This study aims to identify latent classes among shared e-scooter users based on their characteristics and analyze the differences in personal and usage characteristics across these classes. Specifically, the study has the following key objectives: (1) to select variables related to the personal and usage characteristics of shared e-scooter users; (2) to collect data on the personal and usage characteristics of shared e-scooter users; (3) to derive the latent classes of shared e-scooter users; and (4) to test the differences in personal and usage characteristics across the identified latent classes. Variables related to the personal and usage characteristics of shared e-scooter users were selected based on a literature review. Through a survey, data on the personal and usage characteristics of shared e-scooter users were collected. A latent class analysis (LCA) was performed to derive the latent classes of shared e-scooter users. Finally, a chi-square analysis was conducted to test the differences in personal and usage characteristics across the latent classes of shared e-scooter users. The results of this study are as follows. The personal characteristics of shared e-scooter users were identified as age and sex, whereas the usage characteristics were identified as usage frequency, time periods of e-scooter usage, return/rental zones, return/rental places, and types of roads used. Data on sex, age, usage frequency, periods of e-scooter usage, and return/rental locations were collected from 278 shared e-scooter users. Based on information criterion, statistical validation, and the entropy index, four latent classes of shared e-scooter users were identified: “male users with a commuting purpose in business zones,” “male users with a homeward commuting purpose in residential zones,” “female users with a leisure purpose in park/green zones,” and “users in their 20s with a commuting purpose in residential zones.” The results of a chisquare analysis revealed statistically significant differences (p < 0.05) in the personal and usage characteristics across the latent classes. Shared e-scooter user types were classified through Latent Class Analysis (LCA), and differences in personal and usage characteristics were identified across the classes. The preferred usage environments and conditions for each class of shared e-scooter users are determined. Variables related to the return/rental zone and periods of e-scooter usage showed the most significant differences among the classes. These findings can contribute to the development of customized user policies and the improvement of services based on the characteristics of shared e-scooter users.

본 연구는 수도권 소재 D 대학을 중심으로 대학생활 적응양상(대인관 계, 학업활동, 진로준비, 개인심리, 사회체험)에 따른, 잠재계층을 분류하 고 그 영향요인(성별, 나이, 학년, 계열, 전형유형)을 살펴보려는 목적으 로 실시되었다. 연구 대상은 D대학에 재학중인 597명의 대학생이 포함 되었다. 분석결과는 다음과 같다. 첫째, 설명변수, 영향변수 간의 상관분 석에서 GPA(학업성취도)는 대인관계를 제외한 모든 대학생활적응 하위영 역과 통계적으로 유의한 관계가 있었으며, 상관계수는 정적으로 나타났 다. 둘째, 잠재계층분석에서는 4개의 집단으로 유형화되었다. 연구 대상 의 55%는 중도형 대학생활적응 집단(계층 1), 11%는 소극형 대학 생활 적응 집단(계층 2), 27%는 적극형 대학생활적응 집단(계층 3), 7%는 선 택형 대학생활적응 집단(계층 4)로 구분되었다. 셋째, 잠재계층 간 비교 에서는 계층3, 계층1, 계층2 형태는 성취 및 만족도 차이가 순서대로 낮 게 나타났으나, 계층 4의 경우는 성적 및 취업을 제외한 다른 영역에서 는 활동 및 참여가 적은 모습을 보였다. 본 연구는 입학유형에 따른 학 생들의 잠재적 특성을 밝혀내어, 이에 맞는 효과적인 학교생활적응 지원 방안을 모색하고자 하는 데 의의가 있다.

The more the satisfied customers increases in customer satisfaction survey, the more the company has difficultly in improving the customer satisfaction. In addition, the effectiveness of practical application of customer satisfaction survey decreases due to its constitution limitation on its data analysis. To overcome these problems, it is necessary to develop a new method to identify the strategy meanings and find the dissatisfied factors of satisfied customers using the satisfied customers reclassification. This study focuses on the satisfied customer segmentation using Latent Class Analysis. The case study shows that the satisfied customers are divided into three subgroups using Latent Class Analysis and we draw meaning results such as satisfaction and dissatisfaction factors through analyzing each group. This study is expected to play the role as the groundwork for the revitalization of customer satisfaction survey.