All Issue

2023 Vol.41, Issue 6 Preview Page

Article

User Acceptability Assessment for Robo Taxi Using Value Based Adoption and Latent Profile

가치기반 및 잠재프로파일을 통한 로보택시 사용자 수용성 평가

Insu KIM1*
,
Sei-Chang OH2
,
Junghwa KIM3
,
Choongheon YANG4
김 인수1*
,
오 세창2
,
김 정화3
,
양 충헌4
1Senior Researcher, Mobility Department, Korea Agency for Infrastructure Technology Advancement, Gyeonggi 14066, Korea
2Professor, Department of Transportation Systems Engineering, Ajou University, Gyeonggi 443-749, Korea
3Assistant Professor, Urban & Transportation Engineering, College of Creative Engineering, Kyonggi University, Gyeonggi 15442, Korea
4Research Fellow, Department of Highway and Transportation Research, Korea Institute of Civil Engineering and Building Technology, Gyeonggi 10223, Korea
1국토교통과학기술진흥원 모빌리티본부 선임연구원
2아주대학교 교통시스템공학과 교수
3경기대학교 창의공과대학 도시·교통공학과 조교수
4한국건설기술연구원 도로교통연구본부 연구위원
*Corresponding Author
31 December 2023. pp. 753-770
PDF XML Citation
Abstract

Recently, as innovative technologies such as information and communication technology, artificial intelligence, and big data have been converged and combined in the transportation field, fierce competition is underway to preoccupy the future mobility market, including autonomous vehicles. As a derived service case in the autonomous vehicle field, there is an electric vehicle-based Robo Taxi, which has the potential to lead to the spread of autonomous vehicles and various social changes. Therefore, an important prerequisite for successful activation along with commercialization of Robo Taxi is that it should be based on user acceptability. A Technology Acceptance Model has been introduced to analyze the acceptability of people using advanced technologies such as autonomous vehicles, and recently, a Value-based Adoption Model has been applied that considers benefits and sacrifices overall. The purpose of this study was to provide theoretical and practical guidelines to help spread and spread Robo Taxi by analyzing factors that affect users' intention to accept Robo Taxi through Value-based Adoption Model as well as through Latent Profile Analysis to find out what characteristics and effects each combination of factors show and affects within the Robo Taxi adoption group. First, the factors influencing user acceptance of Value-based Adoption Model considering Robo Taxi benefits and sacrifice factors were analyzed. Perceived benefits, perceived sacrifice, vehicle-sharing services, taxis, self-driving cars, and preferences for private cars were selected as independent variables to confirm the impact on perceived value and adoption intention for Gyeonggi-do residents. In addition, Latent Profile Analysis was performed on Value-based Adoption Model variables related to Robo Taxi acceptance, and individual item response patterns and similar response characteristics were grouped into the same group or group and analyzed in detail. In the context of a lack of acceptance research on autonomous driving-based Robo Taxi, this study used Value-based Adoption Model and Latent Profile Analysis to analyze factors for enhancing Robo Taxi acceptance and draw relevant implications. Therefore, it is significant as a meaningful basic study for expanding the acceptability of Robo Taxi, and based on this, it is expected to be of great help in activating Robo Taxi services.

Keywords
latent profile analysis
robo taxi
user acceptability
value-based adoption model
confirmatory factor analysis

최근 교통 분야에 정보통신기술과 인공지능, 빅데이터 등과 같은 혁신 기술이 융·복합되면서 자율주행자동차를 비롯한 미래 모빌리티 시장을 선점하기 위한 치열한 경쟁이 진행되고 있다. 자율주행자동차 분야의 파생된 서비스 사례로 대표적으로 전기차 기반의 로보택시가 있으며, 이는 자율주행자동차의 확산 및 다양한 사회적 변화를 이끌 잠재력을 보유하고 있다. 따라서 로보택시에 대한 상용화와 더불어 성공적으로 활성화하기 위해 중요한 전제조건은 이용자의 수용성을 기반으로 해야 한다는 점이다. 자율주행자동차와 같은 첨단기술을 이용하는 사람들의 수용성을 분석하기 위해 기술수용모델이 도입되었으며 최근에는 이를 확장하여 혜택과 희생 등을 전반적으로 고려한 가치기반수용모델이 적용되고 있다. 본 연구는 로보택시에 대한 사용자들의 수용 의도에 영향을 미치는 요인들을 가치기반수용모델을 통해 분석할 뿐만 아니라, 로보택시 수용 집단 내에서 각 요인들의 조합이 어떠한 특성을 보이고, 어떠한 영향을 미치는지 잠재프로파일분석을 통해 알아봄으로써 로보택시 서비스의 보급·확산에 도움이 되는 이론적, 실무적 지침 제공을 목적으로 하였다. 우선 로보택시 혜택과 희생 요인을 고려한 가치기반수용모델의 사용자 수용성 영향 요인을 분석하였다. 지각된 혜택, 지각된 희생과 차량공유서비스, 택시, 자율주행자동차와 자가용에 대한 선호를 독립변수로 선정하여, 경기도민을 대상으로 지각된 가치 및 수용 의도에 미치는 영향을 확인하였다. 또한, 로보택시 수용성 관련 가치기반수용모델 변수들을 대상으로 잠재프로파일분석을 수행하여 개별 문항 반응 패턴을 가지고 서로 비슷한 응답특성을 나타내는 것을 묶어 동일한 집단 혹은 그룹으로 유형화하고 상세 분석하였다. 본 연구는 자율주행기반 로보택시에 대한 수용성 연구가 부족한 상황에서 가치기반수용모델 및 잠재프로파일분석을 활용하여 로보택시 수용성 제고를 위한 요소들을 분석하였고 관련 시사점을 도출하였다. 따라서 로보택시의 수용성 확장을 위한 의미 있는 기초 연구로서 의의가 있으며, 이를 바탕으로 로보택시 서비스 활성화시 많은 도움이 될 것으로 기대된다.

키워드
잠재프로파일분석
로보택시
사용자 수용성
가치기반수용모델
확인적 요인분석
References
1
Bergman L. R., Magnusson D. (1997), A Person-oriented Approach in Research on Developmental Psychopathology, Development and Psychopathology, 9(2), 291-319. 10.1017/S095457949700206X9201446
2
Casley S. V., Jardim A. S., Quartulli A. M. (2013), A Study of Public Acceptance of Autonomous Cars, Worcester Polytechnic Institute.
3
Chang S. B. (2018), The Effects of Smartphone User Experience(UX) on the Perceived Enjoyment, Perceived Immersion, and Reuse Intention, A Journal of Brand Design Association of Korea, 16(4), 105-114. 10.18852/bdak.2018.16.4.105
4
Choi K. O., Lee H. R. (2019), A Study on Influence Relation of Flight Search Mobile Application Users' Perceived Benefit, Perceived Sacrifice, Perceived Value and Continuous Use Intentions by Using Value-based Adoption Model, Journal of Tourism Sciences, 43(8), 115-35. 10.17086/JTS.2019.43.8.115.135
5
Davis F. D., Bagozzi R., Warshaw P. R. (1989), User acceptance of computer technology: A Comparison of Two Theoretical Models, Management Science, 35(8), 982-1003. 10.1287/mnsc.35.8.982
6
Fornell C., Larcker D. F. (1981), Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics, Journal of Marketing Research, 18(3), 382-388. 10.1177/002224378101800313
7
Frost and Sullivan (2020a), Global Shared and Autonomous Mobility Industry Outlook.
8
Frost and Sullivan (2020b), Industry Convergence and Disruptive Technologies are Key Growth Areas of the Global Shared Mobility Market, 2030.
9
Geiser C. (2012), Data Analysis with Mplus, The Guilford Press. 10.1007/978-3-531-93192-0
10
Goodman L. A. (1974), Exploratory Latent Structure Analysis Using Both Identifiable and Unidentifiable Models, Biometrika, 61(2), 215-231. 10.1093/biomet/61.2.215
11
Gutman J. (1982), A means-end chain model based on consumer categorization, Journal of marketing, 46(2), 60-72. 10.1177/002224298204600207
12
Hassenzahl, M., Tractinsky, N. (2006), User experience: A research agenda, Behaviour and Information Technology, 25(2), 91-97. 10.1080/01449290500330331
13
Hill K. G., Chung I. J., Hawkins J. D., Catalano R. F., White H. R. (2000), Early Adult Outcomes of Adolescent Binge Drinking: Person- and Variable-Centered Analyses of Binge Drinking Trajectories, Alcohol, Clinical and Experimental Research, 24(6), 892-901. 10.1097/00000374-200006000-0002110888080
14
Hipp J. R., Bauer D. J. (2006), Local solutions in the Estimation of Growth Mixture Models, Psychological Methods, 11(1), 36-53. 10.1037/1082-989X.11.1.3616594766
15
Im I. J., Song J. I., Lee J. Y., Hwang K. Y. (2017), Analysis of the Perception of Autonomous Vehicles Using Text Mining Technique, The Journal of The Korea Institute of Intelligent Transport Systems, 16(6), 231-243. 10.12815/kits.2017.16.6.231
16
Kim H. N., Moon H. B. (2022), Heterogeneous Attitudes Toward Autonomous Vehicles: Evaluation of Consumer Acceptance of Vehicle Automation Technology Using a Latent Class Approach, Technology Analysis & Strategic Management, 34, 1402-1417. 10.1080/09537325.2021.1962522
17
Kim H. W., Chan H. C., Gupta S. (2007), Value-based Adoption of Mobile Internet: An Empirical Investigation, Decision Support Systems, 43(1), 111-126. 10.1016/j.dss.2005.05.009
18
Kim H. Y., Lee Y. J., Han Y. S. (2018), The Relationship Between Development Trajectories of Resilience and Social Support Systems Among Early Adolescents: Applications of Latent Class Growth Analysis and Multiple-Group Growth Mixture Modeling, Korean Journal of Child Studies, 39(5), 1-16. 10.5723/kjcs.2018.39.5.1
19
Kim I. S., Jang J. A., Kim J. H. (2023), Understanding User Acceptability Towards to Robo Taxi Based on Value Based Adoption Model, The Journal of The Korea Institute of Intelligent Transport Systems, 22(5), 291-310. 10.12815/kits.2023.22.1.291
20
Kim M. J., Lee S. B. (2018), The Effect of the Perceived Benefit and Sacrifice of Delivery Application Service Users in the Food Industry on Perceived Value and Behavioral Intention: Using the Value-based Adoption Model (VAM), International Journal of Tourism and Hospitality Research, 32(2), 217-33. 10.21298/IJTHR.2018.02.32.2.217
21
Kim N. K., Hwang K. H., Yong S. B. (2021), The Influence of Perceived Value of Fresh Food Early-Morning Delivery Services on Continuous Use Intention: Focusing on the Value-Based Adoption Model, The Journal of Internet Electronic Commerce Resarch, 21(3), 1-26. 10.37272/JIECR.2021.06.21.3.1
22
Kwon S. Y. (2022), A Study on the Factors Affecting the Use Intention of Autonomous Vehicles Based on the Value-based Adoption Model (VAM), Master's Thesis, Dan Kook University.
23
Lee B. J., Moon N. G., Kim D. H. (2022a), Measuring Willingness-to-Pay to Improve Smart Road Infrastructure in the Era of Autonomous Vehicles, J. Korean Soc. Transp., 40(4), Korean Society of Transportation, 555-569. 10.7470/jkst.2022.40.4.555
24
Lee S. H., Yoo S. Y., Kim S. S., Kim E. J., Kan N. W. (2022b), Effect of Robo-taxi User Experience on User Acceptance Field Test Data Analysis, Transportation Research Record, 2676(2), 350-366. 10.1177/03611981211041595
25
Lim M. R. (2014), Latent Class Analysis Using Item response data and Cognitive Diagnosis Results: Using TIMSS 2011 Mathematics open questions group, Ph.D. Dissertation, Dongguk University.
26
Lin T. C., Wu Sh., Hsu J. S. C., Chou Y. C. (2012), The Integration of Value-based Adoption and Expectation-confirmation Models: An Example of IPTV Continuance Intention, Decision Support Systems, 54, 63-75. 10.1016/j.dss.2012.04.004
27
Liu M. G., Wu J. P., Zhu C., Hu K. Z. (2022), Factors Influencing the Acceptance of Robo-Taxi Services in China: An Extended Technology Acceptance Model Analysis, Journal of Advanced Transportation, 2022, 1-11. 10.1155/2022/8461212
28
Lu Z., Du R., Ellen D. J., Park H. S., John C. (2017), Data-enabled Public Preferences Inform Integration of Autonomous Vehicles with Transit-oriented Development in Atlanta, Cities, 63, 118-127. 10.1016/j.cities.2017.01.004
29
Magidson J., Vermunt J. (2002), Latent Class Models for Clustering: A Comparison with K-means, Canadian Journal of Marketing Research, 20, 37-44.
30
McCrae J. S., Chapman M. V., Christ S. L. (2006), Profile of Children Investigated for Sexual Abuse: Association with Psychopathology Symptoms and Services, American Journal of Orthopsychiatry, 76(4), 468-481. 10.1037/0002-9432.76.4.46817209715
31
McKinsey and Company (2020), McKinsey perspective on Robo Taxi and Private AVs.
32
Merz E. L., Roesch S. C. (2011), A Latent Profile Analysis of the Five Factor Model of Personality: Modeling Trait Interactions, Personality and Individual Differences, 51(8), 915-919. 10.1016/j.paid.2011.07.02221984857PMC3185358
33
Nylund K. L., Asparouhov T., Muthén B. O. (2007), Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study, Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535-569. 10.1080/10705510701575396
34
Schmiege S. J., Meek P., Bryan A. D., Petersen H. (2012), Latent Variable Mixture Modeling: A Flexible Statistical Approach for Identifying and Classifying Heterogeneity, Nursing Research, 61(3), 204-212. 10.1097/NNR.0b013e3182539f4c22551995
35
Seo, H. H. (2017), A Study on Intention to Use of Self-driving Cars: Focused on Network Externality and Monetary Consideration, Ph.D. Dissertation, Soong Sil University.
36
Simone P., Liyuwork M. D., Richard N. (2019), Clusters of Potential Autonomous Vehicles Users According to Propensity to Use Individual Versus Shared Vehicles, Transport Policy, 76, 13-20. 10.1016/j.tranpol.2019.01.010
37
Venkatesh V. (1999), Creating Favorable User Perceptions: Exploring the Role of Intrinsic Motivation, Management Information Systems(MIS), 23(2), 239-260. 10.2307/249753
38
Yu M. S. (2022), Development of Management Strategy through Analysis of Adoption Intention and Influence Factors of Autonomous Vehicles: Centering on Value-based Adoption Models, Ph.D. Dissertation, Swiss School Of Business And Management Geneva.
39
Zeithaml V. A. (1988), Consumer Perceptions of Price, Quality and Value: A Means-end Model and Synthesis of Evidence, Journal of Marketing, 52(3), 2-22. 10.1177/002224298805200302
40
Zmud J., Sener I. N., Wagner J. (2016), Consumer Acceptance and Travel Behavior Impacts of Automated Vehicles, Texas A&M Transportation Institute.
Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 41
  • No :6
  • Pages :753-770
  • Received Date : 2023-10-15
  • Revised Date : 2023-11-01
  • Accepted Date : 2023-11-23
  • DOI :https://doi.org/10.7470/jkst.2023.41.6.753
Journal Informaiton Journal of Korean Society of Transportation Journal of Korean Society of Transportation
Archives
: 2015 Vol.34~
  • scopus
  • NRF
  • KOFST
  • crossref cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close