Li Lingyao, Mao Yujie, Wang Yu, Ma Zihui
Department of Civil and Environmental Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA.
Department of Mechanical Engineering, A. James Clark School of Engineering, University of Maryland, College Park, MD, USA.
J Air Transp Manag. 2022 Oct;105:102298. doi: 10.1016/j.jairtraman.2022.102298. Epub 2022 Sep 9.
Airport service quality (ASQ) is a competitive advantage for airport management in today's airport market. Since the COVID-19 health crisis has unprecedentedly influenced airport regulations and operations, effective measurement of ASQ has become crucial for airport administrations. Surveying travelers' attitudes is useful for ASQ assessment but collecting responses could be time-consuming and costly. Therefore, this paper adopts a data-driven crowdsourcing approach to study ASQ during the COVID-19 pandemic by investigating Google Maps reviews from the 98 busiest U.S. airports. To do so, this study develops a topical ontology of keywords regarding ASQ attributes and uses a sentiment tool to derive passengers' attitudes. Through sentiment analysis, Google Maps reviews show more positive sentiment toward and but remain constant about during COVID-19. The lexical salience-valence analysis (LSVA) is then applied to explain such changes by tracking the sentiment of frequent words in reviews. Through correlation and regression analysis, this study demonstrates that is significantly related to , and in pre-and post-COVID periods. Additionally, the effect of , , , and on significantly differs between the two periods. The findings illustrate the effectiveness of leveraging online reviews and offer practical implications for what matters to air travelers, especially in the COVID-19 context.
在当今的机场市场中，机场服务质量（ASQ）是机场管理的一项竞争优势。自新冠疫情对机场规定和运营造成前所未有的影响以来，有效衡量ASQ对机场管理部门而言变得至关重要。调查旅客态度对ASQ评估很有用，但收集反馈可能既耗时又费钱。因此，本文采用一种数据驱动的众包方法，通过调查美国98个最繁忙机场的谷歌地图评论，来研究新冠疫情期间的ASQ。为此，本研究开发了一个关于ASQ属性的关键词主题本体，并使用一种情感工具来得出乘客的态度。通过情感分析，谷歌地图评论显示在新冠疫情期间，对[具体内容1]和[具体内容2]的积极情感更多，但对[具体内容3]的情感保持不变。然后应用词汇显著性-效价分析（LSVA），通过追踪评论中频繁出现词汇的情感来解释此类变化。通过相关性和回归分析，本研究表明，在新冠疫情前后，[具体内容4]与[具体内容5]、[具体内容6]和[具体内容7]显著相关。此外，[具体内容8]、[具体内容9]、[具体内容10]和[具体内容11]对[具体内容12]的影响在两个时期之间存在显著差异。研究结果说明了利用在线评论的有效性，并为航空旅客所关注的事项提供了实际启示，尤其是在新冠疫情背景下。
