基于用户视角的泗水公交服务评价模型

Muhammad Zulfikar Emir Zanggi, N. Arvitrida
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摘要

交通是许多人口众多但城市面积和道路容量有限的国家面临的问题之一。交通系统的另一个问题是服务质量差。泗水是印度尼西亚几个人口最多的城市之一。根据中央统计局2018年的数据,泗水人口达到289万,人口密度为每平方公里8844人。政府已经就这个问题采取了行动。他们想建立一个城市交通系统,这是公交,即Suroboyo公交。这是政府的一个试点项目,旨在通过使用塑料废物作为创新付款来减少泗水的拥堵。建设良好的城市交通体系,必须落实优质的服务。因此,有必要对服务质量进行测量、结构化和全面的评价。SEM(结构方程模型)将适合于这个问题,因为它可以适应具有许多结构和指标的评估模型的复杂性。此外,SEM可以为研究的核心提供结果,以便研究人员确定各个指标的权重。最后,研究者可以为提高服务质量提供准确的建议。将有350名答复者,然后通过AMOS进行处理。模型采用28个指标,8个自变量,1个因变量(用户满意度)。计算结果表明,经过第一次修正后,模型满足假设条件,符合拟合要求。得到的输出是3个自变量已经对用户满意度产生正向影响。同时,其他5个自变量成为服务提供者的输入和改进建议。这五个变量分别是连通性、信息、时间、友好性和安全性。
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Modeling for Services Evaluation of Trans Bus at Surabaya Based on User's Perspective
Transportation is one of the problems faced by many countries with a high population with limited urban areas and road capacity. Another problem with the transportation system is the poor quality of services. Surabaya is one of several most populated cities in Indonesia. According to data from the Central Statistics Agency in 2018, the population in Surabaya reached 2.89 million with a population density of 8,844 people per KM2. The Government already took action about this issue. They would like to build an urban transportation system which is the trans bus, namely Suroboyo Bus. This is a pilot project from the Government to reduce congestion in Surabaya by using plastic waste as innovation payment. To build a good urban transportation system, good services of quality is a must to be implemented. Therefore, it is necessary to evaluate service quality in a measured, structured, and comprehensive way. SEM (Structural Equation Modeling) will be suited to this problem because it can accommodate the complexity of evaluation models with many constructs and indicators. Besides, SEM can provide results to the core of the research, so that researchers can determine the weight of each indicator. In the end, the researcher can provide accurate recommendations for improving the quality of services. There will be 350 respondents which are then processed through AMOS. A model is built using 28 indicators with 8 independent variables and 1 dependent variable (user satisfaction). Based on the calculation results, it shows that the model has met the assumptions and fit after the first modification was made. The output obtained is that 3 independent variables already had a positive influence on user satisfaction. Meanwhile, 5 other independent variables become input and suggestions for improvement to service providers. The five variables are connectivity, information, time, friendliness, and security.
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