Using the Analytic Hierarchy Process (AHP) to Prioritize Augmented Reality (AR) Device Considerations

When designing and developing emerging technologies, understanding user priorities for different product considerations is essential for ensuring that products meet user needs and expectations, leading to better adoption, satisfaction, and overall success in the market. Understanding and characterizing user priorities is critical for making design trade-offs and aligning design and development teams to high-value efforts. This study explores the application of the Analytic Hierarchy Process (AHP) to determine how users prioritize key product considerations in the selection of Augmented Reality (AR) devices. While other prioritization techniques such as Maximum Difference Scaling (MaxDiff) and the ranking method are commonly used to better understand user priorities, AHP provides a more accurate and reliable approach, resulting in priority weights that reflect both the relative importance and the intensity of user preferences across multiple factors.For this study, AR device users completed a web-based survey that included a series of pairwise comparison prompts to prioritize six AR device considerations: comfort, features and functionality, lens quality, price, battery life, and aesthetics. For each of the fifteen pairwise comparisons, participants first indicated which device consideration was more important to them. Then, the participants rated the strength of their preference for the more important factor. The results from a sample of 37 participants revealed comfort was the most significant factor, followed by features and functionality, and lens quality. Price and battery life were also important but ranked lower, while aesthetics was deemed the least important consideration.Utilizing AHP with a panel of remote participants proved to be an effective human-centered approach for prioritizing device considerations for AR devices. The outputs from the AHP analysis not only establish a priority but also priority weights which offer deeper insights into exactly how important each factor is, revealing the relative intensity of user preferences. These priority weights can also be used to quantitatively evaluate products and prototypes, providing a more objective basis for comparison and decision-making. The methods utilized in this study facilitate a deeper understanding of user preferences and priorities which can be applied to the development of many products and emerging technologies.