6

2020년

2

83.3

181.51

225.59

181.51

2021년

2

189

93.99

73.99

93.99

23

2022년

1

20

Total

83

26,567.10 24.29

23

23

21.95

18.11

7.92 134.2 67.65 35.78

26

37.2

86.61

276.4

46.15

141.82

23

23

–20.36 276.4

at 22.8 years. The AUM (Asset Under Management) of the fund managers ranged from a minimum of 85 billion KRW to a maximum of 1.4 trillion KRW, highlighting significant variation among VCs. The fund size ranged from a minimum of 2 billion KRW to a maximum of 154 billion KRW, with a median of 6 years for the fund length. The minimum fund length was 0.4 years, and the maximum length was 11 years.

4.2 The Analysis Results Table 4 presents the results of the analysis on the impact of independent variables on venture fund performance (Net IRR). Multiple regression analysis was conducted, dividing the independent variables into external environmental factors, fund intrinsic factors, and fund structure.

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Table 3 Basic statistics of variables Classification

Median

Standard deviation

Min

Max

Business Cycle

3-year government bond yield

2.72

1.11

0.94

5.51

Stock Market

Stock Market Value (Ratio)

0.89

0.1

0.63

1.26

KOSDAQ_in (%) 1.71 (1-year period return after fund formation)

25.78

–59.78

69.3

Venture Market

Capital Inflow (%) (Total capital inflow from new fund formation annually/ GDP)

0.16

0.09

0.06

0.52

Exit Market

IPO Amount (Annual IPO Amount

97,469.70

67,260.90

12,607

1,88,339

KOSDAQ_exit (%) 7.09 (1-year period return before exit)

23.98

–40.48

59.05

GP experience (years)

22.8

7.9

3.1

23.3

Fund manager personnel (number)

35

14

5

45

Fund manager AUM 262 (Asset Under Management) (in billion)

3,866

850

14,000

Fund Size (in billion) 250

307

20

1,540

Fund length (years)

2.9

0.4

11

GP Capability

Fund Structure

6

Reference: Bank of Korea Statistical System, Korea Exchange website. Excluding IPOs for special listing

4.3 Analysis Results for External Environmental Factors Gresch and Wyss (2011) found that lower average corporate bond rates (BAA) during the fund’s length corresponded to higher IRR. In line with these findings, this study also confirms that lower yields on 3-year government bonds are asso-ciated with higher performance (IRR) in venture funds. Thus, hypothesis 4, which posits that venture fund performance is positively influenced by interest rate levels, is supported [4]. The research findings indicate a positive impact of capital inflow on venture capital. This suggests an increased likelihood of subsequent investments for highgrowth enterprises and illustrates the diverse investment opportunities within the

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Table 4 The results of the multiple regression analysis R

0.718f

R Squared

0.515

Adjusted R-Squared

0.476

Standard Error of Estimate

26.1093

Stat Change Change in R-squared

Change in F

df1

df2

Significance Level Change in F

0.042

6.428

1

75

0.013

Model

Unstandardized Coefficients

CapitalInflow (Log)

1046.178

Government Bond 3-year (Annual Average)

t

Significance Level

B 6.748

0

–47.68

–5.294

0

Fund Length

–6.447

–3.484

0.001

AUM of Asset Manager)

39.057

2.535

0.013

Fund Size

–0.015 g

–0.165

0.87

Experience of Asset Manager

–0.050 g

–0.595

0.554

Number of Employees

–0.084 g

–0.967

0.337

BBB_Interest Rate (Annual Average)

–0.233 g

–1.684

0.096

Stock Market Value (%)

0.084 g

0.601

0.549

KOSDAQ_in (%)

–0.017 g

–0.151

0.88

KOSDAQ_exit (%)

–0.050 g

–0.594

0.554

IPO_exit

0.013 g

0.126

0.9

IPO Amount

0.104 g

0.93

0.356

Follow-on Investment

0.117 g

1.291

0.201

Joint Investment

0.038 g

0.449

0.655

a Dependent

variable: Net IRR, N: 83

domestic venture capital market. Such inflows not only foster growth without escalating competition but also support various enterprises, contributing to positive fund performance. Consequently, the hypothesis that capital inflow during venture fund formation positively influences investment outcomes is supported. The relationship between stock market conditions and fund returns has shown inconsistent results in previous studies, and this study also did not find statistically significant results when analyzing the relationship between 1-year average index returns at the fund’s inception and venture fund perfor-mance. Similarly, there were no significant associations observed between KOSDAQ index at the exit point and the 1-year pre-exit index returns with ven-ture fund performance. Additionally, the analysis of IPO count and IPO amount in relation to fund performance did not yield significant results, leading to the rejection of other hypotheses.

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4.4 Analysis of Fund Intrinsic Factors In previous studies on domestic venture funds, it has been generally found that shortterm fund (1 year or less than 2 years) performance tends to be more favorable than long-term (3 years or more than 5 years) performance. Consistent with this, this study also revealed that fund length (Length) had a statistically significant impact on fund performance. This indicates that funds with shorter lengths tend to exhibit better performance, supporting hypothesis 14. Generally, when fund returns are favorable, it attracts more capital due to the track record of good performance. This increased capital inflow can further enhance fund returns or lead to the formation of subsequent funds. Moreover, with sufficient capital available for investment, venture capital firms are more likely to provide funding to promising companies, leading to more investment opportunities. This, in turn, positively influences investment returns. In this study, the analysis showed that the fund size had no statistically significant impact on fund performance, leading to the rejection of hypothesis 13. The operating experience of venture capital firms plays a crucial role in fund performance. Although early-stage funds may have lower performance, as firms gain experience, their performance improves. However, in this study, the statistical analysis did not find a significant relationship between the operating experience and the number of professionals employed by the venture capital firms (GP staff) and fund performance, resulting in the rejection of hypotheses 10 and 11. Regarding the relationship between fund performance and the venture capital firm’s assets under management (AUM), this study found statistically significant results, supporting hypothesis 12. This suggests that larger AUM positively influences fund performance. Overall, the analysis of fund intrinsic factors provided valuable insights into the determinants of venture fund performance. The results highlighted the positive impact of capital inflow, fund length, and AUM on fund performance, while the relationships with operating experience, GP staff, and fund size were not statistically significant.

5 Conclusions This study conducted an empirical analysis of the factors influencing the investment performance of domestic venture funds that were established between 2007 and 2021 and completed their exits within the recent 10 years (2013–2022), as of the end of 2022. Acquiring empirical data for private equity funds, given their characteristics, is particularly challenging, and conducting research based on recent data is quite rare. Nevertheless, despite these challenges, this study utilized up-to-date data to compare and analyze the impact of external environmental factors, fund intrinsic factors, and fund structure on venture fund performance.

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This study analyzed the external environmental factors affecting the performance of venture funds, which were categorized into economic cycles, stock market conditions, venture market conditions, and exit market conditions. This study also examined the intrinsic factors of fund management capabilities, including experience of the General Partner (track record), human resources, and asset under management (AUM), as well as the fund structure factors of fund size and fund length. The summary of the analysis results is shown below. First, the interest rate of the 3-year government bond has been found to significantly influence fund performance. Lower interest rates are associated with better fund performance, indicating a strong relationship between interest rate levels, reflecting economic cycles, and investment performance. Second, among the external environmental factors, capital inflow has a positive impact on fund performance. This can be attributed to the development of the domestic venture capital market, which provides ample investment opportunities. As a result, the inflow of investment capital acts more as a driver of growth rather than intensifying competition, leading to positive effects on investment performance. These findings differentiate from previous studies and contribute to the existing body of research. Third, among the intrinsic factors, both fund length and asset under management (AUM) have shown significant impacts on fund performance. Shorter-term funds (1 year or less than 2 years) demonstrate better returns compared to longer-term funds (3 years or more than 5 years). Additionally, higher profitability of fund managers facilitates the establishment of subsequent funds, leading to more investment opportunities in promising companies. This positive effect on investment performance contributes to the growth of AUM. Fourth, this study has observed variations in venture fund returns according to the year of establishment, suggesting the need for considering external environmental factors, intrinsic fund factors, fund structure, and vintage effects when constructing portfolios and making venture fund investments.

References 1. Choi, Hyun Hee (2018), “A Study on the Determinants of the Korean Venture Funds Performance: Focusing on Vintage and Venture Capital Financial Performance,” Master’s Thesis, Graduate School of Management of Technology, Korea University. 2. Song, Ingyu (2015), “Performance Analysis, Diversification Effect, Performance Factors and Perfomance Persistency and Learning Effect of Private Funds,” Doctoral Dissertation, University of Seoul. 3. Choi, Hyun-hee, Kim, Young-joon (2020). An Empirical Study on Determinants of Performance in Domestic Venture Funds. Journal of Business Research, 49(2), pp. 279–303. 4. Gresch, N and Rico von Wyss (2011), “Private Equity Funds of Funds vs. Funds: A Performance Comparison,” The Journal of Private Equity, 14(2), pp. 43–58. 5. Diller, C., and C. Kaserer (2009), What Drives Private Equity Returns? Fund Inflows, Skilled GPs, and/or Risk? European Financial Management, 15(3), pp. 643–675.

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6. Phalippou, L., and M. Zollo (2005), “What Drives Private Equity Fund Performance?” Working Paper, Financial Institutions Center at The Wharton School. 7. Aigner, P., S. Albrecht, G. Beyschlag, T.I.M. Friederich, M. Kalepky, and R. Zagst (2008), “What Drives PE? Analyses of Success Factors for Private Equity Funds,” The Journal of Private Equity, 11(4), pp. 63–86. 8. Gompers, P., and J. Lerner (2000), “Money Chasing Deals? The Impact of Fund Inflows on Private Equity Valuation,” Journal of Financial Economics, 55(2), pp. 281–325. 9. Oh, Se Kyung, Choi, Jung Won, Park, Joong Kwon (2016), “An Empirical Study on the Performance and Determinants of Domestic Venture Funds,” Financial Research, 29(3), pp. 343–375. 10. Ljungqvist, A., and M. Richardson (2003), “The Cash Flow, Return, and Risk Characteristics of Private Equity,” Working Paper 9454, NBER. 11. Kaplan, S.N., and A. Schoar (2005), “Private Equity Performance: Returns, Persistence, and Capital Flows,” Journal of Finance, 60(4), pp. 1791–1824. 12. Robinson, D.T., and B.A. Sensoy (2016), “Cyclicality, performance measurement, and cash flow liquidity in private equity,” Journal of Financial Economics, 122, pp. 521–543. 13. Harris, R.S., Tim Jenkinson, and Steven N. Kaplan (2014), “Private Equity Performance: What Do We Know?” Journal of Finance, 69(5), pp. 1851–1882. 14. Chae J., Jee-Hyun Kim and Hyung-Chul Ku (2014), “Risk and Reward in Venture Capital Funds,” Seoul Journal of Business, 20(1), pp. 91–137. 15. Gompers, P., A. Kovner, J. Lerner, and D. Scharfstein (2008), “Venture Capital Investment Cycles: The Impact of Public Markets,” Journal of Financial Economics, 87(1), pp. 1–23. 16. Cho, Sung Sook, and Park, Jeong Seo (2009), “An Emperical Study on the Performance of Korean Venture Capital Funds in Relation to the Management Behavior of the Domestic Venture Capital Firms,” Venture Management Research, 12(2), pp. 1–30.

A Research on Factors Influencing the Survival of Small Businesses: Focusing on Franchise Convenience Stores Youngsu Kim, Gwangyong Gim, Youngkun Yang, and In Mo Yeo

Abstract As of 2020, South Korea had a notably high rate of self-employment, especially in the franchise convenience store sector. A study focusing on Seoul’s major commercial districts used statistical methods to analyze factors affecting the survival of these stores. Key findings indicated that store visibility (number of facing roads), location (proximity to bus stops), and financial aspects (sales revenue and average customer spending) significantly influence the longevity of franchise convenience stores. Keywords Self-employed · Franchise · Survival analysis

1 Introduction According to the study conducted in 2020, the proportion of self-employed individuals in South Korea ranks 6th out of the 38 OECD member countries, with a rate of 24.6%. This indicates that self-employment holds a relatively high share in the domestic market. Compared to G7 countries, the proportion of self-employment in South Korea is among the highest, with a figure of nearly double the average of 12.7% in G7 countries. This demonstrates that self-employment is prevalent in the domestic context.

Y. Kim (B) · Y. Yang · I. M. Yeo IT Policy and Management, Soongsil University, Seoul, South Korea e-mail: [email protected] Y. Yang e-mail: [email protected] I. M. Yeo e-mail: [email protected] G. Gim Department of Business Administration, Soongsil University, Seoul, Republic of Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_9

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The high proportion of self-employment in South Korea is closely related to an unstable employment structure. Due to the lack of stable jobs in the regular employment market and economic uncertainty, many people choose self-employment. In recent years, the growth rate of self-employment has been gradually decreasing alongside the increase in wage levels. However, starting from 2025, when the elderly population exceeds 20% and enters a super-aged society [1], it is expected that the proportion of self-employment will further increase if stable jobs that can accommodate the elderly population are not provided on a large scale [2]. The elderly population will have limited options in the regular employment market, and engaging in economic activities will become a necessary choice [3]. In particular, it is expected that the franchise industry will show the highest potential for elderly population because franchise industry is relatively accessible. Franchise businesses offer a successful business model and support systems, providing relative stability and growth potential [4]. The domestic convenience store market has experienced significant growth during the COVID-19 pandemic, mainly due to its low entry barriers and low startup costs compared to other franchise industries. As of 2021, the sales share of the top three convenience store chains accounted for 15.9% of the total retail market, surpassing large supermaket for the first time since statistics were recorded. This indicates that the franchise convenience store market is economically significant and has a big influence on the domestic retail industry. This study aims to analyze the factors that influence the survival of franchise convenience store outlets. The analysis will be conducted using Cox proportional hazards analysis and logistic regression analysis based on the panel data of all outlets of A franchise located in Gangnam-gu, Seocho-gu, and Songpa-gu, which are the largest commercial districts in Seoul, from January 1, 2020, to April 30, 2023. Cox proportional hazards analysis is a type of survival analysis used to estimate the hazard rate of event occurrence over time. It can be used to determine the impact of specific factors on the survival of convenience store outlets. Additionally, logistic regression analysis is a statistical method used when the dependent variable is a binary variable. It can be used to identify the independent variables that affect the survival of convenience store outlets and the direction of their influence. In this study, the backward elimination method will be used to estimate significant variables based on the results of Cox proportional hazards analysis and logistic regression analysis. The backward elimination method involves initially constructing a model that includes all independent variables and then gradually removing insignificant variables until a final model with only significant variables is obtained [5]. Ultimately, this study aims to estimate the variables that have an impact on the survival of convenience store outlets through empirical analysis.

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2 Theoretical Background 2.1 Self-employed In South Korea, there is no clear legal definition for self-employed individuals. However, previous studies have classified self-employed individuals into several categories based on certain criteria. Classification of Economically Active Population Survey: In the Economically Active Population Survey, non-wage workers are classified, including employers, self-employed individuals, and unpaid family workers. Employers refer to selfemployed individuals who hire paid employees, while self-employed individuals refer to those who operate their businesses without hiring any employees. The term “self-employed individuals” encompasses employers, self-employed individuals, and unpaid family workers, all of whom are considered non-wage workers [6]. Therefore, In South Korea, small businesses are classified for support measures based on industry and employee count: those in mining, manufacturing, construction, and transportation with fewer than 10 regular employees, and service sectors with fewer than 5. The National Tax Service categorizes individual business owners as selfemployed, including general, simplified, and tax-exempt business owners. Internationally, self-employed individuals are defined as those earning income through independent contracts or transactions without fixed wages, often seen as highly skilled in specific professions. South Korea’s classification of self-employed individuals follows the Economically Active Population Survey, special measures for SMEs and small businesses, and the National Tax Service’s criteria.

2.2 Cox Proportional-Hazard Model Cox proportional hazards model, also known as Cox regression, is a statistical technique used to analyze survival data and determine the relationship between independent variables (covariates) and the risk or hazard of an event occurring over time [7]. Developed by Sir David Cox in 1972, the Cox proportional hazards model is a key tool in survival analysis, based on the hazard function which represents the risk of an event over time. It combines a baseline hazard function (risk at a specific time with no covariate effect) with the impact of covariates (independent variables), expressed through a linear combination of their values and regression coefficients. The model assumes constant hazard ratios over time for covariates (proportional hazards assumption) and uses the partial likelihood method for estimation, accommodating right-censored data. It’s widely applied in fields like medicine, epidemiology, economics, and social sciences to analyze time-to-event data and assess survival influences.

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2.3 Logistic Regression Logistic regression analysis is a statistical analysis technique used when the dependent variable is binary or categorical. It models the relationship between the dependent variable and one or more independent variables [8], estimating the probabilities of different outcomes. Logistic regression is a statistical method based on the logistic function, used to model the probability of a binary or categorical dependent variable based on independent variables. It calculates probabilities using the logistic function, P(Y = 1|X) = 1/ (1 + exp(−z)), where z is a linear combination of independent variables and their coefficients. The model employs maximum likelihood estimation to determine the coefficients, maximizing the observed data’s probability. This approach is useful for interpreting the impact of independent variables on event probabilities and is widely applied in various fields like medicine, social sciences, and economics for predicting binary or categorical outcomes.

3 Extraction of Variables Through Prior Research Analysis Previous research on survival rates or survival analysis has primarily focused on the survival and bankruptcy of businesses. It can be broadly categorized into three areas: studies on changes in survival rates, studies on survival factors, and studies on the development of credit rating models. In these studies, explanatory variables such as financial ratios, firm size, market entry rates, growth rates, and economic conditions have been analyzed. However, the results have shown that for retail businesses, location and competition-related factors have a greater influence on strategic decision-making than financial ratios or economic environment-related variables. In fact, location can significantly impact the sales and profits of retail stores, and the choice of location is difficult to change once the store is established, leading to significant losses upon closure. In particular, among retail businesses, franchise convenience stores, which are most sensitive to changes in commercial areas, have the advantage of relatively objective research as they are evaluated for opening through systematic commercial area analysis conducted by the franchise headquarters [9]. Previous studies related to convenience stores have been conducted in areas such as spatial distribution and location, consumer characteristics, marketing strategies, and characteristics of franchise chains. In the case analysis of “Dongsang Store and Daeyeon Store in Busan City” [10], suitable location conditions for convenience stores were identified as areas with a concentration of general hospitals and large buildings, areas with a mix of residential areas and entertainment districts, and areas with a high concentration of office buildings. In the study “Convenience Store Management Owners and Customers in Gwangju City” [11], it was found that there is a tendency for convenience stores to spread

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from downtown to the outskirts, from commercial area-dense areas to residential and academy district areas. The study “Regression Model Estimating Sales based on Traffic Volume Survey” [12] confirmed that traffic volume influences store sales. In the study “Estimation of Sales Increase or Decrease using Floating Population, Competition, Visibility, and Store Area of 35 Convenience Stores” [13], it was found that the most influential factors on sales were competition, pedestrian traffic volume, and visibility. The study “Analysis of Convenience Store Location using GIS Techniques” [14] suggested that the major location factors influencing convenience stores are traffic volume, accessibility, and visibility. The differences between previous research and this study are as follows: First, previous research on survival rates, survival analysis, and location factors had limitations in that they were based on samples rather than conducting a census of a specific population. In this study, the entire A franchise stores operating in Gangnam-gu, Seocho-gu, and Songpa-gu as of January 1, 2020, to April 30, 2023, were surveyed, providing relatively generalizability. Second, while previous studies used a maximum of 7 independent variables for their research, this study utilized more than double that number, specifically 16 variables, ensuring greater reliability compared to previous research. Third, previous studies employed statistical analysis or surveys using only some items, while this study utilized GIS data directly measured from the Seoul Metropolitan Government’s commercial area analysis service, thus distinguishing itself in terms of the objectivity of the data. Based on the previous research, the following variables were selected for application in this study. Factor

Variable

Description

Survival

Opening Date

The date when the store started operating

Population

Accessibility

Closing Date

The date when the store ceased operations

Operating Period

The period from the opening date to the closure date

Mobility Population

The average daily floating population within a 300 m radius

Residential Population

The number of residents within a 300 m radius (based on resident registration)

Employee Population

The number of employees within a 300 m radius (based on employment insurance)

Household Count

The number of households within a 300 m radius (based on resident registration)

Population Density

Population density within a 300 m radius

Bus

The distance between the nearest bus stop and the store

Subway

The distance between the nearest subway station exits and the store (continued)

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(continued) Factor

Variable

Description

Visibility

Facing Roads

The number of Facing Roads adjacent to the store

Competition

Competing Stores

The number of convenience stores within a 300 m radius

Finance

Sales

The average monthly sales from January 2020 to December 2022

Sales Counts

The average monthly Sales Count from January 2020 to December 2022

Average Revenue per Transaction

Sales divided by the Sales Count

Rental Fee

The rental fee for convenience stores within a 300 m radius

Note For stores that operated until the study endpoint on April 30, 2023, the operating period is reflected as “Opening Date to 2023.4.30” for right-censored data

4 Empirical Analysis 4.1 Cox Analysis Result The initial results of Cox Proportional Hazards Analysis are as follows (Tables 1, 2, 3, 4, 5): In conclusion, the Cox proportional hazards analysis model obtained through backward elimination can be formulated as follows: h(t|x) = h0(t) ∗ exp(−0.362090 ∗ Facing Roads + 0.031496 ∗ Bus + 0.006216 ∗ Competing Stores + 0.322837 ∗ Sales Amount − 0.257776 ∗ Sales Count − 0.373006 ∗ Average Revenue per Transaction − 0.013235 ∗ Population Density + 0.030945 ∗ Household Count)

4.2 Logistic Regression Analysis The results before applying backward elimination to logistic regression analysis are as follows (Tables 6, 7): Before applying backward elimination, the variables Facing Roads and Bus were found to have significant values. However, after applying backward elimination, the

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Table1 COX Analysis result (before applying backward elimination) Facing Roads

coef

exp(coef)

se(coef)

z

Pr(>|z|)

−0.357189

0.69964

0.12029

−2.969

0.00298**

Subway

0.017583

1.017738

0.068173

0.258

0.79647

Bus

0.051575

1.052928

0.041875

1.232

0.21808

−0.031668

0.968828

0.079165

Competing Stores

0.006277

1.006296

0.00272

2.308

0.021*

Sales

0.329873

1.390792

0.14849

2.222

0.02632*

Transportation

−0.4

0.68914

Transaction Count

−0.26154

0.769865

0.122746

−2.131

0.03311*

Average Transaction Value

−0.375113

0.687211

0.16586

−2.262

0.02372*

Mobility Population

−0.009097

0.990944

0.05

−0.182

0.85563

Population Density

−0.008095

0.991937

0.033713

−0.24

0.81024

Employee Population

−0.003900

0.996107

0.009476

−0.412

0.68062

0.034475

1.035076

0.016799

2.052

0.04015

−0.009422

0.990623

0.012069

−0.781

0.43501

Household Count Average Rent

Table 2 Cox analysis result (before applying backward elimination)

## Concordance = 0.594 (se = 0.018) ## Likelihood ratio test = 26.82 on 13 df, p = 0.01 ## Wald test = 30.05 on 13 df, p = 0.005 ## Score (logrank) test = 25.11 on 13 df, p = 0.02

Table 3 Cox analysis result (After applying backward elimination) Facing Roads

coef

exp(coef)

se(coef)

z

−0.36209

0.69622

0.115148

−3.145

Pr(>|z|) 0.00166**

Bus

0.031496

1.031997

0.013606

2.315

0.02062*

Competing Stores

0.006216

1.006235

0.00253

2.457

0.01402*

Sales

0.322837

1.38104

0.142461

2.266

0.02344*

Sales Count

−0.257776

0.772769

0.118133

−2.182

0.02910*

Average Transaction Value

−0.373006

0.688661

0.157112

−2.374

0.01759*

Population Density

−0.013235

0.986852

0.007753

−1.707

0.08779

0.030945

1.031428

0.015339

2.017

Household Count

0.04365*

variables Facing Roads, Bus, Sales Revenue, and Average Revenue per Transaction were identified as variables that influence the survival probability of the store. Based on the results of backward elimination, the following regression equation can be derived:

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Table 4 Cox analysis result (after applying backward elimination) exp(coef)

exp(-coef)

lower.95

upper.95

Facing Roads

0.6962

1.4363

0.5556

0.8725

Bus

1.032

0.969

1.0048

1.0599

Competing Stores

1.0062

0.9938

1.0013

1.0112

Sales

1.381

0.7241

1.0446

1.8259

Sales Count

0.7728

1.294

0.613

0.9741

Average Transaction Value

0.6887

1.4521

0.5061

0.937

Population Density

0.9869

1.0133

0.972

1.002

Household Count

1.0314

0.9695

1.0009

1.0629

Table 5 Cox Analysis Result (After applying backward elimination)

## Concordance = 0.588 (se = 0.018) ## Likelihood ratio test = 24.72 on 8 df, p = 0.002 ## Wald test = 28.34 on 8 df, p = 4e-04 ## Score (logrank) test = 22.13 on 8 df, p = 0.005

Table 6 Logistic regression analysis result (before applying backward elimination) Estimate (Intercept) Facing Roads

Std. error

z value

5.06523

5.945671

0.852

−0.895718

0.312855

−2.863

Pr(>|z|) 0.3943 0.0042**

Subway

0.192182

0.163727

1.174

0.2405

Bus

0.213717

0.094434

2.263

0.0236*

Transportation

−0.236991

0.185253

−1.279

Competing Stores

−0.002523

0.004648

−0.543

0.5873

0.175912

0.336751

0.522

0.6014

−0.055751

0.290948

−0.192

Sales Sales Count

0.2008

0.848

Average Transaction Value

−0.265531

0.293226

−0.906

0.3652

Population Mobility

−0.109927

0.112707

−0.975

0.3294

0.048052

0.073846

0.651

0.5152

0.023226

Population Density

−0.712

0.4764

Household Count

0.025323

0.0736

0.344

0.7308

Average Rental Fee

0.023624

0.030842

0.766

0.4437

Employee Population

−0.01654

h(t|x) = h0(t) ∗ exp(5.19743 − 0.92629 ∗ Facing Roads + 0.09608 ∗ Bus + 0.11826 ∗ Sales − 0.28334 ∗ Average Transaction Value)

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Table 7 Logistic regression analysis result (after applying backward elimination) Estimate (Intercept)

Std. error

z value

Pr(>|z|)

5.19473

1.95497

2.659

0.007847**

−0.92629

0.29655

−3.124

0.001787**

Bus

0.09608

0.03409

2.818

0.004833**

Sales

0.11826

0.04065

2.909

0.00362**

−0.28334

0.07997

−3.543

Facing Roads

Average Transaction Value

0.000395***

5 Conclusion This study conducted an empirical analysis on franchise convenience stores, which represent the largest proportion in the domestic self-employment sector, to investigate the factors influencing their survival. Using Cox proportional hazards analysis and backward elimination, the study identified Facing Roads, Bus, Competing Stores, Sales, Sales Count, Average Transaction Value, and Household Count as factors that affect survival. Similarly, logistic regression analysis and backward elimination revealed that Facing Roads, Bus, Sales, and Average Revenue per Transaction are significant factors affecting survival. Notably, Facing Roads, Bus, Sales, and Average Revenue per Transaction were found to be consistent factors across both analyses. Previous studies on survival analysis in domestic research have mainly focused on financial variables related to corporate bankruptcy, while studies on self-employment and convenience stores have been conducted from the perspective of market analysis. However, this study analyzed multiple previous studies to derive various variables and utilized panel data for a specific region without setting a population, which adds significance to the research. With the anticipated full-scale retirement of the baby boomer generation starting in 2026, the proportion of self-employment in the domestic market, which has been gradually declining, is expected to increase explosively again. Particularly, growth in the franchise market is expected to accelerate within the self-employment sector. Considering these circumstances, this study’s objective and evidence-based analysis of survival factors in the franchise convenience store market is expected to have timely and practical applicability. Furthermore, it is believed that this study can help reduce the social costs resulting from indiscriminate market entry. However, despite its significance, this study also has limitations and challenges. Firstly, the data used in this study was based on panel data for a specific region, which may not fully represent the self-employment market in that area. However, conducting large-scale studies that include data from various regions can provide more generalized results. Secondly, the independent variables used in the analysis were identified as significant in this study. However, since there may be additional variables or other factors influencing the survival of self-employment, future research should consider analyzing a wider range of variables for a more comprehensive analysis.

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Third, this study focused on analyzing major variables related to the survival of self-employment. However, the analysis of external factors influencing survival was limited. Therefore, future research that takes external factors into account can contribute to a better overall understanding.

References 1. Kang mi, Lee jaewoo: Cox Proportional Hazards Model Analysis of Survival Factors in Small and Medium Construction Enterprises, Real Estate Studies Volume 15, Issue 2, pp. 41–57 (2009) 2. Hwang, Kwang-Hoon: The Increasing Number of Young Entrepreneurs. Employment Issues, pp. 5–25 (2017) 3. Eom Doong-wook: Factors Determining the Employment of Middle-aged and Older Workers— Focused on the First Year Data of the National Retirement Security Panel, Labor Policy Research, pp. 17–38 (2008) 4. Statistics Korea(2020) 5. Park Jin-hee: Forecast of Manpower Supply and Demand in the Self-Employed Sector, Korea Employment Information Service (2019) 6. Kim Tae-hee, Joo Sung-hee: Study on the Differences in Characteristics According to the Grades of Excellent Franchises, Franchising Journal, pp. 32–53 (2018) 7. Kim Hyung-shin, Moon Sung-woo, Seo Yong-seok: Selection of Major Influencing Factors of Forest Road Landslides Using Structural Equation Modeling and Logistic Regression Analysis, The Korean Geotechnical Society, pp. 585–596 (2022) 8. Choi Kyung-jin, Kim Seok-young, Jeon Hee-joo: Study on the Factors Influencing the Termination Risk of Reverse Mortgage Participants Using the Cox Proportional Hazards Model: Focusing on Reverse Mortgage Beneficiaries, Insurance Research Institute, pp. 43–70 (2022) 9. Kwon Seung-oh: A Study on the Location Selection of Convenience Stores in the Busan Area, Master’s Thesis, Dong-A University (1997) 10. Lee Kyung-soon: Location Analysis of Convenience Stores in Gwangju City, Master’s Thesis, Chonnam National University (1998) 11. Shin Sun-mi: The Impact of Pedestrian Traffic on Retail Sales, Master’s Thesis, Hanyang University (2000) 12. Lee Ho-shin: Locational Factors Affecting the Sales of Convenience Stores, Master’s Thesis, Chungbuk National University (2003) 13. Park Jun-gyu: A Study on the Spatial Characteristics of Convenience Stores in Large Cities, Master’s Thesis, Keimyung University (2003) 14. Hong Ui-taek: A Study on the Location Analysis of Convenience Stores Using GIS Techniques, Korean GIS Association (1995)

A Study on Purchase Intention for Innovative Products: Focusing on Oxygen-Generating Air Purifiers Chae youl Leem, Sang soo Ha, Gwang yong Gim, and Chung ku Han

Abstract This study examines the factors determining consumer purchase intention for innovative products, focusing on oxygen-generating air purifiers, based on the Innovation Diffusion Theory and the Technology Acceptance Model. To achieve this, an online survey was conducted with 259 adults, and the data were analyzed using SPSS. The research findings indicate that perceived usefulness and perceived ease of use, as per the Technology Acceptance Model, positively influence the purchase intention of oxygen-generating air purifiers. From the perspective of perceived usefulness, the relative advantage, compatibility, observability, and trialability components of the Innovation Diffusion Theory were shown to positively impact purchase intention. From the perspective of perceived ease of use, relative advantage, compatibility, complexity, and trialability components also positively influenced purchase intention. This study is significant in providing practical insights for marketing strategies and design considerations to professionals in companies producing oxygen-generating air purifiers. Keywords Oxygen-generating air purifiers · Diffusion of innovation · Technology acceptance model

C. Leem (B) Department of Graduate School of MIS, Soong Sil University, Seoul, South Korea e-mail: [email protected] S. Ha Department of Graduate School of IPTM, Soong Sil University, Seoul, South Korea G. Gim Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] C. Han BI Matrix Co., Ltd., Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_10

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1 Introduction Oxygen is a crucial element for maintaining human life. When people inhale air, oxygen is separated and absorbed through the lungs, delivered to various organs in the body via red blood cells, thereby sustaining life. Typically, the standard oxygen concentration in the air is 21%. However, in environments where the concentration is reduced by 1–2%, individuals may experience symptoms of oxygen deficiency, including headaches, dizziness, cognitive impairment, reduced concentration, decreased problem-solving ability, and sleep disorders [1]. The prevalence of indoor activities in spaces such as residences, workplaces, rest areas, and transportation has increased compared to outdoor activities for modern individuals. Due to harmful external factors such as Covid-19, fine dust, and smog, the trend of living in enclosed indoor spaces has become more widespread. Research indicates that oxygen levels decrease by 0.5% every three hours when heating is applied in a sealed indoor space during the winter [2]. In recent times, there has been a growing market for air purifiers due to the deterioration of indoor air quality and the harmful nature of various air pollutants affecting personal health [3]. However, the market for oxygen generators is not as widespread as that for air purifiers. This study aims to provide insights to market participants by examining the factors influencing the purchase intention of oxygen-generating air purifiers, an innovative product developed with the intention of market expansion, targeting consumers who are already using air purifiers widely in the market.

2 Theoretical Background 2.1 The Concept of Oxygen Generator Oxygen generator is an electronic device that removes pollutants and nitrogen from the air and concentrates oxygen. The oxygen generator utilizes the property of a substance called zeolite, which acts as an adsorbent for gas molecules. Nitrogen, which constitutes 78% of the atmosphere, is more readily adsorbed by zeolite than oxygen. Thus, when air flows into the adsorption BED where zeolite is located, nitrogen is adsorbed onto the BED, resulting in an increase in the proportion of oxygen [4] (Fig. 1)

2.2 The Concept of Air Purifier The concept involves drawing in polluted air through a fan within the device, passing it through various filters and air purification systems to filter or decompose fine dust, bacteria, pollutants, harmful gases, and other contaminants. The purified air is then

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Fig. 1 A schematic diagram of oxygen generator in PSA module

Fig. 2 General filter type air purifier structure

expelled. The primary functions of an air purifier include the removal of particulate matter such as dust, elimination of harmful substances like Volatile Organic Compound (VOC), deodorization, and the removal of mold or bacteria (Fig. 2)

2.3 Diffusion of Innovations Theory (DOI) Rogers’ (1995) Diffusion of Innovations Theory (DOI) is a theory that explains the overall process of innovation adoption by potential users. Innovation refers to ideas, behaviors, or products perceived as new by individuals or adopting units. Diffusion, on the other hand, is the process over time through which innovation is communicated among members of society via specific channels. According to the Diffusion of Innovations Theory, the adoption and acceptance of innovation are determined by the degree to which potential adopters perceive five Perceived Innovation Characteristics. These perceived innovation characteristics include relative advantage, compatibility, complexity, trialability, and observability. This study, based on the theory and prior research, modified these variables to align with the research objectives, and each was measured using a 7-point Likert scale. 1. Relative advantage refers to the evaluation and comparison of the benefits obtained by individuals or organizations before and after the introduction of new

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technology or media. The tendency to adopt increases as the perceived relative advantage of the new technology or medium is greater after its introduction [5]. This signifies the extent to which users perceive the new technology or medium to be more convenient and useful after adoption compared to before [6]. Ultimately, the subjective judgment of users plays a significant role, and the assessment of relative advantage is determined through a comparison between pre-adoption and post-adoption perceptions [7]. Compatibility refers to the subjective perception that the innovation aligns with the experiences, values, and demands of the users. Similarly, it involves the subjective aspects of individual users that vary from person to person, encompassing desires, beliefs, and values [5]. It signifies the extent to which individual users perceive the innovation as fitting with their values or past experiences, and when users feel that the innovation aligns with their values through the introduction of an innovation, the adoption and acceptance tend to occur rapidly [6]. Users generally feel a sense of familiarity when they perceive compatibility [8]. Conversely, if a specific innovation is perceived to significantly violate the existing lifestyle or norms of users, the notion of compatibility diminishes, making adoption and acceptance more challenging [8]. Complexity refers to the psychological aspect of whether the process of adopting and utilizing the innovation is perceived positively by others when users adopt the innovation. Users express a consciousness regarding how others perceive them through the adoption and acceptance of innovation [5]. Users attach importance to the outcomes of innovation, being sensitive to the opinions and evaluations of others in their surroundings, which is why higher observability leads to an increased speed of adoption and acceptance [5]. Observability refers to whether users can try and use the mediation of innovation without difficulty within a certain range when they first encounter the innovation [5]. This indicates the extent to which users can experiment and try before accepting or rejecting the innovation [9]. Through these attempts, users overcome uncertainty and gain more confidence in their beliefs, leading to an acceleration in the speed of adoption [10]. Many users, when faced with innovation for the first time, find it challenging to adopt due to a lack of experience. Therefore, they use trials to eliminate uncertainty [8]. Triability refers to the extent to which potential users can try and experiment with an innovation before adopting it. This reduces anxiety about innovation and increases confidence, facilitating acceptance [8]. Moreover, providing opportunities for improvement, modification, and recreation of innovation increases the likelihood of success in the market [5]. Numerous studies have demonstrated that perceived innovation characteristics can be considered as cognitive indicators of attitudes toward innovation [11]. It has been argued that attitudes mediate the relationship between innovation characteristics and intention, leading to a higher explanatory power of intention [11]. Relative advantage, compatibility, and ease of use, as three innovation characteristics, are considered as behavioral beliefs that directly influence attitudes toward specific behaviors [12]. Studies confirmed that innovation characteristics are significant antecedents of attitudes [13], and

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consumers’ perceived product innovativeness positively influences attitudes [14]. Relative advantage, compatibility, complexity, and observability were validated for their impact on attitudes [11].

2.4 Technology Acceptance Model (TAM) Theory The Technology Acceptance Model (TAM), proposed by Davis [15], is designed to be easily understood even by individuals with low technological literacy. It is widely used in various studies related to information technology due to its high explanatory power. Additionally, the model incorporates various theories such as the Theory of Reasoned Action, Expectancy Theory, and Self-Efficacy Theory, allowing for easy integration with other theories in research on information technology. Davis’s research model analyzes how external variables influence users’ perceived usefulness and ease of use of a system, and how these factors affect users’ intentions and behaviors in using the system [16]. Perceived usefulness, perceived ease of use, and intention to use are recognized as important factors in the Technology Acceptance Model, and their interrelationships have been validated in numerous previous studies [17]. When investigating the impact relationships between perceived usefulness, ease of use, and intention to use in the context of ERP systems, it was found that there was no significant relationship between perceived usefulness and ease of use [18]. In the case of investigating the intention to use video User-Created Content (UCC), a mutual influence relationship was identified [19]. Similar mutual influence relationships were discovered among perceived usefulness, ease of use, and intention to use in the respective field [20]. Building upon these previous studies, the following research hypotheses were formulated (Table 1). Table 1 Hypothesis setting Hypothesis

Established hypothesis

H1

H1-1

Relative advantage will have a positive influence on perceived usefulness

H1-2

Compatibility will have a positive influence on perceived usefulness

H1-3

Complexity will have a positive influence on perceived usefulness

H1-4

Observability will have a positive influence on perceived usefulness

H2

H1-5

Trialability will have a positive influence on perceived usefulness

H2-1

Relative advantage will have a positive influence on perceived ease of use

H2-2

Compatibility will have a positive influence on perceived ease of use

H2-3

Complexity will have a positive influence on perceived ease of use

H2-4

Observability will have a positive influence on perceived ease of use

H2-5

Trialability will have a positive influence on perceived ease of use

H3

H3

Perceived usefulness will have a positive influence on purchase intention

H4

H4

Perceived ease of use will have a positive influence on purchase intention

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Fig. 3 Research model

3 Research Model and Operational Definitions of Variables 3.1 Research Model Based on prior research and hypothesis formulation, this study utilizes the Innovation Diffusion Theory to examine whether five key factors significantly influence the Technology Acceptance Model’s (TAM) perceived usefulness and ease of use. Additionally, the study aims to verify the hypothesis that perceived usefulness and ease of use have a significant impact on the purchase intention of the innovative product, an oxygen-generating air purifier. The research model to test these relationships is shown in Fig. 3.

3.2 Operational Definitions and Measurement Items of Variables In this study, the factors of relative advantages, compatibility, complexity, observability, and trialability were measured using Likert scales to assess their impact on perceived ease of use, perceived usefulness, and purchase intention. Table 2 presents the operational definitions and related prior studies for each variable.

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Table 2 Operational definitions and measurement items of variables Variable

Operational definition

Relative advantages (RA)

The degree to which the innovation is perceived as superior to [6, 7] existing alternatives or ideas

References

Compatibility (COM)

The degree to which the innovation aligns with the internal beliefs, values, and prior experiences of potential adopters

[6, 8]

Complexity (CPX)

The level of difficulty potential users perceive in understanding the innovation and its ease of use

[5, 12]

Observability (OBS)

The visibility of the innovation to members of society and the ease with which the benefits of the innovation can be communicated to others

[8, 9]

Trialability (TRI)

The extent to which potential adopters can try the innovation, reducing anxiety and increasing confidence

[8, 11, 12, 14]

Perceived usefulness (PU)

What usefulness the use of an oxygen-generating air purifier will provide to the user

[17, 18]

Perceived ease of use (PEU)

The convenience of using an oxygen-generating air purifier

[18]

Purchase Intention (INT)

The intention to purchase an oxygen-generating air purifier when buying an air purifier

[16]

4 Empirical Analysis 4.1 Data Collection and Analysis Method For the empirical study, a survey was conducted targeting general adults. The survey was conducted from September 21, 2023, to October 21, 2023, spanning approximately one month. The survey questions were developed based on operational definitions derived from scales validated through literature research. Some measurement items causing confusion among respondents were modified or removed. The survey comprised a total of 51 items, with five items for each of the five perceived innovation factors, five for each of the two perceived usefulness and ease of use factors, and five for the purchase intention of the innovative product, an oxygen-generating air purifier. All survey items were measured on a 7-point Likert scale. Out of the 302 collected responses, 2 with many missing answers and 41 with identical or unserious responses were excluded. The remaining 259 surveys were used for analysis. Before testing hypotheses, the reliability of the questionnaire items was evaluated to ensure the perceived data from respondents were collected reliably. Additionally, descriptive statistical analysis was conducted to understand the demographic characteristics of the sample, and factor analysis was performed to categorize items into

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Table 3 Demographic characteristics Frequency

Category Gender

Age

Education

Male

% 50.6

Female

128

49.4

Total

259

100.0

24

9.3

20s or Below 30s

63

24.3

40s

49

18.9

50s

34

13.1

60s and above

89

34.4

High School Degree or Below

46

17.8

185

71.4

28

10.8

Bachelor’s Degree Graduate Degree or Above Marital Status

131

Married Single

181

69.9

78

30.1

factors. Finally, multiple regression analysis was employed to analyze the relationships between independent variables, mediator variables, and dependent variables. The statistical software SPSS 22 was utilized for these analyses.

4.2 Demographic Characteristics The sample of this study consisted of 50.6% males and 49.4% females, as shown in Table 3. The survey was conducted with an appropriate ratio for each characteristic.

4.3 Validity and Reliability Analysis Validity analysis assesses how accurately measurement tools measure the constructs they intend to measure, and this was tested through factor analysis. For all variables measuring the eight constructs set in this study, factor analysis was conducted, and the results showed factor loading values exceeding 0.5 for each of the eight constructs, indicating that these concepts were appropriately measured. Reliability analysis, which evaluates the ability of measurement tools to consistently measure the phenomenon they intend to measure, was judged using Cronbach’s α coefficient. The reliability analysis confirmed that Cronbach’s α was above 0.6 for all variables, indicating consistent measurement of the intended phenomenon. The reliability analysis

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results can be found in Table 4. Additionally, to enhance the reliability of the survey, two items (variables) with low correlations were removed, and the final results are shown in Table 4.

4.4 Hypothesis Testing The p-values for H1-3 and H2-4 were found to be above 0.05, leading to their rejection. As a result, out of the 12 hypotheses, 10 were accepted (Table 5). The rejected H1-3 hypothesis implies that the perception of users finding the innovative product, the oxygen-generating air purifier, useful is not solely determined by its functionality (complexity) being perceived as simple. On the other hand, the accepted H1-2 hypothesis, with a standard coefficient of 0.625, suggests that among the factors influencing perceived usefulness, the user’s perception of the oxygen-generating air purifier being suitable is the most significant. The rejected H2-4 hypothesis suggests that the ease of observing the oxygen-generating air purifier in the surroundings does not necessarily contribute to providing more convenience to users. Conversely, the accepted H2-3 hypothesis, with a standard coefficient of 0.649, indicates that among the factors influencing perceived ease of use, the simplicity of the oxygen-generating air purifier’s functionality (complexity) is the most significant. In summary, the results highlight that users perceive the oxygen-generating air purifier as more useful when they find it suitable, and they find it easier to use when they perceive its functionality as simple. These findings can offer valuable insights for the development and marketing of innovative products like the oxygen-generating air purifier.

5 Conclusion According to the standardized coefficients in the research results, the purchase intention of the innovative product, the oxygen-generating air purifier, is more influenced by perceived usefulness than perceived ease of use. In other words, when people consider purchasing the oxygen-generating air purifier, they prioritize the improvement in the quality of life and health through product usage rather than the convenience of the product. The interpretation is that, from the perspective of usefulness (value), users do not necessarily find the product useful just because its features (complexity) are simple. Moreover, among the factors that users value, they consider the product that is most suitable for improving their quality of life and health as the most important factor. In terms of ease of use (convenience), users do not find it crucial whether they can easily observe the product. On the contrary, they emphasize that the simplicity of the product’s features and ease of use are more important factors than other considerations. In conclusion, people tend to prioritize the unique value of the product (usefulness and compatibility) over its convenience. Therefore,

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Table 4 Factor analysis results between constructs Construct CPX2

0.865

CPX4

0.836

CPX5

0.818

CPX3

0.818

CPX1

0.783

OBS2

Cronbach α

Factor loading 1

2

3

4

7

0.841 0.8

OBS5

0.782

OBS4

0.75

0.921

COM2

0.75

COM3

0.693

COM5

0.68

COM1

0.665

COM4

0.577

0.905

RA2

0.763

RA4

0.712

RA3

0.689

RA5

0.666

RA1

0.527

0.872

0.826

TRI4

0.802

TRI3

0.775

0.824

0.85

PU2

0.818

PU3

0.815

PU5

0.805

PU4

0.781

0.918

PEU2

0.869

PEU3

0.796

PEU4

0.791

PEU1

0.589

INT2

8

0.876

OBS1

PU1

6

0.919

OBS3

TRI5

5

0.865

0.938

INT1

0.92

INT4

0.917

INT3

0.903

INT5

0.885

0.949

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Table 5 Hypothesis testing results Hypothesis H1

Formulated hypothesis

Standard coefficient

p-value

Acceptance status

H1-1

Relative advantages will have a positive impact on perceived usefulness

0.414

0

Accepted

H1-2

Compatibility will have a positive impact on perceived usefulness

0.625

0

Accepted

H1-3

Complexity will have a positive impact on perceived usefulness

−0.018

0.618

Rejected

H1-4

Observability will have a positive impact on perceived usefulness

0.266

0

Accepted

H1-5

Trialability will have a positive impact on perceived usefulness

0.199

0

Accepted

H2-1

Relative advantages will have a positive impact on perceived ease of use

0.095

0.032

Accepted

H2-2

Compatibility will have a positive impact on perceived ease of use

0.198

0

Accepted

H2-3

Complexity will have a positive impact on perceived ease of use

0.649

0

Accepted

H2-4

Observability will have a positive impact on perceived ease of use

−0.003

0.939

Rejected

H2-5

Trialability will have a positive impact on perceived ease of use

0.193

0

Accepted

H3

H3

Perceived usefulness will have a positive impact on purchase intention

0.705

0

Accepted

H4

H4

Perceived ease of use will have a positive impact on purchase intention

0.38

0

Accepted

H2

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marketing strategies may benefit from further segmenting the target market. Additionally, from a product design perspective, the simplicity or complexity of features may not be a critical consideration, and emphasis should be placed on ensuring the unique value of the product.

6 Limitations and Future Research This study has certain limitations. First, despite the complex relationships among variables in the model, the study collected a relatively small sample. Considering the potential generalization errors and issues related to multicollinearity, future research should aim to expand the sample size when examining the relationships among variables for the oxygen-generating air purifier. Second, consumer information processing and purchasing decision processes may vary depending on the product involvement. Consumer responses, such as attitudes, may differ accordingly. Thus, in future research, it would be meaningful to compare consumer reactions based on the involvement level with the oxygen-generating air purifier. Third, it is predicted that respondents who participated in the survey at the initial entry stage of the oxygengenerating air purifier may have varying levels of knowledge about the product. However, this was not measured in the study. For this reason, future research should analyze data by considering respondents’ knowledge and understanding levels of product functionality, as well as their perception of product characteristics.

References 1. G. W. Kim: A Recent Technology Advance of the Oxy Generator and Its Future Trend, 6–7 (2011) 2. D. C. Moon: A Study on the Development of Oxygen Generator with IoT Functions (2022) 3. J. S. Seo: A Study on the Cleanability of Air Purifier according to Chamber Size. P3 (2021) 4. S. B. Lee: A Study on Indoors and Outdoors Oxy Concentrator Product Design (2018) 5. Everett Rogers: Diffusion of innovations (1995) 6. S. I. Kin: A Study on the Acceptance Intension for Smart Phone by the Innovation Diffusion Theory—Focused on Smart Phone Non Users (2012) 7. C. W. Park: An Empirical Study on the Effects of Personal and Systematic Characteristics on the Acceptance of Technologically Innovative Products (2012) 8. K. S. Lee, S. Y. Han, D. Y. Moon: Teachers’ Perception Affecting the Adoption and Diffusion of Blended Learning (2014) 9. E. Karahanna, D. W. Straub, N. L. Chervany: Information Technology Adoption Across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs (1999) 10. J. W. Lee: The New Policy Values for Social Service and Activating Agenda (2010) 11. C. J. Chou, K. S. Chen, Y. Y. Wang: Green Practices in the Restaurant Industry from an Innovation Adoption Perspective: Evidence from Taiwan (2012) 12. K. F. Yuen, G. Chua, X. Wang, F. Ma, K. X. Li: Understanding Public Acceptance of Autonomous Vehicles using the Theory of Planned Behaviour (2020) 13. G. J. Putzer: Are Physicians Likely to Adopt Emerging Mobile Technologies? Attitudes and Innovation Factors Affecting Smartphone Use in the Southeastern United States (2012)

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14. B. Lowe, F. Alpert: Forecasting Consumer Perception of Innovativeness (2015) 15. F. D. Davis: Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology (1989) 16. U. H. Kim, S. H. Kwon: Choice and Influential Factors for Multimedia UCC: Technology Acceptance Model (TAM, TAM2) (2010) 17. J. H. You, C. Park: Influential Factors on Chinese Consumer’s Usage Intention of Mobile Internet Service (2008) 18. J. W. Kim, H. D. Moon: A Study on the TAM (Technology Acceptance Model) in Different IT Environments (2007) 19. Y. S. Yang, C. H. Shin: Effects of Mobile Phone User Interface Technology and Social Factors on New UI Acceptance in Consumer Use Pattern: From the TAM Perspectives (2011) 20. J. Y. Jin: The Effect of SNS Market Activity Characteristics and Influencer Characteristics on Trust and Brand Attitude Towards Influencers(2022)

The Impact of Company’s ESG Activities on Corporate Reputation Jang-woo Kim, Gwang-yong Gim, Hyong-yong Lee, and Dorjtsembe Zul-Erdene

Abstract This study aims to investigate the causal relationship between ESG management and its impact on corporate reputation. To achieve this, this chapter examined the influence of each key element of ESG management, namely environmental responsibility, social responsibility, and transparency in corporate governance based on research. Additionally, prior research on setting justice and trust theories as the mediating variables was conducted. Corporate reputation can be seen as the overall image of a company, representing intangible assets accumulated over a long period and perceived by stakeholders such as shareholders, consumers, and employees. Although some hypotheses were not accepted, this study provided meaningful evidence that overall ESG management significantly impacts corporate reputation. Keywords ESG management · Justice · Trust · Corporate reputation

1 Introduction There is an increasing spread of social agreement that the value creation of a company comes from its activities in society. Without the development of the society it belongs to, a company cannot guarantee its growth or even its survival. As a result, the J. Kim Department of Grauate School of MIS, Soongsil University, Seoul, South Korea e-mail: [email protected] G. Gim (B) Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] H. Lee Department of Grauate School of ITPM, Soongsil University, Seoul, South Korea D. Zul-Erdene Department of Graduate School of B.A., International University of Ulaanbaatar, Ulaanbaatar, Mongolia © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_11

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emphasis on a company’s social responsibility and role is expanding, as the era when profit maximization was the sole purpose of a company has now passed. Understanding the ultimate goal of strengthening ESG management for companies is crucial in gauging the future direction of ESG management. According to prior research, a majority of companies identify corporate reputation as the primary objective of ESG management. Nowadays, the sustainability of a company requires meeting the needs of various stakeholders, not just buyers of products or services. Thus, the purpose of this study is to analyze how ESG management influences corporate reputation and explore the pathways through which it impacts the reputation of companies.

2 Theoretical Background 2.1 ESG Management The most fundamental and ultimate goal that companies should pursue is profit generation and maximizing profitability. However, companies cannot exist outside the realm of society, and sustainable growth cannot be achieved without considering their social responsibilities. The performance of a company consists of both financial and non-financial aspects, and ESG management has been developed as a framework to assess the non-financial performance and measure the level of social responsibility of individual companies. ESG management seeks sustainable development through long-term perspectives by incorporating environmental responsibility, social responsibility, and transparency in governance structure [1]. In ESG management, environmental responsibility refers to the implementation of eco-friendly practices during the production and use of products and services. It involves developing plans to minimize greenhouse gas emissions and reduce waste disposal, along with regularly assessing the impact of corporate operations on the environment. Social responsibility involves recognizing workers’ rights, protecting their human rights, and evaluating the company’s policies and institutional arrangements for safeguarding labor rights. It also focuses on taking responsibility for products and contributing to the local community as part of the company’s business strategy. Additionally, the protection of customer information has recently emerged as an important aspect of corporate social responsibility. Governance transparency considers shareholder-oriented management as a crucial value. By functioning as the highest decision-making body, the board of directors enhances transparency in corporate management. It grants independent authority and roles to internal and external auditors, thereby encouraging effective governance by executives.

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2.2 Justice Theory The concept of justice has evolved and solidified through various theories, including distributive justice, procedural justice, and interactional justice. Distributive justice, grounded in social exchange theory, recognizes the fair allocation of rewards and costs as a crucial concept. It involves comparing one’s own ratio of obtained rewards to the efforts invested for the organization with the ratio of rewards of others. This comparison leads to a perception of justice [2]. Procedural justice focuses on procedures and methods, emphasizing the importance of the execution process in achieving the ultimate goal. It is defined as the extent to which organizational policies, procedures, guidelines, and criteria are applied fairly by decision-makers [3]. Rational decision-making by organizational members involves the participation of the entire membership rather than decision-making by a few individuals. Interactional justice refers to decision-making based on interpersonal communication. It represents the qualitative level of communication exchanged between organizational members and the organization, determining the perception of organizational justice among members [4]. Interactional justice can be divided into interpersonal justice and informational justice based on its characteristics [5]. Interpersonal justice refers to how the organization treats individuals in a favorable manner during the execution of procedures and decision-making processes [6]. Informational justice refers to the perception of justice among members when sufficient information necessary for decision-making is timely communicated to them [7]. The theory of multiple justice relates to justice within organizations, and in the context of this chapter on the impact of ESG management on corporate reputation, this chapter aims to apply justice to both companies and stakeholders. Furthermore, considering the relevance of ESG management activities, this chapter intends to utilize procedural justice and informational justice as mediators of justice.

2.3 Trust Theory Trust is defined as the belief in the counterpart’s words or promises being trustworthy and the confidence that they will fulfill their obligations and duties in an exchange relationship [8]. It can be characterized as a psychological state where one anticipates that the counterpart’s words and actions will have positive intentions, and expects the counterpart to honestly fulfill their promises within the trading relationship [9]. Trust can be categorized into benevolence trust and expert-based trust. Benevolence trust is rooted in goodwill and the belief that the counterpart will act in a way that benefits them, without self-centered motives. Expert-based trust is based on the counterpart’s expertise, skills, and abilities [10].

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2.4 Corporate Reputation Corporate reputation refers to the overall evaluation of a company’s activities that have been formed over a long period of time through the collective perception of stakeholders. It represents a subjective, comprehensive assessment of a company’s trustworthiness and integrity based on its past managerial activities and behaviors [11]. Thus, corporate reputation can be seen as a distinctive intangible asset of a company, which is accumulated and institutionalized over a long period of time, representing its prominent image rather than its temporary business activities.

3 Research Model and Hypothesis Setting 3.1 Research Model This study aims to analyze the influence of key elements of ESG management, namely environmental responsibility, social responsibility and transparent governance on justice and trust as mediators of corporate reputation. Justice, as a mediator, is divided into procedural justice and informational justice, while trust is divided into benevolence trust and expert-based trust.

3.2 Hypothesis Setting To study the research topic on the impact of ESG management on corporate reputation, 16 hypotheses have been formulated based on the research model shown in Fig. 1. The hypotheses are set as outlined in Table 1.

3.3 Operational Definition of Variables Environmental responsibility refers to the management practices implemented to consider environmental factors in the production and use processes of products and services. Social responsibility involves protecting the rights of workers and consumers, as well as engaging in social contribution activities for local communities and marginalized groups. Transparent governance structures entail protecting shareholders’ rights, ensuring the proper functioning of the board of directors, and appointing independent external audit firms with expertise and independence to ensure transparent disclosure of corporate management information [1]. Procedural

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Fig. 1 Research model

Table 1 Hypothesis setting H1-1

Environmental responsibility will have a positive (+) impact on procedural justice

H1-2

Environmental responsibility will have a positive (+) impact on informational justice

H1-3

Environmental responsibility will have a positive (+) impact on benevolence trust

H1-4

Environmental responsibility will have a positive (+) impact on expert-based trust

H2-1

Social responsibility will have a positive (+) impact on procedural justice

H2-2

Social responsibility will have a positive (+) impact on informational justice

H2-3

Social responsibility will have a positive (+) impact on benevolence trust

H2-4

Social responsibility will have a positive (+) impact on expert-based trust

H3-1

Transparent governance will have a positive (+) impact on procedural justice

H3-2

Transparent governance will have a positive (+) impact on informational justice

H3-3

Transparent governance will have a positive (+) impact on benevolence trust

H3-4

Transparent governance will have a positive (+) impact on expert-based trust

H4

Procedural justice will have a positive (+) impact on corporate reputation

H5

Informational justice will have a positive (+) impact on corporate reputation

H6

Benevolence trust will have a positive (+) impact on corporate reputation

H7

Expert-based trust will have a positive (+) impact on corporate reputation

justice refers to the consistent and coherent decision-making processes and problemsolving methods used in key decisions. Informational justice refers to honest and reasonable communication with stakeholders, conducted in a timely and appropriate manner [4, 5].

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Table 2 Characteristics of the sample Frequencies (number of people)

Categories Gender

Age

ESG Awareness

Female

128

Percentage(%) 43.2

Male

168

56.8

Total

296

100.0

20s

28

9.5

30s

104

35.1

40s

98

33.1

50s or above

66

22.3

Not Familiar

33

11.1

190

64.2

Familiar

54

18.2

Very Familiar

19

6.4

Somewhat Familiar

Benevolence trust reflects the likability based on the extent to which a company demonstrates sincerity and fulfills its promises regarding social demands. Expertbased trust is the image of trust based on the company’s excellent technical expertise and professionalism [10]. Corporate reputation encompasses overall likability that reflects not only the company’s business performance but also its management philosophy and vision [11].

4 Empirical Analysis 4.1 Basic Descriptive Statistics Analysis For this study, a survey questionnaire was developed to collect the necessary data for analysis, using a 7-point Likert scale. The survey targeted individuals aged 20 and above who were working professionals relevant to the research topic. Among the 300 collected responses through online surveys, 4 incomplete responses were excluded, resulting in a final sample size of 296 for analysis. SPSS 22 and SmartPLS 3.0 were used as statistical analysis tools to assess the model fit and test the hypotheses. Frequency analysis was conducted to examine the demographic characteristics of the survey respondents, and the results are shown in Table 2.

4.2 Confirmatory Factor Analysis The measurement model was analyzed using SmartPLS 3.0 to assess internal consistency reliability, convergent validity, and discriminant validity. Internal consistency

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Table 3 The evaluation results of internal consistency reliability Variables

Cronbach’s Alpha

Rho-A

CR

Environmental Responsibility

0.904

0.906

0.926

Social Responsibility

0.890

0.890

0.916

Transparent Governance

0.907

0.908

0.928

Procedural Justice

0.908

0.908

0.929

Informational Justice

0.929

0.929

0.946

Benevolence Trust

0.909

0.909

0.932

Expert-based Trust

0.927

0.929

0.945

Corporate Reputation

0.876

0.881

0.910

reliability measures the reliability of multiple measurement variables used to measure latent variables, and is evaluated as desirable if Cronbach’s Alpha, rho_A, and CR (Composite Reliability) are all above 0.7. The results are shown in Table 3. Convergent validity, which evaluates the consistency among different measurement methods used to measure the same latent variable, is assessed based on external loadings, measurement reliability, and Average Variance Extracted (AVE). For desirable convergent validity, the following thresholds are considered: external loadings should be above 0.7, measurement reliability should be above 0.5, and AVE should be above 0.5. The results are shown in Table 4. Discriminant validity is a method to assess whether variables are mutually independent and well-constructed, measuring the extent to which one variable is different from other variables. To verify this, this chapter conducted an analysis using SmartPLS 3.0 based on Fornell and Larcker’s approach. By comparing the square root of the Average Variance Extracted (AVE) for each latent variable with the correlations between latent variables, this chapter can determine if the square root of AVE for each latent variable is greater than the highest correlation among latent variables. Only then can this chapter conclude the presence of discriminant validity. The results are shown in Table 5.

4.3 Hypothesis Testing This chapter tested the hypotheses regarding the impact of ESG management as mediating variables on justice and trust. This chapter also examined the hypotheses regarding the influence of justice and trust as mediating variables on the dependent variable, corporate reputation. The results are shown in Table 6.

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Table 4 Summary of the evaluation results for convergent validity Variables

Metrics

External loadings

Measurement reliability

AVE

Environmental responsibility

Activities1 Activities2 Activities3 Activities4 Activities5 Activities6

0.854 0.850 0.827 0.806 0.815 0.776

0.729 0.723 0.684 0.650 0.664 0.602

0.675

Social responsibility

Activities 1 Activities 2 Activities 3 Activities 4 Activities 5 Activities 6

0.805 0.812 0.806 0.797 0.825 0.773

0.648 0.659 0.650 0.635 0.681 0.598

0.645

Transparent governance

Structures1 Structures2 Structures3 Structures4 Structures5 Structures6

0.829 0.860 0.834 0.788 0.856 0.791

0.687 0.740 0.696 0.621 0.733 0.626

0.683

Procedural Justice

Procedural Justice1 Procedural Justice2 Procedural Justice3 Procedural Justice4 Procedural Justice5 Procedural Justice6

0.827 0.814 0.850 0.797 0.843 0.831

0.684 0.663 0.723 0.635 0.711 0.691

0.684

Informational justice

Informational Justice1 Informational Justice2 Informational Justice3 Informational Justice4 Informational Justice5

0.884 0.901 0.883 0.874 0.868

0.781 0.812 0.780 0.764 0.753

0.778

Benevolence trust

Benevolence Trust1 Benevolence Trust2 Benevolence Trust3 Benevolence Trust4 Benevolence Trust5

0.865 0.883 0.860 0.836 0.838

0.748 0.780 0.740 0.699 0.702

0.734

Expert-based trust

Expert-based Trust1 Expert-based Trust2 Expert-based Trust3 Expert-based Trust4 Expert-based Trust5

0.886 0.902 0.869 0.881 0.862

0.785 0.814 0.755 0.776 0.743

0.775

Corporate reputation

Corporate Reputation1 Corporate Reputation2 Corporate Reputation3 Corporate Reputation4 Corporate Reputation5

0.873 0.830 0.796 0.729 0.855

0.762 0.689 0.634 0.531 0.731

0.670

0.805

0.617

0.709

0.738

0.706

Informational justice

Transparent governance

Benevolence trust

Environmental responsibility

0.816

0.805

0.804

0.746

0.613

0.734

0.598

0.583

0.605

0.434

0.610

0.880

0.717

0.814

0.797

0.781

0.827

0.601

0.813

0.714

0.882

0.691

0.786

0.827

0.701

0.857 0.822

0.735

Social responsibility

Procedural justice

0.803

0.818

Corporate reputation

Expert-based trust 0.797

Corporate reputation Social responsibility Expert-based trust Procedural justice Informational justice Transparent governance Benevolence trust Environmental responsibility

Variables

Table 5 Summary of discriminant validity evaluation based on Fornell and Larcker’s criterion

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Table 6 Hypothesis Testing Hypothesis

Path t-value p-value Accepted/ coefficients Rejected

H1-1

Environmental Responsibility > Procedural Justice

0.139

2.083 0.037

Accepted

H1-2

Environmental Responsibility > Informational Justice

−0.057

0.897 0.370

Rejected

H1-3

Environmental Responsibility > Benevolence Trust

0.092

1.446 0.148

Rejected

H1-4

Environmental Responsibility > Expert-based Trust

0.263

3.065 0.002

Accepted

H2-1

Social Responsibility > Procedural Justice

0.361

3.883 0.000

Accepted

H2-2

Social Responsibility > Informational Justice

0.528

5.877 0.000

Accepted

H2-3

Social Responsibility > Benevolence-based Trust

0.422

5.919 0.000

Accepted

H2-4

Social Responsibility > Expert-based Trust

0.165

1.59

0.112

Rejected

H3-1

Transparent Governance > Procedural Justice

0.411

4.557 0.000

Accepted

H3-2

Transparent Governance > Informational Justice

0.329

3.91

0.000

Accepted

H3-3

Transparent Governance > Benevolence Trust

0.382

6.577 0.000

Accepted

H3-4

Transparent Governance > Expert-based Trust

0.291

3.552 0.000

Accepted

H4

Procedural Justice > Corporate Reputation

0.165

2.134 0.033

Accepted

H5

Informational Justice > Corporate Reputation

0.055

0.667 0.505

Rejected

H6

Benevolence Trust > Corporate Reputation

0.252

3.443 0.001

Accepted

H7

Expert-based Trust > Corporate Reputation

0.526

12.548 0.000

Accepted

5 Conclusion 5.1 Research Findings and Implications The research findings indicate that environmental responsibility has a positive impact on Procedural Justice and Expert-based Trust, while no significant effects were found on Informational Justice and Benevolence Trust. Social responsibility was found to have positive effects on Procedural Justice, Informational Justice, and Benevolence Trust, but the impact on Expert-based Trust was rejected. Transparent governance was shown to have a positive influence on Procedural Justice, Informational Justice, Benevolence Trust, and Expert-based Trust, as well as other mediating variables. As mediating variables, Procedural Justice, Benevolence Trust, and Expert-based Trust were confirmed to have a positive impact on the dependent variable, Corporate Reputation, while the influence of Informational Justice on Corporate Reputation was rejected. The implications of the study suggest that ESG management activities have an overall positive impact on procedural justice and trust, thereby establishing the mediating role of these variables in linking ESG management to Corporate Reputation.

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While establishing a causal relationship between environmental management activities and informational justice may be challenging, it is somewhat unexpected that environmental management activities do not influence benevolence trust. The finding that transparent governance has a positive influence on all mediating variables highlights its significance as the most crucial factor among ESG activities in shaping corporate reputation.

5.2 Limitations and Future Studies Although there is increasing interest in ESG management, empirical research on its impact remains insufficient. Conducting empirical studies on the influence of ESG management in a situation where the theoretical basis is not well-established posed limitations as it relied on similar previous studies related to corporate social responsibility, sustainable management, and shared value creation. A meaningful future research direction would be to analyze the impact of ESG management not only on corporate reputation but also on corporate performance or value. Conducting such analysis would provide valuable insights into the broader effects of ESG management on business outcomes.

References 1. Galbreath, J. (2013). “ESG in Focus: The Australian Evidence,” Journal of Business Ethics, 118(3), 529–541. 2. Adams, J. S. (1965). “Inequity In Social Exchange,” Advances in Experimental Social Psychology, 2(C), 267–299. 3. Alexander, S. and Ruderman, M. (1987). “The Role of Procedural and Distributive Justice in Organizational Behavior,” Society for Justice Research, 1(2), 177–198. 4. Bies, R. J. and Moag, J. S (1986). “Interactional Justice: Communication Criteria of Jutice,” In R. J. Lewicki, B. Sheppard, and M. H. Bazerman(Eds)., RESEARCH on Negotiation in Organization, Green Witch, JAI Press. 5. Colquitt, J. A. (2001). “On the Dimensionality of Organizational Justice: A Construct Validation of a Measure,” Journal of Applied Psychology, 86(3), 386–400. 6. Greenberg, J. (1990). “Organizational Justice: Yesterday, Today, and Tomorrow,” Journal of Management, 16(2), 399–432. 7. Bies, R. J. and Moag, J. S. (1986). “Interactional Justice: Communication Criteria of Fairness,” Research on Negotiation in Organizations, 1(1), 43–55. 8. Morgan, R. M. and S. D. Hunt (1994). “The Commitment-Trust Theory of Relationship Marketing,” Journal of Marketing, 58(3), 20–38. 9. Cook, J. and T. Wall (1980). “New Work Attitude Measures of Trust, Organizational Commitment and Personal Need Non-fulfilment,” Journal of Occupational Psychology, 53(1), 39–52. 10. White, T. B. (2005). “Consumer Trust and Advice Accepted; The Moderate Role Benevolence, Expertise, and Negative Emotions”, Journal of Consumer Psychology, 15(2), 141–148. 11. Fombrun, C. and C. Van Riel (1997). “The Reputational Landscape”, Corporate Reputation Review, 1–16.

The Impact of ESG Activities in Midsize Manufacturing Companies on Purchase Intentions: Focusing on the Mediating Roles of Corporate Reputation, Brand Image, and Perceived Quality Hui-ryang Eom, Hyun-a Kim, Hee-young Kim, and Gwang-yong Gim

Abstract Recently, ESG has been recognized as a core element of corporate management strategies, Gartner-ing global attention. Leading institutions worldwide, including research firm Gartner Group and the world’s largest asset management company, BlackRock, emphasize the importance of ESG. With the mandatory disclosure of ESG for Kospi-listed companies with assets exceeding 2 trillion won from 2025, government agencies and major domestic companies are actively working to solidify ESG management as a corporate survival strategy. However, the diffusion of ESG management has posed challenges for midsize and small enterprises, such as a shortage of personnel and the financial burden associated with implementation. For this reason, this study aims to investigate the relationship between the ESG activities of midsize manufacturing companies and consumer purchase intentions from a marketing strategy perspective. Specifically, this study seeks to validate the mediating effects of corporate reputation, brand image, and perceived quality in this relationship. Survey results confirmed that ESG activities in midsize manufacturing companies positively influence purchase intentions. Moreover, the study identified partial mediating effects of corporate reputation, brand image, and perceived quality in the relationship between the ESG activities of midsize manufacturing companies and consumer purchase intentions. Based on empirical analysis results, this

H. Eom (B) Department of Graduate School of MIS, Soongsil University, Seoul, Republic of Korea e-mail: [email protected] H. Kim · H. Kim Department of Graduate School of ITPM, Soongsil University, Seoul, Republic of Korea e-mail: [email protected] H. Kim e-mail: [email protected] G. Gim Department of Business Administration, Soongsil University, Seoul, Republic of Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_12

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research contributes academically to future ESG-related studies and provides practical implications for efficient ESG management in terms of a company’s potential for sustainable growth. Keywords ESG activity · Purchase intentions · Corporate reputation · Brand image · Perceived quality

1 Introduction 1.1 Research Background and Objectives Recently, Environmental, Social, and Governance (ESG) considerations have been increasingly recognized as a core element in corporate management strategies, attracting global attention. Prominent institutions such as Gartner Group, a global research firm, and BlackRock, the world’s largest asset management company, emphasize the significance of ESG. With the mandatory ESG disclosure for KOSPIlisted companies with assets exceeding 2 trillion won starting in 2025, government agencies and major domestic corporations are actively focusing on concrete ESG management as a strategy for corporate survival. However, amid the proliferation of ESG management, medium-sized and small companies face challenges due to workforce shortages and cost burdens, making it difficult to align with the trends of the times. The issue lies in the fact that the challenges faced by medium-sized and small enterprises do not exempt them from the overarching trend of ESG management. Thus, the purpose of this study is to examine the relationship between ESG activities of medium-sized manufacturing companies and consumers’ purchase intentions from a marketing strategy perspective. Additionally, the study aims to verify the mediating influence of corporate reputation, brand image, and perceived quality in this relationship.

2 Theoretical Background 2.1 ESG Management ESG management refers to activities related to environmental, social, and governance factors, which constitute non-financial performance elements of a company. As nonfinancial responsibilities of companies increase, and the need for creating a healthy corporate ecosystem grows, ESG management emerges as an essential element for sustainable management, positively affecting a company’s financial performance [1].

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According to prior research, ESG management has a positive impact on corporate performance, demonstrating that companies actively engaged in environmental and social activities with good governance structures create high corporate value. Previous ESG studies until 2021 primarily focused on the relationship between ESG management activities and financial performance from an investor perspective, predominantly within the realms of finance and accounting. However, research on the relationship between ESG management activities and marketing performance has become increasingly active since 2021 [2]. Aligned with this trend, this study explores the impact of medium-sized manufacturing companies’ ESG activities on purchase intentions, focusing on detailed factors of ESG activities. First, environmental activities involve efforts by companies to conserve resources and energy, utilize them efficiently, and minimize greenhouse gas emissions and environmental pollution in their business operations. Second, social activities refer to management practices where companies actively participate in socially responsible activities to harmonize their own development with social relationships. Lastly, governance activities entail institutional mechanisms prioritizing ethics as the utmost value, ensuring transparent, fair, and rational management practices to protect the interests of shareholders and stakeholders [3–8].

2.2 Corporate Reputation Corporate reputation refers to the overall evaluation of an organization’s activities over an extended period, representing stakeholders’ collective perception formed over time [9–11]. Companies with favorable reputations receive various benefits from consumers, ultimately leading to the establishment of brand assets and sustained consumer interest, resulting in subsequent purchasing activities [12].

2.3 Brand Image According to prior research by Jukemura (2019), companies with excellent ESG activities leverage improved brand image and reputation as a source of competitive advantage. Positive brand image is recognized as an important asset that can influence consumer perceptions related to corporate operations, as indicated by Kang and Jamies (2004) [13].

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2.4 Perceived Quality Consumers consider perceived quality as a crucial criterion for product purchases. Companies practicing CSR are believed to act responsibly socially, and, consequently, consumers perceive their products as trustworthy and of good quality [14]. Prior research has shown a positive relationship between perceived CSR and perceived quality [15, 16].

2.5 Purchase Intention Purchase intention refers to the consumer’s willingness or intention to purchase a brand’s product or service, manifested through individual subjective states that result from satisfaction with the brand, company, or its offerings, reflecting personal beliefs and attitudes in actions [17]. There are theories and prior studies proposing that a company’s ESG management can influence consumer attitudes toward the company. Bhattacharya et al. (1995) suggested that the extent to which consumers identify with a particular company positively affects their ongoing purchasing behavior and intention to repurchase the company’s products and services [13].

3 Research Model and Hypothesis Setting 3.1 Research Model This study is based on previous research on corporate ESG management, aiming to investigate the impact of mid-sized manufacturing companies’ ESG activities (environmental, social, governance) on purchase intention. The study further explores the mediating effects of corporate reputation, brand image, and perceived quality in this relationship. The research model is shown in Fig. 1.

3.2 Hypothesis Setting The research topic is the impact of ESG activities of mid-sized manufacturing companies on purchase intentions. Based on the research model shown in Fig. 1, 12 hypotheses have been formulated. The hypotheses are shown in Table 1.

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Fig. 1 Research model Table 1 Hypothesis setting H1

Environmental activities of mid-sized manufacturing companies will positively influence corporate reputation

H2

Social activities of mid-sized manufacturing companies will positively influence corporate reputation

H3

Governance activities of mid-sized manufacturing companies will positively influence corporate reputation

H4

Environmental activities of mid-sized manufacturing companies will positively influence brand image

H5

Social activities of mid-sized manufacturing companies will positively influence brand image

H6

Governance activities of mid-sized manufacturing companies will positively influence brand image

H7

Environmental activities of mid-sized manufacturing companies will positively influence perceived quality

H8

Social activities of mid-sized manufacturing companies will positively influence perceived quality

H9

Governance activities of mid-sized manufacturing companies will positively influence perceived quality

H10

Corporate reputation will positively influence purchase intentions

H11

Brand image will positively influence purchase intentions

H12

Perceived quality will positively influence purchase intentions

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Table 2 Operational definition of variables and measurement items Variable

Category

Operational definition

Independent variable

Perception of environment

Perception of the environment includes Galbreath (2013) the establishment of plans for 고희경 (2021) environmental protection, implementation measures, execution of education programs, development of environmentally friendly products and technologies, and the conduct of campaigns

References

Independent variable

Perception of society

Perception of society involves corporate social contribution activities, protection of workers’ rights, consumer rights protection, and policies related to fair trade

Galbreath (2013) 고희경 (2021)

Independent Variable

Perception of governance

Perception of governance encompasses transparency, fairness, the integrity of management activities related to the board of directors and shareholders

Galbreath (2013) 고희경 (2021) 최왕근 (2023)

Mediating variable

Corporate reputation

Corporate reputation involves the perceived growth potential, reliability, and investment value of the company

곽동현,류기상 (2014) 김정희 (2015)

Mediating variable

Brand image

Brand image refers to the holistic perception that consumers have of the company’s brand

고희경 (2021)

Mediating variable

Perceived quality

Perceived quality refers to consumers’ overall perception of the superiority of the company’s product quality

고희경 (2021)

Dependent variable

Purchase intention

Purchase intention is the intent of consumers to purchase the company’s products due to its ESG activities

Shin et al. (2017) 고희경 (2021)

3.3 Operational Definition of Variables See Table 2.

4 Empirical Analysis 4.1 Data Collection and Analysis For the empirical analysis, this study selected one preferred mid-sized manufacturing company among easily accessible products for consumers: Nongshim, Pizone, Dongseo Food, Daesang Corporation, and Yuhan-Kimberly. The survey was designed

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for self-assessment, where respondents could easily access and respond to the questionnaire. Each survey item used a Likert 7-point scale, and data from 300 valid respondents were utilized for the final analysis. SPSS 22 was employed as the statistical analysis tool to assess the adequacy of the research model and test the hypotheses.

4.2 Exploratory Factor Analysis and Reliability Analysis To verify the validity of the variables, exploratory factor analysis was conducted. Principal component analysis was used as the factor analysis method, and factors were extracted using the Varimax orthogonal rotation method. Factors with a factor loading of 0.5 or higher were considered significant. For reliability testing, a reliability analysis was performed using Cronbach’s Alpha for internal consistency. If Cronbach’s Alpha value was 0.6 or higher, it was deemed reliable. The results of the exploratory factor analysis and reliability analysis are presented in Table 3. Table 3 Exploratory factor analysis and reliability analysis Factor

Measurement items

1

Environmental activity

ENV1 ENV2 ENV3 ENV4 ENV5 ENV6

0.703 0.705 0.747 0.774 0.693 0.781

Social activity

SOC1 SOC4 SOC5 SOC6

Governance activity

GOV3 GOV4 GOV5

Corporate reputation

CR1 CR2 CR3 CR4 CR5

Brand image

BI1 BI3

Perceived quality

PQ1 PQ2 PQ3 PQ4 PQ5

2

3

4

5

6

Cronbach’s Alpha 0.915

0.645 0.578 0.634 0.725

0.855

0.785 0.803 0.730

0.896

0.782 0.733 0.692 0.714 0.819

0.903

0.872 0.644

0.682 0.761 0.762 0.692 0.774 0.641

0.904

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4.3 Hypothesis Testing The hypotheses regarding the impact of mid-sized manufacturing companies’ ESG activities (environmental activities, social activities, governance activities) on mediating variables such as corporate reputation, brand image, and perceived quality were tested. Additionally, the hypotheses regarding the impact of mediating variables (corporate reputation, brand image, perceived quality) on the dependent variable, purchase intention, were also tested. The results are presented in Table 4. As shown in Table 4, Hypotheses H4 and H5, which suggest that the environmental activities and social activities of mid-sized manufacturing companies will positively influence brand image, were rejected as the p-values were greater than 0.05. All other research hypotheses were accepted.

5 Conclusion 5.1 Research Results and Implications This study aimed to analyze the impact of ESG activities, recognized as a core value of global corporate management strategies, on consumer purchase intentions through mediating variables such as corporate reputation, brand image, and perceived quality. The goal was to contribute to a comprehensive understanding of ESG management practices (Environmental, Social Responsibility, Governance) from the perspective of a company’s potential for sustainable growth. Additionally, the study sought to identify factors that positively influence consumer purchase intentions and provide practical implications for marketing activities and academic insights for future ESGrelated research. The key findings of the research can be summarized as follows: First, it was confirmed that ESG activities of mid-sized manufacturing companies have a positive impact on consumer purchase intentions. This aligns with similar findings in numerous prior studies [18]. Despite facing challenges such as manpower shortages and cost burdens in implementing ESG activities, the positive influence on consumer purchase intentions suggests that mid-sized manufacturing companies should actively engage in ESG activities as part of their overall corporate strategy. Second, it was evident that consumers’ positive perceptions of mid-sized manufacturing companies’ ESG activities have a significant positive impact on corporate reputation, brand image, and perceived quality. This aligns with the findings of research on the impact of a company’s ESG activities on purchase intentions [13]. Third, two hypotheses, H4 and H5, were rejected in this study. In other words, it was found that the environmental and social activities of mid-sized manufacturing companies do not have a significant impact on brand image. Similar results were obtained in prior research on CSR activities by Lee and Shin [19].

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Table 4 Hypothesis testing Hypothesis

Standard Coefficient

t-value

p-value

Acceptance

β Value H1

Mid-sized manufacturing companies’ environmental activities -> Corporate Reputation

0.045

6.315

0.000

Accepted

H2

Mid-sized manufacturing companies’ social activities -> Corporate Reputation

0.045

7.523

0.000

Accepted

H3

Mid-sized manufacturing companies’ governance activities -> Corporate Reputation

0.045

9.673

0.000

Accepted

H4

Mid-sized manufacturing companies’ environmental activities -> Brand Image

0.035

0.619

0.537

Rejected

H5

Mid-sized manufacturing companies’ social activities -> Brand Image

0.049

0.870

0.385

Rejected

H6

Mid-sized manufacturing companies’ governance activities -> Brand Image

0.214

3.783

0.000

Accepted

H7

Mid-sized manufacturing companies’ environmental activities -> Perceived Quality

0.302

5.732

0.000

Accepted

(continued)

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Table 4 (continued) Hypothesis

Standard Coefficient

t-value

p-value

Acceptance

β Value H8

Mid-sized manufacturing companies’ social activities -> Perceived Quality

0.256

4.867

0.000

Accepted

H9

Mid-sized manufacturing companies’ governance activities -> Perceived Quality

0.153

2.899

0.004

Accepted

H10

Corporate Reputation -> Purchase Intention

0.469

14.699

0.000

Accepted

H11

Brand Image -> Purchase Intention

0.578

11.882

0.000

Accepted

H12

Perceived Quality -> Purchase Intention

0.379

18.113

0.000

Accepted

Fourth, the most influential mediating factor on purchase intentions was perceived quality, and it was found that perceived quality has the most significant impact on the environmental activities of mid-sized manufacturing companies. Thus, manufacturing companies still need to recognize the importance of quality and pay particular attention to manufacturing quality. In the realm of ESG management activities, a focus on environmental initiatives such as the production of environmentally friendly products and activities to reduce carbon emissions is deemed crucial. The implications are as follows. Based on the empirical analysis results, this study contributes to future research on ESG, providing academic significance. Additionally, practical implications have been outlined for the effective ESG management of midsized manufacturing companies in terms of their potential for sustainable growth.

5.2 Limitations and Future Research Despite presenting meaningful research results in the context of ESG management, this study indicated that the environmental and social activities, among the components of ESG, do not have a significant impact on brand image. Considering that environmental and social activities have been long-standing activities primarily undertaken by large corporations at the government level, further analyses are needed for a

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more comprehensive understanding in the long term. This may include longitudinal studies and qualitative research focusing on a limited subset of target consumers. As this study focused on mid-sized manufacturing companies, future research should consider expanding the scope to include small and medium-sized enterprises (SMEs).

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A Study on the Intention to Utilize Overseas Developers Through Offshoring Approach and Strategy for Economic Cooperation Between South and North Korea Using the Value-Acceptance Model (VAM)—Based on the Case Study of the Kaesong Industrial Complex Insun Kang

Abstract If economic cooperation between North and South Korea (inter-Korean cooperation) progresses, South Korean company will have access to broader business areas and regional economic space, providing greater economic opportunities. However, long-term vision and meticulous planning based on considering the international community’s sanctions on North Korea, protecting investment assets, resolving disputes, and addressing the three barriers of passage, communication, and customs clearance are all crucial for successful investment in North Korea. To achieve this, it is essential for the government and private enterprises to cooperate organically and make multifaceted efforts to transition the inter-Korean relationship from tension to cooperation and induce changes in the market environment by improving the investment environment in North Korea. Next, a step-by-step approach that selects suitable industries in North Korea and gradually expands them will be necessary to solidify the substance of inter-Korean cooperation. Additionally, addressing the issue of overlapping investments and constructing a long-term division of labor structure between North and South Korea are also important tasks. Lastly, proactive institutional improvements that enable North Korea to support business operations and attract investment are crucial. Such proactive measures will promote the opening of the North Korean economy, enhance international connectivity, and facilitate attracting foreign capital and investment. Through these efforts, North and South Korea will open a path to further strengthen cooperation and exchange through economic cooperation. Keywords Kaesong industrial complex · Economic cooperation · Interchange · Development · Epigyny I. Kang (B) Korea Hana Foundation, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_13

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1 Introduction If economic cooperation between North and South Korea (inter-Korean cooperation) progresses, South Korean company will secure a broader business scope and regional economic space, providing greater economic opportunities. However, considering the international community’s sanctions on North Korea, protecting investment assets, resolving disputes, and addressing the three barriers of passage, communication, and customs clearance, it is essential to base the investment in North Korea on long-term vision and meticulous planning. The Kaesong Industrial Complex project began on August 22, 2000, with the signing of the “Agreement on the Construction and Operation of the Kaesong Industrial Zone” between Hyundai Asan from South Korea and the Asia–Pacific Peace Committee (APPC) from the North [1]. Both parties agreed to develop a total area of 65.7 km2 (20 million pyeong), including a factory zone of 26 km2 (8 million pyeong) and development of 40 km2 (12 million pyeong) for residential, tourism, and commercial purposes. The initial phase involved developing 3.3 km2 (1 million pyeong). The Kaesong Industrial Complex experienced quantitative and qualitative growth over the years. However, in January 2016, following North Korea’s fourth nuclear test, the South Korean government unilaterally suspended operations, and the complex has been inactive for the past seven years. Since the adoption of the Kaesong Industrial Complex Development Agreement in 2002, the complex symbolized interKorean cooperation, with more than 55,000 personnel from both North and South Korea engaged in joint production activities. It served as an economic model of mutual prosperity and held symbolic significance for inter-Korean reconciliation. However, due to North Korea’s fourth nuclear test and long-range missile launches, on February 10, 2016, the South Korean government completely halted operations at the Kaesong Industrial Complex through independent sanctions [2]. Thus, this study aims to examine the progress and issues of the Kaesong Industrial Complex project and explore investment approaches and strategic options for substantial activation of inter-Korean cooperation. Based on this analysis, the main objective of this research is to develop ways to enhance the feasibility of North Korea’s acceptance and obtain international consensus to promote active inter-Korean cooperation.

2 Lessons from the Case of the Kaesong Industrial Complex 2.1 Overview of the Kaesong Industrial Complex Project The Kaesong Industrial Complex project began in October 1999 when Hyundai Asan officially proposed the development of a coastal industrial complex to the North Korean side. At that time, locations such as Haeju and Sinuiju were mentioned.

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However, in June 2000, Chairman Kim Jong-il unexpectedly proposed the Kaesong region as a candidate site for the industrial complex, leading to a rapid advancement of the project. Subsequently, Hyundai Asan from the South, the Asia–Pacific Peace Committee, and the National Economic Cooperation Federation from the North concluded the “Agreement on the Construction and Operation of the Kaesong Industrial Zone.” On December 23, 2002, North Korea issued a land-use certificate to Hyundai Asan, guaranteeing the use of the Kaesong Industrial Zone for 50 years, marking the substantial commencement of the project. In January 2004, an agreement was signed that included provisions on the security and safety of access and stay in the Kaesong Industrial Zone and Mount Kumgang Tourist Zone. In the October 4, 2007, North–South Joint Declaration, it was agreed to complete the first phase construction of the Kaesong Industrial Zone at an early date, initiate the second phase development, start rail freight transportation between Munsan and Bongdong, and promptly establish various institutional measures, including passage, communication, and customs clearance. Subsequently, in the inter-Korean Prime Minister’s Meeting held to fulfill the provisions of the Joint Declaration, it was agreed to conduct surveys and geological investigations necessary for the second phase development in December 2007 and commence the construction of the second phase within 2008. Agreements were also reached on the construction of accommodations for workers, road construction for their commute, discussions on train operations, and the commencement of rail freight transportation between Munsan and Bongdong from December 11, 2007. After the production of the first product in the pilot site in December 2004, there was a growing interest among domestic small and medium-sized enterprises (SMEs) to establish their presence in the Kaesong Industrial Complex. In response, the official sale of plots in the main complex was initiated. In January 2005, the Ministry of Unification finalized the sale plan for the Kaesong Industrial Complex main complex through consultations with relevant ministries, the evaluation committee for sales, and other entities. The sale criteria, methods, and plans were established. Initially, the focus was on the textile, apparel, leather goods, bags, and footwear industries, which had the highest demand for entry into the Kaesong Industrial Complex. A total area of 169,000 square meters was designated for priority sale. The Ministry of Unification organized investment explanation sessions nationwide to provide investment information and promote the Kaesong Industrial Complex [3]. Furthermore, in the first half of 2008 It was agreed to commence the operation of commuter trains for Kaesong Industrial Complex workers within 2008. However, as relations between North and South Korea rapidly deteriorated after the inauguration of the Lee Myung-bak administration, with North Korea demanding the abandonment of the “denuclearization, openness, 3000” initiative, the related agreements could not be implemented [4]. During the planning phase of the Kaesong Industrial Complex, a total area of 20 million pyeong (approximately 6.6 million m2 ) was scheduled for development in three stages. The breakdown of the areas is as follows: 6 million pyeong for factory zones, 1 million pyeong for residential areas, 1.5 million pyeong for tourism areas, 500,000 pyeong for commercial areas, and an additional 11 million pyeong

158 Table 1 Scale of Kaesong Industrial Complex development [5]

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Area

Size

Factory area

6 million pyeong (19.7 km2 )

Residential area

1 million pyeong (3.3 km2 )

Tourism area

1.5 million pyeong (4.9 km2 )

Commercial area

500,000 pyeong (1.7 km2 )

Kaesong city

4 million pyeong (13.1 km2 )

Expansion area

7 million pyeong (23 km2 )

Total

20 million pyeong (65.7 km2 )

for expansion, including the city of Kaesong. The following table illustrates the development details for each zone of the Kaesong Industrial Complex. Table 1 is scale of Kaesong Industrial Complex Development zone [5]. However, in February 2013, ahead of the inauguration of the Park Geun-hye administration, North Korea conducted its third nuclear test. Maintaining a hardline stance towards the South, North Korea suddenly announced the withdrawal of all North Korean workers and the temporary suspension of the Kaesong Industrial Complex on April 8. Subsequently, the two Koreas held seven rounds of workinglevel talks regarding the Kaesong Industrial Complex and adopted the “Agreement on the Normalization of the Kaesong Industrial Complex” in April 2013. Despite the resumption of the Kaesong Industrial Complex, North Korea continued its provocations. In January 2016, they conducted their fourth nuclear test, followed by a long-range missile launch on February 7. In response, the Korean government announced the complete suspension of operations at the Kaesong Industrial Complex on February 10.

2.2 Status of the Operation of the Kaesong Industrial Complex From 2005 to 2013, during the operation of the Kaesong Industrial Complex, the production value showed significant growth. It started at $14.91 million in 2005 and increased to $184.78 million in 2007. In 2015, the highest production value was achieved at $563.3 million, which is approximately 37 times the initial production value in 2005. As the Kaesong Industrial Complex expanded, the number of North Korean workers also increased steadily. Starting with 6,013 workers in 2005, the number nearly doubled the following year and reached 56,330 workers in 2015, the year with the highest production value. The number of North Korean workers increased 37 times compared to the initial phase in 2005. The following graph illustrates the trend of production value and the number of North Korean workers in the Kaesong Industrial Complex from its inception in 2005 to the complete suspension in 2015. This Statistic can find at Fig. 1.

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49866

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54988 53448 52329 53947

46284 42561 38931

56330 46997

46950

22538

40185 32332

11160 6013 1491

7373

2005

2006

25142 25468

23378

18478

2007

2008

2009

Production Value(100,000$)

2010

2011

2012

2013

2014

2015

Number of North Korean Workers

Fig. 1 Production value and number of North Korean workers in the Kaesong Industrial Complex [6]

Next, looking at the industry composition based on the production value of the Kaesong Industrial Complex, the textile and apparel sector accounted for the highest proportion at 72%, followed by machinery and metal at 23%. Additionally, the electrical and electronic sector accounted for 13%, while the chemical sector accounted for 9%. These proportions remained relatively stable throughout the operation of the Kaesong Industrial Complex until its closure in 2015, despite variations in the overall production value. As depicted in Fig. 2, the majority of products produced in the Kaesong Industrial Complex were labor-intensive and represented industries classified as low-technology sectors. This percent find at Fig. 2. The operation of the Kaesong Industrial Complex, which lasted for 16 years from 2001 to the complete suspension in 2016, was not only significant in terms of economic aspects but also held symbolic meaning for inter-Korean reconciliation. However, due to North Korea’s fourth nuclear test and missile launches, the South Korean government independently imposed sanctions on February 10, 2016, leading to the complete suspension of the Kaesong Industrial Complex. Compared to the initial development plans, the current development area is only 5%, the number of companies is 6%, and the employment workforce is around 15%. To resume development according to the original plans, several considerations need to be taken into account. While the completion of the first stage, covering 1 million pyeong (3.3 km2 ), was achieved in 2007, the actual number of participating companies reached only about 40% (125 companies) of the planned 300. The development of the second

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Food, 2%

Paper-Wood, 3%

Electricity, 13%

Machinery-Metal, 23% Fiber-Clothes, 72

Fig. 2 2013 years industrial production composition in the Kaesong Industrial Complex [7]

stage (1.5 million pyeong) and third stage (3.5 million pyeong) of the complex, agreed upon by both North and South Korea, has yet to commence[8].

2.3 Challenges in the Operation of the Kaesong Industrial Complex The operation of the Kaesong Industrial Complex, which lasted from 2001 to the complete suspension in 2016, can be regarded as an unprecedented economic cooperation project between North and South Korea, involving over 50,000 North Korean workers and more than 120 South Korean companies. By surpassing $2.6 billion in cumulative production value as of 2015, it achieved significant quantitative growth and provided new opportunities for small and medium-sized enterprises in South Korea that were facing business limits. Moreover, the Kaesong Industrial Complex provided an opportunity for North Korea to experience cooperation based on investment and understand the importance of supportive laws and institutions. In addition, it contributed to political tension reduction and peacebuilding, positioning the complex as a symbolic model of inter-Korean cooperation. However, the complex was completely suspended in 2016, amidst political conflicts between North and South Korea. Furthermore, following North Korea’s fifth nuclear test in 2017, UN economic sanctions and independent sanctions imposed by South Korea further diminished the prospects of resuming the complex. Considering the current international situation, the future possibility of resuming operations remains uncertain. Despite this, it is necessary to establish economic principles to secure the sustainability and potential for development of the complex in preparation for its potential resumption. Multiple academic analyses related to the Kaesong Industrial Complex reveal that the participating small and medium-sized enterprises were generally low-profit businesses that had reached a management threshold within South Korea. While

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these businesses were able to enhance productivity and profitability through their involvement in the complex, this limited focus on struggling enterprises alone cannot serve as the long-term vision for inter-Korean economic cooperation. Although the profitability improvement of the participating enterprises may have been significant, in terms of the overall South Korean economy, the economic impact remains minimal, making it difficult to consider it in line with the goal of inter-Korean cooperation for mutual prosperity. Therefore, South Korea should strengthen the principle of economic separation, while North Korea should prioritize expanded openness. South Korea initially emphasized the principle of economic separation during the early stages of economic cooperation. However, in reality, it became challenging for the economic sector to operate independently, and North Korean institutions participating in inter-Korean cooperation were largely controlled by the ruling party, government agencies, and state-owned enterprises, making some degree of economic linkage inevitable. Throughout the process of economic cooperation, North Korea placed importance on blocking and controlling potential ripple effects on its society [9].

3 Approaches and Form of Economic Cooperation Between South and North Korea 3.1 Strategic Framework for North Korea-Related Projects The Moon Jae-in administration defined peace as the “foremost value and foundation for prosperity” and made efforts to realize a “peace economy” on the Korean Peninsula. President Moon’s “Peace Economy Theory” was established as a key objective for the “New Economic Map of the Korean Peninsula,” and it has been consistently emphasized since the inauguration of the government. The government recognized existing inter-Korean economic cooperation projects such as the Kaesong Industrial Complex and Mount Kumgang tourism as precedents for the peace economy and pursued support measures for inter-Korean economic cooperation, including assistance for affected companies and negotiations between the authorities of both sides [10]. In general, it is expected that South Korean capital should have autonomy in decision-making for North Korea-related projects and that the North Korean regime should create an environment conducive to the activities of investing companies. However, as observed from past experiences with the Kaesong Industrial Complex and Mount Kumgang tourism, the autonomy of South Korean capital is severely limited in practice. Therefore, it is essential to thoroughly understand North Korea’s economic policies and develop business strategies that align with them. Additionally, there is a tendency to anticipate future markets based on the gradual improvement of North Korea’s market environment. However, predicting the speed of market

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improvement is difficult, so it is advisable to determine appropriate business models and scales based on the current market environment. Lastly, investment should be based on profitability. The government and companies should thoroughly examine various internal institutions, customs, and external environments of North Korea before making investment decisions. Positive investment should be pursued only when profitability analysis, considering these factors, yields favorable results. Approaching the North Korean market with vague expectations that the market environment will improve over time may lead to wasting more time for North Korea to grasp market economy principles. For example, paying costs to the North Korean side for various cultural and tourism projects can be relatively easy to acquire compared to funds obtained through manufacturing activities. Consequently, North Korea naturally prefers such projects, while internal market environment improvements may not be given high priority in policy [11].

3.2 Possible Approaches for Private Sector Inter-Korean Cooperation In the area of trade, it is crucial for South Korean companies to engage in close information exchange with North Korean trading companies. They should develop products that meet international standards and establish various safety measures for quality inspections before and after production, as well as ensuring timely delivery. North Korean trading companies possess significant know-how in trading with socialist countries such as Russia, Eastern Europe, and China. Given North Korea’s cautious approach to direct imports from the South due to concerns about ideological contamination and regime stability, actively utilizing trilateral trade arrangements where South Korean products are exported to the North and then re-exported to a third country can be a viable option. Establishing a joint trading company between the two Koreas, with the South responsible for Western markets and the North responsible for socialist markets, could also be an effective approach. In the field of contract manufacturing, it is currently the most realistic form of interKorean economic cooperation. This involves South Korean companies outsourcing production to North Korean counterparts. Since 1992, this type of cooperation has primarily consisted of providing North Korea with raw materials, production technology, and equipment, while importing finished products. Contract manufacturing is often perceived as a form of investment since South Korean companies provide production technology and equipment to the North. However, in practice, it should be viewed as a trade arrangement, as most cases involve selling production equipment to North Korea through long-term installment plans. The South Korean companies have no involvement in the management or operation of North Korean factories, making it difficult to consider it as a true investment. Lastly, in terms of direct investment, according to North Korea’s foreign investment regulations, South Korean companies are allowed to directly invest in North

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Korea. Under the Joint Ventures Law in North Korea, there are three main forms of direct investment: joint ventures (co-investment and co-management by foreign and North Korean parties), cooperative ventures (co-investment with North Korean operation, where the North Korean side manages the venture), and wholly foreignowned ventures (sole investment and operation, limited to special economic zones). However, it is important to note that the investment environment in North Korea is extremely challenging. North Korea is considered one of the riskiest regions for investment globally, with foreign capital holding a highly negative perception of the North Korean market. Furthermore, domestic companies in South Korea are also directly impacted by foreign capital, leading to potential declines in their stock prices.

3.3 Possible Forms of Cooperation at the Private Sector Collaboration between small and large enterprises appears to be the most beneficial form of cooperation at the private sector level. The subcontracting and outsourcing sector, which has been actively engaged in inter-Korean economic cooperation, relies on sourcing raw materials from South Korea and international markets and providing them to North Korea for processing. However, North Korea has expressed a desire to process the raw materials domestically. This indicates North Korea’s interest in moving beyond simple processing methods and exploring the potential for forward and backward linkages in industries. Therefore, collaboration between finished product manufacturers (large enterprises) and suppliers of necessary raw materials (small and medium enterprises) is expected to provide practical assistance and contribute to the progress of inter-Korean economic cooperation. Furthermore, collaboration among large enterprises is also anticipated. Currently, due to the strained inter-Korean relations and the challenging investment environment in North Korea, there is not significant competition among companies for market entry into North Korea. However, if the North Korean market shows even a slight sign of change, as observed in past overseas expansion cases, it is expected that there will be a potential for overlapping investments within the same industry. Unlike other foreign markets, the North Korean market has the potential to become a domestic market in the future, which could lead to intensified competition. Particularly, with investment attraction in North Korea being centralized under the government, active competition among companies could result in long-term market distortions such as duplication and excessive investments. Lastly, it is worth considering the formation of consortia between domestic and foreign companies to engage in inter-Korean cooperation projects. Due to political reasons, North Korea tends to exhibit a more exclusive attitude towards South Korean companies compared to Western companies. This exclusivity extends even to non-governmental organizations providing humanitarian assistance to North Korea, where domestic organizations face numerous constraints on their activities, including restrictions on visits to North Korea. Therefore, instead of insisting on independent market entry into North Korea, domestic companies could benefit from forming

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consortia with Chinese and European companies. This approach would enhance the autonomy of business operations while also diversifying investment risks associated with North Korean projects.

3.4 North Korea’s Economic Development Areas North Korea has implemented economic reform measures, albeit cautiously, in various sectors following its arduous march. These measures, known as the July 1 Economic Management Improvement Measures, include elements of marketoriented reforms such as price rationalization, responsibility-based management in production units, and recognition of the raw materials market. This signifies a reduction of the planned economy and an expansion of market-oriented activities. Granting regional enterprises, the authority to formulate their own production plans and allowing limited market activities indicate the North Korean government’s recognition of its limitations in planning capabilities. However, due to insufficient production and failures in price policies, significant achievements have been limited. The specific details of the July 1 Economic Management Improvement Measures are shown in Table 2. During the Kim Jong-un era, the policy direction emphasizing the four key sectors of electricity, coal, metal, and railway transportation, as well as agriculture and light industry, remained unchanged from the Kim Jong-il era. However, there was a noticeable shift in the formulation of industrial policies, with a consistent consideration of marketization. This marked a difference from the repetitive pattern of market acceptance and rejection observed during the Kim Jong-il era. Kim Jong-un utilized markets as a means to achieve the goals of industrial policy and introduced relatively more open external policies, indicating a degree of change. In particular, he promoted the development of economic development zones and established the “Law on Economic Development Zones” on May 29, 2013, providing institutional foundations. In the same year, 14 economic special zones and development zones were selected, and an additional 6 regions were chosen in 2014. While the lifting of economic sanctions is an essential precondition for achieving results in economic development zones, other important factors include the transparency and reliability of economic policies, the share of the manufacturing sector, and macroeconomic stability.

4 Conclusion To improve the current challenges and activate inter-Korean cooperation, it is crucial to transition from the current state of tension to a more cooperative atmosphere and induce changes in North Korea’s investment environment and market conditions. The government and private enterprises should work together in a synergistic

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Table 2 Key contents of the July 1 economic management improvement measures Measures

Key contents

Price and wage

. Approximation of market prices and state-fixed prices in agricultural markets . Monthly average wage within 2,000 won

Pricing mechanism

. Considering production costs, international market prices, domestic demand, and supply . Limited authority granted to factories for price setting, apart from central and local administrative agencies

Establishment of planning authority . “National Planning Committee” only prepares plans for nationally important projects, total industrial output by province, basic construction investment, etc. . Delegation of other specific projects to institutions, enterprise units, and local administrative agencies Management rights

. Strengthening of independent cost accounting system . Strengthening of cost concept . Abolition of various regulations by central agencies

Raw materials market

. Opening of raw materials market . Supplying a certain percentage of products to the raw materials market

Distribution mechanism

. Distribution based on “income earned” . Payment of bonuses based on profitability . Remuneration for national mobilization efforts by the state

Social security

. Payment for food, consumer goods, and housing prices

manner, focusing on multifaceted efforts. In particular, various initiatives should be undertaken to encourage North Korea to improve its market environment in line with international standards. It is evident that without improvements in North Korea’s business environment, investment may not succeed even if it materializes. Furthermore, in the current context of intensified competition between the United States and China, investment in North Korea is expected to face greater difficulties. Therefore, there are plenty of investment opportunities in other regions, even without specifically targeting North Korea. Consequently, if North Korea improves its market environment to meet international standards, South Korean enterprises can leverage their competitive advantages of shared language and geographical proximity, leading to the expansion of inter-Korean business cooperation. To solidify the substance of inter-Korean cooperation, it is necessary to adopt an approach that selects suitable industries in North Korea and gradually expands them. In the initial stages of the economic special zones, there should be a focus on labor-intensive industries to maximize employment creation and increase the income and purchasing power of North Korean workers. This can help pioneer a domestic consumer goods market in North Korea. In subsequent stages, there should be a transition to technology-intensive industries and expand their scope. Labor-intensive industries have relatively low technological requirements, allowing for the maximum

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utilization of North Korea’s labor potential. This can help enhance the skill levels and productivity of North Korean workers, leading to increased income [9]. However, for long-term growth, a transition to technology-intensive industries is also necessary. Technology-intensive industries require high levels of technology and labor productivity, necessitating efforts to educate and enhance the technical capabilities of North Korean workers. Examples of such industries include electronics manufacturing, information technology, biotechnology, and advanced manufacturing. The selection of industries to attract in North Korea should consider various factors, and this is a matter that policymakers and economic experts should carefully examine. By making appropriate industry choices and adopting a phased expansion approach, it will be beneficial to achieve long-term growth in North Korea. Lastly, North Korea should proactively demand institutional improvements that can support business operations and attract investment. Considering the recent trend of economic law revisions in North Korea, it is assessed that there is a possibility for North Korea to accept demands for institutional improvements. Such improvements in external economic systems can activate foreign investment attraction, which can further lead to synergistic effects through law and institutional improvements. Enhancing North Korea’s business-related laws can improve the business environment and instill greater confidence in investors. These proactive measures can facilitate the opening of the North Korean economy, promote international integration, attract foreign capital, and stimulate investment. Through these efforts, North and South Korea can open up new avenues to strengthen mutual cooperation and exchanges through inter-Korean economic cooperation.

References 1. Hyundai Asan. (2005). Kaesong Industrial District Development Plan. 2. Lee, H.J., Lee, Y.H. (2017). One Year Since the Kaesong Industrial Complex Was Shut Down, the Current Status and Challenges of Inter-Korean Relations. Hyundai Research Institute, Current Issues and Challenges, 17(3), 1–9. 3. Ministry of Unification Kaesong Industrial Complex Project Support Group. (2007). Kaesong Industrial Complex 5-Year Publication Committee. 4. Lim, K.T., Lee, K.W. (2017). Kaesong Industrial Complex Operation Status and Development Plan: Lessons from the 11 Years of Operation of the Kaesong Industrial Complex (2005–2015). Korea Institute for National Unification Institutional Repository, Policy Research, 16(03). 5. Song, J.J. (2011). Study on Policies for Vitalization of Gaeseong Industrial Complex. Journal of SME Policy, 2011(15), 1–184. 6. Korea Ministry of Unification. Main Business Statistics. https://unikorea.go.kr/unikorea/bus iness/statistics. 7. Na, S.K., Hong, Y.K. (2014). A Study on Ways to Strengthen the International Competitiveness of the Kaesong Industrial Complex. Korea Institutes for International Economic Policy Research, 14(5). 8. Han, K.Y. (2016). The Present and Future of the Unified Economy. Hyundai Research Institute Research Report. 9. Jo, S.T. (2020). An Analysis of Firms’ Productivity in Kaesong Industrial Complex and Inter-Korean Economic Cooperation Strategy. Gyeonggi Research Institute Research Report, 2020(07), 1–116.

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10. Lee, H.J. (2022). Review and Future Tasks for the Resumption of the Kaesong Industrial Complex. Unification & Law, Vol. 55, 88–121. 11. Jung, J.H. (2001). Industrial map of North and South Korea for a Unified Korea. The Institute for Peace Affairs, Vol. 211, 44–47.

A Study on the Intention to Use Home Network Services Seong-byeong An, Gwang-yong Gim, Yoon-je Sung, and Sae-yeon Lee

Abstract This study focuses on the factors influencing the integration of home network services in smart architecture, enabling the comprehensive management and control of internet-connected home appliances, lighting, heating, security, and more. Smart architecture provides enhanced energy efficiency, convenience, and safety due to its ability to control various aspects of the household through technological advancements. Researching the impact of home network services on smart architecture could significantly contribute to the advancement of future architectural technology and the improvement of human life quality. Home networks refer to the network within a household that enables remote control functionalities, allowing individuals to operate appliances from the living room, check who is at the door from another room, and even observe, control, and schedule home devices from outside the house. This study constructs an integrated research model for analyzing the factors influencing the adoption of home network services, applying the TAM_IS Model, an extended model based on the Technology Acceptance Model (TAM_IS Model). The research model includes home security, home automation, and home healthcare as independent variables, using utility, ease of use, and satisfaction as mediators, leading to the dependent variable, usage intention. The findings illustrate the impact of independent variables on utility and satisfaction, the influence of ease of use on satisfaction, and how utility and ease of use affect usage intention. For future studies,

S. An (B) Department of Graduate School of MIS, Soongsil University, Seoul, Korea e-mail: [email protected] G. Gim Department of Business Administration, Soongsil University, Seoul, Korea e-mail: [email protected] Y. Sung Woori Bank Co., Ltd., Seoul, Korea e-mail: [email protected] S. Lee J&S Entertainment, Seoul, Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_14

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more research should continue to explore home network services from the perspective of their convergence with Fourth Industrial Revolution technologies and home network service information systems. Keywords Home network services · TAM · IS success model

1 Introduction This study is a home network service that can manage and control home appliances, lighting, heating, and security connected to the Internet in an integrated manner. Smart architecture provides better energy efficiency, convenience, and safety due to technological advancements. This suggests that home network services contribute significantly to the development of smart architecture. The integration of various devices previously operated separately—such as high-speed internet, web Wi-Fi, information devices, home appliances, sensors, or cameras—facilitates a network within households. This allows functionalities such as operating a washing machine from the living room, confirming visitors at the entrance from another room, and controlling household devices from outside the home, enhancing convenience in daily life. Home network services primarily serve home security, aiming to safeguard residences from residential intrusions and various crimes. Home CCTV is a representative product in this category, enabling real-time monitoring of household safety through smartphone integration. Additionally, features like facial recognition door locks, double locks on entrance doors, door open sensors, portable emergency bells, and scream recognition devices are included in the home security setup. Home automation involves remotely or automatically controlling household appliances and functionalities, including lighting, HVAC systems, door locks, security cameras, wireless lighting controls, and self-power wireless switches. Home healthcare is an essential aspect affected by the COVID-19 pandemic, where exercising has been significantly impacted. To address this, rehabilitation robots, elderly-specific remote caregiver applications, detection of insomnia, malnutrition, falls, depression, and needle-less blood glucose monitoring have become prevalent. Furthermore, AI-powered lighting simulates natural sunlight within homes, improving the living environment. This study is based on the TAM_IS MODEL to construct an integrated research model that analyzes the influencing factors of home network services. The model encompasses independent variables such as home security, home automation, and home healthcare. Utility and ease of use act as mediating variables, while satisfaction is set as the dependent variable influencing usage intentions.

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2 Theoretical Background 2.1 Home Network Services 2.1.1

Home Security

Home security aims to safeguard users’ personal information and household safety, ensuring that users of home network services do not feel insecure about the system [1]. To achieve this, various security technologies are implemented within home network services. For instance, user authentication systems, data encryption technologies, and network security measures are employed to protect user information and system safety. Educating and providing information to users is necessary to raise awareness and encourage active participation in home security [2, 3]. Home automation allows users to control various home appliances, lighting, and heating within their households via smartphones, tablets, or other devices. This technology enhances user convenience and offers the potential for energy conservation [2]. Through home automation, users can control home appliances, lighting, and heating, boosting their convenience in day-to-day life. Moreover, by collecting data such as temperature and humidity, home automation systems can automatically regulate heating and air conditioning, thereby improving energy conservation [1, 3]. Home healthcare involves technologies that enable users to monitor and manage their health at home. It provides substantial assistance to users in managing their health conditions [3]. Users can conduct simple health check-ups at home and monitor their health status. Using devices such as smartwatches and weighing scales allows for real-time health checks, offering support in improving health conditions based on the gathered information [1, 2].

2.2 Information System Success Model Delone and McLean [4] classified the variables influencing information system success into system quality, information quality, information system use, user satisfaction, individual impact, and organizational impact, presenting a model to explain the causal relationships between these variables [5]. Seddon [4] proposed a modified model by incorporating perceived usefulness in the aspect of user involvement to overcome the conceptual ambiguity of information system use in the Delone and McLean model [6].

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2.3 Technology Acceptance Model (TAM) The Technology Acceptance Model (TAM) is a model developed based on Davis’s (1989) existing Theory of Reasoned Action, aiming to predict user acceptance intentions during the introduction of new technology. It establishes perceived usefulness and perceived ease of use as factors influencing acceptance intentions. These two factors affect attitudes, and these attitudes, in turn, influence the intention to use the technology. Finally, the intention to use influences the actual usage behavior, elucidating a causal and structural model [7].

3 Research Design 3.1 Research Model This study constructed an integrated research model for the analysis of factors influencing the acceptance model of home network services based on the TAM_IS Model, an extension model of the Technology Acceptance Model (TAM) for home network service usage. The research model comprises independent variables such as home security, home automation, and home healthcare. Using usefulness, ease of use, and satisfaction as mediating variables, the dependent variable was established as the intention to use. Ultimately, our aim was to elucidate how the independent variables affect usefulness and satisfaction and how ease of use influences satisfaction and impacts the intention to use. We sought to analyze the effects of independent and mediating variables on the dependent variable [7] (Fig. 1).

Fig. 1 Study on intention to use home network services

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Table 1 Research hypothesis Research hypothesis

Hypothesis H1-1 H1

H2

Home security will have a positive effect on perceived usefulness

H1-2

Home automation will have a positive effect on perceived usefulness

H1-3

Home healthcare will have a positive effect on perceived usefulness

H2-1

Home security will have a positive effect on perceived ease of use

H2-2

Home automation will have a positive effect on perceived ease of use

H2-3

Home healthcare will have a positive effect on perceived ease of use

H3

Perceived usefulness will have a positive effect on satisfaction

H4

Perceived ease of use will have a positive effect on satisfaction

H5

Satisfaction will have a positive effect on intention to use

H6

Perceived usefulness will have a positive effect on intention to use

H7

Perceived ease of use will have a positive effect on intention to use

3.2 Research Hypothesis See Table 1.

3.3 Operational Definitions and Measurement Items The survey items were organized with a 7-point Likert scale for each factor’s operational definitions and measurement items in Table 2 Operational Definitions and Measurement Items.

4 Empirical Analysis 4.1 Basic Statistics This study utilized SPSS 22 as the statistical analysis tool to assess the adequacy of the research model and verify the hypotheses. To understand the demographic characteristics of the respondents, a frequency analysis was conducted on the collected sample, and the results are detailed in Table 3 Basic Statistics On May 24, 2023, we received 304 survey responses online, and after excluding 10 untruthful responses, we used 294 as the sample for analysis.

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Table 2 Operational definitions and measurement items Category

Operational definition

Measurement item

Home security

The degree to protect from various crimes such as residential invasion

Ability to convey external [2] emergencies to the security office [1] Ability to monitor home interiors [8] from the outside Power to turn off all home appliances from the outside Ability to communicate external emergencies to the police station or security companies Viewing the video of visitors when absent Detecting fire or gas leaks

Reference

Home automation

The degree of controlling home appliances and functions remotely or automatically

Ability to control indoor temperature from the outside Ability to control indoor lighting from the outside Ability to control indoor humidity from the outside Ability to share the audio system from any location in the house. Ability to share the video system from any location in the house

[2] [1] [8]

Home health care

The service provided to Providing services to promote health check the residents’ health Providing remote medical services Ability to collect real-time medical information Ability to self-diagnose diseases Ability to respond promptly to emergencies

[2] [1] [9]

Perceived usefulness

The belief in the help provided in life or use through home network services

Services used through home network services are useful Services used through home network services are valuable Ability to find the information one wants through home network services Services used through home network services are necessary for life Home network services provide what the consumer wants

Perceived ease of use

The perception of ease and convenience in using home network services

The use of home network services is easy Using home network services is convenient The method of using home network services is easily learnable The home network service is simple to operate (continued)

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Table 2 (continued) Category

Operational definition

User satisfaction

The degree of satisfaction Satisfaction with services used gained from using home through home network services network services The services used through home network services are what I wanted The services used through home network services meet my expectations Great satisfaction with the services used through home network services The services used through home network services have unique advantages Satisfaction with the procedure of the services used through home network services

Intention to use The degree of continuous use of home network services

Measurement item

Reference

I will continue to use home network services I will prioritize using home network services I have no intention to replace home network services with another medium unless for specific reasons I will continue to use home network services I will prioritize using home network services I have no intention to replace home network services with another medium unless for specific reasons

4.2 Research Method 4.2.1

Exploratory Factor Analysis

To verify the validity, an exploratory factor analysis was conducted, utilizing the principal component analysis with varimax orthogonal rotation method. A factor loading of 0.5 or higher was considered significant (Shin, 2017). The results of the exploratory factor analysis are presented in Table 4. Variables grouped under each factor are considered as measurement items, and the factor’s variables were labeled as Factor 1 to Factor 6 based on the similarity of items within each factor. Additionally, factor scores were computed for each factor, which will be utilized in multiple regression analysis for hypothesis testing. This approach’s advantage lies in the varimax orthogonal rotation method, which eliminates multicollinearity concerns in multiple regression analysis by ensuring that the factors are uncorrelated, allowing for a more accurate hypothesis testing process.

Generation

Marital status

Age

Gender

Category

67

60 and over

Baby Boomers (1950–1964)

70

103

77

Millennials (1980–1994)

Gen X (1965–1979)

44

Gen Z (1995 and after)

191

73

50 ~ 59

Single

57

40 ~ 49

103

50

Married

47

30 ~ 39

142

20 ~ 29

152

Female

Frequency

Male

Table 3 Basic statistics

23.8

35.0

26.2

15.0

65.0

35.0

22.8

24.8

19.4

17.0

16.0

48.3

51.7

Percentage (%)

Monthly Income

Occupation

Education

Category

35 66 90 71

2 M–3 M won 3 M–4 M won Over 5 M

32

61

9

16

25

113

38

32

41

181

27

45

1 M–2 M won

Less than 1 M won

Other

Student

Freelancer

Technical/Manufacturing

Clerical/Government

Professional

Self-Employed

Graduate Degree

Bachelor Degree

Associate Degree

High School Graduate

Frequency

24.1

30.6

22.4

11.9

10.9

20.7

3.1

5.4

8.5

38.4

12.9

10.9

13.9

61.6

9.2

15.3

Percentage (%)

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Table 4 Exploratory factor analysis result Observation variables

Factors 1

Home automation 1

0.862

Home automation 2

0.839

Home automation 3

0.765

Home automation 4

0.590

Home automation 5

0.483

2

3

4

5

6

Cronbach’s Alpha 0.855

Home health care 3

0.785

Home health Care 4

0.785

Home Health care 2

0.711

Home health care 5

0.588

Home health care 1

0.572

0.830

Home security 6

0.793

Home security 3

0.768

Home security 1

0.715

Home Security 4

0.596

Home security 2

0.362

0.766

Satisfaction 6

0.802

Satisfaction 4

0.785

Satisfaction 2

0.784

Satisfaction 3

0.753

Satisfaction 5

0.663

Satisfaction 1

0.644

0.928

Ease 2

0.837

Ease 1

0.818

Ease 3

0.815

Ease 4

0.808

Ease 5

0.719

0.928

Usefulness 1

0.845

Usefulness 2

0.762

Usefulness 5

0.744

Usefulness 4

0.720

Usefulness 3

0.623

0.890

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Table 5 The multiple regression analysis results of home network factors on perceived usefulness and perceived ease of use Variable

Unstandardized coefficient

Standardized coefficient

Standard error

Beta value

0.047

0.459

T-value

Significance level

9.767

0.000

Home automation

Usefulness Ease of use

0.56

0.208

3.700

0.000

Home healthcare

Usefulness

0.047

0.360

7.655

0.000

Ease of use

0.56

0.192

3.417

0.001

Home security

Usefulness

0.047

0.138

2.935

0.004

Ease of use

0.56

0.011

0.196

0.844

Table 6 Regression analysis results of TAM-IS model variables: usefulness, ease of use, and satisfaction Unstandardized coefficient

Standardized coefficient

Standard error

Beta value

Ease of use

0.054

Usefulness

0.054

Variable

T-value

Significance level

0.234

4.296

0.000

0.290

5.325

0.000

4.3 Hypothesis Testing 4.3.1

Hypothesis Testing on the Relationship Between Home Network Factors and Perceived Usefulness and Ease of Use

To test the hypothesis, multiple regression analyses were performed, using grouped Home Automation, Home Healthcare, and Home Security as independent variables, while Perceived Usefulness and Perceived Ease of Use were set as dependent variables. The outcomes are presented in Table 5, showing the standardized beta coefficients for Usefulness and Ease of Use. It reveals that Home Automation exerts the most significant influence, followed by Home Healthcare, and lastly, Home Security.

4.3.2

Hypothesis Testing for the Relationship Between Perceived Usefulness, Perceived Ease of Use, and Satisfaction in Home Network Factors

To test Hypothesis H3 and H4, multiple regression analysis was conducted with perceived usefulness and perceived ease of use as independent variables and satisfaction as the dependent variable, grouped by exploratory factor analysis. The results are presented in Table 6.

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Table 7 Multiple regression analysis results of home network factors, satisfaction, perceived usefulness, perceived ease of use, and intention to continue use Variable

Unstandardized coefficient

Standardized coefficient

Standard error

Beta value

Ease of use

0.203

0.336

T-value

Significance level

11.262

0.000

Usefulness

0.206

0.513

16.925

0.000

Satisfaction

0.212

0.430

13.797

0.000

4.3.3

Testing the Relationship Between Home Network Factors, Perceived Usefulness, Perceived Ease of Use, Satisfaction, and Intention to Continue Use

To verify hypotheses H5, H6, and H7, multiple regression analysis was conducted, considering satisfaction, perceived usefulness, and perceived ease of use as independent variables and intention to continue use as the dependent variable. The results are presented in Table 7. The standardized beta coefficients show that perceived usefulness has the most significant effect, followed by satisfaction, and lastly, ease of use. As shown in Table 8, hypothesis H2-1 on home security through home network services resulted in a p-value of 0.844, which is greater than 0.05. Thus, the research hypothesis that home security significantly influences ease of use is rejected. However, all other hypotheses, excluding the one regarding home security’s impact on ease of use, were accepted. Even though the users perceived that home network services are easy and convenient to use, the exploratory factor analysis in Table 4 shows well-grouped variables with a Cronbach’s alpha value of above 0.07. All factors appeared normal. Security seems slightly challenging for people to use; however, it impacted usefulness and intention to use. For the research on the impact of security on ease of use, it’s deemed valuable. Home automation and healthcare appeared easier for people to use. Security might have a diverse menu and might not be part of everyday use, yet it impacts usability. Therefore, for improved user convenience and more customized programs and system development, efforts should be made in product design and development tailored to a wider customer base.

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Table 8 Results of hypothesis testing Hypothesis Hypothesis content H1-1

Home security will have a positive effect on perceived usefulness

Acceptance Accepted

H1-2

Home automation will have a positive effect on perceived usefulness

Accepted

H1-3

Home healthcare will have a positive effect on perceived usefulness

Accepted

H2-1

Home security will have a positive effect on perceived ease of use

Rejected

H2-2

Home automation will have a positive effect on perceived ease of use Accepted

H2-3

Home healthcare will have a positive effect on perceived ease of use

Accepted

H3

Perceived usefulness will have a positive effect on satisfaction

Accepted

H4

Perceived ease of use will have a positive effect on satisfaction

Accepted

H5

Satisfaction will have a positive effect on intention to use

Accepted

H6

Perceived usefulness will have a positive effect on intention to use

Accepted

H7

Perceived ease of use will have a positive effect on intention to use

Accepted

5 Conclusion 5.1 Research Result The purpose of this study was to create a research model by incorporating the IS success model and TAM, previously utilized in related studies, and adding the user’s personal characteristics in the context of a home network service information system. The results of this study indicated that security, although perceived as somewhat challenging to use, still influenced usefulness and intention to use. This suggests that there is significance and value in studying security concerning ease of use. While home automation and healthcare appeared more user-friendly, security, with its diverse menu and less everyday usage, might not be easy to use. However, the implication from the study is that designing security to be more user-friendly and catering to the preferences of a broad customer base through program, system, and product development is crucial.

5.2 Implications and Future Studies The home network service is rapidly evolving, and the outcomes of this research suggest several implications and future research directions. One major implication of this study is the exploration of home security, home automation, and home healthcare as independent factors within the home network. Future research might consider systems that people actively engage with, considering users’ enthusiasm. Additionally, it would be beneficial to further study the integration of these three services and functionalities from a service integration perspective.

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References 1. Kim, T. H. (2009). Design of an Intelligent Home Network System Using RFID. Master’s Thesis, Sookmyung Women’s University, 8. 2. Kim, B. S., Han, S. H., & Kang, Y. S. (2012). A Study on Digital Item Purchases and Actual Usage of Social Network Service Users: An End-to-End Perspective. Information Systems Research, 21(2), 97–114. 3. Lee, J. E., & Park, S. Y. (2007). Analysis of Home Network Service Adoption Model Applying TAM Extension Model. Consumer Culture Research, 10(4), 153–172. 4. Ha, Y. K., & Kim, K. S. (2000). Strategies for Building Home Network Services. Proceedings of the Korean Multimedia Society, 287–290. 5. Yeom, D. S., & Jang, S. B. (2018). User Effects of Indoor Digital Signage. Proceedings of the Korean OOH Advertising Society Academic Conference, 15–23. 6. Park, K. B. (2011). A Study on the Introduction of Crime Prevention and Support Programs for the Elderly. Crisisonomy, 7(4), 23–36. 7. Choi, B. H. (2013). Exploring the Value of Smart Mirror Products in the Smart Home Industry. Master’s Thesis, Sungkyunkwan University 10. 8. Park, J. N., & Lee, J. H. (2003). Implementation of Voice Interface in Home Automation. Journal of the Korean Society of Maritime Information and Communication Sciences, 7(2), 300–303. 9. Lim, E. T., Kim, G. Y., Kang, N. Y., Choi, Y. H., & Oh, M. S. (2020). A Study on the Intention to Use Smart Healthcare. Global Management Journal, 17(4), 259–281.

A Study on the Impact of Green Patent Data on ESG Environment Indicators Hyunyoung Kwak and Sungtaek Lee

Abstract Global investment and financial institutions such as BlackRock are exerting pressure by stating that they will no longer invest in companies that do not adhere to ESG management criteria while operating financial products related to ESG. As a result, ESG is recognized as a management activity that companies must necessarily perform. Various evaluation agencies publish ESG indicators to measure and manage such ESG activities. However, companies assessing ESG levels only disclose the comprehensive evaluation results encompassing the three areas of environment, society, and corporate governance, without revealing specific measurement methods. Detailed information is shared only when the evaluated company requests consulting, limiting the ability of the general public or unmeasured companies to confirm and compare ESG management levels. To address these limitations, this study aims to investigate the relationship between publicly available data and ESG indicators. The data used in the research are patent data representing a company’s technological development, secured from the Google Cloud Platform’s BigQuery. ESG indicator data utilized the Dow Jones Sustainability Indices (DJSI) from S&P Global, which received a high-quality assessment from the Sustainability Institute. The research model employed deep learning-based natural language processing technologies, utilizing Long-Short Term Memory (LSTM), Attention, and Transformer models. Keywords ESG · Patent data · Deep learning · LSTM model · Attention model · Transformer model

H. Kwak LG UPLUS, Seoul, Republic of Korea e-mail: [email protected] S. Lee (B) Department of Business Administration, Yong In University, Yongin-si, Gyeonggi-Do, Republic of Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3_15

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1 Introduction Recently, not only in the media such as newspapers and broadcasts but also in major investment institutions, there is a paradoxical argument that, in the wake of the COVID-19 pandemic, companies should not only pursue shareholdercentric economic interests but also utilize ESG (Environmental, Social, Governance) centered management factors, encompassing environmental and social issues, as well as corporate governance, in crucial decision-making. Additionally, activities to measure, evaluate, and disclose the level of ESG management activities have been carried out since the late 1990s, mainly by evaluation agencies managing investment-related indicators. However, companies assessing ESG levels only disclose the comprehensive evaluation results covering the three areas of environment, society, and corporate governance, without revealing specific measurement methods. Detailed information is shared only when the evaluated company requests consulting, as disclosed by the Korea Corporate Governance Institute in 2021. Global asset management companies such as BlackRock, Vanguard, and SSGA (State Street Global Advisors) have launched and operated various financial products, including funds and ETFs, utilizing ESG indicators [1]. These asset management companies demand ESG-centered management activities and ESG disclosure from companies included in the funds they operate. If companies fail to comply, they exert pressure by exercising voting rights at shareholder meetings or suspending investments [2]. The pressure from financial investment institutions is compelling companies to engage in ESG management activities, making ESG a central and expanding aspect of business operations.

2 Theoretical Background 2.1 ESG ESG (Environmental, Social, and Governance) can be defined as a management philosophy that aims not only for the interests of shareholders who own the company but for the interests of all stakeholders associated with the company’s activities. It involves the reestablishment of non-financial management activities related to environmental, social, and corporate governance aspects, with the goal of pursuing the company’s sustainable growth. Ultimately, ESG represents a management ideology that seeks to address many global issues such as the environment, labor, and poverty collectively faced by the world. This ideology should not be exclusive to businesses but should also be embraced by the general public, encouraging shared interest and collaborative implementation to tackle the challenges at hand. ESG-related research until the mid-2010s primarily interpreted concepts such as sustainability or ESG from the perspective of investors. Studies during this period focused on investigating the mutual relationship to determine whether investment

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value increases. Subsequently, with the introduction of systematic frameworks that enable the quantification of a company’s non-financial status, research has diversified beyond measuring the relationship between ESG and corporate performance. Additional areas of exploration now include ESG disclosure methods. However, the central focus of research remains concentrated on investigating the relationship between ESG indicators and a company’s investment value. Notably, there is a scarcity of research on the evaluation criteria for generating ESG indicators.

2.2 Patent In recently, there has been active research leveraging the big data characteristics of patents. Studies utilizing data from patent documents, including titles, abstracts, descriptions, and claims, are being conducted in areas such as prediction, recommendation, classification, retrieval, valuation, visualization, translation, and summarization [3]. While early research on patents focused on technological strategies and identifying technological gaps, advancements in natural language processing have led to an increased emphasis on addressing various issues arising from patents. This includes not only text mining for natural language elements such as titles, abstracts, descriptions, and claims but also deep learning research to tackle diverse challenges within the realm of patents.

2.3 Natural Language Process Based on Deep Learning A patent document can be considered as big data that encapsulates natural language elements, such as titles, abstracts, and claims, which determine the scope of patent rights [4]. Consequently, natural language processing is essential for handling this data.

2.3.1

Natural Language Process Theory

Deep learning models for classification or prediction typically use numerical data in the input layer. Therefore, document data like patents cannot be directly utilized in machine learning or deep learning. To employ them in deep learning models, the textual form of sentences or words needs to be transformed into numerical data, a process known as vectorization. The methodology for this is called Text Embedding. To perform text embedding, suitable text data is required. The sentences or words in patent documents are structured in a format that is not inherently suitable for embedding, as they are designed for human communication rather than machine processing. The process of transforming them into text data that is suitable for embedding and analysis is known as Text Pre-processing.

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Text Embedding Based on Deep Learning

LSTM (Long-Short Term Memory) The LSTM is a specialized type of recurrent neural network developed by Sepp Hochreiter and Jürgen Schmidhuber in 1997. It incorporates a selective memory function to identify what information should be stored for a long time and what should be forgotten. The long-term dependency problem, where the gradient vanishes during backpropagation as the time gap between input data points increases, is a significant limitation of recurrent neural networks. The LSTM model was designed to address this issue. The advantages of LSTM include the ability to continuously update past data and control outcomes through individual memory control. However, it may suffer from slower computation due to memory updates [5].

Attention Model When humans perceive natural language or background scenes, they do not recognize every element but focus on the essential parts for information acquisition [6]. Similarly, in learning, the Attention mechanism allocates weights to different elements, allowing the model to concentrate on the input of necessary information [7]. Initial Attention models used weights on the sequence of internal state values in LSTM models. Recently, LSTM models themselves are being replaced by Attention-based models, where models consisting only of Encoder and Decoder Attention layers demonstrate higher parallelism, better learning efficiency, and superior performance compared to traditional models [8].

Transformer Model Traditional LSTM models process input data sequentially, generating the current state based only on the initially input data. To address this limitation, bidirectional LSTM models and self-attention mechanisms have been introduced. Among these, the Transformer model utilizes self-attention to comprehend the relationships between all words simultaneously, allowing it to assess the semantic relevance weights between words [9]. The self-attention model faces the challenge of input sequence order, and positional encoding is employed as a solution. Positional encoding adds positional vector values corresponding to the order of input data to overcome the issue [8].

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3 Research Method 3.1 ESG Valuation Indicators Analysis ESG criteria have become indicators reevaluating the value of companies, resulting in the proliferation of over 600 ESG-related metrics globally [10]. Given the multitude of available metrics, there is a need to identify those trusted by global investment institutions and companies. To address this, the focus will be on the ‘Rate the Raters 2020’ report released by the Sustainability Institute since 2010, aiming to select reliable indicators in this regard. In South Korea, the major indicators used are ESG indicators published by Sustinvest and the Korea Corporate Governance Service (KCGS), while internationally, institutions primarily use the ESG indicators of S&P’s Dow Jones Sustainable Indices (DJSI) and Morgan Stanley Capital International (MSCI) [11], especially in relation to funds such as ETFs.

3.1.1

DJSI (Dow Jones Sustainable Indices)

The ESG evaluation criteria are classified into three areas with nine subclassifications. A notable aspect is the use of the term “Economic” instead of “Governance,” where corporate governance is included as a sub-classification. Additionally, within this category, there are sub-classifications for research and development, as well as renewable energy, based on industry characteristics. In the environmental area, the evaluation is based solely on publicly available environmental reports, and there is no apparent assessment criterion for green technologies. In this study, we aim to investigate whether technological development activities, especially those related to green technologies, are adequately reflected in ESG metrics. We plan to conduct research using patent data to demonstrate the capabilities of technological development and predict ESG metrics. Among the assessment agencies evaluated by the Sustainability Institute [10], the DJSI (Dow Jones Sustainability Index), rated as having excellent quality, partially mentions technology-related aspects in the industry-specific characteristics section of the governance domain. In the environmental domain, where technology may have an impact, evaluation criteria are not explicitly specified, and the weighting of the evaluation in the environmental domain appears relatively low. By validating DJSI’s environmental indicators, we can indirectly confirm whether environmentally green technological patents are well-reflected in ESG environmental indicators.

3.1.2

ESG Indicator Predicting Research Framework

This study aims to use deep learning-based natural language processing techniques to substantiate the impact of environmentally friendly patents on ESG environmental

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indicators. This study focuses on conducting experiments to determine whether the context or meaning of sentences contained in patent text data, analyzed through natural language processing, can predict ESG indicators. The experiment is divided into three parts. The first part involves testing how accurately green patent technology predicts environmental indicators within a company’s ESG metrics. The second part experiments with predicting to what extent a company’s overall patents accurately predict the company’s comprehensive ESG indicators, comparing the results with the predictive performance of the first experiment. The last part of the experiment explores how a company’s overall patents predict ESG indicators over time.

3.1.3

Data Definition

Selection of Target Companies: For the study’s target company selection, the primary criterion is the reliability of ESG metric calculation. The DJSI World index, evaluating 2,500 companies annually and selecting the top 10% based on market capitalization, is specifically chosen as the ESG research target. In 2020, out of 323 companies included in the DJSI World index, 303 were selected for data extraction. Definition of Dependent Variables: The study defines the dependent variable as the company-specific evaluation results of DJSI indicators, recognized for their excellent quality by the Sustainability Institute. DJSI indicators are categorized into environmental, social, and governance aspects, with an overall indicator derived from their integration. Text Data Definition of Patents: The text information from patents undergoes natural language processing to vectorize features embedded within the context of sentences, utilizing these as independent variables. Text data representing the features of patents includes titles, summaries, descriptions, claims, etc. Among these, summaries have been selected as the text data for analyzing the features of patents.

3.1.4

Green Patent Data Collection

Based on the registration numbers of patents as a reference, I acquired an English dataset from the United States Patent and Trademark Office. This dataset includes patent title, abstract_text, IPC code, and description. By doing so, I obtained the entire patent dataset for companies included in the DJSI World, using the registration numbers as the basis. To extract green patents, it is necessary to define green attributes. Yao Fu [12] defined environmentally sustainable technologies based on the types of environmental technologies defined by the United Nations Environmental Program. These include technologies related to CO2 emission reduction, alternative energy, energy efficiency, and recycling [12].

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4 Experiment Result 4.1 Experiment This study conducts a primary experiment (Experiment 1) to verify the extent to which environmentally friendly patents influence the environmental indicators of ESG. To provide a comparative analysis, an additional experiment (Experiment 2) is conducted to examine the impact of overall patents on the comprehensive ESG indicator. Furthermore, to assess the medium to long-term effects of patents, a third experiment (Experiment 3) investigates the influence of patents on ESG comprehensive indicators one to four years after patent registration. To enhance the accuracy of the experiments and address potential biases in patent data, supplementary experiments are conducted for Experiment 1 and Experiment 2.

4.1.1

Experiment 1: Environmental Indicator Prediction Model for Green Patent

Experiment 1 involves predicting the DJSI environmental indicators of companies with green patents that are included in the DJSI World Index. The hypothesis being tested is whether it is possible to predict the DJSI environmental indicators of companies with environmental patents by learning the relationship between green patents and DJSI environmental indicators. The experiment utilizes data combining green patents and DJSI environmental indicators. The summary information of green patent data is used as input variables, and the environmental indicators of DJSI from the same year, such as the registration year of patents, are used as dependent variables. The experiment employs three deep learning models, namely LSTM, Attention, and Transformer. Additionally, to eliminate bias in patent data, an additional experiment is conducted excluding the top four companies in patent registrations. Fig. 1 illustrates the process of learning to predict environmental indicators for green patents, and Table 1 presents the performance metrics of the final test results. In Fig. 1, 3, graphs (a), (b), (c) on the left represent the learning curves for each model concerning all green patents, while (d), (e), (f) on the right depict the learning curves after excluding the top four companies (Fujitsu: 1,048 patents, NEC: 966 patents, SAP: 492 patents, Siemens: 475 patents) holding green patents. Examining the curves trained with the Transformer model, (c) and (f) in particular, it is observed that overfitting occurs around epoch 4 in the experiment using all green patents, while in the experiment excluding the top four companies, the validation loss is decreasing. This suggests overfitting in the Transformer model due to biased patent data from the top four companies. The experiment excluding these four companies demonstrates a decreasing validation loss, indicating stable learning [13].

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(a)

LSTM Learning Curve of Green Patent

(d) LSTM learning curve excluding the top 4 companies of Green Patent

(b)

Attention Learning Curve of Green Patent

(e) Attention learning curve excluding the top 4 companies of Green Patent

(c) Transformer Learning Curve of Green Patent

(f) Transformer learning curve excluding the top 4 companies of Green Patent

Fig. 1 Learning curve for each DJSI environmental indicator prediction model for green patents Table 1 DJSI environmental indicator predicting performance valuation of green patent Model

Accuracy

Precision

Total (4,385 cases) (%)

Excluding the top 4 patent registration companies (1,404 cases) (%)

Total (4,385 cases) (%)

Excluding the top 4 patent registration companies (1,404 cases) (%)

LSTM

57.13

52.61

67.54

73.37

Attention

63.23

51.18

71.82

71.60

Transformer

74.79

76.78

76.01

79.30

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Since precision is higher than accuracy in all three models, it can be presumed that the results are meaningful for assessing the impact between independent and dependent variables through predictions. Furthermore, the Transformer model, excluding the top four companies, exhibits the highest performance with prediction accuracy and precision reaching 76.78 and 79.30%, respectively. Based on these results, it can be inferred that green patents influence the DJSI environmental indicators.

4.1.2

Experiment 2: ESG Comprehensive Indicator Prediction Model for Patent Registration year

Experiment 2 involves predicting the DJSI comprehensive indicator using all patents from companies included in the DJSI World Index. Since the summary information of patents used as input variables is not specified in this experiment, the dependent variable, DJSI indicators, uses the comprehensive indicator without sub-indicators for environment, social, or governance. The experiment is conducted with three deep learning models. Figure 2 and Table 2 present the experimental results. Examining Fig. 2, the learning curves for all three models show validation loss occurring at epoch 3 or 4, followed by a sharp increase in validation loss, maintaining validation accuracy below 30%. Additionally, the final evaluation results in Table 2 reveal no significant findings in accuracy or precision when predicting the DJSI comprehensive indicator using the overall patents of companies. When comparing the performance evaluation results of Experiment 2 with Experiment 1, Experiment 1’s results are relatively superior. One possible explanation for this difference is that, in Experiment 1, environmentally friendly patents were preselected using the environmentally friendly attribute information defined in previous research. These selected patents were then mapped to DJSI environmental indicators, serving as an effective replacement for the labeling of environmentally friendly patents and environmental indicators. This process likely led to a more accurate and effective learning. In contrast, in Experiment 2, where the entire patents and DJSI comprehensive indicators were compared, the omission of labeling for the relationship between the two datasets likely hindered proper learning. As research on labeling is beyond the scope of this study, it is suggested as a subject for future research.

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(a) LSTM Learning Curve of Entire Patent

(d) LSTM learning curve excluding the top 4 companies of Entire Patent

(b) Attention Learning Curve of Entire Patent

(e) Attention learning curve excluding the top 4 companies of Green Patent

(c) Transformer Learning Curve of Entire Patent

(f) Transformer learning curve excluding the top 4 companies of Green Patent

Fig. 2 Learning curve for each DJSI comprehensive indicator prediction model for entire patents Table 2 DJSI comprehensive indicator predicting performance valuation of entire patents Model

Accuracy

Precision

Total Excluding the top Total (52,160 cases) (%) 4 patent (52,160 cases) (%) registration companies (19,132 cases) (%)

Excluding the top 4 patent registration companies (19,132 cases) (%)

LSTM

28.42

27.79

30.03

31.22

Attention

29.00

26.97

37.14

37.17

Transformer 28.19

31.18

29.24

32.34

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Experiment 3: ESG Indicator Prediction Model Based on the Number of years Since Patent Registration

Analyzing the results of Experiment 2, it is evident that the company’s patent activities do not immediately translate into performance in the same year. However, R&D activities such as patents often manifest as medium to long-term outcomes rather than short-term gains. Similarly, it is important to consider the medium to long-term aspects of ESG performance. Experiment 3 is conducted based on the hypothesis that a company’s patent activities have a medium to long-term impact on ESG performance. To explore this, the experiment predicts DJSI indicators at points 1 to 4 years after patent registration, building on the results of Experiment 2. The results of this experiment are presented in Fig. 3 and Table 3. Observing Fig. 3, the slope of the validation loss decreases with each passing year after patent registration. Examining the final validation results in Table 3, there is an improvement in prediction accuracy and precision as the years progress after patent registration. The prediction accuracy and precision reach a maximum of 69.23 and 69.81%, respectively, after 4 years of patent registration. The accuracy of the Transformer model increases by more than 40 percentage points compared to the first year of patent registration, while the LSTM and Attention models show an increase of 28 to 31%. These results suggest that patent information can have a medium to long-term impact on DJSI’s comprehensive indicator. The utilization of a company’s patent activities as a medium to long-term leading indicator for DJSI’s comprehensive indicator is indicated by the enhanced accuracy and precision over time.

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(a) LSTM learning curve 1 year after entire patent registration

(e) Attention learning curve 1 year after entire patent registration

(i) Transformer learning curve 1 year after entire patent registration

(b) LSTM learning curve 2 year after entire patent registration

(f) Attention learning curve 2 year after entire patent registration

(j) Transformer learning curve 2 year after entire patent registration

(c) LSTM learning curve 3 year after entire patent registration

(g) Attention learning curve 3 year after entire patent

(k) Transformer learning curve 3 year after entire patent

registration

registration

(h) Attention learning curve 4 year after entire patent registration

(l) Transformer learning curve 4 year after entire patent registration

(d) LSTM learning curve 4 year after entire patent registration

Fig. 3 Learning curve for each yearly DJSI comprehensive index prediction model for entire patents

A Study on the Impact of Green Patent Data on ESG Environment …

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Table 3 DJSI comprehensive indicator predicting performance valuation by yearly of entire patents Period/model

Accuracy (%)

Predictoion for LSTM the current year Attention

28.42

–

30.03

–

29.00

–

37.14

–

28.19

–

29.24

–

Transformer Predictoion in 1 year

Predictoion in 2 year

Predictoion in 3 year

Predictoion in 4 year

Growth rate compared to the base year (%)

Precision (%)

Growth rate compared to the base year (%)

LSTM

26.35

−2.07

27.60

−2.43

Attention

31.73

2.73

36.96

−0.18

Transformer

26.07

−2.12

26.60

−2.64

LSTM

37.37

8.95

39.30

9.27

Attention

43.78

14.78

53.28

16.14

Transformer

35.30

7.11

36.10

6.86

LSTM

40.23

11.81

42.76

12.73

Attention

43.20

14.20

48.58

11.44

Transformer

41.86

13.67

42.37

13.13

LSTM

56.48

28.06

62.40

32.37

Attention

60.18

31.18

64.25

27.11

Transformer

69.23

41.04

69.81

40.57

5 Conclusion The study has yielded the following key findings: Analysis of the Relationship between ESG and Corporate Value: Through a review of previous studies, the research concludes that a company’s ESG management activities play a crucial role in leading society towards sustainable growth and contribute significantly to long-term profit generation. Prediction of Environmental Indicators by Green Technology Patents: By predicting environmental indicators of the Dow Jones Sustainability Index (DJSI) with a 77% accuracy using eco-friendly technology patents, the study suggests that active pursuit of eco-friendly technology development positively impacts a company’s future value and improvement of ESG environmental indicators. Medium to Long-Term Impact of Patent Data: It was observed that patent data can influence ESG indicators from the third to fourth year after registration. This implies the potential use of patent information as a leading indicator for medium to long-term ESG performance, reflecting a company’s commitment to sustained growth. Potential of Utilizing Public Data: The study indicates that leveraging public data, such as patent information, could empower companies not included in indices like DJSI to self-assess and estimate their ESG levels. Particularly, companies

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securing patents related to the environment can make more accurate ESG predictions. The results suggest the possibility of extending the research model to broader ESG evaluation criteria and various publicly available datasets.

References 1. Ryu, Jung-sun. “Global ESG Investment and Policy Trends.” Financial Investment Association, 2020. 2. Lee, Hyo-jeong et al. “ESG Management Era: Strategic Paradigm Shift.” Samjung KPMG, 2020. 3. Zhang, L., Li, L., and Li, T. “Patent Mining: A Survey.” SIGKDD Explorations Newsletter, 16(2), 1–19, 2015. 4. Kang, Myung Chul. “Deep Learning-Based Classification of Imbalanced Data: Focused on Patent Precedent Technology Investigation.” Master’s Thesis, Inha University, 2021. 5. Kim, Yoon-jae. “Attention Mechanism-Based Stock Price Forecasting Model Using BiDirectional LSTM.” Master’s Thesis, Hanbat National University, 2019. 6. Chaudhari, S., Mithal, V., Polatkan, G., and Ramanath, R. “An Attentive Survey of Attention Models.” ACM Transactions on Intelligent Systems and Technology, 1(1), 2021. 7. Bahdanau, D., Cho, K., and Bengio, Y. “Neural Machine Translation by Jointly Learning to Align and Translate.” arXiv preprint arXiv:1409.0473, 2014. 8. Seong, So-yun et al. “A Study on Improved Comment Generation Using Transformer.” Journal of the Korea Game Society, 19(5), 103–113, 2019. 9. Vaswani, A., Shazeer, N., et al. “Attention Is All You Need.” 31st Conference on Neural Information Processing Systems, 5998–6008, 2017. 10. Sustainability Institute. “Rate the Raters 2020.” 36–38, 2020. 11. MSCI ESG Research. “MSCI ESG Ratings Methodology.” MSCI Inc., 2020. 12. Fu, Y., Kok, R.A.W., and Dankbaar, B. “Factors Affecting Sustainable Process Technology Adoption: A Systematic Literature Review.” Journal of Cleaner Production, 226–251, 2018. 13. Kim, Myunghwa. “An Empirical Study on Time Series Nonlinear Prediction Models Using Generative Adversarial Networks (GAN).” Doctoral Dissertation, Soongsil University, 2021.

Index

A Adoption, 4, 16, 18, 28, 42–44, 51, 85, 119, 120, 156 Air purifiers, 118 API, 42, 82, 84–86, 91 Architecture, 82–84, 90, 170

B Brand image, 144–146, 148, 150, 152

C Co-creation experiences, 70, 71, 75–77 Corporate reputation, 88, 132–134, 136, 137, 140, 141, 144–146, 148, 150 Cox proportional hazards analysis, 108, 112, 115

D Data quality, 3 Deep learning, 185–187, 189, 191 Diffusion of Innovations Theory (DOI), 119

G Green patent, 188, 189, 191

H Home automation, 170–172, 174, 178–180 Home network services, 170–172, 179

I Information security platform, 28–31, 34, 37, 39 Internal Rate of Return (IRR), 94, 98, 100–102 Investment performance, 94, 95, 97, 98, 104, 105 IT development, 56, 65 IT outsourcing, 56, 60

J Justice, 133–135, 137, 140, 141

K Kaesong Industrial Complex, 156–161 E Electric Vertical Take-Off and Landing (eVTOL), 16 Endpoint security, 30, 32, 33, 39 Environmental, Social, and Governance (ESG), 144, 150, 184–185, 187–189, 191, 193, 195 ESG activities, 141, 144–146, 150 ESG management, 132–134, 137, 140, 141, 144, 146, 150, 152, 184, 195

M MyData, 3, 5–8, 11, 42, 82–88, 90, 91

N Natural language process, 185, 187, 188 North-South Korea Economic Cooperation, 156, 160, 161

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 R. Lee (ed.), Big Data and Data Science Engineering, Studies in Computational Intelligence 1139, https://doi.org/10.1007/978-3-031-53385-3

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198 O Off-shoring, 56, 57, 59, 65 Open source software, 42–45, 49, 51, 52 Oxygen-generater, 118, 119

P Perceived quality, 144, 146, 150, 152 Private fund, 95, 104 Purchase intentions, 68, 70, 118, 122, 125, 144–146, 150, 152

R Reputation, 88, 132, 145

S SECaaS, 28, 29, 39 Security mechanisms, 82, 84, 87, 90 Self-employed, 107, 109

Index Serial multiple mediation, 74, 75 Service recovery, 68–77 Survival of franchise convenience stores, 110, 115 Switching costs, 69–72, 75–77

T Technology Acceptance Model (TAM), 4, 59, 121, 122, 172 Transparency, 3, 132, 164 Trust, 24, 70, 133, 134, 136, 137, 140

U Urban Air Mobility (UAM), 15–18, 24

V Venture capital funds, 94–96 Vintage, 95, 96, 100, 105