The value of visual quality and service quality to augmented reality enabled mobile shopping experience

Q2 Business, Management and Accounting
Alsius David, William D. Senn, D. Peak, V. Prybutok, Charles Blankson
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引用次数: 10

Abstract

Abstract This study demonstrates that visual quality is an emergent factor in explaining behavioral intention in online shopping, as online retail technology continues to evolve. The researchers develop, and test a model, in augmented reality (AR) and non-AR contexts, explaining the effect of aesthetic appeal of the interface of a mobile retail app and position relevance of a product in the user environment on visual quality of the app. The model explains the influence of visual and service quality on user satisfaction toward the app and the influence of user satisfaction on recommendation intention. The data were collected from 102 mobile app users who were asked to use the view-in-room features on a mobile augmented reality (MAR) app. Another group of 155 mobile app users were requested to search for a product using a typical mobile app search interface. Both groups provided their responses via an online survey. The researchers used partial least squares structural equation modeling (PLS-SEM) to analyze the collected data. Results indicate that the model has superior explanatory power when applied to an AR context. This research also provides key insights for practitioners related to improving the rate of mobile app recommendations by their customers.
视觉质量和服务质量对增强现实移动购物体验的价值
摘要本研究表明,随着在线零售技术的不断发展,视觉质量是解释在线购物行为意向的一个新兴因素。研究人员在增强现实(AR)和非AR环境下开发并测试了一个模型,解释了移动零售应用程序界面的审美吸引力和产品在用户环境中的位置相关性对应用程序视觉质量的影响。该模型解释了视觉质量和服务质量对用户对应用程序满意度的影响,以及用户满意度对推荐意愿的影响。研究人员从102名移动应用用户中收集数据,这些用户被要求使用移动增强现实(MAR)应用的室内视图功能。另一组155名移动应用用户被要求使用典型的移动应用搜索界面搜索产品。两组人都通过在线调查提供了他们的答案。研究人员使用偏最小二乘结构方程模型(PLS-SEM)来分析收集到的数据。结果表明,该模型在应用于AR情境时具有较好的解释力。这项研究还为从业者提供了与提高客户移动应用推荐率相关的关键见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Quality Management Journal
Quality Management Journal Business, Management and Accounting-Business, Management and Accounting (all)
CiteScore
4.50
自引率
0.00%
发文量
16
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