探索多功能交通应用程序的采用水平:交通超级应用程序(TSA)用户客户旅程变化的跨理论模型

IF 12.5 Q1 TRANSPORTATION
Muhamad Rizki , Tri Basuki Joewono , Yusak O. Susilo
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引用次数: 0

摘要

本研究利用修改后的变革理论模型(TTMC)来调查交通多功能应用程序或交通超级应用程序(TSA)的采用水平,并研究用户个性、社会人口因素、居住地点、感知建筑环境和使用动机的影响。分析基于在印度尼西亚四个城市收集到的 1,051 名用户的数据。在这项研究中,使用了潜类聚类分析(LCCA),确定了四个不同等级的 TSA 采用水平:过度热衷(OE)(42%),包括广泛探索和使用应用程序功能的用户;探索但不感兴趣(ED)(36%),包括主要使用应用程序的热门功能,如交通、购物和支付服务,但对使用其他服务缺乏兴趣的用户;以购物为导向但兴趣广泛(SBI)(14%),包括那些大量使用购物服务,同时对其他功能表现出浓厚兴趣的用户;以及极简和不了解(MU)(8%),包括那些使用 TSA 核心功能用于交通和购物,同时对应用程序内其他可用功能了解较少的用户。研究发现,与 TSA 的其他功能相比,交通和购物服务的采用率最高。研究结果还表明,围绕使用 TSA 的行为在未来可能会发生变化,采用水平受到享乐和功利动机的影响。OE 用户与权威/控制相关联,倾向于更加无组织。而 SBI 用户则更有创造力,乐于接受新体验。与其他类别的用户相比,OE 用户主要分布在雅加达这个大都市地区,而且主要居住在活动中心附近。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring levels of adoption of multi-function transport apps: Transtheoretical model of change on the customer journey of Transport-SuperApp (TSA) users

This study utilises the modified Transtheoretical Model of Change (TTMC) to investigate the levels of adoption of transport-multi-function apps or Transport-SuperApp (TSA) and also examine the influence of users’ personality, socio-demographic factors, residential location, perceived built environment, and motivations of use. The analysis is based on data collected from 1,051 users in four Indonesian cities. In this study, a latent class cluster analysis (LCCA) was used which identified four distinct classes of TSA level of adoption: Over Enthusiast (OE) (42%) comprising users who extensively explore and utilise the functions of the apps; Exploring but has Disinterest (ED) (36%) comprising those who primarily use apps for popular functions such as transportation, shopping, and payment services, but lack interest in utilising other services; Shopping-oriented but has Broad Interest (SBI) (14%) comprising those who heavily use shopping services while displaying a high interest in other functions; and Minimalist and Unaware (MU) (8%) comprising those who utilises the core functions of TSA for transportation and shopping while exhibiting low awareness of other available functions within the apps. The study found that transportation and shopping services have the highest level of adoption compared to other TSAs’ functions. The findings also suggest that the behaviour surrounding TSA utilization may evolve in the future and adoption levels are influenced by hedonic and utilitarian motivations. The OE users are associated with authority/control and tend to be more disorganised. Whereas SBI users tend to be more creative and open to new experiences. Compared to other classes, OE users are mostly found in Jakarta, a megapolitan area, and primarily reside near activity centres.

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CiteScore
15.20
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