PDF(Pubmed)
Abstract：
Sharing Economy apps, such as Uber, Airbnb, and TaskRabbit, have generated a substantial consumer interest over the past decade. The unique form of peer-to-peer business exchange these apps have enabled has been linked to significant levels of economic growth, helping people in resource-constrained communities to build social capital and move up the economic ladder. However, due to the multidimensional nature of their operational environments, and the lack of effective methods for capturing and describing their end-users\' concerns, Sharing Economy apps often struggle to survive. To address these challenges, in this paper, we examine crowd feedback in ecosystems of Sharing Economy apps. Specifically, we present a case study targeting the ecosystem of food delivery apps. Using qualitative analysis methods, we synthesize important user concerns present in the Twitter feeds and app store reviews of these apps. We further propose and intrinsically evaluate an automated procedure for generating a succinct model of these concerns. Our work provides a first step toward building a full understanding of user needs in ecosystems of Sharing Economy apps. Our objective is to provide Sharing Economy app developers with systematic guidelines to help them maximize their market fitness and mitigate their end-users\' concerns and optimize their experience.
摘要：
共享经济应用,比如Uber,Airbnb,还有TaskRabbit,在过去十年中产生了巨大的消费者兴趣。这些应用程序实现的独特形式的点对点业务交换与显著的经济增长水平有关，帮助资源有限社区的人们建立社会资本，提升经济阶梯。然而,由于其运营环境的多维性质，缺乏有效的方法来捕捉和描述他们的最终用户的担忧，共享经济应用程序通常难以生存。为了应对这些挑战，在本文中，我们研究了共享经济应用程序生态系统中的人群反馈。具体来说,我们提出了一个针对食品配送应用程序生态系统的案例研究。采用定性分析方法，我们综合了Twitter提要和这些应用程序的应用程序商店评论中存在的重要用户关注问题。我们进一步提出并内在地评估了用于生成这些关注点的简洁模型的自动化程序。我们的工作为全面了解共享经济应用程序生态系统中的用户需求迈出了第一步。我们的目标是为共享经济应用程序开发人员提供系统的指导方针，以帮助他们最大限度地提高市场适应性，减轻最终用户的担忧并优化他们的体验。
