Goals - Assumption - Interaction Steps (GAIS): A Practical Method to Determine a Quantitative Efficiency Benchmark for UX Interaction Design Concepts

摘要

One reason for the digitalization megatrend is to further increase the efficiency of digital value chains. The human-machine interaction is typically one of the slowest elements in this chain, and therefore on the critical efficiency path. An efficiency increase of the human-machine interaction can lead to an efficiency increase of the entire digital value chain. To determine the efficiency of a UX design concept, an efficiency benchmark is needed before a UX design concept is created. The paper introduces GAIS (Goal - Assumption - Interaction Steps), a cognitive task analysis method. The author developed the GAIS method for use in industrial projects and validated it in industrial projects. GAIS allows to determine an interaction efficiency benchmark for a given use case. The benchmark is measured in number of interaction steps per use case. An important step in the GAIS method is a systematic reduction of interaction steps by considering manual, semi-automated and automated interaction. An interaction designer can use the determined efficiency benchmark to make an interaction design concept more efficient. The paper describes how GAIS is applied, with several examples (GUI and VUI), and how it guides the interaction design process.