Augmented Reality (AR) has become increasingly popular in different areas of application and is transforming the way mobile commerce is conducted through mobile apps. It imbibes the sensory experience of local presence during online browsing. We investigate the impact of AR-based presentation on sales rank of products using data from Amazon AR View. This is followed by our investigation of the impact of AR on perceived diagnosticity, perceived risk, perceived cognitive load, and emotion of users. Using a mixed-method approach, we find that the use of AR on a mobile app significantly improves sales rank, enhances perceived diagnosticity, and reduces perceived risk. These effects are greater for technology products. Additionally, we find that AR significantly increases perceived cognitive load for non-technology products. We capture various touch movements, such as pan, pinch, and rotate as well as touch pressure, in consumers’ AR interactions and find that they significantly impact the generated emotion. Our research contributes to the literature on mobile commerce and provides directions on when to use the AR interface for product presentation and how to assess consumers’ reactions to it.

Battery diagnostics aims to monitor a lithium-ion battery’s state of health (SOH) by estimating its capacity and degradation parameters over the service life. The SOH estimation informs online maintenance/control decision making, all performed within a battery management system. This talk will first give an overview of battery degradation diagnostics and then discuss the long-term testing and methodology development efforts led by a team of researchers at Iowa State University and the University of Connecticut. An emphasis will be placed on physics-informed machine learning for degradation diagnostics. Methodologies will be demonstrated using an industry-relevant application on implantable-grade lithium-ion batteries.