Emotional Artificial Intelligence and Metaverse (Studies in Computational Intelligence, 1067) 3031164849, 9783031164842

Citation preview

Studies in Computational Intelligence 1067

Roger Lee   Editor

Emotional Artificial Intelligence and Metaverse

Studies in Computational Intelligence Volume 1067

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

The series “Studies in Computational Intelligence” (SCI) publishes new developments and advances in the various areas of computational intelligence—quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output. This series also publishes Open Access books. A recent example is the book Swan, Nivel, Kant, Hedges, Atkinson, Steunebrink: The Road to General Intelligence https://link.springer.com/book/10.1007/978-3-031-08020-3 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

Emotional Artificial Intelligence and Metaverse

Editor Roger Lee ACIS International Headquarters Mount Pleasant, MI, USA

ISSN 1860-949X ISSN 1860-9503 (electronic) Studies in Computational Intelligence ISBN 978-3-031-16484-2 ISBN 978-3-031-16485-9 (eBook) https://doi.org/10.1007/978-3-031-16485-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023, corrected publication 2023 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

Foreword

The purpose of the 1st ACIS International Symposium on Emotional Artificial Intelligence & Metaverse (EAIM) held on August 4–6, 2022 in Danang, Vietnam, was to bring together researchers, scientists, engineers, industry practitioners, and students to discuss, encourage, and exchange new ideas, research results, and experiences on all aspects of emotional artificial intelligence and metaverse and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The symposium organizers have selected the best 15 papers from those papers accepted for presentation at the symposium in order to publish them in this volume. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. In Chap. 1, SungTaek Hwang, JongWoo Park, and Jiah Hwang conducted a study with the aim of empirically identifying the operating principles of innovative behavior, developing universally applicable theories and models for innovative behavior, and providing an important theoretical basis for applying numerous management techniques to the reality. In Chap. 2, Hye Jung Kim, Myeong Sook Park, and Jiin Hwang conducted a case study by applying a Walk-through Audit (WtA) to hotel hospitality services based on an understanding of customer experience management (CEM). In Chap. 3, Mi Sook Moon and Hannah Park conducted a study to identify the structural relationships among organizational identification, psychological well-being, and role performance and the moderating effect of the director’s coaching leadership in order to analyze the effect of positive psychological capital of childcare teachers on organizational citizenship behavior thereby enhancing the competitiveness of infant welfare service institutions and presenting strategic orientation. In Chap. 4, Gina Gim, Hyekyung Bae, and Seon A. Kang present and discuss a research model that converges technology acceptance model, information systems success model, and self-determination theory with the purpose of exploring variables that affect learner satisfaction, mediating the flow theory. In Chap. 5, Han Lee, Dae Hyun Kang, and Seok Chan Jeong present a study for the intelligent maintenance or intelligent predictive maintenance of processing v

vi

Foreword

equipment, using the “Conv2d-LSTM” technique, which reduces the parameters of the LSTM network layer and adds a Conv2d layer, which is applied to the real-time spindle load data to verify the state judgment of the machining tool. In Chap. 6, Junghee Lee, Phuong Huy Tung, Euntaek Im, and Gwangyong Gim Conducted an AI recruitment systems study using EEG-based biological experimental studies with a deep learning method to explore more objectively. The study aimed to approach in a more systematic and scientific way to maximize the effect of recruiting talent. In Chap. 7, Byeongduk Hwang, Myeong Sook Park, Jangwoo Kim, and Gwangyong Gim present a study that optimizes the evaluation items through a meeting of ESG evaluation indicators experts with previous studies to present general ESG evaluation criteria that can be used by mid and small-sized companies that have difficulty in ESG management activities. In Chap. 8, Soo-Yeon Yoon and Jong-Bae Kim present a study of big data (SNS, Naver Blog, YouTube, Google search keyword frequency, etc.) collected online to analyze the recognition and evaluation of un-tact (real-time online or pre-recorded video lecture) and coding education was used as a text mining technique. Analysis was performed. “Coding education”, the keyword of this study, was selected as an analysis keyword, and data was collected through portals/SNSs of Google, Naver, Daum, and YouTube. In Chap. 9, Jin Hee Son presents a study to confirm the effect of youths’ narcissism and self-esteem on compensatory consumption behavior. To this end, a survey was conducted on 550 youths living in Seoul and Gyeonggi-do, Korea, and 528 data were used for analysis. In Chap. 10, Hye-Kyu An, Jin Xian, and Ho-Chul Shin investigate the individual effects of personal motivation such as the functional motivation of volunteers in non-profit organizations and the transformational leadership of their leaders on organizational commitment. In Chap. 11, Saeyeon Lee, Minwoo Lee, Younghoon Kim, and Gwangyong Gim present a new approach to K-POP music content creation by analyzing the factors of soundtrack content that has received a lot of attention from consumers and has been loved by consumers for a long time. In Chap. 12, Se Hyoung Kim, Chae Won Pyun, Jeong Yeon Ryu, Yong Hyun Kim, and Ju Young Kang propose installation locations of barrier-free bus stops by using cluster analysis to address the lack of accessible transportation in Seoul, Korea. In Chap. 13, Myunghee Hong, KangHyun Kim, Soonok Sa, Dan Bee Pyun, Chae Won Lee, Myung-Gwan Kim, Ju Hee Kim, Seogsong Jeong, Sung Soo Yoon, and Hyun Wook Han present a study to report the common adverse events following the COVID-19 vaccines, mRNA-1273, BNT162b2, and JNJ-78436735 in diabetic patients. In Chap. 14, Jun Heo, Doohwan Kim, Seok Chan Jeong, Minseok Kim, and Tae-Hwan Yoon present a study to investigate which factors of the metaverse-based MICE events affect the participants’ loyalty and intention to attend the MICE events and provide empirical data for establishing an effective metaverse-based MICE marketing strategy.

Foreword

vii

In Chap. 15, Jinhee Lee and Beonghwa Jeon present a study to identify the characteristics and consumption propensity of the MZ generation, which is emerging as a major consumer class, and examine the factors for successfully realizing kidult marketing. The research method was investigated, and the results were derived through literature review and existing data analysis. It is our sincere hope that this volume provides stimulation and inspiration and that it will be used as a foundation for works to come. August 2022

Dr. SungTaek Lee Department of AI Yongin University Yongin, South Korea Dr. Trinh Cong Duy Software Development Centre The University of Da Nang Da Nang, Vietnam

Contents

Why do companies become similar, but performance is different?: Understanding of Corporate isomorphism through Self-determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SungTaek Hwang, JongWoo Park, and Jiah Hwang A Study on the Customer Experience Management (CEM) by Applying Walk-Through Audit (WtA); Focused on Hospitality Service Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hye Jung Kim, Myeong Sook Park, and Jiin Hwang A Study on the Factors of Positive Psychological Capital on Organizational Citizenship Behavior: For Teachers of Infant Welfare Service Institutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mi Sook Moon and Hannah Park

1

15

27

The Effect of Self-Determination and Quality of VR-Based Education in the Metaverse on Learner Satisfaction . . . . . . . . . . . . . . . . . . Gina Gim, Hoi Kyoung Bae, and Seon A. Kang

41

A Study on Industrial Artificial Intelligence-Based Edge Analysis for Machining Facilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Han Lee, Dae Hyun Kang, and Seok Chan Jeong

55

AI Recruitment System Using EEG to Explore the Truth of Interviewers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junghee Lee, Phuong Huy Tung, Euntack Im, and Gwangyong Gim

71

Development of ESG Evaluation Indicators from a Policy Perspective—Focusing on the Legislature . . . . . . . . . . . . . . . . . . . . . . . . . . . . Byeongduk Hwang, Myeong Sook Park, Jangwoo Kim, and Gwangyong Gim

85

ix

x

Contents

A Study on the Analysis of Related Keywords on the Perception of Untact Coding Education in the Post-COVID Era Using Big Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soo-Yeon Yoon and Jong-Bae Kim

99

Relationship Between Narcissism, Self-esteem and Youth Consumption Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Jin Hee Son The Effect of Transformational Leadership on the Volunteer Members’ Organizational Commitment in Non-profit Organizations: The Moderating Effects of Motivation on Volunteeing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Hye-Kyu Anh, Jin Xian, and Ho-Chul Shin Analysis on the Life Cycle of Soundtrack Content in K-POP—Data-Driven on Music Platforms . . . . . . . . . . . . . . . . . . . . . . . . . 141 Saeyeon Lee, Minwoo Lee, Younghoon Kim, and Gwangyong Gim Method of Selecting the Optimal Location of Barrier-Free Bus Stops Using Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Se Hyoung Kim, Chae Won Pyun, Jeong Yeon Ryu, Yong Hyun Kim, and Ju Young Kang Common Adverse Events Following COVID-19 Vaccination in Patients with Type 2 Diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Myunghee Hong, Kang Hyun Kim, Soonok Sa, Dan Bee Pyun, Chae Won Lee, Myung-Gwan Kim, Ju Hee Kim, Seogsong Jeong, Sung Soo Yoon, and Hyun Wook Han Examining Participant’s Perception of SPICE Factors of Metaverse MICE and Its Impact on Participant’s Loyalty and Behavioral Intentions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Jun Heo, Doohwan Kim, Seok Chan Jeong, Minseok Kim, and Tae-hwan Yoon Kidult Marketing Reflecting the Characteristics of MZ Generation . . . . 199 Jinhee Lee and Beonghwa Jeon Correction to: Analysis on the Life Cycle of Soundtrack Content in K-POP—Data-Driven on Music Platforms . . . . . . . . . . . . . . . . . . . . . . . . . Saeyeon Lee, Minwoo Lee, Younghoon Kim, and Gwangyong Gim

C1

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207

Contributors

Anh Hye-Kyu Department of Business Administration, Soongsil University, Seoul, Korea Bae Hoi Kyoung Programs in Project Management, Soongsil University, Seoul, South Korea Gim Gina Next Generation R&D Technology Policy Institute, Soongsil University, Seoul, South Korea Gim Gwangyong Department of Business Administration, Soongsil University, Seoul, South Korea Han Hyun Wook Department of Biomedical Informatics, CHA, University School of Medicine, CHA University, Seongnam-si, South Korea Heo Jun Global MICE Major, Dong-Duk Womens University, Seoul, South Korea Hong Myunghee Department of Biomedical Informatics, CHA, University School of Medicine, CHA University, Seongnam-si, South Korea Hwang Byeongduk Department of Business Administration, Soongsil University, Seoul, South Korea Hwang Jiah Department of Innovation Management, Soongsil University, Seoul, South Korea Hwang Jiin Department of Innovation Management, Soongsil University, Seoul, South Korea Hwang SungTaek Department of Business Administration, Soongsil University, Seoul, South Korea Im Euntack Department of Business Administration, Soongsil University, Seoul, South Korea Jeon Beonghwa Information Technology Division Director, National Library of Korea, Seoul, South Korea xi

xii

Contributors

Jeong Seogsong CHA University, Pocheon-si, South Korea Jeong Seok Chan Deparment of e-Business College of Commerce and Economics, AI Grand ICT Research Center, Dong-Eui University, Busan, South Korea Kang Dae Hyun ICT Technical Team, HN Co., Ltd., Seoul, South Korea Kang Ju Young Deparment of e-Business, Ajou University, Suwon, South Korea Kang Seon A. Programs in Project Management, Soongsil University, Seoul, South Korea Kim Doohwan Global MICE Major, Dong-Duk Womens University, Seoul, South Korea Kim Hye Jung Department of Business Administration, Soongsil University, Seoul, South Korea Kim Jangwoo Department of Business Administration, Soongsil University, Seoul, South Korea Kim Jong-Bae Startup Support Foundation, Soongsil University, Seoul, South Korea Kim Ju Hee CHA University, Pocheon-si, South Korea Kim Kang Hyun CHA University, Pocheon-si, South Korea Kim Minseok Deparment of Hotel and Convention Management, Dong-Eui University, Busan, South Korea Kim Myung-Gwan CHA University, Pocheon-si, South Korea Kim Se Hyoung Deparment of Business Analytics, Ajou University, Suwon, South Korea Kim Yong Hyun Deparment of e-Business, Ajou University, Suwon, South Korea Kim Younghoon ICT Solution Departmnt, KEPCO Engineering and Construction Comapnay, Seoul, South Korea Lee Chae Won CHA University, Pocheon-si, South Korea Lee Han Department of e-Business College of Commerce and Economics, DongEui University, Busan, South Korea; ICT Technical Team, HN Co., Ltd., Seoul, South Korea Lee Jinhee Department of Financial Asset Management, Korea Soongsil Cyber University, Seoul, South Korea Lee Junghee Department of Business Administration, Soongsil University, Seoul, South Korea Lee Minwoo Department of IT Policy and Management, Soongsil University, Seoul, South Korea

Contributors

xiii

Lee Saeyeon Department of IT Policy and Management, Soongsil University, Seoul, South Korea Moon Mi Sook Department of Business Administration, Graduate School, Soongsil University, Seoul, South Korea Park Hannah Pomfret School CT, Connecticut, USA Park JongWoo Department of Business Administration, Soongsil University, Seoul, South Korea Park Myeong Sook Graduate of Business Administration, Soongsil University, Seoul, South Korea Pyun Chae Won Deparment of e-Business, Ajou University, Suwon, South Korea Pyun Dan Bee CHA University, Pocheon-si, South Korea Ryu Jeong Yeon Deparment of e-Business, Ajou University, Suwon, South Korea Sa Soonok CHA University, Pocheon-si, South Korea Shin Ho-Chul Department of Business Administration, Soongsil University, Seoul, Korea Son Jin Hee Dept of Youth Coaching and Counseling, Korea Soongsil Cyber University, Seoul, South Korea Tung Phuong Huy Department of Business Administration, Soongsil University, Seoul, South Korea Xian Jin Department of Business Administration, Soongsil University, Seoul, Korea Yoon Soo-Yeon Dept of School of Software Convergence, Kook Min Univ., Seoul, South Korea Yoon Sung Soo CHA University, Pocheon-si, South Korea Yoon Tae-hwan Deparment of Hotel and Convention Management, Dong-Eui University, Busan, South Korea

Why do companies become similar, but performance is different?: Understanding of Corporate isomorphism through Self-determination SungTaek Hwang, JongWoo Park, and Jiah Hwang

Abstract This study inferred that those various individual innovative behaviors expressed to survive social environmental changes are the result of individual’s subjective intrinsic behaviors. The study was conducted with the aim of empirically identifying the operating principles of innovative behavior, developing universally applicable theories and models for innovative behavior, and providing an important theoretical basis for applying numerous management techniques to the reality. Due to the nature of individual’s intrinsic behavior research, it was prepared using an electronic self-report survey. Outliers were removed from the collected data and normality checks were accomplished to perform confirmatory factor analysis, and the causal relationship was verified using the structural equation model. As an analysis method, reliability was verified using the Cronbach alpha coefficient, and factor analysis was conducted to test the validity of the measurement variable. Competence, relationship, and autonomy, which are the main constituents of self-determination, play significant role on innovative behavior, while competence having a mediating effect on relationship and autonomy. Similarly, autonomy was found to have a moderating effect on relationship and competence. The results of this study provided an opportunity to identify the fundamental principles of innovative behavior and understand the phenomenon of corporate isomorphization. The coercive and normative isomorphism caused by changes in external systems and norms is understood as a simple adaptation rather than an innovation. On the other hand, the act of self-awareness and imitation of new standards in exchange with others presupposes the acceptance of more innovative ideas, so if only an autonomous environment is continuously provided, the expression of innovative behavior can be continuously induced. Keywords Isomorphism phenomenon · Self-determination · Innovative behavior · Competence · Relatedness · Autonomy

S. Hwang · J. Park (B) Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] J. Hwang Department of Innovation Management, Soongsil University, Seoul, South Korea © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_1

1

2

S. Hwang et al.

1 Introduction Arnold Joseph Toynbee argued in his book, History of Study, that society grows and prevails as the public mimics the self-determination and self-expression of creative minorities and forms a unified civilization. However, he argued that if creative minorities lose their self-determination and degenerate into dominant minorities with only past achievements and fighting experiences, civilization unity will collapse and perish. This phenomenon is confirmed not only in the company’s management strategy, but also in the homomorphism phenomenon in which the organization’s official structure, business operation method, and various innovation activities for innovation become similar [1]. The sociological-related theory explains that changes in the socio-cultural environment and changes in social relations are the result of the spread of individual’s innovative behavior based on an individual’s perceived desire to imitate for survival. Therefore, since this study understands individual’s innovative behavior inferred from voluntary imitative isomorphization activities, psychological theory of individual behavior, the core of management activities, was first examined to verify why companies differ from each other in terms of performance. Psychological approaches to explaining the causes of human behavior include activism and humanism. First, in the Behavioristic Approach, humans are recognized as target objects that react to preceding stimuli and strengthen to subsequent stimuli. Second, in the Humanistic Approach, humans have a desire for self-realization according to subjective perception, and when certain conditions are met, they perceive themselves as subjective objects that act for problem solving and growth. It is said that people make decisions about external stimuli caused by environmental changes after reasonable consideration of the consequences of their actions [2]. This means that in the Behavioristic Approach, reactive isomorphisms can occur due to stimuli that is the discharge of non-legal elements such as standardization, as well as regulations by laws and rules, which are changes in the external environment. However, it can be said that the spontaneous imitative isomorphism phenomenon is expressed and promoted by the subjective perception of individuals socially embedded in the social hierarchical structure from the perspective of humanism. In this study, to understand the intrinsic behavior, which is inferred as the root cause of individual’s imitative innovation-behavior, the study was conducted grounding the principle of perception and need, from German enlightenment philosopher, G. W. Leibniz’s monadology. This will provide a theoretical basis for developing universally applicable theories and models for future innovation actions and applying numerous management methods to real-world management.

Why do companies become similar, but performance is different?: Understanding …

3

2 Theoretical Background 2.1 Understanding of Innovative behavior In the past, traditional management methods emphasized the efficiency of simple and stable functions centered on instruction and control, but in today’s complex business environment, modern management is required to create new values centered on innovation. Modern management centered on innovation begins with finding new and useful creative ideas through individual innovative thinking and recognition of problems and expression of creative ideas and drawing applicable problem solutions [3]. Therefore, it is very important to find ways to increase individual innovative behavior for the development of organizations and societies [4]. Scott and Bruce [5] presented the concept of innovative behavior and classified the preceding factors of innovative behavior of individual members into three types. First, individual abilities and problem-solving types, second, social exchange relationships with others, and third, the organizational innovation atmosphere were presented as influential prerequisites for innovative behavior.

2.1.1

Personal characteristic

Innovative behavioral people generally have a high tolerance for ambiguity, a strong preference for risk [6], a wide range of interest, and show high intelligence, independence, and confidence, engaging in many creative and innovative behaviors through a multifaceted mindset. In other words, individual characteristic antecedents that cause innovative behavior can be summarized by personal intelligence, creativity, knowledge in one’s field or technical ability, and personal propensity to recognize and solve problems. In the process of performing these innovative actions, the state of not being able to recognize and solve problems becomes a psychological burden for individuals, and cognitive efforts are made to instigate and implement creative solutions. In this situation, solving the emerging problems can be seen to be entirely related to an individual’s cognitive abilities as well as cognitive tendencies.

2.1.2

Exchange characteristic

Innovation is based on individual’s creative ideas, and communication with others is closely related to enriching ideas. Also, it is said that interaction with others on equivalent level in terms of social skills, can best teach how to harmonize with and complement to each other’s skills and abilities [7]. In other words, it can be said that the more sense of purpose is shared inside and outside the organization, the higher the performance on innovation team members make. Kanter [3] presented social exchange relationships such as educational program for innovation, distinct

4

S. Hwang et al.

rewards for innovation, circumstance for sharing information and ideas, participation of members in decision-making, easing barriers between departments through communication, and leaders’ intervention for active and continuous innovation. This was said to be a way to promote the members of the organization’s commitment to innovation. In other words, social exchange can be expected to contribute like economic exchange, but it is based on trust from a long-term perspective that the other party would compensate because the exact time of compensation and the content and value of the reward are uncertain.

2.1.3

Environmental characteristic

The environment that supports social support and autonomy as environmental factors indirectly affects innovative behavior by forming an atmosphere that induces innovative behavior. Therefore, individual’s innovative behavior is affected by environmental factors, and members of the organization experience the organization’s support method for personal welfare as well as how much they are observed [5]. In other words, it is important for the organization to value individual contributions and provide positive feedback such as recognition and praise, while the organization’s members themselves relate to the feeling of being cared for and valued by the organization. Therefore, not only do people feel attached to the organizations that care about their members and respect the autonomy of their tasks, but they also take various innovative actions required to achieve their goals [8].

2.2 Perceptual action of Self-determination Self-determination includes experiences of internal causality perceived as giving an individual significant value and refers to the quality of human function that determines one’s own behavior, not forced by compensation or external pressure [9]. Selfdetermination is a concept like autonomy and refers to making one’s own decisions and taking certain actions in a given environment. In other words, because the level of motivation depends on whether one acts autonomously, the degree to which one feels that he can adjust or control his behavior is self-deterministic. This self-determination uses the universal basic human psychological needs of the original concept to explain human motivation. Among the theories of self-determination, the theory of basic psychological needs considers humans to be born of three psychological needs: Competence [10], Relatedness [11], and Autonomy [12]. This basic psychological desire theory was proposed to explain the dynamic relationship between individual psychological health and well-being as a comprehensive concept for understanding motivation for self-determination [13].

Why do companies become similar, but performance is different?: Understanding …

2.2.1

5

Competence

Competence is a core concept of the Effect Motivation Theory, and it is also described as “a sense of competence” in some contexts as it is also a perception of an individual’s desire to feel competent rather than the actual ability or skills of competencies achieved by an individual. As such, everyone wants to be capable of, to have the opportunity, and to improve their skills, abilities, and talents [14]. In other words, competence is a desire for how much individual technology can be performed by finding optimal conditions [15] and has a meaning like self-efficacy because it allows individual skills and abilities to be exercised and developed.

2.2.2

Relatedness

Relatedness is a concept introduced in the study of Harlow (1958), Bowlby (1979), [11] and refers to an individual’s psychological perception that he has stable social relationships or harmony with others. Thus, individuals who have experienced interaction with others are more sophisticated and cooperative than those who have not [16], and the higher the quality of interaction with others, the newer and more original the individual [17]. As such, relatedness plays a decisive role in enhancing the internalization of external causes but plays a remote role in promoting intrinsic motivation. The theory of self-determination explains that satisfying the desire for relatedness is not directly connected to intrinsic motivation, as the desire for competence and autonomy does, yet it is still necessary to fulfill the desire for relatedness to maintain intrinsic motivation. Therefore, compared to satisfying the needs of competence or autonomy, satisfaction with the needs of relatedness is considered important in individual activities because it is crucial in sustaining intrinsic motivation.

2.2.3

Autonomy

Autonomy is a concept derived from decharms (1976)’s reasoning of personal causality, which means wanting freedom to determine what is important and valuable to oneself, which is also perceiving oneself as the source and source of one’s actions. Therefore, if there is no external control, people feel pleasure and personal fulfillment by the challenging the meaning and value of the work itself. Deci and Ryan [18] argues that this autonomy is the most essential element of the three basic psychological needs, explaining that the sense of competence can be properly exercised only when autonomy is guaranteed. In addition, autonomous behavior refers to behavior in which an individual reflects and adjusts his or her own thoughts according to the subject of the cause perceived internally from the perspective of Attributions Theory.

6

S. Hwang et al.

2.3 Hypothesis Setting In this study, the intrinsic behavior by an individual’s subjective perception was understood as the fundamental principle of self-determining innovative behavior. Therefore, to find out how the basic psychological needs constituting selfdetermination affect the expression and continuation of innovative behavior, the relationship between the variables is investigated through statistical analysis and implications accordingly.

2.3.1

Hypothesis Setting for Independent Variables

[H1a] Perceived competence will have a significant effect on innovation behavior. [H2a] Perceived Relatedness will have a significant effect on innovation behavior. [H3a] Perceived autonomy will have a significant effect on innovation behavior. 2.3.2

Hypothesis Setting for Independent Variables

[H2b] Perceived Relatedness will have a positive effect on innovation behavior by promoting the perceptual action of competence. [H3b] Perceived autonomy will have a positive effect on innovation behavior by promoting the perceptual action of competence. 2.3.3

Hypothesis Setting for Moderating Effect

[H3c] Perceived autonomy will play a controlling role in the relationship between perceived competence and innovation behavior. [H3d] Perceived autonomy will play a controlling role in the relationship between perceived Relatedness and innovation behavior.

3 Research Method 3.1 Data Collection A total of 355 copies of the data collection in this study were distributed and 296 copies were recovered, but 254 copies were used for analysis, excluding 8 unfaithful respondents and 34 copies not belonging to work or any organization.

Why do companies become similar, but performance is different?: Understanding …

7

3.2 Measurement The basic psychological needs scale used in this study was measured using the Korean-style scale developed by Lee Myung-hee and Kim A-young (2008) which based upon questions about competence, relatedness, and autonomy scales formulated by Ryan and Deci (2002). Each of the three sub-factors of competence, relatedness, and autonomy consisted of 10 questions. The total number of the questions is 30, and as a Likert-style 5-point scale from ‘Not at all (1) to’ Very Yes (5), the internal consistency was 0.90, showing high reliability. Innovative behavior, a dependent variable, was measured in the form of self-reporting based on the items of [5, 3]. The total number of the questions is 10, and as a Likert-style 5-point scale from ‘Not at all (1)’ to ‘Very Yes (5), the internal consistency was also 0.90, showing high reliability.

4 Empirical Analysis 4.1 Verification of The Reliability and Validity of Variables This study is intended to apply the structural equation model analysis to analyze the results and test the hypotheses.

4.1.1

Reliability Verification

Variable

Number of questions

Number of questions used

Cronbach’s alpha

Competence

10

5

0.721

Relatedness

10

9

0.892

Autonomy

10

7

0.833

Innovation behavior

10

9

0.926

4.1.2

Reliability Verification

As a result of factor analysis, it was found that each variable used in this study was conceptually distinguished from each other and conceptually valid.

8

S. Hwang et al.

4.1.3

Correlation Analysis

Variable

Average

Standard deviation

(C)

Competence (C)

3.8442

0.4978

1

(R)

(A)

(IB)

Relatedness (R)

3.7205

0.6024

0.331**

1

Autonomy (A)

3.9291

0.4694

0.356**

0.355**

1

Innovation behavior (IB)

3.7091

0.5396

0.341**

0.193**

0.394**

1

4.2 Hypothesis Testing 4.2.1

Hypothesis Verification for Independent Variables

Innovation behavior (Dependent Variable) Sig

Variables

Model 1

Control variable

Independent variable

Gender

Model 2-1

Model 2-2

Model 2-3

SRC

T

Sig.

SRC

T

Sig.

SRC

T

Sig.

SRC

T

Sig.

−0.043

−0.793

0.428

−0.044

−0.837

0.404

−0.075

−1.426

0.155

−0.040

−0.459

0.449

Age

0.436

6.318

0.000

−0.410

6.226

0.000

0.315

4.545

0.000

0.434

6.505

0.000

Academic

0.152

2.170

0.031

0.131

1.963

0.051

0.202

2.996

0.003

0.170

2.508

0.013

Occupation

−0.049

−0.893

0.373

−0.051

−0.986

0.325

−0.033

−0.620

0.536

0.289

5.281

0.000 0.225

4.322

0.000

Competence

−0.076

−1.452

0.148

0.268

5.176

0.000

Autonomy Relatedness

R2

0.290

0.359

0.362

0.340

Adj R2

0.279

0.346

0.349

0.326

F

25.421

27.801

28.110

25.518

Sig.

0.000

0.000

0.000

0.000

* Standardization regression coefficient: SRC

4.2.2

Hypothesis Verification of Mediating Effect

Independent variable

Mediate variable

Dependent variables

Mediating effect SRC verification phase

T

Sig.

R2

Relatedness

Competence

Innovation behavior

Step 1

5.565

0.000

0.109

0.331

Step 2

0.193

3.122

0.002

0.037

Step 3(Independent)

0.090

1.437

0.152

0.124

Step 3(Mediate) 0.311

4.971

0.000 (continued)

Why do companies become similar, but performance is different?: Understanding …

9

(continued) Independent variable

Mediate variable

Dependent variables

Mediating effect SRC verification phase

T

Sig.

R2

Autonomy

Competence

Innovation behavior

Step 1

0.356

6.053

0.000

0.127

Step 2

0.394

6.807

0.000

0.155

Step 3(Independent)

0.312

5.173

0.000

0.201

Step 3(Mediate) 0.230

3.807

0.000

4.2.3

Hypothesis Test for Modulating Effect

The modulating effect of Autonomy in the relationship between Competence and Innovation Behavior

Innovation behavior (dependent variable) Variable

Model 1 SRC

Control variable

Model 2-1 T

Sig.

SRC

T

Model 2-2 Sig.

SRC

T

Sig.

Gender

−0.044 −0.837 0.404 −0.067 −1.308 0.192 −0.070 −1.375 0.170

Age

0.410

6.226

0.000 0.327

4.820

0.000 0.297

4.307

0.000

Academic

0.131

1.963

0.051 .174

2.629

0.009 0.179

2.721

0.007

Occupation

−0.076 −1.452 0.148 −0.071 −1.383 0.168 −0.083 −1.618 0.107

Independent Competence variable Modulate variable

Autonomy

Interaction

Competence*

0.268

5.176

0.000 0.197

3.652

0.000 −0.366 −1.308 0.192

0.216

3.791

0.000 −0.217 −0.992 0.322 0.839

2.050

Autonomy R2

0.359

0.394

Adj R2

0.346

0.380

0.388

F

27.801

26.813

23.881

Sig.

0.000

0.000

0.000

0.405

* Standardization regression coefficient: SRC

The modulating effect of Autonomy in the relationship between Relatedness and Innovation Behavior

0.41

10

S. Hwang et al. Innovation behavior (dependent variable)

Variable

Model 1 SRC

Control variable

Interaction

T

Sig.

SRC

Model 2-2 T

Sig.

SRC

T

Sig.

Gender

−0.040 −0.759 0.449 −0.066 −1.276 0.203 −0.059 −1.147 0.253

Age

0.434

6.505

0.000 0.339

4.890

0.000 0.367

5.236

0.000

Academic

0.170

2.508

0.013 0.203

3.048

0.003 0.205

3.095

0.002

Occupation

−0.033 −0.620 0.536 −0.041 −0.790 0.430 0.036

−0.699 0.485

0.225

Independent Relatedness variable Modulate variable

Model 2-1

4.322

Autonomy

0.000 0.140

2.557

0.011 0.818

2.518

0.012

0.230

3.910

0.000 0.957

2.746

0.006

−1.167 −2.116 0.035

Relatedness* Autonomy

R2

0.340

0.378

0.389

Adj R2

0.326

0.363

0.372

F

25.518

25.037

22.402

Sig.

0.000

0.000

0.000

* Standardization regression coefficient: SRC

4.3 Summary of Hypothesis Verification In the verification of H1a, H2a, and H3a, individual’s innovative behavior is influenced by psychological needs. As a result of the analysis, the basic psychological needs of self-determination were found to have the same positive effect on innovative behavior as in previous studies, and all research hypotheses were adopted. H2b wants to be recognized as a capable person because its self-controls for relationships with others, which promotes self-esteem. In addition, the desire to perceive new standards through having relationships with others and to reach standards is driven by a desire to express better innovative behaviors. As with the analysis results in the prior research, the study theory was adopted because the ability in relational and innovative behavioral relationships appeared to play a role as a medium. H3b is more self-directed and competent for those who feel autonomy, and the functional autonomy of selffunctioning increases self-efficacy, leading to continued participation in innovative behavior. As with the analysis results in the prior research, the ability to act as a medium in relation to autonomy and innovative behavior appeared to play a role, and the research theory was adopted. H3c’s autonomous environment in support of an individual’s sense of competence promotes the internalization of motives, and externally synchronized behavior contributes to promoting self-control. As with the analysis results in the prior research, autonomy appears to play a role in regulating efficiency and innovative behavior, and research theory has been adopted. H3d has an autonomous environment that supports the relationship between others and stable

Why do companies become similar, but performance is different?: Understanding …

11

exchanges, which is associated with higher navigation activities of individuals, and intra-synchronized navigation activities when an individual has a satisfactory experience with a relationship desire. As with the analysis results in the prior research, autonomy appears to play a role in the relationship of innovative behavior, and research theory has been adopted.

5 Conclusions 5.1 Interpretation of Research Results In this study model, it was confirmed that competence, a basic psychological need for self-determination, composed of independent variables, has the most significant effect on the expression of innovative behavior, and relatedness is an important variable to maintain indirect influence and innovative behavior. In addition, autonomy affects the expression and continuation of innovative behavior, while also confirming it as a variable that directly affects people’s behavior. These results are consistent with Ryan and Deci (2002)’s basic psychological desire theory that "people can freely engage in self-determining behavior only when basic psychological needs are satisfied. In addition, hypotheses such as Ryan and Frederick (1997) and Waterman (1993) who argued that to express and maintain voluntary innovative behavior, basic psychological needs must be met throughout the entire process of individual innovation activities. The manifestation of innovative behavior identified by the results of this study can be interpreted that it promotes voluntary innovative behavior as stable exchange relationships with others in an autonomous environment increase individual competence. In addition, it was confirmed that the individual’s ability to adapt to social relations is self-regulated by an autonomous environment. In other words, self-determining innovative behavior can lead to obtaining positive innovative behavior results according to the satisfaction of basic psychological needs, and autonomous imitation relationships trigger active individual innovative behavior with perception of new standards.

5.2 Implications of Research Findings According to the research results, first, individual innovative behavior where corporate innovation begins will be expressed only when individuals’ basic psychological needs such as competence, relatedness, and autonomy are satisfied. Second, to continuously induce positive results of innovative behavior, it is necessary to promote exchange relationships with others and an autonomous environment. Third, an autonomous environment can manage the impact on the expression and elimination of innovative behavior because it promotes individual competence and maintains

12

S. Hwang et al.

an exchange relationship with others. Based on these research results, the understanding of corporate isomorphism was summarized as follows. The coercive and normative isomorphization phenomenon according to changes in external systems and norms is understood as a simple adaptation rather than a subsequent innovation according to universal standards. On the other hand, the act of self-awareness and imitation of new standards in exchange with others presupposes the acceptance of more innovative ideas, so if only an autonomous environment is continuously created, the expression of innovative behavior can be continuously induced. Therefore, the difference in performance due to the homomorphism of companies is expected to be high in imitation innovative companies. In addition, for the sustainable growth of a company, it is necessary to analyze how an autonomous environment affects the expression and maintenance of self-determining innovative behavior and find a way to sustain a stable exchange relationship.

5.3 Future Research Challenges In addition to the variables suggested in this study, future studies will provide an opportunity to broadly understand self-determining innovative behaviors by adding various unconscious perceptual variables such as embedded cognition formed by continuous and repeated exposure to physical environments including but not limited to local culture and architectural space.

References 1. P.J. DiMaggio, W.W. Powell, The new institutionalism in organizational analysis (University of Chicago Press, Chicago, 1991) 2. M. Fishbein, A consideration of beliefs and their role in attitude measurement, in Readings in Attitude Theory and Measurement (Wiley, New York) 3. R. Kanter, When a thousand flowers bloom: structural, collective, and social conditions for innovation in organizations, in Research in organizational behavior, vol. 10, ed. by B.M. Staw, L.L. Cummings (1988), p. 169 4. A.H. Van De Ven, Central problems in the management of innovation. Manag. Sci. 32, 590–607 (1986) 5. S.G. Scott, R.A. Bruce, Determinants of innovation behavior: a path model of individual innovation in the workplace. Acad. Manag. J. 37(3), 580–607 (1994) 6. M. Howell, C.A. Higgens, Champions of technological innovation. Admin. Sci. Q. 35, 317–41 (1990) 7. D.B. Rubin, Comment: which ifs have causal answers. J. Am. Statist. Assoc. 81(396), 961–962 (1986) 8. J.L. Farr, C.M. Ford, Individual innovation, in Innovation and creativity at work, ed. by M.A. West, J.L. Farr (Wiley, New York, 1990), pp. 63–80 9. E.L. Deci, R.M. Ryan, Intrinsic motivation and self-determination in human behavior (Plenum, NY, 1985) 10. S. Harter, Pleasure derived from optimal challenge and the effects of extrinsic rewards on children’s difficulty level choices. Child Dev. 49, 788–799 (1978)

Why do companies become similar, but performance is different?: Understanding …

13

11. R. Baumeister, M.R. Leary, The need to belong: desire for interpersonal attachments as a fundamental human motivation. Psychol. Bull. 117, 497–529 (1995) 12. E.L. Deci, Intrinsic Motivation. (New York: Plenum Publishing Co. Japanese Edition, Tokyo: Seishin Shobo 1975) 13. J. Levesque, Y. Joanette, B. Mensour, G. Beaudoin, J.M. Leroux, P. Bourgouin, M. Beauregard, Neural basis of emotional self-regulation in childhood. Neuroscience 129, 361–369 (2004) 14. R.W. White, Motivation reconsidered: the concept of competence. Psychol. Rev. 66, 297–333 (1959) 15. A. Bandura, Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev. 84, 191–215 (1986) 16. J. Belsky, L.D. Steinberg, The effects of day care: a critical re view. Child Dev. 49(4), 92–949 (1978) 17. W.C. Becker, Teaching reading and language to the disadvantaged—what we have learned from field research. Harvard Educ. Rev. 47(4), 518–543 (1977) 18. E.L. Deci, R.M. Ryan, The “what” and “why” of goal pursuits: human needs and the selfdetermination of behavior. Psychol. Inquiry 11, 227–268 (2000)

A Study on the Customer Experience Management (CEM) by Applying Walk-Through Audit (WtA); Focused on Hospitality Service Cases Hye Jung Kim, Myeong Sook Park, and Jiin Hwang

Abstract This study conducted a case study by applying Walk-through Audit (WtA) to hotel hospitality services based on an understanding of customer experience management (CEM). To apply WtA, the researcher repeatedly tested the hospitality service of Conrad Seoul Hotel, identified the Blueprint of the hospitality service based on personal experience, and pinpointed a total of eight Moment of Truth (MOT) that have a significant impact on customer experience through Customer Journey Analysis. By applying WtA to the identified MOT, the difference in perception between users and related parties regarding the service was confirmed. First, when looking at Conrad Seoul Hotel’s hospitality service from the perspective of CEM, Conrad Seoul Hotel provided various promotional programs and experienced services to users, and through this, users could experience the unique and impressive hospitality service of Conrad Seoul Hotel. However, in terms of the service provided, there was a large difference in service awareness between the users who experienced main services such as room service and promotion program experience, food & Beverage (F&B), and related parties providing services, as the users who experienced services provided by the related parties implied relatively negative encounters. Therefore, if Conrad Seoul Hotel can improve this process in terms of user experience management, the hospitality service quality of Conrad Seoul Hotel can be further improved. As such, this study is meaningful in that it has laid the foundation for understanding customer experience management more clearly and broadly by applying the customer experience management technique to the practical case of a hotel. Keywords Hospitality services · Customer experience management (CEM) · Walk-through audit (WtA) · Moment of truth (MOT)

H. J. Kim Department of Business Administration, Soongsil University, Seoul, South Korea M. S. Park (B) Graduate of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] J. Hwang Department of Innovation Management, Soongsil University, Seoul, South Korea © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_2

15

16

H. J. Kim et al.

1 Introduction Economic value has grown in leaps and bounds, shifting from hard and soft commodities such as the “Extract-Commodity” in the era of the collection economy to “MakeGoods” in the era of the industrial economy to “Service-Delivery” in the era of the service economy, and recently, “Stage-Experience” has become a key economic value. As the importance of experiential products experienced in the experience process increases, many scholars are striving to explain customer experience-based management. Customer experience is fundamentally an emotional value, and it is outside the scope of management because it is an element that an individual feels in life due to intangible events based on the individual’s position and understanding of the situation as well as his past experiences according to the cultural and social background. Therefore, [1] pro-posed a CEM process to efficiently manage customer experiences by analyzing customer experiences and then designing services for customer discriminatory experiences followed by promoting continuous improvement and innovation. This means designing for the customer’s experience and providing an environment in which the operations manager can provide impeccable experiences by adjusting relevant factors [2]. In addition, customer experience management im-proves customer loyalty by identifying customers’ needs and maximizing their satisfaction by providing seamless, high-quality hospitality services. Although, as seen, customer experience management is emerging as a crucial factor in this era of the experience economy, the importance of planning and developing inter-active facilities based on customer experience management systems is often overlooked, requiring a lot of effort and improvement Therefore, by applying Walkthrough Audit to the hospitality service of Conrad Seoul Hotel, operated by the global brand Hilton, this study was conducted to confirm the cognitive differences and different perceptions regarding the hospitality service of the hotel and of the related parties as well as certain areas to improve for better service in the future. In this study, the contact points between hotel users and hospitality service systems were subdivided by applying Service Blueprint analysis, then MOT affecting the user experience was identified through Customer Journey Analysis. Moreover, in order to measure and meaningfully evaluate the user’s experience, the WtA method, formulated by Fitzsimmons and Maurer (1991) to focus on the customer’s perspective, was utilized. Since the application of WtA to hotel hospitality services enables systematic evaluation of users’ perceptions of the hospitality service, customer loss can be prevented in advance by managing their un-satisfactory experiences related to service points. In addition, the applicability and implications of customer experience management are derived by evaluating the difference in perception between users and hotel facility managers in the service delivery process.

A Study on the Customer Experience Management (CEM) by Applying …

17

2 Theoretical Background 2.1 Customer Experience Management (CEM) Experience refers to a result of encountering, undergoing or living through situations. It is a triggered stimulation to the senses. In essence, experiences provide sensory, emotional, cognitive, behavioral, and relational values that replace functional values [1]. In this context, customer experience is defined as a set of contacts in which sensory stimuli, information, and emotions are exchanged between a company and a customer [3]. In other words, customer experience is formed by a mixture of the physical performance that a company provides to customers and the customer’s personal feelings that arise from feeling the difference between what the customer expected at all interfaces [4]. Therefore, companies should build customer loyalty through strategic management and a personalized approach so that customers can perceive the tangible and intangible brand value and have an overall satisfying experience. Therefore, for the sustainable growth of a company, a process of integrating and managing all the factors regarding customer experience like customer experience management is vital. CEM con-sists of a total of four stages. The first stage is to identify all touchpoints on the customer value chain and subdivide the customer experience process into individual touchpoints. The second step is to identify customer events and/or the most critical MOT that has the biggest influence in terms of customer loyalty on the customer value chain. The third step is to develop procedures and services to provide a significant experience to customers in compliance with their values and characteristics. A strategic and differentiated customer experience should be designed in this step. The last step is to systematically assess the customer experience and continuously promote a culture of improvements based upon the indicators from evaluating customer experience activities. Customer experience management examined through such theoretical consideration is a management strategy, and at the same time, a management concept that focuses on process and execution, not the result of a branded process.

2.2 Understanding Hospitality Services Hospitality mainly refers to friendly behavior that welcomes customers who experience services at hotels [5]. And Brotherton [6] defines it as "kindness in welcoming strangers or guests". In other words, hospitality is an important means and concept to provide a pleasant experience to customers and more specifically, an experience of “being moved”. So, King (1995) argues that making customers “feel at home” is one of the most important objectives of hospitality. The field in which this outlook can be applied is constantly expanding as the concept of hospitality is academically examined and developed academically considered by dividing it into specificity, structuring, and mean characteristics. In line with this trend, various scholars

18

H. J. Kim et al.

consider hospitality applications and boundaries, including the structural and behavioral characteristics of tourism, leisure, experience, finance, products, processes, and the service industry. This suggests that it shows infinite possibilities in the field [7]. Research on hospitality in the past has focused on service providers, but recently, there is a big change as the focus is shifting toward the service recipients. This change in perspective suggests that the basic paradigm of marketing or service is switching to consumers, that is, customers, not suppliers. In particular, hospitality from the perspective of the recently emerging experience economy includes both service provision to customers and the physical environment as well as the experience of employees who are service providers [8]. In other words, hospitality can be said to be a complex concept that includes physical, psychological, mental, and emotional interactions. Therefore, hospitality in the hotel industry can be defined as a deliberately designed and introduced simultaneous interchange that enhances the mutual interests of the parties concerned through the provision of Accommodation, F&B, etc. [6]. Therefore, hospitality is being studied on the ethics of welcome, power relationships between owners and guests, and interactions. Hospitality has grown spontaneously in history. This has been formed and developed in terms of social convention rather than academic and theoretical aspects [9]. In the past, hospitality was thought around the human body. In other words, complex sensibilities such as food and beverage expanded the materiality of hospitality. However, in modern times, it is expanding into a phenomenological concept of experiencing a realistic heterotopia that performs a utopian function through hospitality. In other words, it is assumed that multiple spaces or areas that are usually incompatible by themselves in one realistic place can be parallelized through temporal division. For the realization of such hospitality, the design and management of spaces that match the behavior of employees and the manipulation of material objects are becoming important key points. Like many other management issues, it will be possible to maximize the effectiveness of hospitality service within the company and enhance its brand reputation or corporate value if an in-depth evaluation and accurate understanding of customers is achieved.

3 Walk-Through Audit on Hospitality Services Facilities 3.1 Overview of Target Facilities The Conrad Seoul Hotel is named after the founder of the Hilton Hotel, Conrad Hilton. The Conrad Seoul Hotel was planned for three years from 2003, and the construction began in 2006 and was completed in 2012 and opened before the opening of the Seoul International Financial Center. The location of the facility is a hotel attached to IFC, International Finance-ro, Yeongdeungpo-gu, Seoul, Ko-rea, with a 200 m roof and 184 m top floors, and 37 stories above the ground, making it the second-highest building in the Seoul International Financial Center complex. There are various

A Study on the Customer Experience Management (CEM) by Applying …

19

facilities such as a lobby on the first floor, restaurants on the second to ninth floors, a banquet room, a business center, a spa, a fitness club, and a swimming pool on the eighth floor, and a total of 434 rooms on the 10th to 36th floors. In addition, it consists of a sky lounge and restaurant on the 37th floor, providing various hospitality services. Entering the main gate on the first floor, a 36.5-m-long spiral staircase leading to the fifth floor stands out symbolically.

3.2 Design of Walk-Through Audits In this study, in order to apply an experience survey to the hospitality service of Conrad Seoul Hotel, first, a service blueprint was designed to give an effective visual display of the characteristics of the hospitality service process of Conrad Seoul Hotel. Then, a detailed service contact was assessed by applying a custom-er movement analysis technique in order to understand the scope and roles of employees in the process of delivering services to users from Conrad Seoul Hotel and the service contact with users throughout the hospitality service process. The service touchpoint was also analyzed by deriving a detailed MOT by applying the same technique. That is to say, a total of 8 MOTs provide significant experiences to users since they arrive at the Conrad Seoul Hotel until they leave were derived. Also, the WtA analysis technique was applied and a questionnaire from a study by Fitzsimmons and Maurer (1991) was reconstructed by the researcher by modifying and making necessary adjustments for Conrad Seoul Hotel to measure and evaluate the customer experience (Figs. 1 and 2).

Fig. 1 Hospitality service blueprint

20

H. J. Kim et al.

Fig. 2 Customer movement path analysis

4 Research Method The WtA survey on the Conrad Seoul Hotel consists of 31 questions based on the experience of the customers who have actually experienced the hospitality services. The user’s assessment on the questionnaires was measured following the Likert scale, a psychometric response scale in which responses can be specified in five points according to the level of agreement, converting a very negative response to 1 and a very positive answer to 5. The WtA Questions were divided into a total of eight stages, including Arrive and Entry, Reception & Response, Room Interior, Room Service, Subsidiary Facility, Business Centre & Meeting Room, Perform Events, Evaluate Hospitality Services. Each question was de-signed and selected based on the comprehensive hospitality services that users would acknowledge throughout their stay.

4.1 Data Collection The subjects of the survey were 30 users who visited the Conrad Seoul Hotel in person and experienced the overall facility as well as 18 resident employees who provided the hospitality services. Initially, Conrad Seoul Hotel’s general manager was also planned to be included on the list as one of the responders but was later excluded because the position is supervised by the Headquarter’s promotion team due to being managed by a global brand company.

A Study on the Customer Experience Management (CEM) by Applying …

21

Table 1 Arrive and entry Questions

Average score Standard deviation User

Staff

User

Staff

t

p

➀ Is a parking sign easy to recognize?

3.906

4.846

0.554

0.000

8.941

0.000

➁ Are valet and parking facilities convenient?

4.219

4.769

0.659

0.439

2.763

0.008

➂ Is an entrance sign easy to identify?

4.500

4.694

0.672

0.599

1.255

0.216

➃ Is the entrance clean?

4.469

4.846

0.761

0.376

2.218

0.032

4.2 Analysis of WtA Results for Hospitality Services The results of the WtA survey were divided into two groups: users and service staff. Then the average value of each group was calculated and compared. The chart comparing those two values displays the difference in the perception of users and service employees about hospitality services. The results are as follows. As shown in Table 1, it is confirmed that there was the smallest difference between users and staff in opinions regarding the arrival area and the entrance of Conrad Seoul Hotel. Nevertheless, many users gave opinions that parking lot signs are difficult to recognize, thus it needs some adjustments. In addition, adjustments should be made to enhance the convenience of customers in valet parking and parking lots. The level of cleanliness of the entrance is similar to the user’s expectations and experiences, so the quality of hospitality service is not affected, but it may be neglected due to the biased perception of some staff, so continuous management is required. As shown in Table 2, certain degrees of a discrepancy between users and staff are observed in different areas. In particular, when staff fails to give adequate attention while providing a hospitality service can be interpreted as having a work-overload or a simple demonstration of repeated gestures rather than providing an optimized service. In this case, it would be necessary to hire more employees to distribute the workload or to make impactful changes to customer service training. For instance, employees feel that the processing speed of the reception desk is delayed, while users are more understanding and tolerant to-wards the delay by regarding it as a more thorough procedure for being a global brand. In addition, it has been confirmed that users have lower expectations of the procedures in the waiting room after being assisted in the reception. Even the staff responded that the service was not attentive enough. Therefore, if an extra staff member is assigned in the reception area to assist and provide any necessary guidance after receiving the guest, potential problems that may occur around the reception desk area will be solved, reducing friction and increasing positive touchpoints. As shown in Table 3, there is little difference in perception between users and staff about the room, but the experiences in the journey of getting to the as-signed room were evaluated differently. This disparity may have been caused by the familiarity with the hotel. While employees feel familiar with the structure and layout of the

22

H. J. Kim et al.

Table 2 Reception and response Questions

Average score

Standard deviation

User

User

Staff

t

p

Staff

➀ Did a staff greet customers first?

4.281

4.846

0.634

0.376

3.692

0.001

➁ Did a staff meet customers’ eyes and actively respond to them?

3.375

4.539

0.871

0.519

4.487

0.000

➂ Was the reception area well organized? 4.219

4.615

0.608

0.506

2.073

0.044

➃ Was reception processed at a rapid speed?

4.375

3.615

0.833

0.961

2.653

0.011

➄ Did a staff give an explanation on 3.313 waiting area and procedures after receipt?

3.615

0.780

0.768

1.185

0.242

➅ Did a staff smile at customers and say goodbye after completion of a reception process?

4.846

0.822

0.376

3.675

0.001

3.969

Table 3 Room interior Questions

Average score Standard deviation

t

p

User

Staff

User

Staff

➀ Was it easy to find assigned room (Directions and signages on each floor., etc.)?

3.500

4.769

0.474

0.376

5.521

0.000

➁ Do you think the lighting in the rooms and corridors is adequate?

4.094

4.769

0.856

0.439

2.692

0.010

➂ Do you think the facilities and equipment in 4.031 the room are well maintained?

4.846

0.740

0.376

3.761

0.001

hotel, users tend to feel uncomfortable in unfamiliar spaces. Therefore, it is concluded that more guided maps and assistance in direction are necessary. As shown in Table 4, there is little difference in perception between users and employees about room service. As a global brand, various types of room services were provided; the appearance of employees who provide room services was neat, and the service quality was also consistent and friendly. However, the users responded that the attitude of the staff and the wait time upon the user’s requests for room service need to be improved. This difference in perception is caused by less feedback in the process than the physical attitude. Therefore, operational and service improvements are needed to implement processes to communicate with guests in real-time, resolve issues efficiently, and help their under-standing, which will eventually drive higher guest satisfaction. As shown in Table 5, there are inconsistencies in perceptions regarding subsidiary facilities between users and staff. From this result, it can be deduced that there is a clear lack of respect for customers. Especially in the fitness area, It can be inferred that the service provided by the staff was inconsistent and not pro-vided

A Study on the Customer Experience Management (CEM) by Applying …

23

Table 4 Room service Questions

Average score

Standard deviation

t

p

User

Staff

User

Staff

➀ Was the information about the room service well arranged?

3.813

4.846

0.998

0.376

5.045

0.000

➁ Were there various types of room service?

4.375

4.769

➂ Was the response to the room service request friendly and appropriate?

3.500

4.539

0.609

0.439

2.427

0.021

0.950

0.519

4.694

0.000

➃ Was the waiting time for room service appropriate?

3.125

4.846

0.793

0.376

7.454

0.000

➄ Was the appearance of the staff providing room service neat and clean?

4.125

4.615

0.554

0.506

2.757

0.009

➅ Despite the various room service requests, was the staff’s attitude always kind?

4.188

4.769

0.535

0.599

3.194

0.003

equally to all customers. Due to having a strong sales and marketing presence in the Fitness profession, the staff also acted in a more controlling position. The controlling management style and regulations based on responsibilities and safety seem to have caused such differences in perception. So, it is more effective to nurture the guest experience by enforcing autonomous norms within the facilities with continuous interest in customers. Therefore, constant efforts, with a greater focus on customer experience strategy, need to be made to improve the negative experiences. As shown in Table 6, there is a difference in the perception of users and staff in charge of business and meeting rooms. Uniform business support that does not consider the specificity of each user can also affect the hotel brand image in the future. Therefore, it is necessary to develop, and supplement differentiated sup-port programs tailored to each user’s situation and level. In addition, in order to respond to users’ unscheduled individual service requests, it is necessary to identify users’ meeting characteristics and attendees in advance, organize predictable scenarios, and prepare for unexpected situations. It is necessary to foster professional staff to provide effective business support to users rather than simply renting locations and equipment. As shown in Table 7, the reason why users perceive the information mark of wellorganized banquet hall facilities negatively is understood as a desire for the help of employees familiar with the facility rather than a physical system. The staff’s pride in the brand regarding the appropriateness of the planned event and the degree of preparation is relieving some of the users’ anxiety, but there is a big difference in perception in terms of hospitality services. In particular, the reason why many users feel that the planning and composition of the event are inappropriate for publicity is that it is a well-managed event, but it has not been effective. As shown in Table 8, overall satisfaction with Conrad Seoul Hotel is similar to that of employees and viewers. This is understood to be the degree of satisfaction considering the user’s global brand. The reason for this is that despite the difference

24

H. J. Kim et al.

Table 5 Subsidiary facility Questions

Average score Standard deviation

t

p

User

Staff

User

Staff

➀ Did the staff use honorifics when addressing the user?

3.375

4.539

0.871

0.519

4.487

0.000

➁ When entering the subsidiary facility, did the staff guide you through the procedure?

3.844

4.846

0.554

0.000

8.941

0.000

➂ Did the staff in charge greet the user before 4.031 they started to guide the use of the Subsidiary facilities?

4.846

0.723

0.376

6.078

0.000

➃ Were the staff at the subsidiary facilities professionally prepared and welcomed new users?

3.063

4.615

0.948

0.506

3.101

0.000

➄ Did the staff in charge of the Subsidiary facilities provide a detailed overview and features of the facilities?

3.550

3.846

0.916

0.689

1.226

0.2227

➅ Did the staff in charge of the subsidiary 3.000 facilities guide you in a personalized manner, rather than in a passive manner?

4.769

0.842

0.439

7.155

0.000

t

p

Table 6 Business centre and meeting room Average score

Standard deviation

User

Staff

User

Staff

➀ Was the procedure convenient?

3.938

4.846

0.718

0.439

7.218

0.000

➁ Was the secretary’s service provided for the user’s meeting?

3.500

4.769

0.803

0.439

6.789

0.000

➂ Was the user meeting area provided with 3.469 adequate space and equipment?

4.846

1.016

0.376

6.637

0.000

➃ Was there an optional/additional private meeting space after the meeting?

4.615

1.915

0.650

4.103

0.000

t

p

Questions

3.469

Table 7 Perform events Questions

Average score Standard deviation User

Staff

User

Staff

➀ Was the Banquet Hall & function room for 4.125 the event easy to find?

5.000

0.707

0.000

11.000

0.000

➁ Was the size of the event facilities managed properly and cleanly?

3.781

4.846

0.870

0.376

5.733

0.000

➂ Was the plan and composition appropriate for the promotion of the event?

3.313

4.769

0.896

0.439

7.296

0.000

A Study on the Customer Experience Management (CEM) by Applying …

25

Table 8 Perform events Questions

Average score

Standard deviation

t

p

User

Staff

User

Staff

➀ Are you satisfied with the use of Conrad Seoul Hotel?

4.313

4.769

0.592

0.599

2.337

0.024

➁ Do you plan to continue using the Conrad Seoul Hotel in the future?

4.219

4.385

0.792

0.870

0.619

0.539

➂ Would you recommend the hospitality service experience of Conrad Seoul Hotel to people around you?

4.000

4.846

0.916

0.376

4.396

0.000

in perception that occurs at each customer contact point, the intention to use it continuously is due to the perception of brand power. However, the fact that the user’s willingness to recommend is lower than the employee’s means that the actual experience of hospitality service is negative. Therefore, improvement is needed in many areas of hospitality service.

5 Conclusions Through this study, it was confirmed when hotel users evaluate the quality of the service, not only does the service from the hotel itself play an important role but also the process in which it was delivered. In other words, the user must have a fulfilling experience throughout the overall service process to be satisfied with the service as a result. This means balanced operations management is crucial in hospitality services. To provide a satisfactory hotel experience to users, it is necessary to develop a customer-oriented and differentiated service so that users can experience hospitality only for themselves in the service process, rather than insisting on standardized and stable services of global brand hotels. In other words, it is necessary to improve the attitude of operations and employees who rely solely on brand power to avoid developing discriminatory attributes that could misrepresent the brand. Global brands should be transformed into valuable brands by developing relationship values with customers based on their value as a brand.

References 1. B. Schmitt, Experiential Marketing (The Free Press, New York, 1999) 2. M.E. Pullman, M.A. Gross, Welcome to your experience: where you can check out anytime you’d like, but you can never leave. J. Bus. Manag. 9(3), 215–232 (2003) 3. S. Robinette, V. Lenz, Emotion Marketing: The Hallmark Way of Winning Customers for Life (McGraw-Hill, New York, NY, 2002)

26

H. J. Kim et al.

4. C. Shaw, J. Ivens, Building Great Customer Experience (Palgrave Macmillan, New York, NY, 2002) 5. R. Pijls, B.H. Groen, M. Galetzka, A.T.H. Pruyn, Measuring the experience of hospitality: scale development and validation. Int. J. Hosp. Manag. 67, 125–133 (2017) 6. B Brotherton, Towards a definitive view of the nature of hospitality and hospitality management. Int. J. Contemp. Hosp. Manag. 165–173 (1999) 7. P. Nailon, Theory in hospitality management. Int. J. Hosp. Manag. 1(3), 135–143 (1982) 8. N. Hemmington, From service to experience: understanding and de-fining the hospitality business. Serv. Indus. J. 27(6), 747–755 (2007) 9. P. Lugosi, Hospitality and organizations: enchantment, entrenchment and reconfiguration. Hosp. Soc. 4(1), 75–92 (2014)

A Study on the Factors of Positive Psychological Capital on Organizational Citizenship Behavior: For Teachers of Infant Welfare Service Institutions Mi Sook Moon and Hannah Park

Abstract The purpose of this study is to identify the structural relationships among organizational identification, psychological well-being, and role performance and the moderating effect of the director’s coaching leadership in order to analyze the effect of positive psychological capital of childcare teachers on organizational citizenship behavior thereby enhancing the competitiveness of infant welfare service institutions and presenting strategic orientation. According to the findings of this study, first, among the subcomponents of positive psychological capital, all of self-efficacy, hope, resilience, and optimism had positive effects on organizational identification, psychological well-being, and role performance and the effect of hope on role performance was not significant. Second, organizational identification and psychological well-being had positive effects on role performance. Third, organizational identification, psychological well-being, and role performance had positive effects on organizational citizenship behavior. Fourth, multi-group analysis was utilized to verify the moderating effect of coaching leadership and according to the results, the director’s coaching leadership had moderating effects on the effects of organizational identification, psychological well-being, and role performance on organizational citizenship behavior. Keywords Positive psychological capital · Organizational citizenship behavior · Organizational identification · Psychological well-being · Role performance · Coaching leadership · Welfare service

M. S. Moon (B) Department of Business Administration, Graduate School, Soongsil University, Seoul, South Korea e-mail: [email protected] H. Park Pomfret School CT, Connecticut, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_3

27

28

M. S. Moon and H. Park

1 Introduction In the midst of steady decreases in the fertility rate from the 1970s to 2022 and fierce competition among infant welfare service organizations to secure a competitive advantage, which is intensifying, the perception that the role of childcare teachers who provide human services on behalf of institutions at the service encounter is enormous is being magnified [1]. In this respect, one of the subjects that attract the greatest attention recently to improve the quality of childcare services of childcare teachers is positive psychological capital based on human positive psychology [2]. In addition, among the factors that are affected by positive psychological capital, the one that is receiving the greatest attention these days in relation to the creation of outcomes is organizational citizenship behavior, which is voluntarily contributing to organizational performance even without any reward system [3, 4], and it has been told that organizational citizenship behavior, which is faithfully performing the role of discretionary and voluntary contribution as well as the prescribed role performance given to members, is an important variable for an organization to achieve successful performance [5]. As such, previous studies reported the importance of positive psychological capital and organizational citizenship behavior for the creation of organizational performance but most of those studies were conducted with general business organizations and previous studies on the identification of concrete relationships through which positive psychological capital is connected to organizational citizenship behavior conducted with childcare teachers at infant welfare service institutions are still lacking. Therefore, this study is intended to overcome such limitations and empirically investigate the structural path through which the positive psychological capital of childcare teachers is connected to the formation of organizational citizenship behavior. In addition, in the study of Kim and Kim [6] that applied coaching leadership [7], which is a behavior of the leader to promote the learning and growth of organizational members for the achievement of the goal of the organization, to middle managers of infant welfare service institutions, there were positive changes such as personal changes and improvement of communication among members. Since such research data explains that the coaching leadership of the director is a variable that has an important influence on teachers in the infant welfare service institution, it is necessary to actually verify whether the coaching leadership of the director has a moderating effect on the influencing relationships between variables. As described above, the significance of this study is in that after analyzing the structural paths of the effects of positive psychological capital on organizational citizenship behavior by the sub-components of positive psychological capital of childcare teachers (self-efficacy, hope, resilience, optimism) and the moderating effect of the coaching leadership of the director, this study presented academic and practical implications through the provision of strategic orientation for the enhancement of the competitiveness of infant welfare service institutions and securing competitive advantages of the same institutions.

A Study on the Factors of Positive Psychological Capital on Organizational …

29

2 Theoretical Background and Hypotheses 2.1 The Relationship Between Positive Psychological Capital and Organizational Identification, Psychological Well-Being, and Role Performance In a previous study related to positive psychological capital and organizational identification, Romero and Cruthirds [8] demonstrated that positive psychological capital can increase organizational competitiveness by reducing negativity and increasing positivity, and in a previous study related to psychological well-being, it was indicated that positive psychological capital had positive (+) effects on the subjective well-being of adolescents [9], and in a previous study related to role performance, it was said that persons with positive tendencies actively accept and cope with changes and positively (+) affect organizational capability maximization and organizational performance [10]. Through the above previous discussions, Hypothesis 1, Hypothesis 2 and Hypothesis 3 were established as follows. Hypothesis 1. Positive psychological capital will have positive (+) effects on organizational identification. Hypothesis 1–1. Self-efficacy will have a positive (+) effect on organizational identification. Hypothesis 1–2. Hope will have a positive (+) effect on organizational identification. Hypothesis 1–3. Resilience will have a positive (+) effect on organizational identification. Hypothesis 1–4. Optimism will have a positive (+) effect on organization-al identification. Hypothesis 2. Positive psychological capital will have positive (+) effects on psychological well-being. Hypothesis 2–1. Self-efficacy will have a positive (+) effect on psycho-logical well-being. Hypothesis 2–2. Hope will have a positive (+) effect on psychological well-being. Hypothesis 2–3. Resilience will have a positive (+) effect on psychological wellbeing. Hypothesis 2–4. Optimism will have a positive (+) effect on psychological wellbeing. Hypothesis 3. Positive psychological capital will have positive (+) effects on role performance. Hypothesis 3–1. Direction presentation will have a positive (+) effect on role performance. Hypothesis 3–2. Hope will have a positive (+) effect on role performance. Hypothesis 3–3. Resilience will have a positive (+) effect on role performance. Hypothesis 3–4. Optimism will have a positive (+) effect on role performance.

30

M. S. Moon and H. Park

2.2 Relationships Between Organizational Identification, Psychological Well-Being, and Role Performance Among previous studies related to organizational identification and role performance, there is a study indicating that organizational identification has a positive (+) effect on an individual’s active behavior toward the organization [11] and a study indicating that organizational identification has a positive (+) effect on an individual’s work performance attitude [12] and among previous studies related to psychological well-being and role performance, a study indicated that persons with high psychological well-being had high task performance ability [13] and a study indicated that psychological well-being of childcare teachers positively (+) affect the solution of the internal conflicts of infants, child protection, safe environment, knowledge and information, etc. [14]. Based on the above previous studies, Hypothesis 4 and Hypothesis 5 were established as follows. Hypothesis 4. Organizational identification will have a positive (+) effect on role performance. Hypothesis 5. Psychological well-being will have a positive (+) effect on role performance.

2.3 Relationships Between Organizational Identification, Psychological Well-Being, Role Performance, and Organizational Citizenship Behavior In a previous study related to organizational identification and organizational citizenship behavior, O’Reilly and Chatman [15] stated that organizational identification had a positive (+) effect on organizational citizenship behavior (cooperation, altruism, and voluntary non-rewarding behavior). In a previous study on the relationship between psychological well-being and organizational citizenship behavior, Kwon and Lim [16] stated that there is psychological well-being had positive effects on organizational citizenship behavior. In a previous study on role performance and organizational citizenship behavior, Fredrickson [17] said that role performance had a positive effect on organizational citizenship behavior. Based on the above previous studies, Hypothesis 6, Hypothesis 7 and Hypothesis 8 were established as follows. Hypothesis 6. Organizational identification will have a positive (+) effect on organizational citizenship behavior. Hypothesis 7. Psychological well-being will have a positive (+) effect on organizational citizenship behavior. Hypothesis 8. Role performance will have a positive (+) effect on organizational citizenship behavior.

A Study on the Factors of Positive Psychological Capital on Organizational …

31

2.4 The Moderating Effect Relationships Between Coaching Leadership and Variables In a previous study related to coaching leadership and positive psychological capital (self-efficacy, hope, resilience, optimism), Meyer et al. [18] indicated that coaching leadership had a positive (+) effect on self-efficacy through trust relationship formation. Baron and Morin [19] and Moen and Allgood [20] also indicated that coaching leadership had a positive (+) effect on self-efficacy. In a previous study related to coaching leadership and organizational identification, Ha and Do [21] said that the coaching leadership of the director has a positive (+) effect on organizational identification. In a previous study related to coaching leadership and psychological wellbeing, Ko and Do [22] stated that coaching leadership of the director had a positive (+) effect on psychological well-being. In a previous study related to coaching leadership and role performance, Nielsen and Munir [23] stated that coaching leadership had a positive (+) effect on autonomous task performance ability. In a previous study on coaching leadership and organizational citizenship behavior, Smith et al. [24] stated that coaching leadership support activities had a positive (+) effect on organizational citizenship behavior. Based on the discussions of these previous studies as such, Hypothesis 9 was established as follows. Hypothesis 9. Coaching leadership will have moderating effects through group differences in the paths between variables. Hypothesis 9–1. Coaching leadership will have moderating effects on the relationship between self-efficacy and organizational identification into a positive (+) relationship. Hypothesis 9–2. Coaching leadership will have moderating effects on the relationship between hope and organizational identification into a positive (+) relationship. Hypothesis 9–3. Coaching leadership will have moderating effects on the relationship between resilience and organizational identification into a positive (+) relationship. Hypothesis 9–4. Coaching leadership will have moderating effects on the relationship between optimism and organizational identification into a positive (+) relationship. Hypothesis 9–5. Coaching leadership will have moderating effects on the relationship between self-efficacy and psychological well-being into a positive (+) relationship. Hypothesis 9–6. Coaching leadership will have moderating effects on the relationship between hope and psychological well-being into a positive (+) relationship. Hypothesis 9–7. Coaching leadership will have moderating effects on the relationship between resilience and psychological well-being into a positive (+) relationship.

32

M. S. Moon and H. Park

Hypothesis 9–8. Coaching leadership will have moderating effects on the relationship between optimism and psychological well-being into a positive (+) relationship. Hypothesis 9–9. Coaching leadership will have moderating effects on the relationship between self-efficacy and role performance into a positive (+) relationship. Hypothesis 9–10. Coaching leadership will have moderating effects on the relationship between hope and role performance into a positive (+) relationship. Hypothesis 9–11. Coaching leadership will have moderating effects on the relationship between resilience and role performance into a positive (+) relationship. Hypothesis 9–12. Coaching leadership will have moderating effects on the relationship between optimism and role performance into a positive (+) relationship. Hypothesis 9–13. Coaching leadership will have moderating effects on the relationship between organizational identification and role performance into a positive (+) relationship. Hypothesis 9–14. Coaching leadership will have moderating effects on the relationship between psychological well-being and role performance into a positive (+) relationship. Hypothesis 9–15. Coaching leadership will have moderating effects on the relationship between organizational identification and organizational citizen-ship behavior into a positive (+) relationship. Hypothesis 9–16. Coaching leadership will have moderating effects on the relationship between psychological well-being → organizational citizenship behavior into a positive (+) relationship. Hypothesis 9–17. Coaching leadership will have moderating effects on the relationship between role performance → organizational citizenship behavior into a positive (+) relationship.

3 Research Method A total of 368 copies of the data collection in this study were distributed and 328 copies were recovered. After that, 310 copies were used for analysis, excluding unfaithful respondents (Table 1).

4 Analysis and Result As a result of the analysis, it was found that each variable used in this study was conceptually distinguished from each other and conceptually valid (Tables 2 and 3). As a result of this study, all of Hypothesis 1 through Hypothesis 8 were adopted except for Hypothesis and Hypotheses 9–15, 9–16, and 9–17 were adopted (Tables 4 and 5).

A Study on the Factors of Positive Psychological Capital on Organizational … Table 1 Characteristics of sample

Category and items Gender Age

Educational background

Sample size

Position

Male

3

1.0

307

99

20 s

57

18.4

30 s

56

18.1

40 s

119

38.4

50 s

73

23.5

60 s

5

1.6

high school graduation

51

16.5

Graduation from junior college

152

49

Graduation from college

97

31.3 3.2

1 or 2 years

43

13.9

3 or 4 years

70

22.6

5 or 6 years

60

19.4

7 or 8 years

37

11.9

Over 9 years

100

32.3

Homeroom teacher 204

65.8

English assistant teacher

25

8.1

Nuri assistant teacher

18

5.8

Overtime teacher

26

8.4

The chief and principal teacher

37

11.9

12

3.9

place of work Home nursery

Total

Ratio (%)

Female

Graduate school or 10 higher Career

33

Private nursery

228

73.5

National and public nursey

50

16.1

Corporate and workplace nursey

17

5.5

Etcetera

3

1.0

310

100.0

34

M. S. Moon and H. Park

Table 2 Convergent validity and reliability verification Variable Self-efficacy

Hope

Resilience

Optimism

Organizational identification

Psychological well-being

Role performance

Measurement items

Standardization coefficient

C.R.

AVE

Crb alpha

0.951

0.796

0.939

0.930

0.768

0.914

0.956

0.879

0.951

0.938

0.752

0.925

0.937

0.749

0.922

0.949

0.823

0.938

0.941

0.841

0.923

Self-efficacy 5

0.831

Self-efficacy 4

0.888

Self-efficacy 3

0.865

Self-efficacy 2

0.899

Self-efficacy 1

0.856

Hope 5

0.882

Hope 4

0.763

Hope 2

0.902

Hope 1

0.891

Resilience 3

0.960

Resilience 2

0.908

Resilience 1

0.923

Optimism 5

0.845

Optimism 4

0.845

Optimism 3

0.840

Optimism 2

0.822

Optimism 1

0.863

Organizational identification 5

0.831

Organizational identification 4

0.771

Organizational identification 3

0.874

Organizational identification 2

0.818

Organizational identification 1

0.868

Psychological well-being 4

0.923

Psychological well-being 3

0.840

Psychological well-being 2

0.883

Psychological well-being 1

0.911

Role performance 3

0.899

Role performance 2

0.887

Role performance 1

0.900 (continued)

A Study on the Factors of Positive Psychological Capital on Organizational …

35

Table 2 (continued) Variable

Measurement items

Standardization coefficient

C.R.

AVE

Crb alpha

Organizational citizenship behavior

Organizational citizenship behavior 5

0.813

0.943

0.768

0.912

Organizational citizenship behavior 4

0.818

Organizational citizenship behavior 3

0.844

Organizational citizenship behavior 2

0.817

Organizational citizenship behavior 1

0.819

Table 3 Discriminant validity Variable

1

1. Self-efficacy

0.796*

2. Hope

0.470

0.768*

3.Resilience

0.392

0.297

0.879*

4. Optimism

0.476

0.443

0.331

0.752*

5. Organizational identification

0.540

0.519

0.415

0.470

0.749*

6. Psychological well-being

0.520

0.476

0.543

0.529

0.466

0.823*

7. Role performance 0.571

0.432

0.487

0.522

0.528

0.577

0.841*

8. Organizational citizenship behavior

0.523

0.436

0.567

0.532

0.609

0.620

0.534

2

3

4

5

6

7

8

0.768*

Note * AVE (Average Variance Extract)

5 Conclusions Suggestions according to the hypothesis and research results are as follows. First, according to the results of Hypothesis 1, it is necessary to prepare a program and policy to strengthen the sub-components of positive psychological capital for childcare teachers. Second, according to the results of Hypothesis 2, a psychological support program with enhanced positivity is needed. Third, according to the results of Hypothesis 3, a fair distribution policy plan is needed. Fourth, according to the results of Hypothesis 4–8, a plan to form organizational citizenship behavior, which is the core of organizational creation, is needed through organizational identification, psychological well-being, and role performance improvement. Finally, according to the results of Hypothesis 9, it is necessary to recognize and apply proper coaching

36

M. S. Moon and H. Park

Table 4 Results of hypotheses tests Hypothesis

Path

H1

Positive Psychological Capital → Organizational identification

St. Estimate

C.R

Result

H1-1

Self-efficacy → Organizational identification

0.272

4.425***

Supported

H1-2

Hope → Organizational identification

0.301

5.123***

Supported

H1-3

Resilience → Organizational identification

0.160

3.086**

Supported

H1-4

Optimism → Organizational identification

0.166

2.807**

Supported

H2

Positive Psychological Capital → Psychological well-being

H2-1

Self-efficacy → Psychological well-being

0.189

3.343***

H2-2

Hope → Psychological well-being

0.171

3.18**

Supported

H2-3

Resilience → Psychological well-being

0.352

7.215***

Supported

H2-4

Optimism → Psychological well-being

0.266

4.773***

Supported

4.085***

Supported

H3

Positive Psychological Capital → Role performance

H3-1

Self-efficacy → Role performance

0.248

Supported

H3-2

Hope → Role performance

0.041

0.711

Not Supported

H3-3

Resilience → Role performance

0.171

3.131**

Supported

H3-4

Optimism → Role performance

0.180

3.043**

Supported

H4

Organizational identification → Role 0.137 performance

2.208*

Supported

H5

Psychological well-being → Role performance

0.196

2.944**

Supported

H6

Organizational identification → Organizational Citizenship Behavior

0.220

3.956***

Supported

H7

Psychological well-being → Organizational Citizenship Behavior

0.345

5.771***

Supported

H8

Role performance → Organizational Citizenship Behavior

0.341

5.298***

Supported

***

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

leadership, develop and apply related programs, expand coaching education opportunities for the director’s professional coaching skills, and secure coaching leadership experts in childcare. The implications of this study are first, that this study proposed the programs and policies to reinforce the sub-components (self-efficacy, hope, resilience, optimism) of the positive psychological capital of childcare teachers and the development of programs to improve teachers’ relationships that would enhance the depth

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

0.410

Psychological well-being → Organizational citizenship behavior

Role performance → Organizational citizenship behavior

H9-16

H9-17

***

0.383

Organizational identification → Organizational citizenship behavior

H9-15

−0.029

0.152 0.121

Organizational identification → Role performance

Psychological well-being → Role performance

H9-13

0.105

Optimism → Role performance

H9-12

H9-14

0.204

−0.034

Hope → Role performance

Resilience → Role performance

H9-10

0.306

Self-efficacy → Role performance

H9-9

H9-11

0.354 0.285

Resilience → Psychological well-being

Optimism → Psychological well-being

H9-7

0.089

Hope → Psychological well-being

H9-6

H9-8

0.074 0.189

Optimism → Organizational identification

Self-efficacy → Psychological well-being

0.121

Resilience → Organizational identification

H9-3

H9-4

0.412

Hope → Organizational identification

H9-2

H9-5

0.359

Self-efficacy → Organizational identification

H9-1

0.181

0.737

0.16 −0.309***

0.07 4.292***

0.269

0.151

0.208

0.135

0.084

0.21

3.972

1.143

1.248

1.031

2.277

−0.314

2.755

0.251

0.33

2.948

0.233

4.425***

0.201

0.213

0.231

0.216

0.959

1.945

0.872

1.718

4.732***

3.988***

St Estimate

St Estimate

C.R.

High Group

Low Group

Path

Hypothesis

Table 5 The moderating effects of coaching leadership

7.794***

2.254

1.175

3.201

2.201

3.006

2.037

1.252

3.025

3.730***

5.348***

3.552***

2.902

2.676

3.14

2.761

2.195

C.R.

6.06***

−2.249*

−2.806**

0.760

−0.183

0.592

−0.982

0.856

−0.930

−0.204

−0.424

1.330

0.264

1.188

0.909

−1.683

−1.328

△χ2

A Study on the Factors of Positive Psychological Capital on Organizational … 37

38

M. S. Moon and H. Park

and quality of relationships so that a sense of isolation would not be formed even in the case of those with strong psychological attitudes to-ward hope out of the positive psychological capital. Second, in order to improve the organizational identification, psychological well-being, and role performance of childcare teachers, this study presented a fair distribution policy plan at the organizational level and proposed a customized psychological support program with reinforced positivity and a competency strengthening program through delegation of authority at the individual level. Third, this study proposed the director’s proper recognition of coaching leadership, the expansion of opportunities for coaching education, and securing of professional manpower for coaching leadership in the field of childcare.

References 1. M.K. Park, H.J. Lee, A study on the effects of director’s emotional leadership on early childhood teacher’s organizational commitment and customer orientation. Korean Public Manag. Gazette 31(4), 171–194 (2017) 2. T.O. Park, M.J. Kim, The effects of positive psychological capital and innovative behavior on job performance: focused on the moderating effect of perceived organizational support. Commerc. Educ. Res. 33(5), 43–76 (2019) 3. D.W. Organ, M. Konovsky, Cognitive versus affective determinants of organizational citizenship behavior. J. Appl. Psychol. 74(1), 157 (1989) 4. S.B. MacKenzie, P.M. Podsakoff, R. Fetter, The impact oforganizational citizenship behavior on evaluations of sales person performance. J. Mark. 57(1), 70–80 (1993) 5. C.Y. Kim, A study on the effects of psychological capital on the organizational citizenship behavior among child care teachers. Acad. Soc. Glob. Bus. Admin. 2017(2), 171–188 (2017) 6. Y.J. Kim, D.W. Kim, The changes in the leadership of child care center middle anager based on coaching leadership program. J. Early Childhood Educ. 19(5), 501–524 (2015) 7. S.J. Stowell, Coaching: a commitment to leadership. Train. Dev. J. 42(6), 34–39 (1988) 8. E.J. Romero, K.W. Cruthirds, The use of humor in the work-place. Acad. Manag. Perspect. 20(2), 58–69 (2006) 9. Y.N. Ko, The mediating effects of social support and positive psycho-logical capital in the relationship between gratitude and subjective well-being in adolescents. J. Learn. Center. Curric. Instruct. 19(2), 1325–1345 (2019) 10. B. Li, H. Ma, Y. Guo, F. Xu, F. Yu, Positive psychological capital: a new approach to social support and subjective well-being. Soc. Behav. Pers. 42(1), 135–144 (2014) 11. J.C. Turner, Discovering the social group: a self-categorization theory (Basil Blackwell, Oxford, England, 1987) 12. R. Van Dick, Identification in organizational contexts: linking theory and research from social and organizational psychology. Int. J. Manag. Rev. 3(4), 265–283 (2001) 13. M.E.P. Seligman, Learned optimism: how to change your mind and your life. (Vintage, Now York, 2006) 14. L.G. Katz, Talks with teachers of young children. (Norwood, NJ, Ablex, 1995) 15. C.A. O’Reilly, J. Chatman, Organizational commitment and psychological attachment: the effects of compliance, identification and internalization of prosocial behavior. J. Appl. Psychol. 71(3), 492–499 (1986) 16. Y.S. Kwon, J.K. Lim, A study on the impact of public servant’s authentic leadership on organizational citizenship behavior and organizational silence: the mediating effect of perceived organizational sup-port. The Korea Loc. Admin. Rev. 29(2), 297–322 (2015)

A Study on the Factors of Positive Psychological Capital on Organizational …

39

17. B.L. Fredrickson, The role of positive emotions in positive psychology: the broaden-and-build theory of positive emotions. Am. Psychol. 56(3), 218–266 (2001) 18. J.P. Meyer, N.J. Allen, I.R. Gellatly, Affective and continuance commitment to the organization: evaluation of measures and analysis of concurrent and time-lagged relations. J. Appl. Psychol. 75(6), 710 (1990) 19. L. Baron, L. Morin, The impact of executive coaching on self-efficacy related to management soft-skills. Leadersh. Organ. Dev. J. (2010) 20. F. Moen, E. Allgood, Coaching and the effect on self-efficacy. Organ. Dev. J. 27(4), 69 (2009) 21. M.S. Ha, M.H Do, Impact of the coaching leadership of daycare center principals on the organizational commitment and turnover intention of daycare teachers. J. Korean Coach. Res. 8(3), 5–24 (2015) 22. E.J. Ko, M.H. Do, The effect of childcare center directors’ coaching leadership on the psychological well-being of childcare teachers. J. Korean Coach. Res. 9(3), 77–99 (2016) 23. K. Nielsen, F. Munir, How do transformational leaders influence followers’ affective wellbeing? Exploring the mediating role of self-efficacy. Work Stress 23(4), 313–329 (2009) 24. C. Smith, D.W. Organ, J.P. Near, Organizational citizenship behavior: its nature and antecedents. J. Appl. Psychol. 68, 653–663 (1983)

The Effect of Self-Determination and Quality of VR-Based Education in the Metaverse on Learner Satisfaction Gina Gim, Hoi Kyoung Bae, and Seon A. Kang

Abstract While online learning had traditionally been implemented to aid in-person education, it has recently evolved into a critical tool for remote education. In particular, the advent of the COVID-19 pandemic has accelerated the spread of online learning and its significance. However, this wave of innovation in the education field has revealed the lack of research on how online education should be systematically provided and which educational aspects should be considered to enhance learner satisfaction in online settings. With the recent emergence of the Metaverse, VR education is once again proposed as a major tool for online learning despite existing limitations in research which put both systematic and educational aspects into consideration. Hence, this study presents and discusses a research model that converges technology acceptance model, information systems success model, and self-determination theory with the purpose of exploring variables that affect learner satisfaction, mediating the flow theory. Results showed that most variables of both self-determination theory and information system quality within VR education impact learner satisfaction. In particular, learners’ flow—complete immersion in learning—functions as an important mediating variable for learner satisfaction. These findings suggest designing and running a systematic platform that reflects self-directed learning is imperative to bring the best educational practices into the Metaverse. Keywords Virtual reality · Metaverse · Self-determination theory · Flow · Technology acceptance model · Information systems success model

G. Gim (B) Next Generation R&D Technology Policy Institute, Soongsil University, Seoul, South Korea e-mail: [email protected] H. K. Bae · S. A. Kang Programs in Project Management, Soongsil University, Seoul, South Korea e-mail: [email protected] S. A. Kang e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_4

41

42

G. Gim et al.

1 Introduction Online education is a learning system in which learners transcend time and space in an Internet-based, digitally implemented learning environment and perform a variety of educational tasks through interaction and self-directed learning activities. In recent times, due to the COVID-19 pandemic, online learning has become not only an alternative to in-person learning but also a new growth engine for future education. In particular, the recent rise of the Metaverse has called forth a surge of attention in the use of educational content based on virtual reality (VR) or augmented reality (AR), and a growing body of research has explored specific variables that affect learner satisfaction within this novel form of learning. VR and AR technology utilized in the Metaverse allow learners to freely manipulate their avatars in a threedimensional virtual space [1, 2]. The functional characteristics of VR- and AR-based content in the Metaverse are the augmentation of spatial movement and social interaction via avatars, which allow users to build social relationships and emotional connections with each other. In addition, learners can easily perceive their learning space as they are able to move to other spaces in the Metaverse through physical movement, such as walking. Because the Metaverse allows users to have spatial mobility, learners can enjoy learning experiences that combine movement and spatiality [3]. Such spatial movement enabled in the Metaverse is a unique function that cannot be served in the existing online education in the form of videoconferencing. Despite recent discussions on the explosive demand for the Metaverse and possibility of its use, only a small body of prior work has investigated learner satisfaction of VR- and AR-based educational content. For hands-on learning, the use of video content is insufficient to increase learner motivation and engagement and clearly communicate the content being taught. Accordingly, our society in this day and age calls for new teaching and learning strategies in which the instructor can resolve the difficulties of class preparation and the learner can enhance his or her academic achievement. The latest platform for such remote, interactive, and real-time teaching and learning may be the Metaverse because the advantages of the Metaverse—spatial mobility and social interaction—would help overcome shortcomings of the past VRand AR-based online education [1]. Thusly, our research questions were established with the above considerations and desired outcomes in mind: 1. Which quality variables of the Metaverse platform affect learner satisfaction? 2. How do learners’ self-determination affect their flow in relation to userfriendliness and ease of use of the platform?

The Effect of Self-Determination and Quality …

43

2 Theoretical Background 2.1 Metaverse and Virtual Reality The term Metaverse, first coined in Neil Stephenson’s science fiction novel Snow Crash in 1992, is a combination of the Greek prefix “meta,” which means after or beyond, and “universe [4].” Metaverse can be described as a collective virtual space or a combination of physical and digital environments incited by extended reality (XR) and the convergence between the Internet and web technologies [1]. The Metaverse incorporates a variety of new technologies [1, 2, 5]. Digital twins generate mirror images of the real world, VR and AR provide immersive three-dimensional experiences, and the advancement of 5G network supports largescale Metaverse devices. In addition, wearable sensors and Brain-Computer Interface (CI) facilitates interactions among users within the Metaverse, artificial intelligence (AI) enables large-scale Metaverse creation and rendering, and blockchain and non-fungible tokens (NFT) play an important role in determining users’ authentic rights [3]. Although the terminology was first introduced approximately 20 years ago, the Metaverse has recently begun to draw new attention as the COVID-19 pandemic accelerated changes in technological, socioeconomic, and cultural aspects. In sociocultural context, the pandemic stimulated demands for new relationships and spaces and economically, called for a novel business model interlocked with digital transformation. According to prior studies, Duan et al. [5] highlighted representative application programs based on infrastructure, interaction, and ecosystem and provided a threelayer Metaverse architecture, including a brief timeline for Metaverse development. This study introduced a virtual world in which thousands of users can interact simultaneously within an equally simulated three-dimensional space and addressed how it can affect our society as a whole in terms of business, education, social science, technological science, and social computing. Park and Kim [6] defined the world as an electronic memory and the Internet as a virtual reality to which users log in on a daily basis. Their key focus was on the safe preservation of information, data evaluation, and recognition. Diaz et al. [7] proposed the virtual world as an alternative medium for education and highlighted the immersive realism, ubiquity of approach and identity, interoperability, and expandability of the Metaverse. As noted above, prior studies on the Metaverse are largely confined to its application and social impact whereas the effectiveness of its use and consumer satisfaction remain largely uncharted territory.

44

G. Gim et al.

Fig. 1 Technology acceptance model (TAM). Note Reprinted from “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” by Davis, F. D., 1989, MIS Quarterly, 13(3), 319–340

2.2 Technology Acceptance Model The Technology Acceptance Model (TAM) is a research model adopted to examine users’ attitudes and behavioral intentions toward novel products or technologies and identify variables that affect their use. Technology Acceptance Model (TAM), introduced by Davis [8] for user verification of information systems, has initiated an array of follow-up studies at home and abroad, several of which are still being conducted. As depicted in Fig. 1, TAM is not only simple and clear with a firm theoretical foundation but also suitable for verifying the acceptance of information technology as it can be easily modified and expanded. TAM also introduced perceived usefulness and perceived ease of use as important beliefs in technology acceptance. According to Davis [9], the user’s intention to use a new system, which is influenced by the user’s attitude, actually determines his or her use of the new system. In addition, user’s attitude is affected by perceived usefulness and perceived ease of use.

2.3 Information Systems Success Model The Information Systems (IS) Success Model was first developed by DeLone and McLean [10] who defined six key indicators for information systems success: system quality, information quality, use, user satisfaction, individual impact, and organizational impact. Since then, DeLone and McLean [11] modified the existing model to include intention to use at the same level as use in the relationship between use and user satisfaction. In addition, it was suggested that use, intention to use, and user satisfaction impact the net benefits. The three main quality variables to be examined in this study are as follows.

The Effect of Self-Determination and Quality …

45

Information quality refers to the quality characteristics—accuracy, usefulness, and timeliness—of outputs that an information system produces. As summarized by Juan and Marylene [12], information quality is also a critical determinant of satisfaction within e-learning information systems. Even in learning settings that allow communication, data sharing, and discussion among instructors and learners, users would not be satisfied if the provided information is not accurate or reliable. System quality indicates the technological quality of an information system that users assess while using the system as it collects and processes accurate information to support communication [10]. Service quality refers to the quality of services that an information system provides or the degree to which the information system meets the user needs in terms of services provided. SERVQUAL, developed by Parasuraman, Zeithaml and Berry [13], evaluates service quality by measuring the difference between consumer expectations and perceptions of the service. Due to the challenges that are unique to online services, such as issues with the Internet server, website connectivity, and security, the same evaluation tools constructed to test offline services cannot be applied to measure online services. Recent studies have slightly modified the existing SERVQUAL model to measure service quality in online settings, as cited and used in this study.

2.4 Self-Determination Theory Self-Determination Theory is a macro-psychological theory of human motivation suggesting humans act on their own motivation without external influence. This implies that a person grants oneself self-determination by internalizing his or her social role or external pressure and, in turn, perceiving these extrinsically motivated behaviors as his or her own choice. A key concept within self-determination theory is that of basic psychological needs—the need for autonomy, competence, and relatedness—the satisfaction of which is essential to enact self-determining behaviors [14]. The need for autonomy, derived from the theory of personal causation by deCharms [15], reflects the human desire to believe the cause or agent of one’s action belongs to oneself, regulate one’s plans and actions, and have the freedom to decide what is important and valuable to oneself. Ryan and Deci [16] argue that autonomy is differentiated from independence, the state of being free from external influence, and that autonomy and dependence are not necessarily conflicting concepts. Since autonomy reflects the phenomenological experience of one’s will and choice, an antonym for autonomy is heteronomy in which one feels controlled and manipulated rather than dependent [17]. Simply put, autonomy does not always negate relying on others or require separation from others [16]. If one chooses to rely on others, that itself is an autonomous behavior. The need for competence, a core concept of effectance motivation theory by White [18] and Harter [19], indicates that all humans aspire to be capable and enhance their abilities, skills, and talents when given the opportunity. Competence also played a

46

G. Gim et al.

key role in Deci [20]’s early theory of intrinsic motivation. The need for competence is sometimes described as a sense of competence as it does not indicate the desire for acquired skills but rather one’s perception of wanting to feel competent. Research has shown that positive feedback and autonomy satisfy one’s competence needs and consequently promote intrinsic motivation [21]. In addition, a student’s competence needs positively correlated with his or her autonomous motivation for learning, teacher’s perception of the student’s competence [14], student’s positive attitude toward school, and student’s academic performance [22]. Relatedness refers to the state of having meaningful relationships and interactions with others. It is in human nature to desire to feel connected to and cared for by others and included in the society [16]. The need for relatedness is similar to the desire for affection or a sense of belonging in a group or community. According to Vallerand et al. [23], learners with high self-determination perceive relatively more positive emotions in class, find more joy in learning activities, and demonstrate higher satisfaction with school life.

2.5 Flow Theory and Learner Satisfaction Flow is defined as the “state of concentration so focused that it amounts to absolute absorption in an activity” without expecting any reward as the activity itself is already a reward [24–26]. In the flow state, one’s consciousness and behavior are integrated, ability to regulate one’s behavior is enhanced, and the distinction among one’s sense of identity, stimulation, response, and time is eliminated [27]. Learning flow is a psychological progression in which learners continuously interact with their learning process and activities in order to perform tasks or achieve learning goals [28]. In other words, learning flow is a state of concentration in which individuals are completely immersed in their learning as they interact optimally with their learning environment [29]. According to Csikszentmihalyi [24], the exposure to learning flow not only induces one’s interest and active participation in learning but also impacts the overall quality of his or her life through the experience of creativity, peak experience, enjoyment, talent development, and self-esteem. Zhao et al. [30] argue that selfdetermination affects learners’ overall satisfaction and enjoyment in class as their perception of interest and value in their learning allows them to be intrinsically motivated for active learning. Learners with intrinsic motivation experience a variety of cognitive strategies, learning persistence, and positive emotions that help build self-determination, which in turn leads to flow state. In an experimental study in which learning flow was implemented in school education, Kowal and Fortier [31] emphasized the significance of educational environments as one’s learning flow was affected by the educational system, student learning, learning resources, and faculty. Customer satisfaction, a subjective reflection of how a customer feels about a company’s products, services, or capabilities, has been extensively studied by

The Effect of Self-Determination and Quality …

47

researchers from various perspectives. According to Deci et al. [32], customer satisfaction is an overall psychological state that reflects the difference between one’s expectations and perceptions of a product, service, or capability before and after the encounter. Learner satisfaction, which can be explained in the same context as customer satisfaction, is examined from two perspectives in this study. While economic satisfaction refers to the perceived difference between the expected benefits of paid education and the results experienced by learners, psychological satisfaction is an emotional state of mind developed towards educational institutions as learners undergo implemented learning processes. In this study, learner satisfaction is defined as the degree of economic and psychological satisfaction obtained through a variety of experiences that educational institutions provide.

3 Research Method 3.1 Research Model and Hypotheses The research model (Fig. 2) and hypotheses based on the aforementioned points are as follows.

3.2 Data Collection For this study, a survey on the satisfaction of VR- or AR-based educational content was conducted from January to March 2022 on 535 participants with experience in using VR- or AR-based educational content. The sample consisted of 238 male respondents and 297 female respondents. In terms of lifetime VR or AR usage experience, 2–10 times accounted for the largest percentage with 322 respondents. 90 participants responded within 1–2 times, 82 participants responded 10–20 times, and 41 participants responded 20 times or more.

4 Data Analysis 4.1 Structural Equation Modeling (SEM) Analysis In order to test the hypotheses from the collected data, a structural equation modeling (SEM) analysis was conducted using Smart PLS 3.0, which aims to analyze the causal relationship in highly complex structural models [33].

48

Fig. 2 Research model H1a. Information Quality has a positive effect on Perceived Usefulness H1b. Information Quality has a positive effect on Perceived Ease of Use H1c. Information Quality has a positive effect on Perceived Flow H2a. Service Quality has a positive effect on Perceived Usefulness H2b. Service Quality has a positive effect on Perceived Ease of Use H2c. Service Quality has a positive effect on Perceived Flow H3a. System Quality has a positive effect on Perceived Usefulness H3b. System Quality has a positive effect on Perceived Ease of Use H3c. System Quality has a positive effect on Perceived Flow H4a. Perceived Autonomy has a positive effect on Perceived Usefulness H4b. Perceived Autonomy has a positive effect on Perceived Ease of Use H4c. Perceived Autonomy has a positive effect on Perceived Flow H5a. Perceived Competence has a positive effect on Perceived Usefulness H5b. Perceived Competence has a positive effect on Perceived Ease of Use H5c. Perceived Competence has a positive effect on Perceived Flow H6a. Perceived Relatedness has a positive effect on Perceived Usefulness H6b. Perceived Relatedness has a positive effect on Perceived Ease of Use H6c. Perceived Relatedness has a positive effect on Perceived Flow H7a. Perceived Usefulness has a positive effect on Perceived Flow H7a. Perceived Usefulness has a positive effect on Learner’s Satisfaction H8a. Perceived Ease of Use has a positive effect on Perceived Flow H8b. Perceived Ease of Use has a positive effect on Learner’s Satisfaction H9. Perceived Flow has a positive effect on Learner’s Satisfaction

G. Gim et al.

The Effect of Self-Determination and Quality …

49

Table 1 Convergent validity test result Variable

Measurement item

Outer loading

AVE

Autonomy

Autonomy1, Autonomy2, Autonomy3, Autonomy4

0.778, 0.749, 0.777, 0.822

0.611

Competence

Competence2, Competence3, Competence4, Competence5

0.801, 0.762, 0.758, 0.811

0.614

Ease of use

EaseofUse1, EaseofUse2, EaseofUse3, EaseofUse4, EaseofUse5

0.81, 0.799, 0.766, 0.800, 0.836

0.644

Flow

Flow1, Flow2, Flow3, Flow4, Flow5

0.761, 0.765, 0.762, 0.835, 0.844

0.631

InfQ

InfQ1, InfQ2, InfQ3, InfQ4, InfQ5

0.79, 0.764, 0.829, 0.763, 0.763

0.612

Relatedness

Relatedness1, Relatedness2, Relatedness3, Relatedness4, Relatedness5

0.741, 0.769, 0.761, 0.831, 0.776

0.602

Satisfaction

Satisfaction1, Satisfaction2, Satisfaction3, Satisfaction4, Satisfaction5

0.763, 0.826, 0.735, 0.803, 0.803

0.619

ServQ

ServQ1, ServQ2, ServQ3, ServQ4

0.812, 0.820, 0.786, 0.757

0.631

SysQ

SysQ2, SysQ3, SysQ5

0.797, 0.857, 0.717

0.628

Usefulness

Useful1, Useful2, Useful3, Useful4, Useful5

0.82, 0.834, 0.794, 0.841, 0.812

0.673

4.1.1

Convergent Validity Test

Outer loading is a key indicator showing the fitness through standardized regression coefficients. Measured variables with the outer loading of 0.7 or higher are considered highly satisfactory and can be maintained [34]. Average Variance Extracted (AVE) refers to the average amount of variance that a latent construct is able to explain in the observed variables, and an AVE of 0.5 or higher indicates adequate convergence [35]. The outer loading and AVE value of the observed variables were calculated through Smart PLS 3.0 and those that did not exceed the above thresholds were eliminated. The observed variables used in the analysis are presented in Table 1.

4.1.2

Reliability Test

Reliability refers to the degree to which the observed variable produces consistent results. This study evaluated the consistency and homogeneity among observed variables through internal consistency analysis and confirmed that all variables had Cronbach’s Alpha 0.7 or higher [36], Composite Reliability of 0.7 or higher [37], and Dijkstra-Henseler rho_A of 0.7 or higher [38] as shown in Table 2.

50

G. Gim et al.

Table 2 Reliability test result

4.1.3

Variable

Cronbach’s alpha

Rho_A

Composite reliability

Autonomy

0.789

0.795

0.863

Competence

0.791

0.796

0.864

EASE OF USE

0.862

0.864

0.900

Flow

0.853

0.856

0.895

InfQ

0.841

0.844

0.887

Relatedness

0.835

0.839

0.883

Satisfaction

0.846

0.851

0.890

ServQ

0.805

0.807

0.872

SysQ

0.706

0.742

0.834

Usefulness

0.878

0.879

0.911

Discriminant Validity Test

Discriminant validity tests whether measures of constructs that are not supposed to be related are, in fact, not highly correlated to each other under the assumption that each variable is independent. As seen in Table 3, this study confirmed that all variables had Heterotrait-Monotrait Ratio of 0.9 or lower, securing the discriminant validity [39]. Table 3 Discriminant validity test result 1

2

3

4

5

6

7

8

9

10

1 2

0.593

3

0.724

0.660

4

0.773

0.596

0.849

5

0.806

0.552

0.817

0.789

6

0.681

0.610

0.727

0.791

0.641

7

0.723

0.576

0.872

0.832

0.743

0.730

8

0.843

0.526

0.779

0.754

0.801

0.710

0.716

9

0.753

0.548

0.787

0.808

0.764

0.760

0.768

0.756

10

0.818

0.623

0.886

0.864

0.861

0.747

0.883

0.830

0.747

Notes1 = Autonomy, 2 = Competence, 3 = EaseofUse, 4 = Flow, 5 = InfQ, 6 = Relatedness, 7 = Satisfaction, 8 = ServQ, 9 = SysQ, 10 = Usefulness

The Effect of Self-Determination and Quality …

4.1.4

51

Hypothesis Test

According to the results of the hypothesis testing, our hypotheses that autonomy has a positive effect on ease of use, competence has a positive effect on flow, information quality has a positive effect on flow, service quality has a positive effect on flow, and system quality has a positive effect on perceived usefulness were rejected as they demonstrated p-value greater than 0.05. The results are presented in Table 4. According to the path analysis of the research model as depicted in Table 4, hypothesis H4b that perceived autonomy has a positive effect on perceived ease of use was rejected. This suggests that participants of this study already have experiences of autonomously using VR- and AR-based content that perceived autonomy does not significantly affect ease of use and that people take VR- and AR-based educational courses because they are more convenient than other online content. Table 4 Hypothesis test result Independent

Dependent

Weight

P-value

Result

Autonomy

Ease of use

0.01

0.812

Reject

Autonomy

Flow

0.093

0.023*

Accept

Autonomy

Usefulness

0.143

0.001**

Accept

Competence

Ease of use

0.183

0***

Accept

Competence

Flow

0.011

0.708

Reject

Competence

Usefulness

0.106

0***

Accept

Ease of use

Flow

0.213

0***

Accept

Ease of use

Satisfaction

0.314

0***

Accept

Flow

Satisfaction

0.199

0***

Accept

InfQ

Ease of use

0.307

0***

Accept

InfQ

Flow

0.077

0.111

Reject

InfQ

Usefulness

0.34

0***

Accept

Relatedness

Ease of use

0.154

0***

Accept

Relatedness

Flow

0.198

0***

Accept

Relatedness

Useful

0.178

0***

Accept

ServQ

Ease of use

0.182

0***

Accept

ServQ

Flow

0.007

0.874

Reject

ServQ

Useful

0.206

0***

Accept

SysQ

Ease of use

0.163

0***

Accept

SysQ

Flow

0.131

0.002**

Accept

SysQ

Usefulness

0.046

0.263

Reject

Usefulness

Flow

0.247

0***

Accept

Usefulness

Satisfaction

0.375

0***

Accept

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

52

G. Gim et al.

Hypothesis H1c that information quality has a positive effect on perceived flow was rejected, suggesting that information quality, including the accuracy or timeliness of the information, does not affect flow but rather affects perceived usefulness or perceived ease of use. This indicates the content of the information itself is unrelated to one’s flow state which also explains why hypothesis H2c that service quality has a positive effect on perceived flow was rejected. As hypothesis H3c that system quality affects flow was accepted, it is rationally convincing that system stability or software and hardware support impacts one’s flow state. However, the rejection of hypothesis H3a that system quality affects perceived usefulness was unexpected. The stability or capabilities of the system affects learners’ flow or ease of use rather than usefulness, suggesting the quality of the content plays a more critical role in affecting usefulness. In addition, all three variables within self-determination theory—autonomy, competence, and relatedness—affected usefulness, ease of use, and flow, all of which are influential to learner satisfaction. Therefore, this study contributes to the discussion of self-determination theory as an important educational and/or psychological foundation in studies that measure learner satisfaction with the usage of Metaverse-based VR content.

5 Conclusions This study examined the effect of VR- and AR-based content in the Metaverse on learner satisfaction with the focus on three quality variables of the platform and self-determination theory. This study also used an interdisciplinary approach as both information technological perspectives and educational and psychological perspectives were examined, establishing a connection among technology acceptance model, information systems theory, and self-determination theory. Results of the study demonstrated that self-determination theory extensively impacts studies on VR-based education in the Metaverse, confirming that selfdetermination theory serves as an equally important factor in virtual education as it did in prior research on online education. In addition, the unexpected finding in that neither information quality nor service quality affected one’s flow while system quality did suggests the system stability of VR-based content in the Metaverse is a critical determinant of learners’ flow. Limitations of this study mainly stem from possible diversity in learners’ responses due to the lack of popularization in the use of VR-based content in the Metaverse. Therefore, a more detailed and refined study based on the characteristics of each learner or the provided content is needed. One of our future research tasks is to further investigate whether and how VRbased education within the Metaverse produces actual educational outcomes beyond learner satisfaction. An experimental research, in which learners’ academic performance is measured after first-handedly using VR devices to explore educational activities in the Metaverse, may offer a more accurate and explicit framework for future research that measures learners’ academic performance.

The Effect of Self-Determination and Quality …

53

References 1. L.H. Lee, T. Braud, P. Zhou, L. Wang, D. Xu, Z. Lin, A. Kumar, C. Bermejo, P. Hui, All one needs to know about Metaverse: a complete survey on technological singularity, virtual ecosystem, and research agenda (2021). arXiv:2110.05352 2. Q. Ynag, Y. Zhao, H. Huang, Z. Zheng, Fusing blockchain and AI with Metaverse: a survey (2022). arXiv:2201.0320 3. Y. Wang, Z. Su, N. Zhang, D. Liu, R. Xing, T.H. Luan, X. Shen, A survey on Metaverse: fundamentals, security, and privacy (2022). arXiv:2203.0266 4. J. Joshua, Information bodies: computational anxiety in Neal Stephenson’s snow crash. Interdiscip. Lit. Stud. 19(1), 17–47 (2017) 5. H. Duan, J. Li, S. Fan, Z. Lin, X. Wu, W. Cai, Metaverse for social good: a university campus prototype, in Proceedings of the 29th ACM International Conference on Multimedia (2021) 6. S.M. Park, Y.G. Kim, A Metaverse: taxonomy, components, applications, and open challenges. IEEE Access (2022) 7. J. Díaz, C. Saldaña, C. Avila, Virtual world as a resource for hybrid education. Int. J. Emerg. Technol. Learn. (iJET) 15(15), 94–109 (2020) 8. F.D. Davis, A Technology Acceptance Model for Empirically Testing New End-user Information Systems. Cambridge, MA (1986) 9. F.D. Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989) 10. W.H. DeLone, E.R. McLean, Information system success: the quest for the dependent variable. Inf. Syst. Res. 3(1), 60–95 (1992) 11. W.H. DeLone, E.R. McLean, The DeLone & McLean mode of is success: a ten-year update. J. Manage. Inf. Syst. 19(4), 9–30 (2003) 12. C. Juan, G. Marylene, Understanding E-learning continuance intention in the workplace: a self-determination theory perspective. Comput. Human Behav. 24, 1585–1604 (2008) 13. A. Parasuraman, V.A. Zeithaml, L.L. Berry, A conceptual model of service quality and its implications for future research. J. Mark. 49(4), 41–50 (1985) 14. E.L. Deci, R.M. Ryan, The “What” and “Why” of goal pursuits: human needs and the selfdetermination of behavior. Psychol. Inq. 11(4), 227–268 (2000) 15. R. deCharms, Enhancing Motivation: Change in the Classroom. Irvington, NY (1976) 16. R.M. Ryan, E.L. Deci, An Overview of Self-determination Theory: An Organismic-Dialectical Perspective (2002) 17. R.M. Ryan, J. Lynch, Emotional autonomy versus detachment: revisiting the vicissitudes of adolescence and young adulthood. Child Dev. 60, 340–356 (1989) 18. R.W. White, Motivation reconsidered: the concept of competence. Psychol. Rev. 66, 297–333 (1959) 19. S. Harter, Developmental perspectives on the self-system, in Handbook of Child Psychology: Formerly Carmichael’s Manual of Child Psychology, ed. by Paul H. Mussen (1983) 20. E.L. Deci, Intrinsic Motivation (Plenum, New York, 1975) 21. R.J. Vallerand, G. Reid, On the causal effects of perceived competence on intrinsic motivation: a test of cognitive evaluation theory. J. Sport Psychol. 6, 94–102 (1984) 22. M. Miserandino, Children who do well in school: individual differences in perceived competence and autonomy in above-average children. J. Educ. Psychol. 88, 203–214 (1996) 23. R.J. Vallerand, M.R. Blais, N.M. Briere, L.G. Pelletier, Construction and validation of the motivation toward educational scale. Can. J. Behav. Sci. Revue Can. 21, 323–349 (1989) 24. M. Csikszentmihalyi, Flow: The Psychology of Optimal Experience (Harper Perennial, New York, 1990) 25. M. Csikszentmihalyi, If we are so rich, why aren’t we happy? Am. Psychol. 52(10), 821 (1999) 26. E.L. Deci, R.M. Ryan, Intrinsic Motivation and Self-Determination in Human Behavior (Plenum Press, New York, 1985) 27. M.Csikszentmihalyi, Play and intrinsic rewards. J. Humanistic Psychol. (1975)

54

G. Gim et al.

28. K.C. Chen, S.J. Jang, Motivation in online learning: testing a model of self-determination theory. Comput. Hum. Behav. 26(4), 741–752 (2010) 29. Z. Guo, L. Xiao, C. Van Toorn, Y. Lai, C. Seo, Promoting online learners’ continuance intention: an integrated flow framework. Inf. Manage. 53(2), 279–295 (2016) 30. L. Zhao, Y. Lu, B. Wang, W. Huang, What makes them happy and curious online? An empirical study on high school students’ internet use from a self-determination theory perspective. Comput. Educ. 56(2), 346–356 (2011) 31. J. Kowal, M.S. Fortier, Motivational determinants of flow: contributions from selfdetermination theory. J. Soc. Psychol. 139(3), 355–368 (1995) 32. E.L. Deci, R.M. Ryan, M. Gagné, D.R. Leone, J. Usunov, B.P. Kornazheva, Need satisfaction, motivation, and well-being in the work organizations of a former eastern bloc country: a cross-cultural study of self-determination. Pers. Soc. Psychol. Bull. 27(8), 930–942 (2001) 33. D. Barclay, C. Higgins, R. Thompson, The Partial Least Squares (PLS) Approach to Casual Modeling: Personal Computer Adoption and Use as an Illustration (1995) 34. J.F. Hair, C.M. Ringle, M. Sarstedt, PLS-SEM: indeed a silver bullet. J. Market. Theory Pract. 19(2), 139–152 (2011) 35. C. Fornell, D.F. Larcker, Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1), 39–50 (1981) 36. L.J. Cronbach, Coefficient alpha and the internal structure of tests. Psychometrika 16(3), 297– 334 (1951) 37. C.E. Werts, R.L. Linn, K.G. Jöreskog, Intraclass reliability estimates: testing structural assumptions. Educ. Psychol. Measur. 34(1), 25–33 (1974) 38. T.K. Dijkstra, J. Henseler, Consistent partial least squares path modeling. MIS Q. 39(2), 297– 316 (2015) 39. J. Henseler, C.M. Ringle, M. Sarstedt, A New criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 43(1), 115–135 (2015)

A Study on Industrial Artificial Intelligence-Based Edge Analysis for Machining Facilities Han Lee, Dae Hyun Kang, and Seok Chan Jeong

Abstract Unlike the past, the smart factory environment requires many changes from the 2 M (Man and Machine) side to digitalization and intelligence. In other words, as an alternative to the shrinking vacancy of workers for manufacturing competitiveness, the transition to an artificial intelligence-based real-time predictive maintenance environment or an intelligent production system is highly demanded. Therefore, research on intelligent or artificial intelligence-based real-time predictive maintenance of automated processing facilities, which occupies a large proportion in the general production system, is quite significant. In this study, for the intelligent maintenance or intelligent predictive maintenance of such processing equipment, first, the “Conv2d-LSTM” technique, which reduces the parameters of the LSTM network layer and adds a Conv2d layer, is applied to the real-time spindle load data to verify the state judgment of the machining tool. Second, the real-time diagnostic performance for machining quality (status analysis for each tool) was verified by applying the spectrogram image using “STFT” to the “Pretrained Network model” for the machining load data. As a result of the study, it was verified that the learning time was shortened, and the performance was excellent (loss value lowered) This work was supported by the Technology Innovation Program (20019327, Industrial IoT based Lightweight Manufacturing System Technology Development for 24hr unmanned Production System) funded by the Ministry of Trade, Industry & Energy(MOTIE), Korea and the Grand Information Technology Research Center support program (IITP-2022-2020-0-01791) supervised by the IITP (Institute for Information and communications Technology Planning and Evaluation) funded by the Ministry of Science and ICT (MIST), Korea. H. Lee Department of e-Business College of Commerce and Economics, Dong-Eui University, Busan, South Korea e-mail: [email protected] H. Lee · D. H. Kang ICT Technical Team, HN Co., Ltd., Seoul, South Korea e-mail: [email protected] S. C. Jeong (B) Department of e-Business College of Commerce and Economics and AI Grand ICT Research Center, Dong-Eui University, Busan, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_5

55

56

H. Lee et al.

compared to the application of “Bi-LSTM” in the state judgment through the prediction of the machining tool load. It was possible to confirm the quality judgment through classification. Based on the results of this study in the future, it is expected that production facility intelligence will be possible through weight reduction and diversification of artificial intelligence models that enable edge analysis for various production facilities such as processing facilities and robots. Keywords Tool monitoring · Predictive maintenance · AIoT · Edge computing · Deep learning

1 Introduction In the transition to Industry 4.0, overall mechanical systems will become more intelligent, with increased uncertainty and complexity. In this situation, manufacturing facilities will have increased demand for reliable equipment and systems that can control uncertainty, and the life of equipment using appropriate maintenance strategies [1]. In addition, the implementation of smart factories in small and medium-sized manufacturing companies is of paramount importance not only for the company itself but also for strengthening the national industrial competitiveness. Despite this importance, small and medium-sized manufacturing companies are facing many practical restrictions in terms of finance, labor, and technology in building and upgrading smart factories. In particular, alternatives to prevent the decline in productivity and quality due to the gradual decrease in the labor force and the absence of professional and technical personnel due to aging are essential. As such, there is a need to overcome the reality and limitations of small and medium-sized manufacturing companies, as well as establish an intelligent smart factory environment that can support faster and more efficient manufacturing site management and decision-making. In an automated facility-based production system, it can be said that the state of machining tools has a great influence on major performance indicators such as work safety/productivity/quality. In addition, in the advanced smart factory environment, a system capable of collecting and processing large amounts of data quickly and effectively is required for intelligence of the production system or intelligence of various production facilities. Accordingly, this study recognized the need for artificial intelligence-based realtime analysis/prediction technology for abnormal detection and machining (production) quality of automated facilities that can have a positive effect on major performance indicators at manufacturing sites. And for this purpose, an AI-based edge analysis technology that can predict and determine abnormality detection for facilities and machining quality at the same time was studied. In this study, for more efficient facility anomaly detection using spindle load data of machining tools, “Conv2dLSTM” model-based prediction and effective machining quality analysis, a prediction technique using “FFT (Fast Fourier Transform and STFT (Short Time Fourier Transform)” was presented and verified.

A Study on Industrial Artificial Intelligence-Based Edge …

57

2 Related Researches Deep-learning is a technique in machine learning that uses neural network-based nonlinear transformation techniques to abstract multidimensional data and extract key information in this process. Previously, human-defined feature factors were extracted from data to solve classification and prediction problems. However, deep learning allows us to autonomously extract feature factors from learning data and solve classification and prediction problems based on them [2]. Currently, the power plant uses a subjective method of surface-replicating damaged parts of material with film and visually evaluating them through a microscope. To compensate for this shortcoming, Choi [3] developed a convolutional neural network degradation assessment technique to assess the degradation of high temperature facilities. In addition, various studies are being conducted for artificial intelligence-based failure diagnosis. Oh et al. [4] extracted feature factors used for journal bearing failure diagnosis using deep learning technology and performed failure diagnosis. As a result, it was confirmed that the failure diagnosis accuracy of the health feature factor extracted through deep learning was higher than that of the health feature factor extracted based on expert knowledge. Song [5] studied the anomaly detection technique through data-based analysis of IMS bearing vibration for the analysis of anomaly detection in rotating devices such as aircraft engines, wind generators, motors, etc., Nam [6] and Kang [7] studied techniques that can diagnose failures through neural networks using operating sound data by device state based on the different patterns of frequency bands activated by operating sound depending on failure conditions [6, 7], Studies applying the CNN-LSTM technique to fine dust prediction and highway speed prediction can also be found [8, 9]. Although many studies have been conducted on the facility abnormality detection analysis using vibration or sound data or CNN-LSTM in fields other than fault diagnosis, it is difficult to find a study on the facility abnormality detection and processing quality at the same time using facility spindle load data. Therefore, the effectiveness of the artificial intelligence technology-based edge analysis technology conducted in this study can be sufficiently expected.

3 Design of Intelligent Edge Analysis System In automated machining facilities such as CNC (Computerized Numerical Control), the condition of machining tools such as wear and breakage of machining tools has an absolute influence on machining quality. In this study, two AI models for inferring the load of the tool spindle using the spindle load data collected when the CNC operates was proposed as follows. First, an AI model that analyze the state of machining tools by applying the “Conv2dLSTM” technique, which reduces the parameters of the LSTM network layer and adds the Conv2d layer, and second, for the collected load data, the spectrogram image

58

H. Lee et al.

Fig. 1 Intelligent edge analysis system process flow

characteristic using “STFT” is extracted from the load value of the spindle motor in units of machining cycle and applied to the “CNN Pretrained Network model” for real-time diagnosis of machining quality and machining tool status classification. Figure 1 shows the flow of the intelligent edge analysis system proposed in this study.

3.1 Data Collection Raw Data collection for CNC machining tool state analysis and machining quality judgment analysis was performed using IIoT middleware developed in previous study [10], and spindle load data of the machining tool was collected in 50 ms units, and 100 ms (average of two data) data were used from learning process to prediction.

3.2 Data Pre-processing In the data pre-processing stage, as shown in Fig. 2, the collected spindle load data is classified into Train/Valid/Test data sets for pattern analysis and learning to enable AI model learning and validation. In this study, data pre-processing for processing tool condition analysis for facility abnormality detection and data pre-processing for processing quality analysis were divided into two categories. (1) Data pre-processing for tool state analysis Data pre-processing for machining tool status analysis can be divided into 3 steps as shown in Fig. 2. (i) In the Data Pattern Analysis stage, it is checked whether

A Study on Industrial Artificial Intelligence-Based Edge …

59

Fig. 2 Data preprocessing for tool state analysis

production downtime or production start and end can be clearly recognized from the collected CNC M/C machining program number, tool code, and spindle motor load data, (ii) Data preprocessing is performed through data normalization through time sampling and reselling processing in DeNoising and Rescaling stage and finally, (iii) DataSet Extraction separates data sets for learning and verifying AI models. Figure 3 shows the data pre-processing process for such tool state analysis. (2) Data pre-processing for machining quality analysis In the preprocessing for processing quality analysis, unlike the preprocessing for machining tool state analysis, (i) the frequency component of the signal is identified by converting the time domain into the frequency domain, i.e., FFT transformation that decomposes the frequency components of the signal, (ii) To identify the frequency components at each point in time, the time series data were divided into a certain time section and the STFT method of identifying the frequency components

Fig. 3 A example of FFT and STFT

60

H. Lee et al.

in each time section was applied to extract data characteristics through spectrogram imaging and generate a data set for Train/Valid/Test. Figure 3 shows an example of FFT and STFT processing [11, 12].

3.3 Intelligent Edge Analysis Model (1) AI model for tool state analysis In this study, instead of the “Bi-LSTM Model” applied in the previous study [10], “Conv2d-LSTM” that reduced the parameters of the LSTM network and added a Conv2d layer for real-time spindle load data to improve performance such as the learning rate of machining tool load prediction. Model” was applied to verify the state judgment of the machining tool. In general, LSTM networks show better performance (lower loss) as the layer becomes deeper and the hyperparameter (unit) becomes larger depending on the complexity of the data pattern, but the computational amount is large and the computational efficiency through the computing power (GPU) is not good. Therefore, by introducing an efficient CNN algorithm for GPU operation, the number of hyperparameter units of LSTM was reduced, and by adding a CNN network layer, the AI model performance (loss) and learning time were shortened, and the inference time per real-time case was shortened. The “Conv2d-LSTM Model” was input-processed to understand the data more closely than before through spatial data characterization by overlapping temporal characteristics, and the algorithm reflected the spatial characteristics of the data through 2D-CNN and modeled to learn time series characteristics by using it in LSTM. Figure 4 shows the basic concepts of LSTM and Conv2D-LSTM model. (2) AI model for machining quality analysis Based on the image data set generated through STFT-based data preprocessing for machining quality analysis, this study aims to reduce learning time and improve model performance by using pretrained networks such as VGGNet to increase prediction accuracy, and by re-learning only the weights and bais of some layers, the model technique for classifying the tool state and judging the quality was applied, and more than 99% accuracy was secured with only a small amount of data set. Figure 5 shows the structure of the VGG 19 model, which is an example of the pretrained network applied in this study [13].

A Study on Industrial Artificial Intelligence-Based Edge …

Fig. 4 A basic concept of LSTM versus Conv2D-LSTM model

Fig. 5 A example of pretrained network

61

62

H. Lee et al.

Fig. 6 The sample of collected raw data

4 Test and Validation 4.1 Data Collection and Experiment Design In the experimental verification of this study, about 86,000 data collected for 7 days in 100 ms units from the roughing process CNC were used as raw data. A total of 6 machining tools are used to process one product in the CNC facility of the process. The collected data consists of a process code, spindle load, tool code, etc., as shown in Fig. 6. In this study, experimental verification was carried out individually for two models, the previously presented machining tool condition analysis model and machining quality analysis model.

4.2 Verification for AI Model of Tool State Analysis (1) Data pre-processing As in the study [10], average value downsampling was processed at 100 ms cycle for spindle load data (load 1–4) collected at 50 ms cycle, and then data was normalized through 0–1 Min–Max scaling with MAX value of spindle load value Normalization) was treated. In the study [10], real-time prediction was made by learning only the machining section, but in this study, by adding a tool code as an input to learn data in the idle state, not only the simplification of pre-processing but also real-time inference By simplifying the complexity according to the data condition (analysis of processing/non-processing conditions), the complexity was removed so that separate judgment processing for processing/non-processing during real-time reasoning was unnecessary. Figure 7 shows the comparison between the source data collected and the data after pre-processing.

A Study on Industrial Artificial Intelligence-Based Edge …

63

Fig. 7 The comparison chart of real data versus pre-processed data

(2) AI modeling and Training The total model parameters of the AI model designed in this experiment were 8,881,865 and the Conv2D layer (unit:128) was constructed as two layers, and two consecutive Bi-LSTM (unit:256) layers were accumulated to construct the entire model. A flatten layer was placed to transmit the Conv 2D output to the Bi-LSTM input, and a Time Distributed layer was added before the Dense layer for final regression analysis to ensure no data loss. Figure 8 shows the validation results of the Conv2D-LSTM model presented in this study for the state analysis of the machining tool. As a result of learning the data collected on the first day of operation (837,832 data), Validation MAE was 0.00471 for 0 ~ 500 epochs, and as a result of learning the data collected on the second day of operation (863,346 data), Validation MAE 0.00155(for 500–1,000 epochs) was confirmed. Also, in the case of the Bi-LSTM model proposed in the previous study (Lee et al., 2022), the learning time was too long, so the training was carried out by dividing the daily data into 20 equal parts. But In this study, it was confirmed that the total learning time was about 40 h even though the entire data was learned at once, and based on this, learning was conducted in units of data for the whole day. In other words, in this experiment, it took an average of 4 min 38 s per epoch, 40 h 37 min for 500 epochs, and the performance of MAE 0.00155 was confirmed, which means that the learning time was improved by 1.6 times compared to the performance of MAE 0.002524 after 3200 lessons in the previous study. Table 1 shows the comparison of the learning performance of the Conv2D-LSTM versus the Bi-LSTM.

64

H. Lee et al.

Fig. 8 Conv2D-LSTM model loss & validation

Table 1 Comparison of learning performance with Conv2D-LSTM versus Bi-LSTM Experiment

Used data

Learning epochs

500 epochs learning time

Final learning time

Validation MAE

Conv2D-LSTM

Data for 2 days

1,000 epochs

40 h 37 min

81 h 14 min

0.00155

Bi-LSTM

Data for 7 Days

3,200 epochs

36 h 36 min

234 h 24 min

0.002524

4.3 Verification for AI Model of Machining Quality Analysis (1) Data pre-processing In this study, data pre-processing was performed on the CNC spindle load data collected in real time for prediction and judgment on the machining quality of CNC machine using artificial intelligence as follows. First, FFT was applied in 50 ms units to the collected time series spindle load data to extract about 1900–1950 load waveforms during 1 cycle of product processing, and resampling was performed from the extracted load waveform at a sampling rate (fs) of about 100 Hz. Second, it was confirmed that the image characteristics of the tool state were most clearly extracted when STFT was applied to the resampled load waveform under the following setting conditions. Figure 9 shows the results of data preprocessing using FFT and STFT. (2) AI modeling and Training Pretrained networks such as VGGNet (Visual Geometry Group Net) are used to reduce learning and increase prediction accuracy for effective prediction of

A Study on Industrial Artificial Intelligence-Based Edge …

65

Fig. 9 Result of data pre-processing using FFT and STFT

machining quality or tool condition based on the data set generated through STFTbased data preprocessing for machining quality analysis. For this purpose, 1,200 cases of normal data and 600 cases of wear and breakage data of 5 tools were extracted from the pre-processed data for a total of 6,000 cases of defective data. These tool wear data were continuously randomly generated at 1.3–1.6 times the normal tool load data, and tool breakage data were additionally generated through data generation techniques such as continuously and randomly generated with values in the range of 100–300. And, using these data, we checked the validity of 5 types of pretrained networks such as NASNetLarge, Xception, VGG16, VGG19, and InceptionResNetV2 for tool condition and quality prediction. As a result, VGG19 was selected as the main pretrained network in this study. Among the pretrained networks, three models were learned through finetuning for Xception, VGG19, and InceptionResNetV2, and the final decision was made through an ensemble technique to which the average value according to the three model results was applied.

5 Results According to the analysis of the collected actual data, it was confirmed that the processing tool spindle load (0–300 ms) when the facility is inoperative and the load value when the processing tool is broken or worn were almost similar. Accordingly, in this study, the performance of the artificial intelligence model can be improved by learning not only the normal operating state data but also the data of the nonoperating (idle) section. In addition, the real-time monitoring system developed in the study [10] was improved to confirm the results of real-time spindle load data analysis and analysis on machining quality. (1) Results of tool state analysis As with the Bi-LSTM model, to check the prediction performance of the Conv2DLSTM model, the actual collected data on the 8th day and the real-time prediction data through AI inference were continuously compared with the data for the last 3 s. Using the Conv2d-LSTM model proposed in this study, predicting the spindle load value for machining tool state analysis showed performance of 0.0917 for 3 s

66

H. Lee et al.

(30 data) average MAE 0.0016 for normal data and 3 s average error rate (MAE) for abnormal data as shown in Fig. 10. In conclusion, it can be confirmed that the Conv2D-LSTM model that considers both spatial and time-series characteristics contained in the data shows better performance in terms of learning rate, etc. (2) Results of machine quality analysis Based on data pre-processing using STFT, pre-trained networks such as Visual Geometry Group Net (VGGNet) were used to reduce learning and increase predictive accuracy, and 6,000 bad data/1,200 normal data/ 600 fault data were extracted. Figure 11 shows an example of a spectrogram image according to the state of the tool among a total of 7,200 cases of 13 types of normal and abnormal (6 types of tool wear and 6 types of tool break) data among the pre-processed data. For example, if we look at the spectrogram is image when the tool is worn, we can see that unlike the normal image, we can obtain images that are lighter in color and darker in the section where wear occurs. And when the tool is broken, there is no change in the frequency component, indicating an image that look like a straight line or is cut off.

Fig. 10 Normal and abnormal detection results with Conv2D-LSTM

Fig. 11 Spectrogram using STFT

A Study on Industrial Artificial Intelligence-Based Edge …

67

These tool wear data were continuously randomly generated at 1.3–1.6 times the normal tool load data, and tool breakage data were additionally generated through data generation techniques such as continuously and randomly generating values in the range of 100–300. And using these data, the validity of 5 types of pretrained networks, such as NASNetLarge/Xception/VGG16/VGG19/InceptionResNetV2, was checked for tool status and machining quality prediction. As a result, in the case of NasNetLarge, Xception, and InceptionResNetV2 models, an overfitting was found within a short time, and in the case of the same series, VGG16/VGG19, an appropriate level of validity was confirmed and used as the main pretrained network in this study. Of the pretrained networks, three models were trained through fine tuning of the VGG19/Xception/InceptionResNetV2 models, and the final decision was made through the ensemble method that applied the average value according to the three model results. The results of real-time spindle load prediction and tool condition prediction of CNC equipment can be checked in the real-time machining tool load prediction monitoring system as shown in Fig. 12. In this study, the system was designed to detect abnormalities through the prediction of the machining tool spindle load and to analyze the machining quality according to the machining tool status at the same time. That is, equipment abnormality detection is performed through real-time machining tool spindle load prediction, and the machining quality according to machining tool status (wear, broken, etc.) of the machined product at the end of the machining cycle can be analyzed. As shown in Fig. 12, the real-time spindle load prediction for equipment abnormality is predicted to be in a normal state, but the analysis of the tool state after the machining cycle is over shows that the probability that the machining tool is worn out is 99.64% or more. As a result of comparing with the actual data for the corresponding section, it was confirmed that the tool wear section was predicted normally. Accordingly, it was confirmed that the error in real-time spindle load prediction caused by non-operation data could be determined not only on the condition of the machining tool such as breakage and wear, but also on the machining quality caused by it through secondary analysis and verification of the data for the entire cycle. However, in this study, a model was selected for a CNC facility with six machining tools and an experiment was conducted. Other Pretrained Network models may perform better. Therefore, it is necessary to verify that various ImageNet models can be applied in a variety of ways in consideration of production characteristics or facility characteristics of complex manufacturing sites.

68

H. Lee et al.

Fig. 12 Results of machining quality analysis

6 Conclusions In this study, an AI-based edge analysis technique was conducted that enables quality prediction through real-time machining spindle load prediction and machining tool state analysis of automated facilities. In particular, we presented an edge-based AI model that can quickly detect facility abnormalities and analyze conditions in the field. For this analysis, we developed a prototype of a real-time AI machining tool load prediction system and verified the possibility of commercialization. Through this, it is expected to contribute a lot to the advancement of smart factory or the development of intelligent manufacturing facilities. In the future, additional research on the lightening of artificial intelligence models for real-time inference in a more stable Edge environment and additional customization of optimal pretrained network model selection and finetuning will be needed depending on the number of learning data and complexity of data characteristics. Additional research on artificial intelligence models for processing facilities for multi-species production is also needed.

References 1. S.H. Lee, B.D. Youn, Industry 4.0 and the direction of failure prediction and health management technology (PHM), J. Korean Soc. Noise Vib. Eng. 25(1), 22–28 (2015) 2. B.D. Youn, T.W. Hwang, S.H. Jo, D.K. Lee, K.M. Na, Diagnosis and prediction of engineering system status using artificial intelligence. J. Korean Soc. Mech. Eng. 57(3), 38–41 (2017) 3. W.S. Choi, Deterioration evaluation method of high-temperature parts of power generation equipment based on Deep Learning. Mon. Electr. J. 496, 15–20 (2018)

A Study on Industrial Artificial Intelligence-Based Edge …

69

4. H.S. Oh, B.C. Jeon, J.H. Jung, B.D. Youn, Smart diagnosis of journal bearing rotor systems: unsupervised feature extraction scheme by deep learning, in Proceedings of the Annual Conference of the PHM Society, vol. 8, no. 1 (2016), pp. 1–8 5. Y.W. Song, H.S. Lee, H.S. Park, Y.J. Kim, J.Y. Jung, A signal processing technique for predictive fault detection based on vibration data. J. Soc. E-Bus. Stud. 12(2), 111–121 (2018) 6. J.I. Nam, H.J. Park, A neural network based fault detection and classification system using acoustic measurement. J. Korean Soc. Manuf. Technol. Eng. 29(3), 210–215 (2020) 7. K.W. Kang, K.M. Lee, CNN-based automatic machine fault diagnosis method using spectrogram images. J. Inst. Converg. Signal Proc. 21(3), 121–126 (2020) 8. C.H. Hwang, K.W. Shin , CNN-LSTM combination method for improving particular matter contamination (PM2.5) prediction accuracy. J. Korea Inst. Inf. Commun. Eng. 24(1), 57–64 (2020) 9. B.G. Park, S.H. Bae, B.K. Jung, Speed prediction of urban freeway using LSTM and CNNLSTM neural network. J. Korea Intell. Trans. Syst. 20(1), 86–99 (2021) 10. H. Lee, D.H. Kang, S.C. Jeong, A Study on the predictive maintenance system for CNC Facilities based on intelligent cutting tool load prediction. ICIC Express Lett. 16(3), 265–273 (2022) 11. K. Chen, Denosing Data with Fast Fourier Transform (2020). https://kinder-chen.medium. com/denoising-data-with-fast-fourier-transform-a81d9f38cc4c. Accessed 02 Dec 2021 12. VMV-TECH, STFT (Short Time Fourier Transform)-Series #1’ (2017). https://m.blog.naver. com/vmv-tech/220936084562. Accessed 15 Dec 2022 13. Y. Zheng, C. Yang, A. Merkulov, Breast cancer screening using convolutional neural network and follow-up digital mammography, in Proceedings SPIE, Computational Imaging III, vol. 10669 (2018)

AI Recruitment System Using EEG to Explore the Truth of Interviewers Junghee Lee, Phuong Huy Tung, Euntack Im, and Gwangyong Gim

Abstract COVID-19 pandemic is behind the implementation of the “AI recruitment system.” The number of companies trying to introduce AI recruitment systems is increasing because the non-face-to-face method is recommended due to the COVID19 pandemic and the management change of the organization comes with the development of IT technology. Behind the positive evaluation that the development of AI technology improves the efficiency of work, the demand for fair and transparent recruitment procedures has been increasing as controversy over fairness and objectivity has increased due to various hiring irregularities. This study aimed to approach in a more systematic and scientific way to maximize the effect of recruiting talent. In the previous study, voice and video were identified based on ML. In situations where the problem of truth and falsehood is raised, this study conducted EEG-based biological experimental studies with a deep learning method to explore more objectively. Also, the experimental design applied biological experiments between brain activity patterns and brain regions as signals from EEG-based 14 channels to explore the truth/false authenticity of the experimenters. As a result of the experiment, the best performance and effect were shown in the CNN model with an accuracy of 91% truth and 89% false among the comparative analysis of Decision Tree, Random Forest, and CNN. Keywords AI recruitment system · Artificial intelligence · EEG signal · Truth and lies · EEG (electroencephalogram) · CNN This research is supported by Soongsil University. J. Lee Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] P. H. Tung · E. Im · G. Gim (B) Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] P. H. Tung e-mail: [email protected] E. Im e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_6

71

72

J. Lee et al.

1 Introduction Deception is referred to as an expression or action that hides the truth, and deception detection is referred to as a concept for revealing the truth [1]. The main function of the AI recruitment industry is to find the right talent for the job [2], which is the biggest strategic point directly connected to the profitability of the organization [3]. On the other hand, although there are many advantages to applying AI to recruitment [4], other specific social limitations still exist [5]. The reason for this is that it is limited to evaluating the competence and potential of applicants who try to intentionally hide their shortcomings as much as possible [6]. It is premature to conclude that AI interviews enhance objective fairness while there is no certainty about the fairness of the algorithm [8]. In this study, a connection analysis was applied for the authenticity between true and false brain activity patterns and brain regions. This study was interviewed in almost the same way as the actual interview environment in EEG-based biological experiments. As for the questions, open questions were applied to evaluate the ability to solve situations assuming arbitrary situations used in interview protocol interviews. In the interview, a network connection between truth and lies was constructed for each topic. The classification results showed the best performance among Decision Tree, Random Forest, and CNN comparative analysis methods, with the accuracy of CNN models being 91% true and 89% false. Thus, this study aims to provide an important meaning that can be practically helpful in the development and implementation of a new AI system applying a neural pattern of deception.

2 Theoretical Background 2.1 Social Response to AI Recruitment System In previous studies, ML studies through voice and face, in which the image analysis system of the AI recruitment system appears in various ways in real-time, have been continuously studied. Amid growing controversy over the reliability of truth and falsehood, few studies have been conducted on EEG-based patterns of true and false brain activity that can be identified during interviews by interviewers. In a study by Gao et al., it is stated that “deception has proven to be associated with greater activation within the prefrontal cortex compared to the truth,” and “deception is associated with many functions of higher cognitive functions during lying because it is a rather complex mental activity” [9]. In particular, fraud detection through interviews cannot be generalized for any reason, but it can provide a limited evaluation of who knows the stimulus. Consequently, connection analysis can provide unique information about truth and lying brain activity, so it can be applied to interviews [1]. When asked on the social network, “What are the negative effects of AI interviews on the job market?” it replies “Easy-to-pass model answers may spread in secret

AI Recruitment System Using EEG to Explore …

73

and become nominal.” In fact, experts may miss some of the essential personality and deceiving behaviors in the recruitment process. It is difficult to evaluate the knowledge and experience of work only with voice and video [1]. Also, problems with datasets and implicit biases embedded in algorithms between developers can arise. The complexity of the algorithm may bias AI decision-making, which is a prerequisite for public trust and conviction [10]. The abolition of false interviews continues to occur steadily, and the question of how to establish evaluation standards has also been raised [11].

2.2 Introduction to AI Recruitment Process This study will introduce the seven stages of the entire process of ‘in FACE’ provided by Soo-Han [12]. Step 1. This is the user registration stage. Step 2. The basic interview is a total of three questions: 1. self-introduction 2. strengths and weaknesses of personality 3. motivation for the application. The AI voice analysis system is used as a basic basis for finding the job suitability of applicants by analyzing the frequency of use of positive words, negative words, and neutral words [24]. ML research through voice and face was continuously conducted. However, there is also a disadvantage that they can intentionally hide the truth [4]. Step 3. It’s a personality test. The main focus is on grasping job-related characteristics, attitudes, and values such as the applicant’s sense of order, responsibility, diligence, sociality, leadership independence, adaptability, concentration, and stability. Step 4. Deal with the situation. It is the answer to present specific situations and conditions and how to deal with them. Step 5. Reward preference. Evaluate decision types and information utilization types. Step 6. AI games are aimed at presenting 6 to 10 games with little repetitive learning effect and identifying basic skills such as the propensity, memory, and quickness of applicants to cope with the game [13]. Developers say that the AI recruitment system is the most suitable because it was developed in line with the recruitment of new people in their 20s and 30s. “In particular, the older you are, the worse the results were,” he said. Step 7. In-depth interviews are ’tail-biting questions’. Currently, there are about 15 companies that provide overseas AI Recruitment Platforms including Fetcher, Py-metrics, Textio, XOR, Hiretual, Humanly, Paradox, Eightfold, My interview, Talkpush, AllyO, Loxo, Seekout, LinkedIn, Facebook, Face Net. In Korea, there are JOBFLEX, inFACE, Win SiDAERO, Avril HR for Recruit, View Inter HR, and Copy Killer HR.

74

J. Lee et al.

2.3 Artificial Intelligence The term AI was first coined by John McCarthy at the 1956 Dartmouth College workshop in a paper by Russell and Norvig [25]. Work on AI was initiated by Alan Turing, a British mathematician and computer scientist during World War II [1]. Artificial intelligence is a term commonly used in software, machinery, and computers, and will also contribute to specialized fields such as applied science and psychology in the future, predicted John McCarthy predicted [6]. Subsequently, research by Herbert Simon, Allen Newell, Claude Shannon, Nathaniel Rochester, Marvin Minsky, and John McCarthy developed early computer models of human cognition [10]. New technologies are changing rapidly for companies to gain a strategic competitive advantage [4, 7], and the use of AI technology in various business areas is increasing unprecedentedly [14]. It will be a key part of AI technology with artificial intelligence similar to humans.

2.4 EEG (Electroencephalogram) EEG is a record of the electrical activity of nerve cells in the cerebral cortex through electrodes attached to the scalp. EEG has a frequency of 1–50 Hz and an amplitude of approximately 10–200 µV, and was first attempted in 1929 by German physiologist Hans Berger [15]. A way to find people who cheat based on electrical activity in the brain was pioneered by Farwell in 2001 [16]. Recently, EEG has been performing physiological measurements to distinguish the lying brain [17]. It is possible to directly evaluate the internal state of the interviewee through the EEG signal. EEG is even more reliable because perception based on EEG data or other physiological bases cannot be disguised or altered naturally and artificially. Thus, a biological experimental study on the dataset of EEG truth and lie detection was conducted in this study. A comprehensive review of EEG functions was conducted to improve model performance. Several FSs were used to systematically compare functions in one database, and the comparison results showed stronger results.

3 Research Methodology 3.1 Experiment Design Figure 1 shows the experimental design of this study.

AI Recruitment System Using EEG to Explore …

75

Fig. 1 Decision tree, random forest, CNN performance comparison

In this study, an experiment was conducted to distinguish whether participants were telling the truth or lies. The experimental environment was prepared in the same environment as the AI interview, and the experimental setting was prepared by two laptops and a 14-channel Mobile Brain headset as shown in Figs. 2 and 3. Fig. 2 Experimental setup

Fig. 3 Emotive Epoc X

76

J. Lee et al.

In this experiment, participants were interviewed by developing 10 open-ended questions. The following is the pre-preparation for each step to proceed with the experiment. (1) Pre-preparation for each step Step 1(preparation). Two laptops and a 14-channel Mobile Brain headset were prepared, and 10 open-ended questions for situation-solving ability were prepared for the interview content assuming a random situation used in an actual interview. Step 2. The participant and researcher sit face to face. The researcher asks the participant 10 questions. Participants will answer the truth first. EEG signals are recorded during the response. Step 3. This set of answers is completely different from the truth answered in Step 2. The researcher again asks 10 questions, and the participant answers a false answer. EEG signals are recorded during the response. To classify true and false, Emotiv Epoc X was selected as the device, and a total of 14 channels (Fig. 4) were selected to collect signals from participants. To perform the task, Emotiv Epoc X, which supports full support and raw data and file storage functions, was used as the device. This study used the Emotiv Pro app to analyze EEG data by utilizing the powerful cloud computing infrastructure which is the biggest feature of the software. Fig. 4 14 channel

AI Recruitment System Using EEG to Explore … Table 1 14 channels

77

Channel number

Channel label

Channel number

Channel label

1

AF3

8

02

2

F7

9

P8

3

F3

10

T8

4

FC5

11

FC6

5

T7

12

F4

6

P7

13

F8

7

01

14

AF4

The proposed method is non-invasive, efficient, and has low time limitations, making it suitable as an application program [30]. This study prepared two laptops. One laptop was used for interview experiment interviews with participants, and the other was used to run the Emotive PRO app to collect signals. Table 1 shows the list of EEG channels where signals were captured. Three researchers were on standby to collect EEG signals, and to conduct an interview, 10 open-ended questions on situational resolution ability were prepared assuming any situation that could be used in an actual interview in advance. Participants were five graduate students in their 20 s and 30 s who are about to get a job. The interview was conducted without being able to see the questions in advance as in the actual interview environment. The experiment took 30 min per person, including precautions, and data from 5 participants were collected. This study implemented a prediction model by preprocessing the data of two out of five people, respectively. The ratio of truth to false was 1:1, and 22 truth responses, 22 false responses, and a total of 44 responses were derived from the data of two people. The contents were the prepared content and the contents from the questions. Questions were based on 2021 Vicky Oliver’s Harvard Business Review Ascend questions [18].

3.2 Signal Acquisition from EEG Figure 5 is a table of signal collection by running the Emotive PRO app with 14 EEG channels from AF3 to AF4. The signal in Fig. 4 is recorded during the interview, and 14 signals from AF3 to AF4 are included. It is a table recorded by time zone and frequency.

78

J. Lee et al.

Fig. 5 EEG RAW data of 14 channel

3.3 Data Pre-processing: Artifact Removal EEGs recorded in electrodes placed on the scalp can basically provide information about brain activity, but signals that attempt to interpret the recorded signals are artifacts. As a result, the presence of electrical signals of non-neurogenic origin is always hindered [1]. The function of the artificial IC may be visualized using various expressions. Artifacts have physiological origins such as eye movement or muscle contraction, or non-biological causes such as electrode high impedance or electrical device interference [22]. EEGLAB provides several convenient visual representations of IC properties that enable the accurate identification of artificial ICs. In the process of collecting EEG signals, artifacts, which is ’facial muscle movement, head, neck and body movement, eye movement’, must be removed. Artifact is removed by the following 5 methods [20]. 1. Remove noisy components with weak autocorrelation 2. Remove components that are too focal and thus unlikely to correspond to neural 3. Remove components with weak signal to noise ratio between arbitrary baseline and interest time windows 4. Remove components whose time course correlates with any channel 5. Remove components based on the joint use of spatial and temporal features (ADJUST method).

3.4 Eeg Features Extraction The feature extraction method is reviewed to determine the authenticity of the candidates’ truth and falsehood in the EEG. The EEG feature extraction process is largely extracted through three steps [19].

AI Recruitment System Using EEG to Explore …

79

Step 1: Time Domain Features. Step 2: Frequency Domain Feature. Step 3: Time–Frequency Domain. This study aims to review feature extraction methods used for EEG signal analysis in EEG raw data and to systematically compare and evaluate 29 features for each dataset of [20] 14 channels that allow qualitative evaluation of features and electrodes. In this study, a total of 409 features were extracted from 14 channels with 29 features for each channel. EEGLAB is an open-source MATLAB toolbox widely used for electrophysiological data analysis. EEGLAB allows users to import various data formats, preprocess and visualize filters, resamples, means, and epochs of data. MATLAB combines desktop environments tailored to the iterative analysis and design process with programming languages that directly represent matrix and array math [21].

4 Classification Models Implement 4.1 Method of Feature Selection and Filter Method Since using all the raw data features in modeling is very inefficient in terms of computing power and memory, this study selected and used only some necessary features. Three classification methods for Feature Selection include 1. Filter Method: measuring the relevance between features; 2. Wrapper Method: measuring the usefulness of Feature Subset. 3. Embedded Method: measuring the usefulness of Feature Subset, but using built-in metrics [22]. The Filter Method (Fig. 6) used in this study is a method of finding out the correlation between features using a statistical measurement method and using a feature to see if it has a high correlation coefficient. The filter method includes information gain, chi-square test (chi squared), variance threshold, fisher score, and correlation coefficient, often represented by a heat map to visualize the correlation [21].

All feature

Selecting the

Learning

Data sets

Best subset

Algorithm

Fig. 6 Filter method statistical measurement method

Performance

80

J. Lee et al.

Fig. 7 Correlation analysis

4.2 Correlation Analysis with Extra Trees Classifier In the importance analysis of this work, we utilize Extra Trees Classifier to implement a meta-estimator that fits multiple randomized decision trees for various subsamples of the dataset. Overfitting was controlled by improving prediction accuracy using averaging. The reason for the correlation analysis is to know the effect of data on other data and to determine the data for the analysis model. When looking at the heatmap, it was confirmed that the correlation between ’mte’ and ’max’ was the highest (Fig. 7).

4.3 Feature Selection This study used the Extra Tree Classifier for feature selection. The importance of a feature is calculated as the total reduction in the criteria brought by the feature, also called GNI importance. Only features with an importance value of 0.01 or more are maintained. As seen in Table 2, variables with a sub-criticality of 0.01 or less were removed, and 31 features were selected among 406 features. Table 2 31 select picture function Ranking

Variable

Relative importance

Ranking

Variable

Relative importance

1

1d_6

0.11683

7

1d_7

0.03077

2

bpg_6

0.0909

8

bpa_8

0.02923

3

mte_11

0.06772

9

kurt_1

0.0239

am_2

0.02216

min_13

0.0101

4

bpg_7

0.06242

10

5

mte_6

0.04188

…

6

n1d_5

0.03547

31

AI Recruitment System Using EEG to Explore …

81

Table 3 Decision tree, random forest parameters of each model Decision tree

Random forest

CNN

Criterion: gini max depth = 5 min samples leaf = 1

Criterion: gini max depth = 5 min samples leaf = 1 n estimators = 10

Filters = 64 kerner size = 2 pool size = 2 optimizer: Adam epochs = 100 batch size = 64

4.4 Binary Classification Modeling The dataset of this study is classified into learning data and test data. The learning data ratio was classified as 55%, and the test data ratio was classified as 45%. From the data of two people, 22 truth responses, 22 false responses, and a total of 44 responses were derived. The ratio of truth to lie was 1:1. This study learned the predictive model with 24 data out of 44 responses and tested the model performance with test data with 20 data. Therefore, in this experiment, since the amount of data was not large, a predictive model was implemented at a ratio of 55%: 45%. As shown in Table 3, this study trained the classification model with three universal models: Decision Tree, Random Forest, and CNN.

4.5 Models’ Evaluation As shown in Table 4, the model performance evaluation test was conducted with 11 truths, 9 lies, and a total of 20 data. As seen in Table 4, for classification methods, the Confusion Matrix was set with X-axis as predict and Y-axis as actual. Table 4 Models’ evaluation (decision tree, random forest, and CNN) Classification (Truth & Lie)

Model performance evaluation test result Decision tree

Random forest

CNN

Lie(10)

Truth(10)

Lie(6)

Truth(14)

Lie(9)

Truth(11)

Lie(9)

True Lie(7)

False Truth(2)

True Lie(5)

False Truth(4)

True Lie(8)

False Truth(1)

Truth(11)

False Lie(3)

True Truth(8)

False Lie(1)

True Truth(10)

False Lie(1)

True Truth(10)

82

J. Lee et al.

Fig. 8 Formulas for precision, recall, and F1 score

Table 5 Decision tree, random forest CNN classification model evaluation Label

Precision

Recall

F1 score

Total of tested samples

Lie

0.70

0.78

0.74

9

Truth

0.80

0.73

0.76

11

Random forest

Lie

0.83

0.56

0.67

9

Truth

0.71

0.91

0.80

11

CNN

Lie

0.89

0.89

0.89

9

Truth

0.91

0.91

0.91

11

Decision tree

4.6 Machine Learning Classification Indicators (Precision, Recall, F1 Score) It is to create an ML algorithm that can model Classification and then statistically check how well the model works. The equation is shown in Fig. 8 [23]. As seen in Table 5, the classification method was evaluated by machine learning classification models such as precision, reproduction rate, and f1-score with Confusion Matrix. Both precision and reproducibility were high in the comparative analysis of Decision Tree, Random Forest, and CNN. The f1-score was 0.89–0.91, with CNN methods showing the best performance with 91% true and 89% false.

5 Conclusions Although there have been many studies on image analysis and voice analysis of the ML-based AI recruitment system, which is the existing interview method, behind the positive evaluation of work efficiency, the perception that the existing AI recruitment system alone cannot be fair recruitment should be studied. The problem of developers’ biased algorithms and interviewers learned and intended methods of evaluating false interviews are due to the increased demand for reliability from businesses and job seekers. Thus, in this biological experimental study, signals from EEG-based 14 channels, a non-invasive method, were used to receive unique information between brain activity patterns and brain regions for trick detection. Also, the Decision Tree, Random Forest, and CNN methods were applied to compare and analyze. As a result of the classification, the accuracy of the CNN method was 91% true and 89% false,

AI Recruitment System Using EEG to Explore …

83

showing the best performance. In the EEG-based biological experimental study, few existing theoretical studies were conducted in the interview part of the AI recruitment system. The study is meaningful in preparing 10 open-ended situational problem questions assuming any situation used in the actual interview, conducting practice for participants, and providing a theoretical framework for research on the authenticity of truth and falsehood based on EEG. This is meaningful in practical research that can be used for AI recruitment interviews. The limitation of this study is that the amount of data used in the study is not large. Future research should continue the research on a more systematic deep learning approach by securing a large amount of supplemented data. This study is expected provide an important meaning that can be practically helpful in the development and implementation of an AI recruitment system applying a new neural pattern of deception.

References 1. M.D. Kohana, A.M. Nasrabadib, A. Sharific, M.B. Shamsollahi, Interview based connectivity analysis of EEG in order to detect deception. Med. Hypotheses 136, 109517(Mar 2020) 2. G Uma, Sowmya N, Rama Krishna G, Artificial intelligence: a conceptual study. Int. J. Mech. Eng. Technol. (IJMET) 8(7), 63–70 (Jul 2019) 3. N. Nawaz, Artificial intelligence is transforming recruitment effectiveness in CMMI level companies. Int. J. Adv. Trends Comput. Sci. 8(6) (Nov–Dec 2019) 4. A.F.S. Borges, F.J.B. Laurindo, M.M. Spnola, R.F. Gonalves, C.A. Mattos, The strategic use of artificial intelligence in the digital era: systematic literature review and future research directions. Int. J. Inf. Manage. 57, 102225 (2021) 5. N. Oswal, M. Khaleeli, A. Alarmoti, Recruitment in the era of industry 4.0: use of artificial intelligence in recruitment and its impact. Pjaee 17(8) (2020) 6. G.Y. Son, W.-H. Lee, K.-J. Han, S.-H. Kyung, Machine learning for EEG signal-based feature vector formation and emotion recognition. J. Korean Telecommun. Soc. (2020) 7. N. Haefner, J. Wincent, V. Parida, O. Gassmann, Artificial intelligence and innovation management: a review, framework, and research agenda. Technol. For. Soc. Change 162, 120392 (2021) 8. Y.K. Dwivedi, L. Hughes, E. Ismagilova, G. Aarts, et al., Artificial Intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manage. 57, 101994 (2021) 9. M. Karnati, A. Seal, A. Yazidi, O. Krejcar, LieNet: A Deep Convolution Neural Networks Framework for Detecting Deception (IEEE, 2021), pp. 2379–8920 10. M. Soleimani, A. Intezari, N. Taskin: Cognitive biases in developing biased Artificial Intelligence recruitment system, in Proceedings of the 54th Hawaii International Conference on System Sciences (2021) 11. O. Allal-Cherif, A.Y. Aranega, R.C. Sanchez, Intelligent recruitment: how to identify, select, and retain talents from around the world using artificial intelligence. Technol. For. Soc. Change 169, 120822 (2021) 12. P. Soo-Han, All about AI competency test, (Corporation)educ (2020) 13. Tae woo Park, Hankyoreh (2021). https://h21.hani.co.kr/arti/economy/economygeneral/49403. html 14. I. Munoko, H.L. Brown-Liburd, M. Vasarhelyi, The ethical implications of using artificial intelligence in auditing. J. Bus. Ethics 167, 209–234 (2020)

84

J. Lee et al.

15. H.-J. Lee, S. Dong-Il, S. Dong-Gyu, User’s emotion classification algorithm using brain waves. J. Korean Telecommun. Soc. (J-KICS)’14–02 39C(02) (2014) 16. N. Baghel, D. Singh, M.K. Dutta, R. Burget, V. Myska, Truth Identification from EEG Signal by using Convolution Neural Network: Lie Detection. 978-1-7281-6376-5/20/© (IEEE, 2020) 17. A. Bablani, D.R. Edla, V. Kuppili, Deceit identification test on EEG data using deep belief network, in 9th ICCCNT, July 10–12, IISC, Bengaluru, vol. 43488 (IEEE, 2018) 18. V. Oliver, 10 Common Job Interview Questions and How to Answer Them. Harvard Business Review Ascend (2021) 19. R. Jenke, A. Peer, M. Buss, Feature extraction and selection for emotion recognition from EEG. IEEE Trans. Affect. Comput. 5(3), 327–339 (2014) 20. R. Jenke, A. Peer, M. Buss, Feature extraction and selection for emotion recognition from EEG. IEEE Trans. Affect. Comput. 5(3) (Jul–Sep 2014) 21. https://wooono.tistory.com/249 22. https://www.mathworks.com/products/matlab.html 23. https://blog.naver.com/PostView.nhn?blogId=wideeyed&logNo=221531940245 24. J. Lee, J. Kim, G.Y. Gim, Factors affecting the intention to use artificial intelligence- based recruitment system: a structural equation modeling (SEM) approach, in ICIS 2021: Computer and Information Science 2021 (Summer 2021), pp. 111–124 25. P.N. Russell, Artificial intelligence: a modern approach by stuart. Russell and Peter Norvig contributing writers, Ernest Davis...[et al.] (2010)

Development of ESG Evaluation Indicators from a Policy Perspective—Focusing on the Legislature Byeongduk Hwang, Myeong Sook Park, Jangwoo Kim, and Gwangyong Gim

Abstract This study optimized the evaluation items through a meeting of ESG evaluation indicators experts with previous studies to present general ESG evaluation criteria that can be used by mid and small-sized companies that have difficulty in ESG management activities. Based on this, AHP analysis was conducted on 54 survey data (31 companies and 23 National Assembly members), excluding 29 out of a total of 83 surveyors who exceeded the consistency index of 0.2. As a result of the analysis, first, the environment (E) area was evaluated as the most important task for all surveyors at 47.6%. In the evaluation items by area, 34.4% for pollution and waste in the environmental (E) area, 28.8% for human rights in the social (S) area, and 37.1% for ethical management in the governance (G) area were evaluated as the most important tasks. Next, looking at the total weight results for each evaluation item, pollution and waste (16.3%) were found to be the most important tasks. Second, the result was that corporate officials gave a relatively 6–7% more weight to related laws and regulatory violations such as environmental laws and regulatory violations than legislative officials. This can be interpreted that corporate officials are paying more attention to legal risks caused by regulatory violations due to excessive punishment for companies and anti-corporate sentiment. In the development and evaluation of ESG evaluation indicators, the promotion that ignores the opinions of companies that can be called the direct parties can lead to a gap between the field and the indicators. In the future, it is necessary to develop and evaluate ESG indicators that can further reflect the voices of the field. Keywords ESG · Environmental · Social · Governance · Policy · Legislature B. Hwang · J. Kim · G. Gim (B) Department of Business Administration, Soongsil University, Seoul, South Korea e-mail: [email protected] B. Hwang e-mail: [email protected] J. Kim e-mail: [email protected] M. S. Park Grad. of Business Administration, Soongsil University, Seoul, South Korea © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_7

85

86

B. Hwang et al.

1 Introduction Due to the continued COVID-19 pandemic, the business environment of companies in all industries is being driven to the worst. Moreover, leading global investment financial institutions such as Vanguard, and Blackrock, the world’s No. 1 asset operator, operate the ESG ETF (Exchange Traded Foundation). Accordingly, it is openly exerting pressure to no longer invest in companies that do not apply ESG evaluation indicators. Global asset managers are demanding ESG-oriented management activities and ESG disclosure for companies incorporated into their funds. If this is not carried out, pressure is being exerted at the general shareholders’ meeting, such as exercising the right to vote or stopping investment [1]. As a result, companies have no choice but to be subordinated to ESG-related evaluations in order to receive investment. However, most rating agencies such as MSCI ESG Ratings, Thompson Reuters ESG Score, Sustin Best, and Korea Corporate Governance Service, which assess the ESG levels of individual companies, will only disclose the results of the three major areas: Environment (E), society (S), and corporate governance. Specific measurement methods of evaluation elements are not disclosed. As a result, the reality is that even if different evaluation results are released for the same company, market participants, including companies, cannot know the clear reason. It is causing great confusion for not only individual companies but also market participants. Moreover, in the case of Korea, a number of bills related to corporate ESG management activities have been proposed, centering on the National Assembly, a legislative body. As a result, the predictability and reliability of domestic companies’ ESG evaluation indicators are greatly undermined. Thus, this study aims to stratify ESG evaluation indicators by analyzing ESG management factors to be considered by companies and evaluation items commonly presented by domestic and foreign evaluation institutions through previous studies related to ESG evaluation indicators. The evaluation items were optimized through a four-member ESG evaluation index meeting consisting of ESG team officials from the Federation of Korean Industries (Jeon Kyung-ryeon), ESG lawyer from Kim & Jang Law Office, and an aide to the National Assembly’s writing committee. Based on the optimized evaluation items, a survey was conducted on working-level members of the K-ESG Alliance under the Federation of Korean Industries (Jeon Kyung-ryeon), the National Planning and Finance Committee (the Ministry of Trade, Industry and Energy), 4th-level aides, and 5th-level senior secretaries. Through this, this study intends to present universal ESG evaluation indicators that can be used by individual companies from a legislative policy point of view, especially small and medium-sized companies that have difficulty in ESG management activities.

Development of ESG Evaluation Indicators from a Policy …

87

2 Theoretical Background 2.1 The Concept of ESG and Importance ESG stands for Environment, Social, and Governance. It refers to three key elements of corporate management to achieve sustainability that focuses on environmental, social responsibility, and sound and transparent governance. In the case of the environment (E), factors that encompass the overall environmental impact that occurs in the process of a company’s management activities are included. Recently, carbon neutrality and the use of renewable energy related to climate change have emerged as important factors. In the case of society (S), elements such as corporate rights, obligations, and responsibilities for various stakeholders such as executives and employees, customers, communities, and partners are included. Recently, issues such as human rights, health, and safety have emerged. In the case of governance (G), diversity, ethical management, and audit organizations of the board of directors are emphasized as areas for the rights and responsibilities of the company’s management, board of directors, shareholders, and various stakeholders. Recently, ESG has spread to a global trend, and as the interest of all members of society, including investors, consumers, and the government, has increased, it has emerged as a key factor in the survival and growth of companies, not choices. First of all, from a corporate perspective, ESG can be an essential element that must be internalized into the purpose of a company as a social value to increase corporate value in a future society. Next, in terms of capital procurement, ESG is an essential management factor in corporate capital procurement at a time when ESG is attracting attention as a core value of investors in various fields. Finally, in terms of sustainability, ESG factors developed under the comprehensive concept of sustainability are essential elements of risk management for companies’ sustainable growth.

2.2 Prior Studies In previous studies, the impact of each factor on corporate performance was examined. According to Amel-Zadeh and Serafeim, the performance of the ESG portfolio was found to follow the ESG standards selected by investors. Also, it was found that the importance of each factor was different by country, industry, and company, and companies focusing on the importance of each industry showed better performance [2]. A survey of 126 financial analysts who used ESG information for portfolio composition and management showed that they performed more analysis of ESG performance at the corporate level than at the industry level [3]. Although there have been some academic studies focusing on national risk management related to ESG, only a few papers have examined the importance of the elements of the ESG framework by country.

88

B. Hwang et al.

Looking at the paper that studied the importance of the category level for each ESG element, it was found that the weight of elements by category varies according to industrial characteristics [4]. Studies have shown that the relative importance of environmental (E), social (S), and governance (G) factors vary by sector, and explained how to assign 100% weight to environmental categories. Industries such as materials, industrial goods, economic consumer goods, and essential consumer goods industries assigned the same weight to each element due to the specificity of the industry. In addition, weights for categories may change over time [5]. Issues related to governance are generally neutral in terms of sector relevance, but issues related to environmental and social factors were highly sector-related. Refinitiv weights environmental (E), social (S), and governance (G) elements by 43%, 31%, and 25%, respectively [6]. Summarizing previous studies related to ESG, related studies until the early and mid-2010s mainly focused on research on the correlation between corporate value and ESG from the perspective of sustainability or investors. Since then, a systematic ESG framework that can quantify non-financial factors of companies, centered on asset managers and credit rating agencies, has been presented, diversifying into research on disclosure methods in addition to research on ESG and corporate performance. However, to this day, many studies are focused on studying the value of a company’s investment in relation to ESG.

2.3 AHP (Analytic Hierarchy Process) The AHP technique was proposed by Saaty [7] as a hierarchical analytical decisionmaking method that hierarchically classifies the various attributes that constitute the problem, and evaluates the optimized alternatives by identifying and listing the importance of each attribute. Decision Hierarchy is established by classifying decision-making problems into levels of interrelated decisions. And it is a method to capture the evaluator’s knowledge, experience, and intuition through judgment by pairwise comparison between elements constituting the hierarchical structure. AHP has been mainly used for decision-making in situations where there are several conflicting criteria. If consistency is perfectly maintained in the AHP analysis, the relative importance of the matrix can be calculated using only one row. However, in the real world, it is impossible to maintain consistency with respect to relative importance. Therefore, it is derived by estimating the relative importance, and in this process, it is necessary to determine whether an appropriate level of consistency among experts is being maintained [8]. Saaty [7] stated that the CR value would be 0 if the consistency was perfect, and conversely, the CR value would be greater than 0 as the consistency of judgment worsened. If the CR value is greater than 0.1, it is necessary to re-judgment and make corrections [9]. However, in some social science research studies, considering that it is difficult to secure independence between the upper and lower standards due

Development of ESG Evaluation Indicators from a Policy …

89

to the nature of the questionnaire questions, the allowable range is within 0.2 (20%) [10]. There are two methods for synthesizing expert opinions: arithmetic mean and geometric mean. In a study comparing bankruptcy prediction models, when qualitative information was used, using geometric mean to synthesize expert opinions was better than using arithmetic mean. As the bankruptcy prediction showed high accuracy, it was said that the geometric mean could more robustly synthesize expert opinions [11].

3 Research Design In this study, through previous studies related to ESG evaluation indicators, ESG management factors to be considered by companies and evaluation items commonly presented by domestic and foreign evaluation institutions were analyzed. Based on this, ESG evaluation indicators were classified, and evaluation items were minimized through a meeting of four ESG evaluation indicators experts, including ESG team officials from the Federation of Korean Industries, ESG lawyers from Kim & Chang Law Office, and aides from the National Assembly. Based on the optimized evaluation items, a survey was conducted on ESG managers of member companies of the KESG Alliance under the Federation of Korean Industries and Trade Unions, fourthgrade aides, and fifth-grade senior secretaries of the National Assembly who are highly related from a legislative policy perspective. Based on this data, an Analytic Hierarchy Process (AHP) analysis was conducted.

3.1 Derivation of Evaluation Indicators for Korean ESG Models Through Previous Studies In this study, based on the items commonly presented by five domestic and foreign ESG evaluation institutions (MSCI, Refinitiv, Sustinvest, KCGS and K-ESG guidelines) through previous studies of ESG evaluation indicators, the Korean ESG model evaluation indicators were composed of a total of 24 categories in three areas, as shown in Fig. 1. Based on the ESG definition described above, the item composition was set as a social value that an organization should pursue through ESG management, based on issues commonly presented by four domestic and foreign evaluation institutions based on three areas: environment (E), society (S), and governance (G). The Korean ESG model evaluation index consisted of a total of 3 areas and 24 subcategories. For the subcategories for each area presented in this study, three

90

B. Hwang et al.

Fig. 1 Stratified Korean ESG model metrics through advance researches

or more commonly overlapping items were selected among the evaluation indicators of four domestic and foreign evaluation institutions. To reflect Korean management characteristics and legal and institutional consistency, the K-ESG guidelines published by the Korean government in December 2021 were referred.

3.2 Optimization of Evaluation Items for Korean ESG Models In the case of the Korean ESG model evaluation index proposed through previous studies, the evaluation index was derived by reflecting some of the common elements of four domestic and foreign evaluation institutions and K-ESG guidelines. As a result, the evaluation item category was excessively expanded, resulting in that it was not suitable for AHP analysis. In this study, a meeting was held by four people, including a person in charge of the ESG team belonging to the Federation of Korean Industries, a lawyer in charge of ESG at the Kim & Chang Law Office, and an aide to the parliamentary committee. Here, 24 Korean ESG model evaluation items proposed through previous studies (8 environmental areas, 8 social areas, 8 governance areas) were integrated and adjusted

Development of ESG Evaluation Indicators from a Policy …

91

Fig. 2 Optimized Korean ESG model

to optimize to 12 items (4 environmental areas, 4 social areas, and 4 governance areas) as shown in Fig. 2.

3.3 Definition of Optimized Korean ESG Model Area and Evaluation Items In this study, 12 Korean ESG model evaluation items optimized through previous studies and expert interventions were presented. Looking at the evaluation items by area, it was resource depletion, pollution and waste in the environment (E) area, greenhouse gases, and environmental law/regulation items. Human rights, community, customer satisfaction, and social law/regulation violations in the area of society (S). It was organized into ethical management in the domain of governance (G), shareholder rights, board composition and activities, and items that violate governance laws/regulations. The definitions of environmental (E), social (S), and governance (G).

92

B. Hwang et al.

Fig. 3 Survey results by pillar of entire respondents

4 Empirical Analysis Results 4.1 Item Weight Results for Overall Survey In this study, a survey was conducted on 51 ESG managers from K-ESG Alliance affiliated with the Federation of Korean Industries and 32 senior secretaries from the National Assembly, the Commerce, Industry and Energy Committee, and the 5thdegree secretary. AHP analysis was conducted on 54 survey data (31 companies and 23 members of the National Assembly), excluding 29 out of a total of 83 surveyors who exceeded the consistency index (Consistency) 0.2. As a result of the analysis, as shown in Fig. 3, the environment (E) area was evaluated as the most important task at 47.5%. Society (S) (37.0%) and governance (G) (15.5%) were in the above order. Next, the results of the weight of the evaluation items by area are shown in Fig. 4. First of all, pollution and waste (34.4%), greenhouse gas (29.9%), environmental law and regulatory violations (19.7%), and resource depletion (16.0%) were derived in the sub-evaluation items for the environment (E). Next, in the social (S) area, human rights (28.8%), violations of social laws and regulations (27.3%), customer satisfaction (22.3%), and local communities (21.7%) were in the above order. Finally, in the area of governance (G), ethical management (37.1%), violation of governance laws and regulations (24%), shareholder rights (22.8%), and board composition and activities (16.0%) were derived in above order. Finally, looking at the total weight results for each evaluation item, as shown in Fig. 5, pollution and waste were 16.3%, greenhouse gas 14.2%, and human rights 10.6%.

4.2 Comparative Analysis of Weighted Results on Enterprise and Legislative Officials In this study, on behalf of the company, a survey was conducted on 51 ESG officials from member companies belonging to the K-ESG Alliance under the Federation of Korean Industries. 31 people showed a significant consistency index. Also, 23 people showed significant consistency as a result of a survey of 32 fifth-degree secretary

Development of ESG Evaluation Indicators from a Policy …

93

34.4% 29.9% 19.7%

16.0%

Pollution

GHG

Environmental law

Resource depletion

0.3437

0.2990

0.1974

0.1599

28.8%

27.3% 22.3%

21.7%

Human rights

Social law

Customer satisfaction

Community

0.2876

0.2727

0.2226

0.2170

24.0%

22.8%

37.1%

16.0%

Ethical management

Governance law

Shareholder rights

Board composition

0.3713

0.2401

0.2285

0.1602

Fig. 4 Survey results by metric of pillar of entire respondents

and fourth-grade aides belonging to the National Assembly, the Commerce, Industry and Energy Committee, and the Environment and Labor Committee, which affect the setting of ESG evaluation standards in the legislative field. In this study, AHP analysis was conducted on a significant questionnaire with a consistency index of 0.2 or less. The result of the analysis of the upper area is shown in Table 1. Both corporate and legislative officials were evaluated as the most important task in the environment (E), followed by society (S) and governance (G). Next, the results of the weight of the sub-evaluation items by subject (corporate and legislative officials) are shown in Tables 2 and 3. The peculiarity was that

94

B. Hwang et al.

3.7%

3.5%

2.5% Board composition

5.8%

Shareholder rights

7.6%

Ethical management

8.0%

Resource depletion

8.2%

Community

Environmental law

Social law

Human rights

GHG

Pollution

10.6% 10.1% 9.4%

Governance law

14.2%

Customer satisfaction

16.3%

0.1633 0.1420 0.1064 0.1009 0.0938 0.0824 0.0803 0.0760 0.0575 0.0372 0.0354 0.0248 Fig. 5 Survey results by metric of entire respondents

Table 1 Survey results by pillar of respondents by job groups (business and parliamentary officials)

Business (%)

Parliamentary (%)

Environmental

48.7

45.8

Social

36.8

37.2

Governance

14.5

17.0

corporate officials gave a relatively 6–7% more weight to related laws and regulatory violations such as environmental laws and regulatory violations than legislative officials. This can be interpreted that corporate officials are more interested in legal risks caused by regulatory violations. Various interpretations are possible for the background. First of all, Korea’s backward corporate legislation and overregulation can be cited. According to a report by the Korea Economic Research Institute, 2,205 out of 2,657 punishment items of 285 economic laws, 83% of them, have already punished companies and CEOs at the same time, and the punishment regulations continue to increase [12]. It can be inferred that it is an environment in which corporate officials have no choice but to invest enormous time and money in compliance with legal rules and regulations. Although there should be efforts to improve corporate behavior and awareness, the research suggests that it is necessary to create an environment where companies can be more active in achieving ESG goals rather than simply focusing on passive responses to avoid punishment.

5 Conclusion and Future Research Companies have no choice but to be subordinated to ESG-related evaluations in order to receive investment from investment institutions such as global asset managers.

Development of ESG Evaluation Indicators from a Policy …

95

Table 2 Survey results by metric of pillar of respondents by job groups (business and parliamentary officials) Pilar Environmental

Social

Governance

Metrics

Business (%)

Parliamentary (%)

Resource depletion

13.3

20.2

Pollution and waste

33.2

35.8

GHG

31.0

27.7

Environmental law/regulation violations

22.5

16.3

Human rights

25.2

33.7

Community

20.6

22.9

Customer satisfaction

23.5

20.5

Social law/regulation violations

30.6

23.0

Ethical management

35.0

39.9

Shareholder rights

22.4

23.3

Board composition and activities

15.6

16.4

Governance law/regulation violations

27.0

20.4

Table 3 Survey results by metric of respondents by job groups (business & parliamentary officials) Pillar Environmental

Social

Metric

Parliamentary (%)

Resource depletion

6.5

9.3

Pollution and waste

16.2

16.4

GHG

15.1

12.7

Environmental law/regulation violations

11.0

7.5

9.3

12.5

Human rights Community

7.6

8.5

Customer satisfaction

8.6

7.6

11.3

8.6

Social law/regulation violations Governance

Business (%)

Ethical management

5.1

6.8

Shareholder rights

3.2

3.9

Board composition and activities

2.3

2.8

Governance law/regulation violations

3.9

3.5

However, most evaluation institutions only disclose the comprehensive evaluation results but do not disclose the specific measurement method of the evaluation elements. This is causing great confusion for not only individual companies but also market participants. In the case of Korea, a number of bills related to corporate ESG management activities have been proposed, centering on the National Assembly, a legislative body. As a result, the predictability and reliability of domestic companies’ ESG evaluation indicators are greatly undermined. Thus, ESG evaluation items were optimized in this study through a meeting of experts on ESG evaluation indicators in previous studies. Based on this, AHP analysis was conducted on 54 survey data (31

96

B. Hwang et al.

companies and 23 members of the National Assembly), excluding 29 out of a total of 83 surveyors who exceeded the consistency index of 0.2. As a result of the analysis, first, the environment (E) (47.5%) area was evaluated as the most important task in the upper area according to the overall analysis results of corporate and legislative officials. In the evaluation items by area, pollution and waste (34.4%) in the environment (E) area, human rights (28.8%) in the social (S) area, and ethical management (37.1%) in the governance (G) area were evaluated as the most important tasks. Finally, looking at the overall weight results for each evaluation item, 16.3% of pollution and waste were found to be the most important tasks. Second, the results of weights for each item for companies and legislative officials are as follows. The result was that corporate officials gave a relatively 6–7% more weight to related laws and regulatory violations such as environmental laws and regulatory violations than legislative officials. This can be interpreted that corporate officials are paying more attention to legal risks caused by regulatory violations due to excessive punishment and anti-corporate sentiment. Summarizing the analysis results of this study, both corporate and legislative officials evaluated similar weights in the upper areas of the ESG evaluation index, that is, the environment (E), society (S), and governance (G). However, there was a significant difference in the sub-evaluation items. In the development and evaluation of ESG evaluation indicators, the promotion that ignores the opinions of companies that can be called direct parties can lead to a gap between the field and the indicators. In the future, it is necessary to develop and evaluate ESG indicators that can further reflect the voices of the field. As for the limitations of this study and the future improvement direction, the final analysis for this study was conducted using the weights of each item collected using AHP, but an empirical analysis on whether this analysis was appropriate was not conducted. In future studies, empirical verification for the general public should be conducted based on expert AHP analysis derived in this study. In addition to legislative officials, there should be research on institutional investors who are having a huge impact on corporate ESG management activities. Finally, theoretical and empirical studies on whether exploratory attempts to utilize expert knowledge that are not adopted due to lack of consistency in AHP analysis are really valid should be made. Future studies are needed to examine and empirically verify whether the method attempted in this study is reasonable or whether there is another more useful method.

References 1. J. Lee, G.R. Kim, D.B. Lim, D.H. Park, C.H. Jeon, ESG Management Era, Strategic Paradigm Transformation (Seoul, SamjeongKPMG, 2020) 2. R.G. Eccles, G. Serafeim, The performance frontier: innovating for a sustainable strategy. Harvard Bus. Rev. 91(5), 50–56, 58, 60, 150 (2013). https://www.hbs.edu/faculty/Pages/item. aspx?num=44737. Accessed 24 July 2021

Development of ESG Evaluation Indicators from a Policy …

97

3. E. van Duuren, A. Plantinga, B. Scholtens, ESG integration and the investment management process: fundamental investing reinvented. J. Bus. Ethics 138(3), 525–533 (2016). https://doi. org/10.1007/s10551-015-2610-8 4. J. Bender, T.A. Bridges, C. He, A. Lester, X. Sun, A blueprint for integrating ESG into equity portfolios (2017). https://papers.ssrn.com/abstract=3080381 5. Zoltán Nagy Executive Director, MSCI Research, Linda-Eling Lee Managing Director, MSCI Research, & Guido Giese Executive Director, MSCI Research, ESG ratings: how the weighting scheme affected performance. Msci.Com (n.d.). Accessed 24 May 2022 https:// www.msci.com/www/blog-posts/esg-ratings-how-the-weighting/01944696204. Accessed 24 July 2021. https://www.msci.com/www/methodology/refinitiv-esg-scores-methodology.pdf. Accessed 10 Jun 2021 6. Refinitiv, ESG Scores Methodology (2021). https://www.refinitiv.com/content/dam/marketing/ en_us/documents/ 7. T.L. Saaty, What is the analytic hierarchy process?. in Mathematical Models for Decision Support. (Springer, Berlin, Heidelberg, 1988), pp. 109–121 8. K.T. Cho, S.J. Kim, D.S. Kim, Y.W. Cho, J.I. Lee, Research Paper: Priority setting for future core technologies using the AHP-with major fields in rural development and resources. J. Korean Soc. Rural Plann. 9, 41–46 (2003) 9. T.L. Saaty, Decision making with dependence and feedback: the analytic network process, n.d. Poznan.Pl. http://www.cs.put.poznan.pl/ewgmcda/pdf/SaatyBook.pdf. Accessed 26 May 2022 10. S.-J. Hong, Y.-D. Lee, S.-K. Kim, J.H. Kim, Evaluation of risk factors to detect anomaly in water supply networks based on the PROMETHEE and ANP. J. Korea Water Resour. Assoc. 39(1), 35–46 (2006) 11. C.S. Woo, G.Y. Gim, S.B. Kang, Comparative study of default prediction model using LOGIT analysis and AHP analysis. Korean J. Financ. Manage. 14(2), 229–252 (1997) 12. J.J. Yoo, J.G. Yoo, 83% of 2,657 punishment items in 285 economic laws can be punished by the CEO (2019). http://www.keri.org/web/www/news_02?p_p_id=EXT_BBS&p_p_lifecy cle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-1&p_p_col_count=1&_ EXT_BBS_struts_action=%2Fext%2Fbbs%2Fview_message&_EXT_BBS_sCategory=&_ EXT_BBS_sKeyType=&_EXT_BBS_sKeyword=&_EXT_BBS_curPage=1&_EXT_BBS_ optKeyType1=&_EXT_BBS_optKeyType2=&_EXT_BBS_optKeyword1=&_EXT_BBS_ optKeyword2=&_EXT_BBS_sLayoutId=0&_EXT_BBS_messageId=355822#:~:text=% EA%B2%BD%EC%A0%9C%EB%B2%95%EB%A0%B9%20%EC%B2%98%EB%B2% 8C%ED%95%AD%EB%AA%A9%202%2C657,%EC%96%91%EB%B2%8C%20%EA% B7%9C%EC%A0%95*%EC%9D%84%20%EB%91%90%EA%B3%A0%20%EC%9E% 88%EB%8B%A4

A Study on the Analysis of Related Keywords on the Perception of Untact Coding Education in the Post-COVID Era Using Big Data Analysis Soo-Yeon Yoon and Jong-Bae Kim

Abstract The novel infectious disease COVID-19, which started in December 2019, has plunged the world into a pandemic era. Accordingly, countries around the world are concerned about the spread of COVID-19, and have promoted large-scale gatherings and education through immigration control and social distancing. In particular, from March 15, 2020, elementary, secondary, high school, and university have conducted an unprecedented online opening of school in Korea’s school education operation, and distance education classes have been conducted starting with the third year of middle and high schools across the country. In this process, students, faculty, and parents were faced with unexpected non-face-to-face distance education, and experienced confusion, tension, and uncertainty about the future society. Due to the spread of COVID-19, the online school curriculum has exposed the limitations and problems of the existing face-to-face school education system. In particular, coding education aimed at understanding and using software can interest students through face-to-face computer practice education. In addition, face-to-face education is important for non-IT majors to realize the importance of SW education and improve their convergence thinking skills. In this study, big data (SNS, Naver blog, youtube, google search keyword frequency, etc.) collected online to analyze the recognition and evaluation of untact (real-time online or pre-recorded video lecture) coding education was used as a text mining technique. Analysis was performed. ‘Coding education’, the keyword of this study, was selected as an analysis keyword, and data was collected through portals/SNSs of Google, Naver, Daum, and YouTube. In addition, after refining/morpheme analysis based on the collected list, word frequency, TF-IDF, N-gram, and topic modeling were analyzed through text mining, and matrix analysis, matrix chart, and sentiment analysis were performed. The results of this study will be used as basic data for college students’ coding education. This research is supported by Kookmin University. S.-Y. Yoon Dept of School of Software Convergence, Kook Min Univ., Seoul, South Korea e-mail: [email protected] J.-B. Kim (B) Startup Support Foundation, Soongsil University, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_8

99

100

S.-Y. Yoon and J.-B. Kim

Keywords Bigdata · Untact · Covid19 · Text-mining · Coding education · Software · Association rule · TF-IDF · N-gram · Topic modeling

1 Introduction After the COVID-19 outbreak that spread around the world in 2020, the education industry has been avoiding face-to-face classes, and using the advantages of online education or online and offline education, flipped learning and blended learning. We are oriented towards non-face-to-face classes. Therefore, SW education, which has a high proportion of experiential learning through practice, requires a study on the education method using the online platform. However, most of the research cases [1–4] of flip learning-based and blended learning-based SW education are research cases targeting elementary school students, and it is difficult to find research cases targeting middle and high school students, college students, and the general public. Although the Ministry of Education recently reviewed the introduction of the ’distance education certification evaluation system’ for quality control of online education at universities, its implementation is expected to take place after 2024 [5]. As such, 2022, which is greeted without academic and institutional preparation for online education, heralds a new change not only in SW education but also in the entire domestic education industry. In addition, in the field of education, advanced and customized online education that utilizes the results of big data-based analysis in the curriculum is gradually spreading. In particular, with the trend of Web 3.0 and the generalization of smart devices in big data analysis, unstructured big data shows an explosive increase, entering the era of unstructured big data [6]. Big data goes through a process of storing and analyzing collected data and expressing the results. At each stage, detailed areas and related technologies are emerging, and research on big data is being actively conducted in various fields [7–9]. These big data analysis techniques have been studied in the e-learning environment, such as methods of application, education policy agenda deduction, and education issues deduction automation technology. Accordingly, in this study, the direction of a desirable and future-oriented online SW education process by conducting a perception analysis and emotional analysis of online SW education through big data of portal/SNS along with consideration of online SW education through prior research. Would like to present Therefore, the research questions of this study are as follows. Research Question 1. What is the social perception of online SW education on big data? Research question 2. What are the main keywords of big data for online SW education?

A Study on the Analysis of Related Keywords on the Perception …

101

Research Question 3. What is the public’s sensibility about online SW education in big data?

2 Theoretical Background 2.1 Big Data Big data refers to a set of data with three-dimensional characteristics of capacity, variety, and speed. Big data does not mean only formal and meaningful words or numbers, but all structured and unstructured data generated and circulated in real time can be defined as big data [6]. Big data encompasses huge data sets that are difficult to handle with existing management and analysis systems, and platforms and analysis techniques to solve them. Data analysis techniques were already used in statistics, computer science, and machine learning, but they have been developed with the advent of big data [9]. Big data goes through a process of storing and analyzing collected data and expressing the results, and detailed areas and related technologies appeared at each stage [7–9].

2.2 Online Education Domestic Research Trends The main factors to pay attention to in online education research are learning motivation, learning satisfaction, and learning continuity, and learning motivation is recognized as the most important variable for predicting learning achievement [2]. Therefore, research on online education is focused on methods to induce learners’ learning motivation [3, 4]. The existing field of online education has been focused on the area of e-learning, which depends on the excellence of the educational content provided by instructors. However, with the development of educational technology, new educational methodologies that combine online and offline education such as blended learning and flip learning, or that utilize the advantages of both methods, have received attention, and the online education area has become a complex area that is not affected only by educational content. Figure 1 is the result of analyzing trends in online education over the past 10 years. E-Learning, Flipped Learning, and Blended Learning are the results of the Korean Journal Citation Index (www.kci.go.kr).), distance education, cyber education, smart education, and online education related keywords and duplicate documents among 9,936 domestic documents searched with conditions from 2010 to June 2020 6 graphs represent the results of analysis of 2,038 documents, excluding 7,898 documents in which related words were not found in keywords, titles, and abstracts.

102

S.-Y. Yoon and J.-B. Kim

Fig. 1 Online education research trend graph for the last 10 years (2010–2020)

The graph expresses the number of publications per year as a value obtained by dividing the number of publications by 12 months so that the average number of publications per month by year can be compared. Fig. The e-learning graph shown in 1 tends to decrease rapidly as 2020 approaches, and distance education, smart education, and cyber education tend to decrease slowly as 2020 approaches. On the other hand, blended learning tends to increase slowly with the advent of flip learning, and flip learning begins to appear in 2015 and tends to increase rapidly. As a result, the keyword of e-learning is gradually not being used, and keywords for new educational methodologies such as flip learning, a learning method that combines online and flip learning, are attracting attention. This means that today’s online education is a complex area that needs to consider efficient educational methodologies such as blended learning and flip learning rather than focusing only on educational content.

2.3 Understanding SW Education As social interest in the 4th industrial revolution such as big data, IoT, and artificial intelligence technology increases, interest and expectations for SW, which is the basis of the 4th industrial revolution, are increasing, but the supply of SW-related manpower falls short of social demand. Accordingly, the government is promoting large-scale projects such as the SWcentered university project [10] and the AI graduate school project [11] to focus on nurturing world-class artificial intelligence experts, and announces the AI national strategy to achieve an economy of 455 trillion won by 2030. A goal to create an effect was presented [12]. In addition, recently, efforts are being made to spread software and AI at the national level, such as preparing and implementing plans to spread artificial intelligence and software for all citizens. However, big data and artificial intelligence technologies are fields that require a high degree of expertise, and it takes a long time to nurture professional manpower. In addition, although the curriculum for nurturing SW professional manpower is being studied, it is difficult to find an educational model or program for non-SW

A Study on the Analysis of Related Keywords on the Perception …

103

majors. Lastly, SW manpower nurturing is mostly short-term and sporadic education and training-oriented programs rather than nurturing professional manpower from the basics through a systematic education process [13, 14]. Therefore, big data, artificial intelligence, and IoT education require an education model that can be systematically educated from the extension of SW education. In this regard, Seungki Shin proposed an artificial intelligence education framework based on computational thinking [5]. Shin Seung-gi’s research describes that “contents for developing AI-based problem-solving skills should be structured based on abstraction, the most important element of the thinking process for problem-solving in computer science” [5]. Computing thinking skills education is not a passive programming education or computer utilization education to inject knowledge, but rather a problem-solving ability that can acquire SW-related knowledge such as big data, artificial intelligence, and IoT based on the abstraction skills acquired in the process. This means that a linked education system should be established.

3 Research Method 3.1 Dataset and Methodology In this study, text mining analysis, discourse analysis, topic analysis, and sentiment analysis were performed on online big data to analyze the perception and evaluation of online ’SW education’ that ordinary people think. To this end, data collection was conducted on portal sites and SNS, and specific sites include Naver (webpage, blog, cafe, news, knowledge IN, academic information), Daum (web document, blog, news, cafe), and Google. (Web documents, news, Facebook), YouTube, and Twitter. In this study, as online education, which was called future education, progresses as daily education after COVID 19-pandemic, the importance of online education is being emphasized along with online education for the past two years (March 17, 2020 to March 24, 2022). Naver (webpage, blog, cafe, news, knowledge IN, all academic information), Daum (web document, blog, news, cafe), Google (web document, news, Facebook) including ‘SW education’ as the main keyword, YouTube, and Twitter were extracted and analyzed. A total of 7,837 keywords were extracted, and the data collected through refining/morphological analysis were separated and purified. The final data used in this study was analyzed for 3,942 keywords in the top 100. The distribution by source of portal sites and SNS collected in this study is as follows (Fig. 2).

104

S.-Y. Yoon and J.-B. Kim

Fig. 2 Distribution by source of keyword search related to SW education

3.2 Dataset Analysis Method In this study, TEXTOM, a web-based analysis solution certified by the Korea Information and Communication Technology Association (TTA), was used, and after big data was collected, a textmining analysis procedure was performed. The collected data was firstly analyzed by extracting and analyzing the words of nouns, pronouns, and adjectives by designating 6 words as the analysis counter range based on 3 words before and after from the online document containing the keyword ‘SW education’. Second, unnecessary words were extracted and analyzed. Symbols, numbers, and promotional text were excluded. In addition, for relevance analysis of keywords, connection centrality analysis was performed, and keywords with high frequency and high connection centrality were analyzed. After conducting word frequency analysis, N-gram analysis, and TF-IDF analysis through text mining, connection centrality analysis, matrix analysis, matrix chart, topic modeling analysis, and sentiment analysis were performed.

4 Result 4.1 Online SW Education Big Data Word Frequency Analysis Online SW education keyword analysis, the most frequent words in the texts related to SW education were extracted centered on nouns. in the top rank ranked The 25

A Study on the Analysis of Related Keywords on the Perception …

105

keywords were education, coding, academy, computer, student, software, employment, school, program, convergence, major, robot, artificial intelligence, engineering, and programming. If we classify this, first, the types of software education such as coding, software, artificial intelligence, programming, Python, language, and Java were shown as the perception of online SW education. Second, it appeared in terms of operating and developing academies, schools, employment, specialization, government subsidy, universities, institutions, instructors, training, etc. Third, it appeared in terms of the objects and methods of online SW education such as education, study, class, learning, elementary school, subject, and talent. (Table 1) shows the word frequency analysis of portal sites and SNS related to online SW education. Word Cloud, which charted the relative frequency of key words according to word frequency for online SW education, was education, computer, coding, school, Table 1 Frequency analysis of the top 50 words for SW education

Ranking

Keyword

Frequency

Importance (%)

1

Education

17,324

8.721

2

Coding

12,630

6.358

3

Academy

6602

3.323

4

Computer

6416

3.230

5

Student

2305

1.160

6

Software

1769

0.890

7

Employment

1671

0.841

8

School

1618

0.814

9

Program

1502

0.756

10

Fusion

1461

0.735

11

Major

1406

0.707

12

Robot

1303

0.655

13

a.i

1223

0.615

14

Engineering

1218

0.613

15

Programming

1217

0.612

16

Technology

1215

0.611

17

Specialty

1193

0.600

18

Government subsidy

1120

0.563

19

University

1066

0.536

20

Agency

1042

0.524

21

Basic

1028

0.517

22

Developer

1012

0.509

23

Certificate

957

0.481

24

Consignment

939

0.472

25

Python

936

0.471

106

S.-Y. Yoon and J.-B. Kim

Fig. 3 Word Cloud for key words related to SW Education

academy, program, software, Python, robot, convergence, certification, engineering, etc. The analysis of word cloud for key words related to online SW education is as follows (Fig. 3).

4.2 SW Training Matrix Analysis and Charts A matrix analysis of ‘SW education’ conducted in this study was performed. Matrix analysis is to provide information that analyzes the correlation between keywords based on matrix data created by co-occurrence between refined words. In this study, the perception of SW education was analyzed based on information on the possibility of simultaneous appearance of words related to SW education, and the correlation between key keywords was analyzed. As a result of the correlation analysis, it was analyzed that education, coding, academy, computer, student, software, employment, school, program, convergence, major, robot, artificial intelligence, engineering had a significant correlation. The matrix keyword correlation of SW education analyzed in this study is as follows (Table 2). As a result of the matrix analysis conducted in this study, the matrix chart of key words related to SW education is as follows (Fig. 4). Table 2 Correlation between keywords in SW education matrix Education

Education

Coding

Academy

Coding

0.1345

0

Academy

0.1229

0.2070

0

Computer

0.0930

0.0074

0.1825

Computer

Student

Software

0

Student

0.1315

0.1631

0.0967

0.0075

0

Software

0.0774

0.11014

0.1176

0.0652

0.0742

0

A Study on the Analysis of Related Keywords on the Perception …

107

Fig. 4 Discourse analysis of key words related to SW education

4.3 Discourse Analysis of SW Education In this study, a discourse analysis was conducted on SW education. Discourse analysis is an analysis that shows the relationship between words that appear simultaneously (co-appearance) in a document, and it is a method of clustering according to the relationship pattern between words using correlation. As a result of discourse analysis on SW education in this study, it was revealed that the central university, university, target, company, donation, basics and programming, study, development, KEPCO, developer, school, engineering, etc. The discourse analysis of the results of this study is as follows (Fig. 5).

Fig. 5 Discourse analysis of key words related to SW education

108

S.-Y. Yoon and J.-B. Kim

4.4 Word Sentiment Analysis for SW Education Sentiment analysis conducted in this study was The keywords of SW education are divided into positive/neutral/negative. As a result of this study, the positive and negative aspects of SW education were comparatively analyzed. Positives accounted for 74.23% of the total and negatives accounted for 25.77% of the total. The negative emotions of rejection, sadness, and fear were high. Sentiment analysis conducted in this study are as follows (Table 3), and emotional words for SW education The word cloud is as follows (Fig. 6). Table 3 Frequency analysis of the top 50 words for SW education Division

Frequency (cases)

Emotional intensity ratio (%)

Frequency ratio (%)

Positive

2926/3942

74.78/100.0

74.23/100.0

Denial

1016/3942

25.22/100.0

25.77/100.0

Detail sensitivity

Frequency of sub-sensitivity ( cases)

Detail sensitivity ratio (%)

Crush

1954

50.92

Pleasure

318

8.17

Interest

654

15.69

Sadness

294

9.35

Reluctance

563

12.68

Fear

86

1.71

Anger

49

0.93

Ache

12

0.16

Surprised

12

0.39

Total

3942

100%

Fig. 6 Emotional words for SW education word cloud

A Study on the Analysis of Related Keywords on the Perception …

109

5 Conclusion In order to suggest a new SW education direction suitable for the era of the 4th industrial revolution, this study tried to obtain implications for SW education by examining the social perception of SW education through big data and analyzing the matrix and emotion. To this end, online data on ‘SW education’, ‘coding’, and ‘IT education’ was collected and refined. After that, text mining, matrix analysis, discourse analysis, and sentiment analysis were performed, and the results of the study were visualized as data. The conclusions about the results analyzed in this study are as follows. First, as a result of analyzing the data collected online to understand the social perception of ‘SW education’, the words with high frequency are education, coding, academy, computer, student, software, employment, school, program, convergence, etc. appeared in the order of the highest frequency. And as a result of analyzing the co-occurrence frequency, coding-education, coding-hagwon, robot-coding, academy-coding, computer-hagwon, computerengineering, and consignment-education were shown in the order. In addition, the most frequent word in the document was academy, coding, student, school, employment, software, consignment, robot, computer, and government subsidy in order. The result of this text mining research is that SW education of ordinary people on SNS and portal sites is mainly recognized as about coding classes, robot classes, and computer classes in places such as academies through education. In addition, SW education learns coding, computers, and robots through programs, and it is recognized that such education leads to majors, employment, and development. Second, as a result of matrix analysis through the keyword ‘SW education’, as a result of analyzing the words with high correlation between keywords, it was found to be education, coding, academy, computer, student, software, employment, school, program, and convergence. Was found to have a positive correlation. It can be seen that, in general, the space where SW education is conducted is recognized as hagwons and schools, and SW education is recognized as receiving education on coding, software, programs, and convergence. Therefore, in order to receive SW education, it is recognized that they go to the relevant institution and receive professional education. Third, as a result of analyzing the relationship between the words appearing in the document on ‘SW education’, if it is divided into two discourses, SW education is related to business, basic, central university, weekend, expert, university, university and programming, related, It can be divided into study, academy, learning, school, professor, science, and engineering. It was analyzed that receiving programming as a necessary education for universities and companies was recognized as a study related to engineering, science, and programs through hagwons and schools. Lastly, as a result of conducting emotional analysis as well as superficial recognition of ‘SW education’, positive responses were innovative and higher than negative responses. This is a positive reaction to SW education, and it was found that they had positive expectations and emotions such as particularly good, recommended,

110

S.-Y. Yoon and J.-B. Kim

special, best, fun, and growing. And the negative responses were difficult, complicated, difficult, difficult, etc. Through these results, SW education is recognized as students learning various coding, Python, and programming through schools and private institutes, so it is necessary to build a system that can learn SW education not only in a physical space but also online. This study will be the basic data for presenting the direction of SW education that can be desirable, sustainable, and satisfy the needs of consumers by examining the direction of SW education and the direction of SW education recognized by the general public through previous research.

References 1. Ministry of Education, [Explanatory Material] The Ministry of Education Plans to Develop and Implement various methods to manage the quality of distance education at universities (20.07.21). https://www.moe.go.kr/boardCnts/view.do?boardID=295&boardSeq=81292& lev=0&searchType=null&statusYN=W&page=1&s=moe&m=020401&opType=N 2. J.S. Kim, The Determinants of University Students‘ Satisfaction and Performance in e-Learning Environment, doctoral dissertation. Ph.D. dissertation, Inje University, 2006 3. E.C. Lee, Analysis of effects of learning motivation on the interaction in online cooperation learning. J. Korea Content. Assoc. (Jour. of KoCon.a) 17(7), 416–424, 2017 4. S.I. Park, Y.K. Kim, An inquiry on the relationships among learning-flow factors, flow level, achievement under on-line learning environment. J. Yeolin Educ. 14(1), 93–115 (2006) 5. S. Shin, Designing the instructional framework and cognitive learning environment for artificial intelligence education through computational thinking. J. Korean Assoc. Inf. Educ. (JKAIE) 23(6), 639–653 (2019) 6. H.S. Lee, A Study on Drought Area and Severity by Big Data Analysis. Master’s dissertation, Graduate school in Hanseo University, 2014 7. YoungOk Kwon, Data analytics in education: current and future directions. J. Intell. Inf. Syst. 19(2), 87–100 (2013) 8. J.S. Park, Take advantage of the training agenda developed by Education Big Data-Focused on Social Network Analysis, KERIS research report, KR 2014–10, 2014 9. J.E. Son, A study of the automatic classification technologies for the educational issues big data, Master’s dissertation, Graduate school in DongEui University, 2014 10. Institute for Information & communication Technology Planning & evaluation. Public Announcement of SW-centered university support. https://www.iitp.kr/kr/1/business/busine ssNotify/view.it?ArticleIdx=676&count=true&page=1 11. Ministry of Science and ICT. Public Announcement of Ministry of Science and ICT No. 2018–0576. https://ezone.iitp.kr/common/anno/02/form.tab?PMS_TSK_PBNC_ID=PBD201 800000243 12. Ministry of Science and ICT. Announcement of National Artificial Intelligence (AI) Strategy. https://www.msit.go.kr/web/msipContents/contentsView.do?cateId=_policycom2& artId=2405727 13. H.M. Jeong, Y. Song, A study on factors affecting the effectiveness of big data training-based on perception of participants in consortium for HRD ability magnified program. J. Learn. Center. Curric. Instr. (JLCCI) 18(4), 29–45 (2018) 14. S. Jung, J. Do, A case study on operation of big data educational program. J. Educ. Cult. (JOEC) 25(5), 621–640 (2019) 15. MOE, MSIP, Human Resource Development Plan for the SW oriented society, 2015 16. H. Song, M. Ryu, S. Han, Impact on learning motivation of a SW-STEAM education program using the flipped learning. J. Korean Assoc. Inf. Educ. (JKAIE) 22(3), 325–333 (2018)

A Study on the Analysis of Related Keywords on the Perception …

111

17. S.H. Kim, Analysis of the effectiveness of SW education based on flipped-learning. J. Educ. 39(2), 1–20 (2019) 18. M. Lee, S. Ham, The development and application of a teaching and learning model based on flipped learning for convergence software education in elementary schools. J. Korean Assoc. Inf. Educ. (JKAIE) 22(2), 213–222 (2020) 19. U.S. Song, J.M. Gil, Development and application of software education program based on blended learning for improving computational thinking of pre-service elementary teachers. KIPS Trans. Softw. Data Eng. (KTSDE) 6(7), 353–360 (2017) 20. K. Ju, M. Lee, H. Yang, D. Ryu, The 4th industrial revolution and artificial ntelligence: an introductory review. J. Korean Operat. Res. Manag. Sci. Soc. 42(4), 1–14 (2017) 21. Ministry of Education. Public Announcement Ministry of Education No. 2015–74 (Attachment 10). https://www.moe.go.kr/boardCnts/view.do?boardID=141&boardSeq=60747&lev=0&sea rchType=null&statusYN=W&page=20&s=moe&m=040401&opType=N 22. Y.S. Han, Effectiveness of problem-based learning based programming education: focus on computational thinking. Asia-pacific J. Multimed. Serv. Convergent Art Humanit. Sociol. (AJMAHS) 8(7), 433–445, 2018

Relationship Between Narcissism, Self-esteem and Youth Consumption Behavior Jin Hee Son

Abstract The purpose of this study is to confirm the effect of youths’ narcissism and self-esteem on compensatory consumption behavior. To this end, a survey was conducted on 550 youths living in Seoul and Gyeonggi-do, Korea, and 528 data were used for analysis. The results of the study are as follows. First, the narcissism of youths who participated in this study was 3.13 points, the self-esteem was 3.48 points, and the compensatory consumption behavior was 3.14 points. Second, as a result of regression analysis, narcissism and self-esteem had a statistically significant effect on youths’ compensatory consumption behavior. Third, as a result of confirming the effects of demographic and social background variables, self-esteem, self-directed narcissism, and others’ dependent narcissism on rational consumption behavior through hierarchical regression analysis, self-esteem and self-directed narcissism had a significant effect on rational consumption behavior. Finally, as a result of confirming the effect of related variables on irrational consumption behavior, it was confirmed that the dependent narcissism of others had an effect on irrational consumption behavior. Through these research results, the significance and limitations of this study were presented and discussions for subsequent studies were presented. Keywords Narcissism · Self-esteem · Rational consumption behavior · Irrational consumption behavior

1 Introduction The modern society we live in reveals and emphasizes our individuality and opinions due to the rapid development of technology and economic growth. It also constantly competes fiercely with others, which can be defined as a society of Narcissism [1]. Narcissism is an excessive love and privilege for oneself [2]. Narcissism are higher in individualistic cultures compared with more collectivistic cultures [3]. J. H. Son (B) Dept of Youth Coaching and Counseling, Korea Soongsil Cyber University, Seoul, South Korea e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_9

113

114

J. H. Son

Youth in a middle position from childhood to adulthood is a time when selfidentity should be formed [4], but confusion of identity is experienced by conflict and wandering in a peripheral position that is not incorporated into the existing social structure. In addition, it shows an egocentric tendency due to cognitive distortion [5]. In particular, modern youths are more immersed in themselves than others, imitate and admire media characters to satisfy their desire for Narcissism [6]. Narcissism often appears in youth culture. In particular, since digital devices such as the Internet and smartphones have developed, narcissism has been excessively revealed on the network. Youths with strong egocentricity post their daily routines such as food and clothes on SNS in real time, and edit and post their photos. Youths who are interested in peer relationships say, “I guess I’m a nice person” as they “like” or have more views on their posts.‘, I think he likes me. Youths exaggerate themselves to make themselves look okay to others in their daily lives and in the digital world. Narcissism is divided into overt narcissism and covert narcissism [7]. Overt narcissism is presented as a diagnostic criterion in the DSM. This mainly shows an exploitative interpersonal relationship, demanding exaggerated perception of one’s importance, vanity, sense of privilege, and excessive praise from others [8]. On the other hand, covert narcissism is too compassionate to the evaluation of others to receive negative evaluation, and shows a defensive attitude to protect the vulnerable self by blocking the situation to be evaluated [9]. Previous studies explained the characteristics of narcissism in connection with self-esteem [10]. It was confirmed that when positive narcissism is activated, positive self-concept and self-esteem can be formed and lived as an adaptive being [11]. Compensation consumption is the first term used by Caplovitz [12]. The notion of early compensation consumption stems from a desire to make up for and make up for something that is lacking in oneself. Compensation purchase refers to the use of purchase as compensation for stress, disappointment, frustration, loss of autonomy, lack of self-esteem, etc. Scherhorn [13] analyzed compensation delinquency as a means of expressing self-value, furthermore, for self-restoration, self-esteem, and self-realization needs [14]. Youths account for about 14% of Korea’s total population, the third-largest age group with a population distribution, and are emerging as new leaders in the consumer market. Adolescent consumers express themselves and form relationships through consumption. They are sensitive to trends and trends and use various information technologies easily as they grow with digital culture. In addition, youths respond sensitively to trends, dress themselves up to follow them, and perform narcissistic consumption behavior. In many previous studies, it was confirmed that self-esteem had an effect on compensation consumption. Self-esteem is an evaluation of the value of an image formed from one’s personal expression and refers to a positive or negative judgment, attitude, and emotion evaluation of one’s characteristics and abilities [15]. In general, people with high self-esteem can express their opinions well and play active roles. It also establishes specific goals and actively practices them [16], i.e., recognizes

Relationship Between Narcissism, Self-esteem …

115

itself as valuable, competent, and important [17]. On the other hand, people with low self-esteem tend to materially satisfy the actual self through compensation motives, so they can use compensation consumption behavior [18, 19]. This study aims to provide basic data to find a direction to form the right consumption culture and habits of youths by revealing the relationship between self-love and self-esteem of youths and compensation delinquency.

2 Theoretical Background 2.1 Narcissism The situation called the Narcissistic epidemic is increasing in modern society [20]. The endorsement rate for “I am an important person” has increased from 12% in 1963 to 77–80% in 1992 in youths [21]. The concept of narcissism originated from the story of a boy named Narcissus in Greek mythology who fell in love with his beauty when he saw his face reflected in the pond, and a daffodil bloomed there. Later, narcissism meant narcissism due to excessive love for oneself. Narcissism became an important concept of psychology, starting with Ellis in 1898 and through Freud in 1910. The theoretical study of narcissism has been largely composed of a psychoanalytic approach, a sociocultural approach, and a personality psychological approach based on the diagnostic criteria of DSM, and in recent years, Morf and Rhodewalt [22]’s dynamic self-regulation model, which integrated empirical research, has been reviewed. In addition, the American Psychiatrc Association (APA) in 1980 was supplemented by diagnostic criteria for narcissistic personality disorders with DSM-III in 1980 and later revised DSM-IV [23]. According to Kohut’s [24] theory of narcissism, John et al. [25] said that narcissism is an essential element in development as a healthy trait, not a pathological factor, by focusing attention on his psychology. Stolorow [26] also stated that the healthy factor of narcissism is an essential factor for enhancing self-esteem and personal cohesion and stability. According to this theoretical basis, it has recently been seen that both positive and negative aspects are assumed rather than defining narcissism as a single concept of pathological narcissism or normal narcissism. Therefore, various approaches are being made in defining narcissism. Socio-cultural scholars viewed narcissism as a social substance. Millon [27] saw that parents treat their children as perfect and special beings and overestimate them unrealistically, thereby forming the basis of narcissism. Unlike Millon [28], Masterson [29] presented a comparative cultural perspective, saying that the overprotective hypothesis of children is more appropriate than the neglect hypothesis due to parents’ child-centered parenting attitudes or blood relationships (S.E.,Park, 2004, re-citation) [30]. In addition, a dynamic self-regulatory model appeared, explaining that narcissistic dynamics act as a selfregulatory process to construct a desirable narcissism and satisfy self-evaluating

116

J. H. Son

needs. Past research indicates that narcissism decreases with age. Younger individuals have higher narcissism scores than older individuals [31–33]. In the narcissism of youths, Bleiberg [34] said that positive narcissism can be formed if it develops based on the right understanding and response to narcissism in youth. Narcissism was recognized as an important developmental concept as an element of normal development, and various approaches were attempted accordingly. Accordingly, attention has recently been focused on revealing the aspect of narcissism as a positive trait, not a pathological aspect.

2.2 Self-esteem The concept of self-esteem has developed with slight differences among scholars since it was first mentioned by William James [35]. Although there are differences in perspectives seen by scholars, self-esteem is a psychological variable that plays an important role in adaptation to human life and is related to a positive or negative attitude of evaluation of oneself (J.H., Hwang, 2014) [36]. This definition of selfesteem can be divided into evaluation and emotional elements. Among the scholars who divided self-esteem into evaluation elements, Rosenberg [15] defined himself as self-worth. According to Gilmore [37], self-esteem was defined as an evaluative attitude by which an individual evaluates himself, and Newman [38] defined it as the result of an evaluation of himself between two experiences of his competence and social acceptance. On the other hand, among scholars who identified self-esteem as an emotional factor, Brownfain [39] said, “The degree to which an individual accepts and values himself.” Long, Henerson, & Ziller [17] defined self-esteem as the value or importance of assuming that it belongs to oneself through comparison with others. Among scholars who considered both evaluation and emotional factors, Coopersmith [40] defined self-esteem as a positive or negative evaluation in forming and maintaining one’s ego to the extent that one feels competent and valuable. He explained the factors that contribute to the formation of self-esteem in four ways: significonce, competence, virtue, and power. Significance refers to the degree of feeling recognized by others who think they are important. This contributes greatly to the formation of self-esteem through the recognition and praise of others. The second is competence, which refers to the degree of an individual’s ability that appears by satisfying the desire for achievement by performing tasks that are considered socially important to him. This means that individuals influence the formation of high selfesteem according to their successful performance of their tasks. The third is the desire for what an individual thinks is important as a virtue. This means that the experience of achievement in achieving moral and ethical norms must be reconstructed and interpreted through the desire for an individual to be considered important [35]. The fourth refers to the degree of ability to control along with the exercise of influence on others as power. This means that an individual is free from negative emotions and can maintain calm by controlling and controlling the degree of damage or damage inflicted on him. Such high self-esteem can successfully perform relationships with

Relationship Between Narcissism, Self-esteem …

117

others, but low self-esteem is said to be maladaptive and self-critical to relationships with others. In addition, Rawls [41] called self-esteem a degree of confidence in one’s value and stability. Branden [42] explained self-esteem by dividing it into internal self-esteem and external self-esteem. It was said that external self-esteem manifested its behavioral characteristics by how one is evaluated by others in relation to others. This is based on respect, praise, status, reputation, and social success by others. On the other hand, internal self-esteem is a sense of respect for oneself that recognizes oneself as valuable. In this way, when both internal self-esteem and external selfesteem are not felt, the individual is obsessed with an inferiority complex and feels hopeless. According to Orth and colleagues, individuals establish their sense of self, including their roles, values, and interests during adolescence and young adulthood [43]. Self-esteem increases with age as personality traits mature and social roles begin to manifest. Self-esteem, like narcissism, is a key concept necessary to achieve self-development as an element that appears in the overall development process of an individual’s life. In particular, adolescence is an important period for forming a sense of self-esteem because adolescence is a developmental transitional period and forms an identity by adapting to various situations. Accordingly, self-esteem in adolescence reported the influence relationship with various factors through many previous studies. Self-esteem in adolescence affects life satisfaction, academic, school adaptation, interpersonal ability, and acted as a factor in youths’ psychological factors.

2.3 Youth Consumer Compared to child consumers, youths are independent of their parents and begin to engage in consumption behavior through self-directed purchase decisions that they choose and act on. In other words, the subjectivity of purchasing decisionmaking is improved, and the role and behavior of consumers are also expanded. In this way, through adolescence, as they grow through various socialization processes, they experience consumption-related experiences [43]. In addition, the attachment of adolescence has an individual aspect of moving from parents to peer groups. Accordingly, for youths, peers are important beings that can satisfy their emotional needs. Therefore, in the consumption behavior of youths, they identify themselves with social relationships such as peers, popular stars, and teachers rather than their families, conceptualizing themselves in groups and constantly expressing their individuality. Therefore, during this period, the characteristics of sympathetic consumption and imitation consumption appear due to the influence of social relations such as peer groups and popular stars. Finally, consumption behavior in adolescence can be seen as a process of transitioning to adult consumer behavior. Since adolescence is a process of transitioning from dependent and simple consumption behavior in childhood to independent consumption behavior as a complete adult, consumption experiences in childhood and adolescence continue until adulthood. Accordingly,

118

J. H. Son

adolescent consumers can engage in value confusion and independent consumption behavior amid rapid social changes, but due to the lack of consumption experience, undesirable consumption behaviors such as show-off consumption, impulse consumption, and overconsumption may appear. Youth consumers do not have the right consumption behavior established due to a lack of consumption experience, so it is important to understand the consumption behavior of youths and establish the right consumption behavior.

3 Research Methods 3.1 Subject of Study The purpose of this study is to confirm the effect of youths’ narcissism and selfesteem on consumption behavior To this end, a survey was conducted on 550 youths living in Seoul and Gyeonggi-do, Korea, and a total of 528 surveys were set as samples, excluding survey responses that were not sincere or were not appropriate for analysis. Table 3 is the result of frequency analysis by classifying the demographic and sociological characteristics of the subjects of this study.

3.2 Analysis Method This study primarily conducted a reliability analysis to understand the confidence level of the measurement tools used, and next, descriptive statistics and frequency analysis were conducted to identify the demographic and sociological characteristics of youths. To solve the research problem, the difference in the level of sub-factors of narcissism, self-esteem, and consumption behavior according to the demographic and sociological background of youths was analyzed through T-test and One-way ANOVA.

4 Research Results 4.1 Youth’s Narcissism, Self-esteem, Consumption Behavior Table 2 shows the levels of self-directed narcissism, which are sub-variables of youths’ narcissism, and others’ dependent narcissism. As a result of examining the level of narcissism among youths, the total level of narcissism was 3.13 points (SD = 0.50). The level of the sub-variable of narcissism was 3.14 points (SD = 0.61) for self-directed narcissism and 3.13 points (SD = 0.53) for dependent narcissism of

Relationship Between Narcissism, Self-esteem … Table 1 Demographic and sociological background of the study subject

119

Category Gender School Area

N Male

223

42.2

Female

305

57.8

Middle schoo students

186

35.2

High school students

342

64.8

Seoul

272

51.5

Gyeonggi-do Total

Table 2 The level of youth narcissism

Table 3 The level of youth self-esteem

%

256

48.5

483

100.0

M

Category

SD

Narcissism

3.13

0.50

Self-directed Narcissism

3.14

0.61

Dependence on others Narcissism

3.13

0.53

Category

M

SD

Self-esteem

3.48

0.61

others, all showing the same score. These results show that among the tendencies of narcissism, self-directed narcissism and dependent narcissism of others are the same in youths, and both types have an average score of narcissism of medium or higher. The level of self-esteem of Youths is shown in Table 3. As a result of examining the level of self-esteem of youths, the overall level of self-esteem was 3.48 points (SD = 0.61) indicating the level of severe injury. These results imply that youths’ degree of love and value themselves tends to be higher than the normal level. The level of consumption behavior of Youths is shown in Table 4. As a result of examining the level of consumption behavior of youths, the total level of consumption behavior was 3.14 points (SD = 0.49). The level of sub-variables of consumption behavior was 3.34 points (SD = 0.71) for rational consumption behavior and 2.95 points (SD = 0.48) for irrational consumption behavior, which was higher than that of irrational consumption behavior. These results show that the proportion of reasonable consumption is higher than that of youths’ consumption behavior is unreasonable.

4.2 The Effect of youth’s Narcissism and Self-esteem on Rational Consumption Behavior Table 5 show regression analysis was conducted to find out the effect of youths’ narcissism and self-esteem on rational consumption behavior. Youths’ narcissism

120

J. H. Son

Table 4 The level of youth consumpiotn behavior Category

M(SD)

consumption behavior

3.14(0.49)

Rational consumption behavior

Planned purchase

3.34(0.71)

Economical-practical purchase Impulse purchase

Irrational consumption behavior

3.31 (0.86) 3.36 (1.07)

2.95(0.48)

2.69 (0.75)

Show-off purchase

2.94 (0.57)

Wasteful purchase

3.22 (0.74)

(β = 0.139, p = 0.001), and self-esteem (β = 0.834, p = 0.000) had a statistically significant effect on consumption behavior. Hierarchical regression analysis was conducted to find out the effect of youths’ narcissism and self-esteem on rational consumption behavior. Table 6 show to find out the effect of narcissism and self-esteem on rational consumption behavior. It shows the results of hierarchical multiple regression analysis by inputting demographic background variables in step 1, self-esteem in step 2, and narcissism sub-variables in step 3. Table 5 The effect of youths’ narcissism and self-esteem on consumption behabior Model

B

SE

Constant

0.237

0.184

Beta

t

p 1.290

0.198

Self-esteem

0.139

0.041

0.120

3.395

0.001

Narcissism

0.834

0.050

0.585

16.576

0.000

Table 6 The effect of youths’ narcissism and self-esteem on rational consumption behabior Variable

Model 1 β

Model 2 t

β

Model 3 t

β

t

Gender

−0.170

−2.709*

−0.218

−3.611***

−0.232

−5.132***

School

−0.098

−1.515

−0.073

−1.180

−0.134

−2.859*

Area

−0.010

−0.161

−0.035

0.595

−0.065

−1.478

0.339

6.969***

−0.074

−1.645

Dependence on others narcissism

−0.053

−0.973

Self-directed narcissism

0.867

16.502***

Self-esteem

F

3.120*

14.695***

87.397***

Adjusted R2

0.018

0.101

0.502

R2 change

0.012

0.094

0.496

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

Relationship Between Narcissism, Self-esteem …

121

According to the results, the influence of gender was statistically significant in the first stage model (β = 0.−170, p < 0.05). In the second stage, self-esteem was added, and the second stage model was found to be significant (F = 5.502, p < 0.01). In addition, it was found that gender (β = 0.−218, p < 0.001) was a significant variable among demographic and sociological background variables, and self-esteem gender (β = 0.339, p < 0.001) was found to have the greatest influence. As a result of introducing self-esteem, the amount of explanation increased by 8.2% to explain rational consumption behavior by 9.4% (R2 = 094), and since the β value is greater than 0, the higher the self-esteem, the higher the rational consumption behavior. In addition, since gender has a β value less than 0, it can be said that female students have reasonable consumption behavior compared to male students. In step 3, a sub-variable of narcissism was added, and the three-stage model was found to be significant (F = 87.397, p < 000). In addition, as in step 2, gender (β =0.−232, p < 0.001) was found to be a significant variable among demographic and sociological background variables, and self-directed narcissism (β = 0.867, p < 0.001) was found to have a significant influence among sub-variables of narcissism. As a result of introducing the sub-variable of narcissism, the explanation amount increased 40.2 to 49.6%, and since the β value is greater than 0, the higher the self-directed narcissism, the higher the rational consumption behavior. In the results of this study, it can be said that consumption education should also be considered as the psychological mechanism of narcissism has a great influence on the psychological mechanism of narcissism.

4.3 The Effect of youth’s Narcissism and Self-esteem on Irrational Consumption Behavior Hierarchical regression analysis was conducted to find out the effect of youths’ narcissism and self-esteem on irrational consumption behavior (Table 7). In order to find out the effect of youths’ narcissism and self-esteem on affordable and practical purchases, gender, school, and region were invested in step 1, and self-esteem was input in step 3, and the results of hierarchical multiple regression. According to the results, the first-stage model was not significant. In the second stage, self-esteem was introduced, and the second stage model was found to be not significant. In step 3, a sub-variable of narcissism was introduced, and the three-stage model was found to be significant. Among the sub-variables of narcissism, dependent narcissism of others (β = 0.520, p < 0.001). It was found that only was the variable that had the greatest influence. As a result of introducing the sub-variable of narcissism, the amount of explanation increased by 22.7%, and irrational consumption behavior increased by 24.1% (R2 = 0.241). Since the β value of the dependent narcissism of others is greater than 0, the higher the dependent narcissism of others, the higher the irrational consumption behavior.

122

J. H. Son

Table 7 The effect of youths’ narcissism and self-esteem on irrational consumption behabior Variable

Model 1

Model 2

Model 3

β

t

β

t

β

t

Gender

−0.101

−2.079

−0.090

0.049

−0.047

−1.086

School

0.035

−0.699

−0.041

−0.817

0.010

0.222

Area

0.076

1.586

0.070

1.462

0.098

2.320*

−0.081

−2.053*

−0.048

−1.127

Dependence on others Narcissism

0.520

10.128

Self-directed Narcissism

−0.027

−0.544

Self-esteem

F

2.384

2.853*

28.933***

Adjusted R2

0.013

0.021

0.250

R2

0.008

0.014

0.241

change

*p < 0.05, **p < 0.01

In summary, it can be said that youths who love themselves healthily with a clear purpose in making frugal and practical purchases increase their frugal and practical purchases, and that the more they value and value their existence, the more frugal, and practical consumption habits are. In addition, it can be seen that youths with excessive sensitivity to the gaze of others and the purpose of it is not good at making frugal and practical purchases. Accordingly, previous studies on consumption focused on environmental factors such as economic education and pocket money education for consumption education. However, it can be seen that not only environmental factors but also psychological mechanisms are working in many parts in consumption behavior. Accordingly, it can be seen that not only environmental variables but also psychological mechanisms should be dealt with in consumption education.

5 Conclusions According to the results of this study, it was found that the narcissism of others was not significant with rational consumption behavior, but self-directed narcissism had a significant effect. Unlike the existing negative perception of narcissism, it was confirmed that narcissism positively affects rational consumption, which also acts as a positive factor in the tendency of narcissism. In other words, in order to pursue healthy and reasonable consumption of youths in a society where indiscriminate and excessive consumption behavior of youths is prevalent, it is necessary to develop selfdirected narcissism among narcissism tendencies. This self-directed narcissism helps youths take pride in themselves and carry out consumption behavior independently.

Relationship Between Narcissism, Self-esteem …

123

In order to carry out such reasonable consumption behavior, it can be said that it is a psychological mechanism that must be actively encouraged and nurtured to develop self-directed narcissism. In this study, female students made luxury and wasteful purchases higher than male students, female students made planned purchases higher than male students, and high school students made planned purchases higher than middle school students. Accordingly, appropriate measures should be taken to improve the extravagant and wasteful purchasing habits that appear higher in female students, and appropriate consumption education should be provided to male and middle school students as male students make less planned purchases than high school students. In other words, it can be seen that consumption education should be conducted in consideration of differences between groups in appropriate consumption education. Until now, narcissism and self-esteem have been recognized as different concepts. In particular, self-esteem has been perceived negatively compared to what has been accepted as a positive psychological mechanism. Looking at previous studies, selfesteem showed a positive correlation with positive variables such as life satisfaction, psychological well-being, and subjective well-being and implicit narcissism have been reported to have negative and low self-esteem, and low life satisfaction due to their sensitive and vulnerable nature. According to the results of this study, it was found that narcissism is difficult to see as a psychological mechanism completely different from self-esteem as narcissism and self-esteem show a positive correlation. That is why narcissism should be subdivided and examined. Therefore, in this study, as narcissism was divided into other people’s dependent narcissism and selfdirected narcissism, others’ dependent narcissism was negatively correlated with self-esteem, and self-directed narcissism showed a positive correlation. This suggests that narcissism is also a positive psychological mechanism as well as self-esteem. These results show that self-esteem and self-directed narcissism are deeply related to rational consumption behavior in light of consumption behavior. In other words, according to the research results that narcissism has a positive effect on high selfesteem and reasonable consumption behavior, the perception of narcissism, which has been perceived negatively, should be enhanced.

References 1. C. Lasch, The Culture of Narcissism: American Life in an Age of Diminishing Expectations. WW Norton & Company (2018) 2. S.J. Han, Overt and Covert Self-Relevant Cognition of Narcissists. Seoul University, The master thesis (1999) 3. J.D. Foster, W.K. Campbell, J.M. Twenge, Individual differences in narcissism: inflated selfviews across the lifespan and around the world. J. Res. Pers. 37(6), 469–486 (2003) 4. E.H. Erikson, Insight and Responsibility: Lectures on the Ethical Implication of Psychoanalytic Insight (W. W. Norton, New York, 1964) 5. D. Elkind, Egocentrism in adolescence. Child Dev. 38, 1025–1034 (1967) 6. S.Y. Lee, M.S. Yoo, Relationships of self-esteem and narcissism with aggression among Korean adolescents. Study Youth Stud. 17(7), 101–128 (2010)

124

J. H. Son

7. P. Wink, Two faces of narcissism. Personal. Process. Individ. Differ. 61(4), 590–597 (1991) 8. F. Rhodewalt, C. Morf, On self-aggrandizement and anger: a temporal analysis of narcissism and affective reactions to success and failure. J. Pers. Soc. Psychol. 74(3), 672–685 (1998) 9. S.M Kwon, S.J Han, Narcissistic personality disorder. (Hakjisa, Seoul, 2000) 10. S.H. Lee, The Effect of the Self- Regulated Learning Strategies on the Elementary School Students’s Creativity and Academic Achievement Education, Daegu University. The master thesis (2006) 11. Y.H. Cha, The Effect of the Self-Regulated Learning Strategies on the Elementary School Students’s Creativity and Academic Achievement. Graduate School of Education, Daegu University, The master thesis (2009) 12. K. Caplovitz, The origins of social emotions and self-regulation in a low income group. Cogn. Emot. 19(7), 953–979 (1968) 13. G. Scherhorn, The addictive trait in buying behaviour. J. Consum. Policy 1(13), 33–51 (1990) 14. G.S. Lee, A study on the influencing factors of Korean society’s exaggerating consumption tendency. Jangan Univ. Soc. Sci. Res. 11, 251–268 (2002) 15. M. Rosenberg, Society and Adolescent Self-Images (Princeton University Press, Princeton, NJ, 1965) 16. S.Y. Kim, S.Y. Lim, H.M. Choi, The relationship among stress in clinical practice, depression and self-esteem in nursing college students. J. Converg. Cult. Technol. 1(4), 59–64 (2015) 17. S. Coopersmith, The Antecedents of Self-Esteem (W. H. Freeman, San Francisco, 1967) 18. I.S. Song, Compensatory buying behavior of urban female consumers. J. Life Sci. Res. 3(1), 5–25 (1993) 19. D.Y Kwon, A Study on Compensatory Consumption of the Baby-boom and the Echo Generations, Seoul National University master’s thesis (2016) 20. J.M. Twenge, W.K. Campbell, The Narcissism Epidemic: Living in the Age of Entitlement. (Free Press, New York, NY, US, 2009). [Google Scholar] 21. C.R. Newsom, R.P. Archer, S. Trumbetta , Gottesman II. Changes in adolescent response patterns on the MMPI/MMPI-A across four decades. J. Personal. Assess. 81(1), 74–84 (2003) 22. C.C. Morf, F. Rhodwalt, Unraveling the paradoxes of narcissism: a dynamic self-regulatory processing model. Psychol. Inq. 12, 177–196 (2001) 23. American Psychiatric Association, in Diagnostic and Statistical Manual of Mental Disorders, 4th edn. (1994) 24. H. Kohut, The Analysis of the Self (International Universities Press, New York, 1971) 25. G. John, R. Elsa, E. Lauren E, Narcissistic personalities in DSM III Comprehensive Psychiatry 23(5), 409–420 (1982) 26. R. Stolorow, On the phenomenology of anger and hate. Am. J. Psychoanal. 32, 218–220 (1972) 27. T. Millon, Disorders of Personality (Wiley, New York, 1981) 28. J.F. Masterson, The Narcissistic and Borderline Disorders (Brunner & Mazel, New York, 1985) 29. S.E. Park, Self-evaluation and attributional style of overt-covert narcissists. Seoou National University (2004) 30. E. Bleiberg, Normal and pathological narcissism in adolescence. Am. J. Psychother. 48(1), 30–51 (1994) 31. H. Cai, V.S.Y. Kwan, C.A. Sedikides, sociocultural approach to narcissism: the case of modern China. Eur. J. Pers. 26(5), 529–535 (2012) 32. F.S. Stinson, D.A. Dawson, R.B. Goldstein, S.P. Chou, B. Huang, S.M. Smith et al., Prevalence, correlates, disability, and comorbidity of DSM-IV narcissistic personality disorder: results from the wave 2 national epidemiologic survey on alcohol and related conditions. J. Clin. Psychiatry 69(7), 1033–1045 (2008) 33. T.J. Trull, S. Jahng, R.L. Tomko, P.K. Wood, K.J. Sher, Revised NESARC personality disorder diagnoses: gender, prevalence, and comorbidity with substance dependence disorders. J. Pers. Disord. 24(4), 412–426 (2010) 34. M.R. Leary, J.P. Tangney, Handbook of Self and Identity. Guilford Press (2003) 35. J.H. Hwang, The Influence of Teenager’s Covert Narcissism on Peer Relationship. Sookmyung Women’s University. The master thesis (2003)

Relationship Between Narcissism, Self-esteem …

125

36. J.V. Gilmore, The Productive Personality. Albion-Pub. Company (1974) 37. B.M. Newman, P.R. Newman, Development Through Life. The Dorsey Press (1983) 38. J. Brownfain, Stability of the self-concept as a dimension of personality. J. Abnorm. Soc. Psychol. 47, 596–606 (1952) 39. G. Sunil, Analysis of self system and meaning of life among youth. Int. J. Res. Modern. 3(2), 24–37 (1968) 40. J.A. Rawls, Theory of Justice. (Belknap Press/Harvard University Press, 1971) 41. N. Branden, What Is Self-Esteem? 1st, Asker/Oslo, Norway, August 9 (1990) 42. R.L. Morre, G.P. Moschis, An Analysis of the Acquisition of Some 12(2), 277–291 (1978) 43. U. Orth, J. Maes, M. Schmitt, Self-esteem development across the life span: a longitudinal study with a large sample from Germany. Dev Psychol. 51(2), 248–259 (2015)

The Effect of Transformational Leadership on the Volunteer Members’ Organizational Commitment in Non-profit Organizations: The Moderating Effects of Motivation on Volunteeing Hye-Kyu Anh, Jin Xian, and Ho-Chul Shin Abstract This study investigated the individual effects of personal motivation such as the functional motivation of volunteers in non-profit organizations and the transformational leadership of their leaders on organizational commitment. First, it was predicted that the transformational leadership of leaders in non-profit organizations had a positive effect on organizational commitment. Second, it was predicted that the interaction with the individual’s six volunteer motives between transformational leadership and organizational commitment had a positive effect on organizational commitment overall. To verify this research hypothesis, a survey was conducted on 300 members who volunteered in 30 non-profit volunteer organizations called L Club, one of the NGOs, and hypotheses were established and verified based on existing previous studies. As a result of empirical analysis, the hypothesis that transformational leadership will have a positive effect on organizational commitment was supported. Also, the interaction between transformational leadership and the six volunteer motives of individuals who participated in volunteer activities had a positive effect on organizational commitment overall (rejecting career motivation). These results are significant in that they proved the positive influence of transformational leadership, which has been revealed only for private companies until now, by applying it to non-profit organizations. Keywords Non-profit organization · Transformational leadership · Organizational commitment · Volunteer motives

H.-K. Anh · J. Xian · H.-C. Shin (B) Department of Business Administration, Soongsil University, Seoul, Korea e-mail: [email protected] H.-K. Anh e-mail: [email protected] J. Xian e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_10

127

128

H.-K. Anh et al.

1 Introduction Volunteerism is defined as a long-term planned prosocial behavior that occurs in an organizational environment that benefits strangers [1]. Dedicated and enthusiastic volunteers are a valuable asset and a huge resource for nonprofit organizations operating in various fields such as health, education, community services, churches, and sports [2]. Bath and Avolio [3] suggested that transformational leadership that induces members to think critically and creatively can affect members’ immersion. This is also supported by the study of Walumbwa and Lawler [4]. Tella et al. [5] argued that organizational commitment is the strongest motivation to improve one’s efficiency and skills, and argued that it greatly affects an individual’s successful performance. Also, a study by Ismail and Yusuf [6] found that transformational leadership had a significant positive (+) correlation with the members’ commitment. This can be predicted that transformational leadership is effective leadership in determining organizational commitment by members of the organization. Non-profit organizations rely heavily on volunteers to achieve their organizational goals [7]. Also, the role of a leader is important because volunteers can voluntarily withdraw their services from a group. Some researchers have conducted research to motivate volunteers [8]. Clary and Snyder [9] presented a functional approach to volunteer motivation and argued that volunteer work provides opportunities to meet individual needs and needs. Non-profit members may be related to the motivation underlying plans to pursue, create, and act on specific goals. For example, even volunteers who pursue the same service, some may volunteer for their career or personal feelings. Transformational leadership research has been conducted not only in the educational and military fields but also in the business and service fields. Most of them are studies conducted on private companies or for-profit organizations, and studies examining that leaders’ transformational leadership in non-profit organizations has a positive effect on organizational commitment through organizational members are relatively insufficient. Thus, this study aims to examine how it affects organizational commitment by proving the interaction between transformational leadership and personal motivation. First, the study will study the relationship between the transformational leadership of members of a non-profit organization and organizational commitment in more detail. Among the various leaderships, transformational leadership leads to actions that allow organizational members to perform beyond their own interests and beyond their expected goals [1, 12]. This is because the leader appeals to the ideals and values of the members of the organization and presents and implements the vision of the work to be performed. Also, previous studies have shown that transformational leadership has a positive effect on work attitudes and behaviors at the organizational level [10, 13]. Based on these research results, this study aims to verify the relationship between leaders’ transformational leadership and organizational commitment, focusing on members of non-profit organizations.

The Effect of Transformational Leadership on the Volunteer Members’ …

129

Next, this study will verify the moderating effect of volunteer motivation in the influence of transformational leadership on organizational commitment in non-profit organizations. The factors that motivate volunteers are expected to be important because volunteers provide their time without formal compensation. In non-profit organizations, members with low levels of motivation will be more influenced by leadership than those who do not, which can be predicted to further improve their immersion or satisfaction with volunteer work. Hence, this study aims to verify what relationship changes occur when volunteering acts as a moderating variable in the relationship between transformational leadership and organizational commitment in non-profit organizations.

2 Theoretical Backgrounds and Hypotheses 2.1 Transformational Leadership and Organizational Commitment Leadership is one of the most widely discussed topics in human resource management [14] and plays an important role in determining organizational effectiveness, such as satisfaction and commitment to the organization. Lok and Crawford [15] declared that leadership plays an important role in determining the success and failure of organizations and institutions. Transformational leadership is one of the most notable theories in various leadership theories. The transformational leadership theory emphasizes that it is the co-topic of leaders to promote positive change in organizations and motivate and inspire subordinates for outstanding performance [16]. Leaders who exercise transformational leadership enable members to think creatively, analyze problems from various perspectives, and use technology to explore new and better solutions to problems [17]. Mowday et al. [18] defined organizational commitment as an attitude of accepting individual organizational goals and values and making the best efforts for the organization. Henkin and Marchiori [19] defined organizational commitment as the feeling of a member who becomes a member of an organization and recognizes its goals, values, norms, and ethical standards. Tella et al. [5] argues that organizational commitment is the strongest motivation to greatly affect an individual’s successful performance and improve his or her efficiency and skills. Immersion into the organization is a very important factor because it is a good indicator of the organization’s goals, purpose, productivity, and turnover. Looking at previous studies on leadership and organizational commitment, leadership is considered an important variable that promotes organizational commitment. Bath and Avolio [3] suggested that transformational leadership that induces members to think creatively can affect members’ commitment. This was supported by the study of Walumbwa and Lawler [4]. A study conducted by Avolio et al. [20] on nurses in public hospitals in Singapore revealed that transformational leadership has a positive

130

H.-K. Anh et al.

effect on organizational commitment. A study by Limsili and Ogunlana [21] argued that transformational leadership among various leadership styles was a more effective form of leadership and that members’ productivity and organizational commitment were promoted by transformational leadership. In addition, a study on the transformational leadership of Ismail and Yusuf [6] and the commitment of members showed a significant positive (+) correlation between the two variables. Based on the results of these previous studies, it can be argued that transformational leadership is the most effective leadership in determining the organizational commitment of members. Thus, the following hypothesis was established. Hypothesis 1: Transformational leadership will have a positive effect on organizational commitment.

2.2 Moderating Effects of Volunteer Motivation Various studies have been conducted on the role of leadership in for-profit organizations, but few studies have systematically investigated the role of leadership in non-profit organizations. Transformational leadership focuses on the creation of long-term effects and values and does not require obedience from members. Rather, it is to change the framework of members’ beliefs, values, desires, and thoughts so that they can create new opportunities and change them to achieve their task goals [11]. With the transformational approach to leadership, interest in the substitution theory of leadership has increased [22]. Jermier and Kerr [23] criticized the absolute importance of leader behavior assumed in existing leadership studies and tried to find factors that could replace leadership. As a result, the alternative factors of leadership were classified into the degree of professionalism, ability, experience, knowledge, training, and performance-related factors. Also, groups with high internal satisfaction and cohesion obtained from job performance were classified. In the study of Howell and Dorfman [24], alternative factors for leadership that influence organizational commitment were identified. The leadership substitution theory presented the personal characteristics of members who can replace or neutralize various traditional leadership behaviors [23, 24]. For example, in terms of the identity of volunteering, the job activities undertaken by individual volunteers are very independent. Also, individual characteristics such as inner motivation (value, understanding, protection, or interpersonal motivation) or task performance perception (promotion or career motivation) can be considered to replace or neutralize transformational leadership. According to the theory of leadership substitution [22], members with a high level of motivation do not have much influence from leaders in volunteer organizations, so the influence of members on volunteer activities is limited. However, in the case of members with low motivation levels, it is expected to be greatly influenced by external factors such as leadership.

The Effect of Transformational Leadership on the Volunteer Members’ …

131

People with a low level of motivation for volunteer work are more likely to rely on social clues or hints (e.g., transformational leadership) than people with a high level of motivation. They want to be recognized for themselves or act in accordance with other people’s expectations or get permission or recognition [25, 26]. This is because clues are closely related to behavior, and the dependence on role-related information derived from leaders is very high. This is consistent with Saks and Ashforth’s [27] argument that individuals with low self-esteem will be more affected. Eventually, those with low motivation will respond more favorably to the leader’s positive assessment and show an unfavorable attitude toward negative assessment than those with high motivation. Therefore, according to the substitute for leadership theory [22], members with a high level of motivation do not have much influence from leaders of volunteer organizations, so their influence on volunteer activities is limited, but members with a low level of motivation are expected to be greatly influenced by external factors such as leaders. In volunteer organizations such as non-profit organizations, organizational commitment is expected to be higher than when members of the organization react differently or have low motivation depending on the degree of transformational leadership. Thus, the following hypothesis was established. Hypothesis 2: When transformational leadership affects organizational commitment, it will affect lower members more than members with higher levels of value motivation. Hypothesis 3: When transformational leadership affects organizational commitment, it will affect lower members more than members with higher levels of motivation for understanding. Hypothesis 4: When transformational leadership affects organizational commitment, it will affect lower members more than members with higher levels of motives for enhancement. Hypothesis 5: When transformational leadership affects organizational commitment, it will affect lower members more than members with higher levels of protective motives. Hypothesis 6: When transformational leadership affects organizational commitment, it will affect lower members more than members with higher social motives levels. Hypothesis 7: When transformational leadership affects organizational commitment, it will affect lower members more than members with a higher level of career motives.

132

H.-K. Anh et al.

3 Methods 3.1 Data Collection This study conducted a survey of members who are conducting volunteer activities in 30 non-profit organizations called L Club, one of the NGOs located in the country. A total of 300 questionnaires were distributed and 260 of them were recovered. A total of 249 questionnaires were used, except in cases where the response was unfaithful.

3.2 Operational Definition and Measurement of Variables 3.2.1

Transformational Leadership

Bass [11] classified transformational leadership into four categories which are called charisma attributes and behavior. The four categories are ideal influence, inspirational motivation, intellectual stimulation, and individual consideration. In this study, transformational leadership was defined as the degree of transformational leadership of the boss perceived by his subordinates. Measurement items developed by Bath and Avolio [3] were used to measure transformational leadership and the questions were measured on a Likert 5-point scale.

3.2.2

Organizational Commitment

Organizational commitment to members of the psychological attachment on the basis of the attachments was compliant with the internalization of action, action, organizational value movements, and so on it consists of defined [29]. To measure organizational commitment, 9 questions were derived by referring to previous studies of Mowday et al. [18] and the questions were measured on the Likert 5-point scale.

3.2.3

Volunteer Motivation

This paper utilizes the Volunteer Functions Inventory (VFI) of Clary et al. [28]. Volunteer motivation such as value, understanding, enhancement, protective, social, and career are classified into six types, and five questions are presented for each motivation type. A total of 30 questions ‘I am more interested in the organization to which I belong’ were measured on the Likert 5-point scale.

The Effect of Transformational Leadership on the Volunteer Members’ …

133

4 Results 4.1 Demographic Analysis of Survey Participants Of the total 249 respondents used in the analysis, 188 respondents (76.7%) were men with a high proportion of men. It was found that 234 married respondents (94.7%). Regarding education, 57 respondents were high school graduates (23%). Regarding the total period of participation was the highest with 84 respondents (33.7%) were over 10 years. Regarding the size of non-profit organizations was the highest 109 respondents (44.3%) answered less than 30 members, 68 respondents (27.6%) answered less than 50 members, and 11 respondents (4.5%) answered less than 100 members. Regarding jobs was the highest 149 respondents were self-employed (61.3%).

4.2 Validity, Reliability, Descriptive Statistics, and Correlations An exploratory factor analysis was conducted to verify the validity of each variable. The principal component analysis method was used to extract the factors of all the variables to be measured, and the varimax rotation method was used as the factor rotation method. The selection of questions was set based on whether the factor loading values of each questionnaire exceeded 0.4 or were separated into factors expected by the researcher. First, it was found that transformational leadership, an independent variable, and organizational commitment, a dependent variable, were combined into two factors. As a result of factor analysis of volunteer motivation, a moderating variable, it was divided into six sub-factors and extracted. However, some items were extracted mixed with other factors or the factor loading did not exceed 0.4. As a result of conducting factor analysis excluding these four questions, it was classified into six factors. The excluded items are 1 question in value motive (value 6), 1 question in enhancement motive (enhancement 28), 1 question in protective motive (protective 5), and 1 question in social motive (personal 20). In the reliability analysis, Cronbach’s alpha coefficient was calculated to examine the internal degree of the measurement tool. Van de Ven and Ferry [30] stated that it is considered reliable if the Cronbach’s alpha value is 0.6 or more at the analysis level of the organizational unit, and the reliability of the measurement tools used in this study is 0.6 or more between 0.836 and 0.968. The reliability analysis results of this study are presented in diagonal brackets of the correlation result table in Table 1.

0.98

0.42

4.01

4.06

4.01

3.45

3.87

3.51

3

4

5

6

7

8

0.77

0.69

4.24

11

0.49

1.14

0.82

2.59

3.98

2.13

14

15

16

0.54**

−0.15

0.03

0.18**

0.76**

0.78**

0.79**

0.57**

0.72**

0.60**

−0.03

−0.01

0.18**

0.59**

0.62**

0.38**

0.56**

0.41**

-0.11

−0.01

0.08

−0.05

0.02

0.15*

-0.03

0.16*

−0.05

−0.08

0.07

0.03

−0.07

0.06

0.12

0.001

0.20**

0.04

0.64**

0.51**

0.71**

0.69**

(0.94)

(0.87)

3

0.56**

2

0.62**

(0.96)

1

0.10

0.06

−0.08

0.15*

0.09

0.16*

0.01

−0.08

0.59**

0.79**

0.53**

0.82**

(0.94)

4

0.06

0.06

−0.06

0.13*

0.02

0.17**

0.05

−0.05

0.62**

0.77**

0.56**

(0.93)

5

0.11

0.02

−0.04

0.02

0.10

0.14*

−0.10

−0.01

0.72**

0.59**

(0.84)

6

0.12

0.05

−0.04

0.11

0.04

0.12

-0.05

−0.07

0.65**

(0.86)

7

0.12

0.03

−0.06

0.07

0.07

0.11

−0.09

0.05

(0.90)

8

-0.03

0.12

0.14* 0.18**

0.07

0.17**

0.02

0.24**

0.16*

0.37**

0.05 0.18**

−0.02

0.23**

–

0.13*

11

0.22**

–

10

0.07

−0.12

–

9

−0.06 0.10

0.54** 0.18**

0.36**

–

13

−0.04

0.03

–

12

0.06

0.18**

–

14

0.15*

–

15

–

16

**p < 0.01, < 0.05, 1. Transformational leadership, 2. Organization commitment, 3. Value motives, 4. Understanding motive, 5. Enhancement motive, 6. Protective motive, 7. Social motive, 8. Career motive, 9. Gender, 10. Married, 11. Age, 12. Education, 13. Period of participation, 14. Position, 15. Service period, 16. Membership

*p

1.13

1.34

2.69

4.58

12

13

0.22

1.23

0.94

9

10

0.82

1.02

0.85

0.82

0.82

0.81

3.97

3.99

1

SD

2

M

Table 1 Result of correlation

134 H.-K. Anh et al.

The Effect of Transformational Leadership on the Volunteer Members’ …

135

4.3 Results of Hypotheses In this study, hypothesis 1 was established that transformational leadership will have a positive (+) effect on organizational commitment. The results of multiple regression for hypothesis verification are presented in Tables 2 and 3. The relationship with organizational commitment was examined by first inputting gender, marriage status, age, education, the total length of subscription, position, service period, and membership as control variables, and then introducing transformational leadership, an independent variable. As shown in [Model 2], transformational leadership was found to have a positive (+) effect on organizational commitment, and hypothesis 1 was supported (Table 2). Hypothesis 2–7 was to verify whether value motivation, understanding motivation, enhancement motivation, protective motivation, social motivation, and career motivation had a moderating effect on the relationship between transformational leadership and organizational commitment. To verify this, a multiple regression analysis was conducted, and the results are presented in Table 3. According to the procedure for verifying the moderating effect proposed by Baron and Kenny [31], transformational leadership, which is an independent variable and a control variable, was put into the regression equation of organizational commitment, which is a dependent variable. In the second step (Model 2), value motivation, understanding motivation, promotion motivation, protection motivation, interpersonal motivation, and career motivation were input. In the third stage (Model 3), Table 2 Results on regression analysis

Division Variable

DV: organizational commitment Model 1 Model 2 β

B CV

Gender

−0.12

−0.03

Married Statues

−0.12

−0.08

0.11

0.07

−0.07

−0.01

Age Education The total length of subscription

0.24***

Position

−0.14*

−0.08

Service Period

−0.13

−0.10

Membership IV

0.29**

0.17*

0.04 0.60***

TL

R2

0.12

0.45

△R2

0.12

0.33

*** p < 0.001, ** p < 0.01, * p < 0.05, IV: Independent Variable, DV:

Dependent Variable, CV: Control Variable, TL: Transformational Leadership

136

H.-K. Anh et al.

Table 3 Results on moderating effect of motives on volunteer Variable

IV MV

DV: organization commitment Model 3

Model 3

Model 3

Model 3

Model 3

Model 3

β

β

β

B

β

β

TL(A)

0.78***

0.76***

0.79***

0.83***

0.1.10***

0.74***

VM(B)

1.07*** 1.07***

UM(C)

1.26***

EM(D)

1.02***

PM(E)

1.43***

SM(F)

0.88**

CM(G) Interaction

A*B

−0.84** −0.87***

A*C

−1.08***

A*D

−0.84*

A*E

−1.50***

A*F

−0.65

A*G R2

0.69**

0.68**

0.70**

0.58**

0.67**

0.58

△R2

0.66**

0.66**

0.69**

0.56**

0.65**

0.56

*** p

< 0.001, ** p < 0.01, * p < 0.05, IV: Independent Variable, DV: Dependent Variable, MV: Moderate Variable, TL: Transformational Leadership, VM: Value Motive, UM: Understanding Motive, EM: Enhancement Motive, PM: Protective Motive, SM: Social Motive, CM: Career Motive

transformational leadership and value motivation, understanding motivation, promotion motivation, protection motivation, interpersonal motivation, and career motivation interaction terms were introduced. As a result of regression analysis, hypotheses 2–6 were supported, and hypothesis 7 was rejected. It was schematized in a graph in the manner proposed by Cohen and Cohen [32] to examine the moderating effect more clearly (Figs. 1, 2, 3, 4 and 5).

5 Discussion The first purpose of this study was to examine the system and process of the association between transformational leadership and the organizational commitment of members of non-profit organizations. Second, with the first objective that transformational leadership on the manager’s side can affect organizational commitment for the efficient operation of the organization, the study aimed to explain how the participants’ functional motivation conducts continuous volunteer activities. The results are as follows.

The Effect of Transformational Leadership on the Volunteer Members’ …

Fig. 1 Value motive moderating effective

Fig. 2 Understanding motive moderating effective

Fig. 3 Enhancement motive moderating effective

137

138

H.-K. Anh et al.

Fig. 4 Social motive moderating effective

Fig. 5 Protective motive moderating effective

First, it was found that transformational leadership, an independent variable, had a positive (+) effect on organizational commitment, a dependent variable. This is consistent with Limsila and Ogunlana [21]’s study that transformational leadership is the most effective form of leadership that causes organizational members’ emotions to recognize their goals, values, norms, and ethical standards. It is also consistent with the study that the productivity and organizational commitment of organizational members are promoted by transformational leadership. These results suggest that transformational leadership in non-profit organizations is the most effective leadership in determining the organizational commitment of members. Second, between transformational leadership and organizational commitment, in terms of participants participating in volunteer activities, it was considered what

The Effect of Transformational Leadership on the Volunteer Members’ …

139

moderating effect their functional motivation showed. The results showed that value, understanding, promotion, and interpersonal motivation had statistically significant moderating effects at a confidence level of more than 95% between transformational leadership and organizational commitment, and protection motivation had a significant moderating effect at a confidence level of 90%. The results imply that there is a statistically significant correlation between transformational leadership and organizational commitment. In terms of participants in non-profit organizations, functional motivation acts as a moderating variable for situations or motivations that replace or neutralize the behavioral effects of transformational leaders. As a result of this study, this connection can also be applied to volunteers and non-profit organizations, and it means that it should be treated more importantly given the characteristics of the relationship between volunteers and non-profit organizations. However, this study has some limitations in generalizing the research results because the sample consists of very selective volunteers and members of NGOs called L Club. In addition, since the measurement scores of each variable were collected and analyzed from the study participants in this study, it suggests that there may be a problem of common method bias and that there may be a social desirable bias in the form of a self-report questionnaire. The need to compare and analyze the results of this study is raised due to diversifying leadership and diversifying regulatory variables. Also, this study has a limitation of only considering the individual functional motives of non-profit organizations. In future studies, it is necessary to increase the generalization of research results by verifying the moderating effect from the perspective of organizations such as organizational characteristics and task characteristics.

References 1. L.A. Penner, Dispositional and organizational influences on sustained volunteerism: an interactionist perspective. J. Soc. Iss. 58(3), 447–467 (2002) 2. M.L. Vecina, F. Chacón, D. Marzana, E. Marta, Volunteer engagement and organizational commitment in nonprofit organizations: What makes volunteers remain within organizations and feel happy? J. Commun. Psychol. 41(3), 291–302 (2013) 3. B.M. Bass, B.J. Avolio (eds.), Improving Organizational Effectiveness Through Transformational Leadership (Sage, 1994) 4. F.O. Walumbwa, J.J. Lawler, Building effective organizations: Transformational leadership, collectivist orientation, work-related attitudes and withdrawal behaviours in three emerging economies. Int. J. Hum. Resourc. Manag. 14(7), 1083–1101 (2003) 5. A. Tella, C.O. Ayeni, S.O. Popoola, Work motivation, job satisfaction, and organisational commitment of library personnel in academic and research libraries in Oyo State, Nigeria. Libr. Philos. Pract. 9(2), 13 (2007) 6. A. Ismail, M.H. Yusuf, The relationship between transformational leadership, empowerment and organizational commitment: a mediating model testing. Timisoara J. Econ. 2(6), 101–110 (2009) 7. J. Bidee, T. Vantilborgh, R. Pepermans, G. Huybrechts, J. Willems, M. Jegers, J. Hofmans, Autonomous motivation stimulates volunteers’ work effort: a self-determination theory approach to volunteerism. Voluntas: Int. J. Volunt. Nonprofit Organ. 24(1), 32–47 (2013)

140

H.-K. Anh et al.

8. V.M. Catano, M. Pond, E.K. Kelloway, Exploring commitment and leadership in volunteer organizations. Leaders. Organ. Dev. J. (2001) 9. E.G. Clary, M. Snyder, A functional analysis of altruism and prosocial behavior: the case of volunteerism (1991) 10. K.B. Lowe, K.G. Kroeck, N. Sivasubramaniam, Effectiveness correlates of transformational and transactional leadership: a meta-analytic review of the MLQ literature. Leadersh. Q. 7(3), 385–425 (1996) 11. B.M. Bass, Leadership and Performance Beyond Expectations (Collier Macmillan, 1985) 12. T.A. Judge, R.F. Piccolo, Transformational and transactional leadership: a meta-analytic test of their relative validity. J. Appl. Psychol. 89(5), 755 (2004) 13. U.R. Dumdum, K.B. Lowe, B.J. Avolio, A meta-analysis of transformational and transactional leadership correlates of effectiveness and satisfaction: an update and extension, in Transformational and Charismatic Leadership: The Road Ahead 10th Anniversary Edition (Emerald Group Publishing Limited, 2013) 14. W.J. Kuchler, Perceived leadership behavior and subordinates’ job satisfaction in Midwestern NCAA Division III athletic departments. Sport J. 11(2) (2008) 15. P. Lok, J. Crawford, The effect of organisational culture and leadership style on job satisfaction and organisational commitment: a cross-national comparison. J. Manag. Dev. (2004) 16. G.A. Yukl, Leadership in Organizations, 5th edn. (Upper Saddle River, NJ, 2002) 17. J. Schepers, M. Wetzels, K. de Ruyter, Leadership styles in technology acceptance: Do followers practice what leaders preach? Manag. Serv. Qual.: Int. J. (2005) 18. R.T. Mowday, L.W. Porter, R. Steers, Organizational linkages: the psychology of commitment, absenteeism, and turnover (1982) 19. A.B. Henkin, D.M. Marchiori, Empowerment and organizational commitment of chiropractic faculty. J. Manipulative Physiol. Ther. 26(5), 275–281 (2003) 20. B.J. Avolio, W.L. Gardner, F.O. Walumbwa, F. Luthans, D.R. May, Unlocking the mask: a look at the process by which authentic leaders impact follower attitudes and behaviors. Leadersh. Q. 15(6), 801–823 (2004) 21. K. Limsila, S.O. Ogunlana, Performance and leadership outcome correlates of leadership styles and subordinate commitment. Eng. Constr. Architect. Manag. (2008) 22. S. Kerr, J.M. Jermier, Substitutes for leadership: their meaning and measurement. Organ. Behav. Hum. Perform. 22(3), 375–403 (1978) 23. J.M. Jermier, S. Kerr, “Substitutes for leadership: their meaning and measurement”—Contextual recollections and current observations. Leadersh. Q. 8(2), 95–101 (1997) 24. J.P. Howell, P.W. Dorfman, Substitutes for leadership: test of a construct. Acad. Manag. J. 24(4), 714–728 (1981) 25. J. Brockner, Self-esteem at Work: Research, Theory, and Practice (Lexington Books/DC Heath and Com, 1988) 26. D.C. Ganster, J. Schaubroeck, Work stress and employee health. J. Manag. 17(2), 235–271 (1991) 27. A.M. Saks, B.E. Ashforth, The role of dispositions, entry stressors, and behavioral plasticity theory in predicting newcomers’ adjustment to work. J. Organ. Behav. 21(1), 43–62 (2000) 28. E.G. Clary, M. Snyder, R. Ridge, Volunteers’ motivations: a functional strategy for the recruitment, placement, and retention of volunteers. Nonprofit Manag. Leadersh. 2(4), 333–350 (1992) 29. C.A. O’Reilly, J. Chatman, Organizational commitment and psychological attachment: the effects of compliance, identification, and internalization on prosocial behavior. J. Appl. Psychol. 71(3), 492 (1986) 30. A.H. Van de Ven, D.L. Ferry, Measuring and assessing organizations (1980) 31. R.M. Baron, D.A. Kenny, The moderator–mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51(6), 1173 (1986) 32. J. Cohen, P. Cohen, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd edn. (Erlbaum, Hillsdale, NJ, 1988)

Analysis on the Life Cycle of Soundtrack Content in K-POP—Data-Driven on Music Platforms Saeyeon Lee, Minwoo Lee, Younghoon Kim, and Gwangyong Gim

Abstract This study proposes a key element by analyzing the life cycle of soundtrack content. Based on the theoretical background of K-POP fandom, superstar effect, piggyback effect, branding theory, and product life cycle theory, this study analyzed how independent variables accepted in creation affect the soundtrack content life cycle. The results of the experiment showed that the life cycle of the soundtrack was not maintained because it was limited to the simple number of soundtrack playback and the number of listeners. It was confirmed that the longer the number of likes (counts) added to the active expression of music consumers, which is fandom, the longer the life cycle of the soundtrack lasted. The participation of the music arranger served as a variable in maintaining the chart within the music platform. As the research that shows the popularity factor of K-POP being the “chorus of addictive music” indicates, the popularity of soundtrack increases the lifespan according to the competence of the arranger. This study aims to present a new approach to K-POP music content creation by analyzing the factors of soundtrack content that has received a lot of attention from consumers and has been loved by consumers for a long time.

The original version of this chapter was revised: The Author name has been corrected from “Saeyeon Leem” to “Saeyeon Lee”. The correction to this chapter is available at https://doi.org/10.1007/9783-031-16485-9_16 S. Lee · M. Lee Department of IT Policy and Management, Soongsil University, Seoul, South Korea e-mail: [email protected] M. Lee e-mail: [email protected] Y. Kim ICT Solution Departmnt, KEPCO Engineering and Construction Comapnay, Seoul, South Korea 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 2023, corrected publication 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_11

141

142

S. Lee et al.

Keywords K-POP Fandom · Superstar effect · Brandwagon effect · Brand theory · Life-cycle

1 Introduction K-POP is Korean pop music that has become an independent genre around the world. According to the 2020 Content Industry Survey, the total amount of exports to the content industry was 14 trillion won, up 16.3% year on year. Among them, music content exports achieved 825 billion won [1]. This was the main reason for the increase in exports within the global platform due to the Korean Wave. For example, BTS topping the Billboard main single chart supports this. As these examples show, K-POP is gaining sensational popularity around the world. To continue this popularity, it is necessary to study environmental factors on the life cycle of excellent K-POP music content. K-POP soundtrack content is completed as a single song by integrating various elements such as consumer (fandom), music genre, creative direction, and producer concept. After that, life as a music content begins. The model was constructed based on the main factors of the K-POP life cycle and variables that play the most important role. Data of 24 monthly charts from 2020 to 2021 provided by music platform companies, dependent variables (cumulative scores by ranking) of 2,400 songs, and 15 independent variables were preprocessed. The correlation of variables through exploratory factor analysis (EFA) of SPSS 22, linear regression analysis, and decision tree model were analyzed. Through this, the factors of the music life cycle were presented to K-POP music producers and musicians. It also suggests the direction in which a higher and more elegant K-POP culture can be formed. This will serve as good motivation for artists, music producers’ brand image, and economic profits to continue to grow through the successful launch of K-pop musicians and continuous production activities of fandom.

2 Theoretical Background 2.1 Assessment of Service Efficiency Fandom is a combination of “fanatic” meaning fanatic and “dom” meaning country. In the dictionary sense, it refers to a fan club organization [2]. A voluntary community that supports and enthusiastically likes a particular star or field and a culture that they share. The fandom culture of k-pop began to have a collective and subjective character after the 1990s. It began to change into a fandom culture [3] Fandom is a huge intangible asset of the entertainment company that plays a role in value production,

Analysis on the Life Cycle of Soundtrack Content in K-POP …

143

Fig. 1 Source of fandom formation and growth [5]

not passive consumers, and produces various contents. Music production companies contribute to generating profits. Also, it is a loyal group that has a common goal of growing the value of artists and has a greater joint desire than a minority monopoly in content consumption. Fandom actively presses ‘likes’ and writes ‘comments’ within the soundtrack platform, playing a significant role in the ranking rise and life cycle. Among K-POP stars, BTS and IU are the groups with the most fandom in Korea, ranking first and second on the annual chart [4]. Recognizing the importance of the influx of fans, K-POP fandom is actively engaged in sales activities in various Internet social network communities by making them become so-called “fandom” to attract new people [5] (Fig. 1).

2.2 Superstar Effect The Superstar Effect Quarters Never Win: The (Adverse) Incentive Effects of Competing with Superstars study began with researcher Jennifer Brown studying golfers’ performance when Tiger Woods participated in or was absent from a golf tournament. The study found that the presence of Tiger Woods alters golfers’ performance [6]. In popular music, the superstar effect is explained as an effect that is already proven starry or that artists who are gaining popularity among consumers stay in the rankings longer. Representatively, Michael Jackson’s 1982 album “Thriller” topped the Billboard album chart for 37 weeks, topping the album chart for the longest time in history, and “Butter” by BTS (BTS) remained at the top of the Billboard “Hot 100” for nine weeks. The lifetime of content in the soundtrack market is still affected by the superstar effect. The superstar effect is significant in the importance of the star’s role as time increases, the increase in music-sharing fans, and the value of the time fans focus on artists, and the music consumer’s decision to switch from Vista to Star is high.

144

S. Lee et al.

2.3 Brandwagon Effect It refers to a psychological phenomenon in which humans act according to something or a consumption phenomenon in which they purchase goods according to fashion because others are mainly doing it regardless of their beliefs [7] Humans tend to align their beliefs and actions with the community. This is the same as music consumers who are sensitive to trends and trends are sensitive to music content shared through the media. Hence, the soundtrack, which is on the top of the music platform, has already been selected by many consumers and induces subsequent music consumers to purchase it without a separate search, contributing to the extension of life.

2.4 Brand Theory (K. L. Keller, 1993) The brand theory is a theory that is actively applied in the marketing field as the quality difference of products decreases due to the development of technology. To overcome the limitations of qualitative differentiation of soundtrack content, it is necessary to induce consumption of soundtrack content consumers by focusing on differentiated images and services unique to the brand.

2.5 Product Life Cycle Theory (Raymond Vernon) The production of a product generally goes through the development, growth, and maturity stages of a new product, and each stage of the product life cycle is related to the organization and technical characteristics of a company [8]. Hence, the life span of a product is the stage of its life, which refers to the period during which it appears vigorously in the market in terms of the life span and supply of the product, and then disappears after a certain period of time [9]. Soundtrack content also begins to sell when consumers stream music, and consumers increase according to the popularity of music content. At some point, it gradually decreases, losing life as a music content.

2.6 Prior Studies In a study on the development of a music box office success prediction model using deep learning (2020), prediction model performance was derived in two stages. After building a prediction model by putting it into the DNN model without data erasure, performance measurement and linear regression analysis are performed to examine the significance probability. Only significant variables are put into the DNN model to build a predictive model and design it in a structure that predicts

Analysis on the Life Cycle of Soundtrack Content in K-POP …

145

the period of residence in the music chart, which is a dependent variable. Singer’s power, influence, teaser video, likes, and comments were influenced by the initial box office success [10]. In the ranking change pattern classification of soundtracks through time-series cluster analysis (2020), pattern classification according to the music source ranking is performed through a time-series clustering methodology for time series data on the music chart. The result showed that the superstar effect and the piggyback effect of the artist influence consumers’ choice of soundtrack [11]. Global Success Factors of K-POP Music [12] study analyzed musical characteristics based on the top 20 most viewed K-POP’s YouTube music videos. Musical characteristics such as music production methods, overseas producer appointment, artistproducer relationship, genre combination, English lyrics use, and dance-optimized bits were analyzed. Global success factors were found by delivering familiar melodies to foreignrs by using overseas music trends and overseas composers. Global success factors were found by delivering familiar melodies to foreigners by using overseas music trends and overseas composers [12]. In the field of statistical music recognition (MIR) using meta-information (track count, artist name, streaming), embedding methods are most often used for extracting music features. The embedding method of the Librosa library [13] is relatively simple, and machine learning is possible with a small number of learning elements, which has the advantage of low learning time [14]. Non-statistical music embeddings vary in music extraction embeddings depending on the purpose of analysis in MIR [15]. Preference comparison experiments can be conducted with only the soundtrack, such as predicting the preference of music consumers, and the initial life cycle of K-pop soundtrack contents can be predicted by deriving preference prediction results with only the soundtrack information without the help of meta-information.

3 Research Method 3.1 An Analysis of Variables Affecting the Life Cycle of K-POP To reduce the error range as much as possible, focusing on theoretical research and music platform data through previous research, the music platform (Genie Music) that disclosed the most variables in the data was selected and analyzed. Among them, Genie Music’s K-POP platform was the only domestic music platform that released the total number of listeners and a total number of playback of each music data. From January 2020 to December 2021, 17 independent variables from No. 1 to No. 100 on the music platform and the section entering the chart ranking from No. 1 to No. 100 were scored. Data of 526 unique songs were analyzed after removing unnecessary variables and missing values among 2,400 songs (Figs. 2 and 3).

146

Fig. 2 Analysis methodology

Fig. 3 Research process

S. Lee et al.

Analysis on the Life Cycle of Soundtrack Content in K-POP …

147

Table 1 K-POP platform music data variables No Data variables 1

Digital release date

2

Digital albums out of ranking day

3

24 cumulative ranking points (9 points for 1–10th place—0 points for 91–100th place

4

Singer’s name (person)

5

Featuring (person)

6

Genres (Ballad 1/Dance 2/Rock 3/Hip Hop 4/Wrap 5/Pop 6/Indie 7/OST8/R&B9/Trot 10)

7

Lyricist (person)

8

Composer (person)

9

Arranger (person)

10

Production company

11

Distributor

12

Total playback (number)

13

Listener (number)

14

Good (number)

15

Comments (number)

16

Music video (with or without)

3.2 Data Collection and Analysis Overview This data contains 16 variables and 526 songs. Frequency analysis and correlation analysis of each variable, linear regression analysis, and variables affecting the life cycle of K-POP soundtrack content between neural network models of decision trees were compared and analyzed (Table 1).

4 Research Results 4.1 Frequency Analysis Frequency analysis is used to determine what distribution characteristics the input data have on the frequency table. This analysis is useful to check the data for errors before applying it to statistical analysis methods. By analyzing the frequency of independent variables, it was shown that the number of singers, genres, songwriting, songwriting, arrangement, production company, distributor, and music video influenced the life cycle of music contents (Table 2).

526

0

526

0

N

NA

0

526

First-time

0

526

Last-time

0

526

484

42

483

43

458

68

526

0

526

0

526 0

526 0

0

526 0

526

Number of Genre Feat Ballad1/Dance2/Rock3/Hip-hop4/Rap5/Pop6/Indie7/OST8/R&B9/Teuroteu11

0

526 1

525 0

526 0

526

Listener Good Comments Music video (O, X) (number)

0

526

Cumulative-score Number Feat of singers

Lyricist Composers Arranger Production Distributor Total playback company count

Singer

Song

Statistics

Table 2 Frequency table

148 S. Lee et al.

Analysis on the Life Cycle of Soundtrack Content in K-POP …

149

4.2 Correlation Analysis Correlation analysis is a statistical analysis method that analyzes the close degree of the relationship between variables, which is the correlation, and is the method that indicates to what extent the relationship expression between variables is reliable in regression analysis [16]. The correlation between the scores for each chart ranking, which is a dependent variable, and other variables was analyzed to confirm the correlation between the scores and the variables. As a result of the confirmation, the correlation with the score, that is, the life cycle, was relatively significant compared to other variables in the order of likes (number) > overall play (number) > listeners (number) > comments (number) > arranger (name) (Table 3).

4.3 Linear Regression Analysis Dependent variable: Ranking cumulative score (soundtrack life cycle). A multi-linear regression analysis was conducted to find out how consumer participation and participation steps in music creation affect the music life cycle within the soundtrack platform for the soundtrack life cycle. For the analysis method, input (Enter) was selected. Some variables in the model with a VIF (Variance Inflation Factor) of 5 or less and a TOL (tolerance) limit of 0.2 or more were insignificant in correlation with other variables, so there was no problem in multicollinearity when analyzing data. As a result of the analysis, the regression model viewed as F = 37.079 (p < 0.001) is suitable. adj. = 0.416 showed 41.6% explanatory power. The number of likes is β = 0.400 (p < 0.001) had a significant effect on the soundtrack life cycle. Also, the standardization coefficient value was analyzed as four variables (such as number of likes, number of listeners, total number of plays, and arrangement number) that had a great influence on the soundtrack life cycle. These four β signs are positive (+), so if the number of likes increases by 1, it can be said that the lifespan of soundtracks increases by 0.001. Consumer participation in the soundtrack platform for the soundtrack life cycle and how the music creation step affects the music life cycle were compared through the value of the standardization coefficient. The number of likes is β = 0.400 and the number of singers is β = −0.05 which means that the number of singers has a relatively higher effect on the soundtrack life cycle than the number of singers. When trying to fit a linear model in time series analysis, the problem that the error term is not independent but follows the first-order self-session model frequently occurs. If the number of samples observed is n, the Dubin-Watson statistic d is defined as follows.

−0.012

0.785

Sig

0

0.139

0.001

Sig

Correlation

Sig

0.008

0.115

0.826

0.01

0.016

0

0.629

Sig

Correlation

0.01

0.127

−0.02 0.654

0.45

0.014

0.06 0.04

0.014

-0.09

−0.107 −0.107

0.28

−0.05

0

0.221

0

0.749

1

D

0.031

−0.094

0.124

−0.07

-0.105

0

Sig

0.733

0.016

0.003

0.134

0

0.185

1

C

0.002

0.143

0.578

0.613

Correlation

Correlation

0.01

Sig

0

0.786

0.012

Correlation

0.120

0.523

Sig

Sig

0.378

−0.029

Correlation

Correlation

0.402

0.242

Sig

0

0.002

−0.051

−0.136

1

B

Correlation

Sig

Correlation

A

1

Correlation

0.005

0.127

0.061

0.085

0.114

−0.072

0.553

−0.027

0

0.404

1

E

0

0.249

0.315

0.047

0.711

0.017

0.511

0.031

1

F

0.002

0.138

0

0.862

0

0.845

1

G

0.039

0.090

0

0.860

H

0.001

0.141

1

I

1

J

Note A: Cumulative Ranking Points, B: Singer’s name, C: Featuring, D: Lyricist (person), E: Composer (person), F: Arranger (person), G: Total playback (number), H: Listener (number), I: Good (number), J: Comments (number)

J

I

H

G

F

E

D

C

B

A

Table 3 Correlation analysis table

150 S. Lee et al.

Analysis on the Life Cycle of Soundtrack Content in K-POP …

151

Table 4 Linear regression table Variable

Non-standardization coefficient

Standardization t(p) coefficient

B

B

SE

Constant

−5.791

2.972

Singer (1)

−0.061

0.451

−0.005

P

−1.949

0.052

−0.135

0.893

TOL

VIF

0.801

1.248

Featuring (2)

0.340

1.458

0.009

0.233

0.816

0.781

1.281

Lyrics (3)

0.089

0.827

0.005

0.108

0.914

0.655

1.527

Composing (4)

0.141

0.784

0.008

0.180

0.857

0.635

1.576

Arrange (5)

2.124

Total playback count (6)

1.399E-7

Listeners (7)

3.828E-6

Good Num (8) Comment (9)

1.024

0.083

2.075

0.039

0.793

1.262

0.000

0.159

2.176

0.030

0.239

4.183

0.000

0.111

1.443

0.150

0.217

4.600

0.001

0.000

0.400

5.007

0.000

0.201

4.978

0.000

0.000

0.028

0.725

0.469

0.887

1.127

F(p)

37.079

Adj. R2

0.416

Durbin-Watson

1.381

It is the residual estimated by the least-squares method. The d value for the soundtrack life cycle is 1.381, and the error terms are independent of each other. When trying to fit a linear model in time series analysis, the problem that the error term is not independent but follows the first-order self-session model frequently occurs. Assuming that the number of samples observed is n, the Dubin-Watson statistic d is defined as follows: n 2 j=2 (e j − e j−1 )  d= n 2 j=1 e j 

Here, e j = y j − y j is the residual estimated by the least-squares method. The d value for the soundtrack life cycle is 1.381, and the error terms are independent of each other (Table 4).

4.4 Decision Tree Model Decision trees are a method of classifying and predicting by schematizing a tree structure model according to appropriate decision rules. This allows researchers to easily understand and explain the process, find factors that affect target variables in decision tree analysis, and determine the importance of each factor. A schematic flowchart can express how the incidence rate varies depending on the important

152

S. Lee et al.

Fig. 4 Decision tree model importance

factors affecting each process [17]. Another advantage is not requiring assumptions such as linearity in a nonparametric way and is easy to identify interactions between factors [18] (Fig. 4). The factor that had the greatest influence on the life cycle of the soundtrack analyzed through the decision tree model was ‘likes’. The number of songs to be analyzed was 526, of which the likes (number) ratio accounted for 100%. Next, it was analyzed in the order of 95.7% of the total number of playback (number), 90.4% of listeners (number), and 50.8% of comments. It can be seen that the role of KPOP fandom has an absolute effect on the dependent variable, the life cycle (ranked cumulative score).

4.5 K-POP Life Cycle Prediction Model Open data of the Genie Music platform chart were pretreated to analyze the frequency of tetravalent type, correlation analysis, linear regression analysis, and decision tree model with SPSS 22. As a result, there were differences for each model, but the independent variable likes were analyzed as the most significant result. It can be seen that the activities of K-POP fandom have more influence on the music charts than simply listening to the music. In the linear regression analysis, the number of likes, plays, and listeners was excluded, and the number of arrangements affected the life cycle of the sound source. This is also directly related to the popularity factor of K-POP. The most popular factor

Analysis on the Life Cycle of Soundtrack Content in K-POP …

153

Fig. 5 Life cycle classification tree

analysis in the 2021 K-POP Fandom Trend Report of OPENSURVERY showed that the first place was ‘chorus (rhythm) of addictive music’ with 33.2%. The second place was ‘attractive K-POP singers’ (appearance, style) with 30.6%, which showed a high response rate from music consumers regarding the rhythm. It can be seen that the quality of the content affects the music life cycle of K-POP digital content, not limited to simple continuous listening and the number of listeners (Fig. 5). Table 5 is the result of analyzing the music chart of 526 songs in 24 months by calculating it as an arbitrary score. There were 17 songs distributed above the 50th place for 24 months, accounting for only 3.23% of the total 526 songs. As a result of analyzing the top 10 songs out of 17, BTS’s “Dynamic” and “Boy With Luv” were ranked at the top of the chart. IU’s Blueming and Eight songs were ranked. At the top of the music chart, two large teams of singers each rank two songs, and the solid fandom and superstar effects that support the two singers are reflected in the music chart, continuing to extend their popularity. In an early fandom inflow survey to continue the life cycle of soundtrack, “the first time in life to start idol fan activities” was found to be the highest in the senior year of elementary school [19] while men began fan activities as adults. The continued influx of new fandom becomes the driving force behind the sustainable life cycle of sound sources.

5 Conclusion and Limitations To continue the life cycle of soundtrack, fandom is the most important factor and continuous fan inflow is necessary. Early branding can be seen as the beginning. A fandom is built according to the branding effect of K-POP soundtrack, and the life

154

S. Lee et al.

Table 5 Data ranking cumulative score Ranking

Score

Period (M)

Cumulative score

1–10

9

24

216–193

Contants 0

0

%

11–20

8

24

192–169

0

0

21–30

7

24

168–145

2

0.38

31–40

6

24

144–121

8

1.52

41–50

5

24

120–97

7

1.33

51–60

4

24

96–73

12

2.28

61–70

3

24

72–49

33

6.27

71–80

2

24

48–25

79

15.01

81–90

1

24

24–1

349

66.34

91–100

0

24

0

36

6.84

cycle of the music is determined by the superstar effect and the piggyback effect. Although it is not possible to prioritize the branding effect and the fandom effect in the life cycle of the sound source, prioritizing the fandom and branding will have a greater impact on the life cycle of the soundtrack. In the life cycle of music, likes were analyzed as the factor that influenced the music chart the most. Hence, it is necessary to analyze the stage of consumption behavior of fandom and continuous research on the influx of new fans and the growth of artists’ fandom. This study had limitations in that diversity was insufficient by studying only the online music chart responses of one site. Further study is needed to study the database of various domestic music platform sites and overseas music platform sites, examples of activities of K-POP fandom, and the life cycle of soundtrack. Through this, further research is needed on the changes in music charts according to fandom activities and the effects of offline and media on fandom and music charts. In addition, there have been no studies using preference prediction as a dependent variable in the field of music recognition (MIR). This is due to the preconceived notion that even if an excellent embedding method or a good machine learning model is used, it will not be easy to predict the preference of music that is dependent on individual emotions. A meaningful study in AI music (AI composed music) should be conducted in the future by researching whether it is possible to predict the success of music data when the new music is released without meta-information by binary classification using CNN, which does not have the role of meta-information.

References 1. Y. Hyeongseon, 2020 contents industry mid-to long-term market forecast (KOCCA, 2020) 2. C. Park, Snowball fandoming (2020), p. 138 3. W. Haseong, The effect of fandom activity on psychological contentment and everyday life activities: focus on female Korean and Chinese BTS fandoms (2018), p. 10

Analysis on the Life Cycle of Soundtrack Content in K-POP …

155

4. https://www.idol-chart.com/ranking/year/ 5. H. Lee, The beginning of the 2022 enterprise renaissance—the K-pop industry’s redemption. Yuanta Res. 43–47 (2022) 6. J. Brown,Quitters never win: the (adverse) incentive effects of competing with superstars. J. Polit. Econ. (2011) 7. D.J. Kim, The long-run implications of network effects and learning by doing in a digital economy: a note (2020) 8. Y-IL. Kim, Ji Eun La, K.J. Na, The effects of design attributes on product life cycle in product design (2012) 9. H.B. Thorelli, S.C. Burneet, The nature of product life cycles for industrial goods businesses. J Market 45(4), 97–108 (1981) 10. D.-Y. Lee, B.-H. Chang, A study on development of prediction model for Korean music box office based on deep learning (2020) 11. I.-J. Yoo, D.-H. Park, Derivation of soundtrack’s ranking change through time series clustering (2020) 12. S.-Y. Lee, M.-H. Chang, Analysis global success factors of K-POP music (2019) 13. B. McFee, C. Raffel, D. Liang, D. Ellis, M. McVicar, E. Battenberg, O. Nieto, librosa: Audio and music signal analysis in Python, in Proceedings of the 14th Python in Science Conference, Scipy (2015), pp. 18–24 14. M. Defferrard, K. Benzi, P. Vandergheynst, X. Bresson, FMA: a dataset for music analysis, in Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017 (2017), pp 316–323 15. M. Ashraf, G. Geng, X. Wang, F. Ahmad, F. Abid, A globally regularized joint neural architecture for music classification. IEEE Access 8 (2020) 16. L. Jongsu, The public administration dictionary (2009) 17. A.C. Miller, N.J. Foti, E.B. Fox, Breiman’s two cultures: You don’t have to choose sides (2021) 18. Y.K. Seo, H.-Ok Kim, Comparison of the decision tree model on smartphone dependence between the polar income adolescents (2021) 19. K-pop Fandom Trend Report (2021), https://opensurey.co.kr

Method of Selecting the Optimal Location of Barrier-Free Bus Stops Using Clustering Se Hyoung Kim, Chae Won Pyun, Jeong Yeon Ryu, Yong Hyun Kim, and Ju Young Kang

Abstract Recently, there has been a greater need for barrier-free bus stops as the number of elderly people has rapidly grown, and social interest in the right to transport for the disabled has increased. Barrier-free bus stops refer to stops without obstacles, like flowerbeds and trash cans, to ensure the disabled, the elderly, etc., are comfortable getting on and off the bus. These stops may also have additional support facilities such as enhanced sidewalk block maintenance, braille block reinforcement, and low-floor bus location indications. However, the transportation environment for those in need is still not improving. Of the buses operating in Seoul, only 47.3% are low-floor buses designed for wheelchairs, and there are not enough barrier-free bus stops. In addition, notwithstanding the recent protests regarding the right to travel for the disabled. However, barrier-free bus stops have not increased, and the right to travel is not guaranteed. According to the Nodeul Independent Living Center for the Disabled, after monitoring mobility in Jongno-gu since 2019, it was found that the accessibility for wheelchairs and blind route information in Jongno-gu was minimal. Jongno-gu is an essential stopover for bus routes and is the core of Seoul’s transportation system, with a floating population of more than two million. Thus, the lack of accessible transportation is an issue. This study address these problems, by This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2018-0-01424) supervised by the IITP (Institute for Information & communications Technology Promotion). S. H. Kim Deparment of Business Analytics, Ajou University, Suwon, South Korea e-mail: [email protected] C. W. Pyun · J. Y. Ryu · Y. H. Kim · J. Y. Kang (B) Deparment of e-Business, Ajou University, Suwon, South Korea e-mail: [email protected] C. W. Pyun e-mail: [email protected] J. Y. Ryu e-mail: [email protected] Y. H. Kim e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 R. Lee (ed.), Emotional Artificial Intelligence and Metaverse, Studies in Computational Intelligence 1067, https://doi.org/10.1007/978-3-031-16485-9_12

157

158

S. H. Kim et al.

proposing installation locations of barrier-free bus stops by using cluster analysis. The analysis was conducted using information on ‘convenience facilities with provisions for the disabled’ (ease of use for the disabled), ‘institutional transportation facilities for the vulnerable’ (welfare facilities), and ‘the population getting on and off of transportation’ (accessibility of transportation). This study used convenience, welfare, and transportation accessibility to determine stops. Keywords Barrier-free · Clustering · k-means · Recommendation · Big-data · Geo-coding · Bus stops

1 Introduction The need for barrier-free bus stops has been increasing alongside the growth in the population of elderly people and the expanded focus on the right to accessible transportation for the disabled [1]. A barrier-free bus stop is an obstacle-free bus stop that allows the disabled and the elderly to access transportation easily. Barrierfree stops may also have additional facilities like increased sidewalk maintenance, reinforcement of braille blocks, and low-floor bus stop indicators [2]. However, there is still a need for improvement in accessibility to transportation for the vulnerable. In Seoul, only 47.3% of busses are wheelchair accessible, and there are not enough barrier-free bus stops. There have also been recent demonstrations by disabled rights groups to support guaranteeing the right to transportation for all. In response, in 2016, the Seoul Metropolitan Government installed fifteen “barrier-free stops” on a trial basis to improve the use environment of city bus stops, stating that they would install additional barrier-free stops. However, these bus stops have not yet appeared, and the right to travel for the vulnerable is not properly guaranteed. The Nodeul Independent Living Center for the Disabled has been monitoring the mobility of the vulnerable in Jongno-gu since 2019. Their results show accessibility for wheelchairs, visually impaired route information, and the efficiency of sound signals at Jongno-gu bus stops are inadequate. This confirms that the transportation rights of the vulnerable are not guaranteed in Jongno-gu, an essential stop for bus routes and the core of Seoul’s transportation system, with a floating population of more than two million. To address these issues, this study proposes locations for barrier-free bus stop installations using cluster analysis. The cluster analysis was conducted using information on ‘convenience facilities with provisions for the disabled,’ representing ‘ease of use.’ It also examines ‘institutional transportation facilities for the vulnerable,’ representing ‘welfare facilities’ of local governments and state agencies. Finally, ‘the population getting on and off transportation’ variable refers to transport accessibility. This study used convenience, welfare, and transport accessibility to determine stops. This study’s data was analyzed using ArcGIS, Tableau, and Python. First, necessary data were collected from the big data campus in Seoul, and pre-processing of

Method of Selecting the Optimal Location of Barrier-Free …

159

geographic data was conducted using ArcGIS. Convenience facilities and facilities for the disabled within a 1 km radius of a bus stop were added to combine data. After that, these facilities were counted based on bus stops. Next, the Max–Min Scale was performed for the cluster analysis, and the optimal number of clusters was derived. Finally, the cluster analysis was conducted, and the results were analyzed. This paper is structured as follows. Chapter 2 describes related prior studies, and Chap. 3 describes the research process, including pretreatment. Chapter 4 derives and presents the candidate groups for the stops determined from the cluster analysis results. Chapter 5 presents the limitations and conclusions of this study.

2 Literature Review 2.1 Prior Research on Site Selection Existing studies have been conducted to determine the supply of low-floor buses and how to revitalize these buses for those in need. Research has analyzed the details of bus transportation card transactions of the elderly to derive the spatial clustering phenomenon [3]. It was proposed that installing facilities and supplying low-floor buses that enhance accessibility for the elderly should be prioritized through spatial cluster analysis in Seoul. Some research has analyzed the residential distribution and purpose of the transportation-vulnerable, evaluated the existing low-floor bus routes in terms of convenience, and suggested a new low-floor bus route adjustment plan. Based in Gwanak-gu, Seoul, the P-Median model and the VRP model, which are spatial optimization models, presented free shuttle bus routes optimized for use by the elderly population and offered an efficient improvement of transportation welfare services. This is meaningful as it discusses more specific and practical alternatives to improve the mobility of the elderly population [4]. Through regression analysis, the elderly and disabled population were used to identify the living areas and patterns of the transportation vulnerable in Seoul using R. This statistical analysis tool can manipulate and visualize data affecting the disabled’s living area [5]. Considering the characteristics of the vulnerable, the moving speed of pedestrians, location, departure time, arrival time, purpose of use, and frequency of use by day of the week were analyzed using information on the use of special transportation operated by the vulnerable transportation support center. Using spatial analysis techniques such as overlapping analysis, buffer analysis, and space division, a method of selecting lowfloor bus stop locations to reflect the distribution and demand of the elderly and disabled was proposed. The preceding study above is significant in that it discusses specific and practical alternatives to improving the mobility of the transportation vulnerable. It can contribute to distributing the demand for accessible transit by selecting useful locations for low-floor bus stops. However, additional research is needed on the location

160

S. H. Kim et al.

of barrier-free bus stops because people with disabilities may be unable to get on low-floor buses because they cannot get to the bus stops. As well, it is difficult to get wheelchairs on buses due to frequent failures of low-floor bus wheelchair boarding devices.

2.2 A Study on Transportation Vulnerability Social interest in the transportation vulnerable population is gradually increasing, and the perception of citizens is improving in this regard. Accordingly, related policies and research cases are being conducted to ensure the right to transportation [1]. According to the Seoul Metropolitan Government’s Public Transportation Facilities Accessibility Evaluation Index: Urban Railways and History, the Seoul Metropolitan Government’s Transportation Vulnerability Policy is evaluated only by the installation rate [6]. The current status of transportation indicates that there is a need for wheelchair buses, especially as outdoor activities for the elderly and the disabled are increasing [7]. One study on transportation vulnerability proposed a mobility policy based on a satisfaction analysis of the transportation vulnerable [8]. This study found that satisfaction with buses was the lowest among public transportation, and it was proposed to change the dispatch interval and operation route of low-floor buses and adjust the specifications of stops. Another study stated that research on the technology usage of the vulnerable is uncommon in Korea and that successful technology realization and use can be achieved only when the requirements are identified and designed from a user’s point of view, focusing on the traffic characteristics [8]. Accordingly, among the technology supporting accessible transportation, a model of a walking service can be combined with ICT (Information & Communication Technology) was proposed. Further research suggested introducing an elderly-responsive bus that is easier to use as the interval between bus stops is less than existing intervals. A developed method for finding areas where community buses are needed to support the elderly living alone is necessary [9]. As a representative policy, the Seoul Metropolitan Government is pursuing a “Seoul-type barrier-free building certification system.” This system evaluates and certifies the proper installation and management of convenience facilities to ensure that citizens can use them. Since 2010, Seoul-type certification halls have been attached to all buildings. Since 2015, a partial certification system has been implemented to support the need to certify accessible buildings for the disabled. Accordingly, bus stops, which are convenience facilities, must also be certified. The Ministry of Health and Welfare’s Act on the Promotion of Convenience for Persons with Disabilities, the Elderly, and Pregnant Women (hereinafter referred to as the Act on the Promotion of Mobility for Persons with Disabilities) and the Ministry of Land, Infrastructure, and Transport are guaranteed by law. In 1998, the central government implemented the barrier-free living environment certification system (hereinafter referred to as the BF certification system) so that everyone, including the socially disadvantaged, could be guaranteed an accessible living environment. Goyang City

Method of Selecting the Optimal Location of Barrier-Free …

161

established eight barrier-free bus stops in 2021. The eight locations were selected by conducting big data analysis based on the movement and living radius of the transportation vulnerable population, the number of people getting on and off at each bus stop, and general movement.

3 Research Method 3.1 Research Methodology Procedure Figure 1 shows the sequence of analyses. First, Seoul Bus Stop Dataset, Facilities for the Disabled Location Dataset, and Seoul Disabled Rehabilitation Center Location Dataset were collected from the Seoul Big Data Campus. Next, it was merged with facility data within a 1 km radius of the bus stop. After that, datasets were aggregated based on each bus stop in Seoul. The data collection and preprocessing were carried out using this method to produce the training dataset. After that, we performed clustering and analysis. First, Max–Min scaling for clustering analysis was conducted. Then, after deriving the optimal number of clusters, clustering analysis was finally performed.

3.2 Datasets The dataset was collected from the Big Data Campus in Seoul. First, to collect the location information of bus stops and population data for getting on and off, the locations of public transportation facilities provided by the Seoul Metropolitan Government (Smart Card Company) and statistics on daily transportation usage by time segment were taken out. As shown in Fig. 2, the locations of bus stops in Seoul used for analysis were visualized. Next, the location map of convenience facilities and spatial data of facilities for the disabled were taken out. The daily statistics on public transportation in Seoul were compiled using data from one hour per day at subway stations and bus stops throughout Seoul. The location information data of public transportation facilities in Seoul shows the location of all bus stops and subway station entrances in Seoul as of December 2021. The spatial data of facilities for the disabled in Seoul include the name, address, phone number, and location of facilities for the disabled, and the analysis was conducted based on updated data in October 2021. The location map data of facilities for the disabled was collected in 2016 and provides the name, address, and location of facilities available to the disabled in Seoul.

162

S. H. Kim et al.

Fig. 1 Research Methodology Procedure

3.3 Clustering Methods Algorithms used for clustering can be roughly divided into two methods: hierarchical clustering and point assignment clustering. In this study, non-hierarchical cluster analysis was used, and the types of non-hierarchical clusters include K-Means and DBSCAN. K-Means is a central-based clustering method founded on the assumption that similar data is distributed based on the center point. This is a method of grouping while updating in the direction of minimizing the distance between the center point and the data within each group, given n data and k (