Sylvain Sénécal’s scientific contributions

As the digital era is witnessing huge commercial success driven by design innovations focusing on usability as well as utility, businesses have also been positioning user experience (UX) research as the cornerstone of the competitiveness of their products and services. However, UX data analysis can be costly and demanding in terms of monetary and human resources, making it imperative to develop a cost-effective solution to assist UX evaluation. In this article, we present the design, development, and deployment process of the UX Calculator (uxcalc.web.app), a lightweight web-based tool that enables practitioners to run statistical tests tailored to the main analyses involved in a UX assessment. This tool allows designers to perform two-group comparisons and get robust conclusions about the UX performance without extra training in statistical programming. The interaction logic of this tool, together with the methodological considerations, practical implications, and future work for the tool’s improvement are discussed in detail.KeywordsEvaluation Methods and TechniquesUser ExperienceUsability TestUX PerformanceUX Calculator

Personalized product recommendations are widely used by online retailers to combat choice overload, a phenomenon where excessive product information adversely increases the cognitive workload of the consumer, thereby degrading their decision quality and shopping experience. However, scientific evidence on the benefits of personalized recommendations remains inconsistent, giving rise to the idea that their effects may be muted unless the consumer is actually experiencing choice overload. The ability to test this idea is thus an important goal for marketing researchers, but challenging to achieve using conventional approaches. To overcome this challenge, the present study followed a design science approach while leveraging cognitive neuroscience to develop a real-time neuro-adaptive interface for e-commerce tasks. The function of the neuro-adaptive interface was to induce choice overload and permit comparisons of cognitive load and decision quality associated with personalized recommendations, which were presented according to the following three conditions: (a) not presented (control), (b) perpetually presented, or (c) presented only when a real-time neurophysiological index indicated that cognitive workload was high. Formative testing cycles produced a neuro-adaptive system in which the personalization of recommendations and neuro-adaptivity function as intended. The artifact is now ready for use in summative testing regarding the effects of personalized recommendations on cognitive workload and decision quality.KeywordsNeuro-adaptive interfacedigital technologiese-commercechoice overloadcognitive loaddecision-makingdesign science