Unraveling Usability: An In‐Depth Framework of Constructs, Attributes, and Items

The concept of usability, which has a long history, is receiving increased attention due to the emergence of various services brought about by advancements in diverse technologies. Despite this increased focus and numerous studies, limitations remain such as a lack of statistical validation, missing attributes linking constructs to measurable items, and the absence of measurable items for attributes. To address these issues, this paper proposes a three‐level framework (constructs–attributes–items) that facilitates the structural understanding and evaluation of usability. First, we collected specific items that previous studies have utilized to measure usability constructs. With these items, we employed artificial intelligence algorithms and machine learning techniques to derive attributes for the preliminary framework. Subsequently, we conducted a survey to validate the framework. As a result, we derived seven attributes and 38 items for effectiveness, three attributes and nine items for efficiency, and five attributes and 14 items for satisfaction. We anticipate that this framework can be applied to measure and analyze usability in iterative design processes, particularly in the development of new services that are integrated with emerging technologies such as artificial intelligence and augmented reality.