Investigating the Combination of Adaptive UIs and Adaptable UIs for Improving Usability and User Performance of Complex UIs

ABSTRACT

An Object-Oriented User Interface Customization (OOUIC) framework has been previously proposed as a systematic approach toward reducing the complexity and improving usability and user performance. The OOUIC suggests combining adaptive and adaptable User Interfaces (UIs). Adaptive UIs reduce complexity while adaptable UIs enable customization for specific needs. The objective of this paper is to assess the combination of adaptive and adaptable UIs for improving usability and user performance of complex UIs. Complex UIs are the original interfaces of a complex system. Adaptive UIs automatically tailor UIs to be relevant to a user role but fail to satisfy an individual’s unique needs. Adaptable UIs enable a user to adjust UIs to meet needs for achieving specific tasks but are hard to use. Combining these two techniques enables users to customize complex UIs more easily. This research can be applied directly to the design and redesign of Product Lifecycle Management (PLM) systems. These systems have been consistently enhanced for their lack of usability. In this study complex UIs, adaptive UIs, adaptable UIs, and combination UIs were assessed by measuring 96 participants’ perceived usability, task time, and eye tracking data. Significant results were found on usability score (F (3, 189) = 12.14, p < .001), time spent (F (3, 189) = 63.40, p < .001), and fixation count (F (3, 189) = 39.72, p < .001), indicating that adaptive, adaptable, and combination UIs had better usability and user performance than complex UIs. The usability of combination UIs and adaptable UIs was better than adaptive UIs. Combining adaptive and adaptable UIs can facilitate users to simplify complex UIs.

Additional information

Funding

This research paper was supported by the National Natural Science Foundation of China (Grant NO. 71471033 & 71771045) and the “Double First-Class” Disciplines Construction Project of Northeastern University (Grant No. 02050021940101).

Notes on contributors

Le Zhang

Le Zhang obtained his Ph.D. in Industrial Engineering in 2018 from Purdue University. He received his BS in industrial engineering in 2012 from University of Science and Technology Beijing (China).

Qing-Xing Qu

Qing-Xing Qu is a doctoral student in the Department of Industrial Engineering, School of Business Administration at Northeastern University (China). He was a visiting scholar in the School of Industrial Engineering at Purdue University from 2015 to 2017. He received his MS in industrial engineering in 2014 from Northeastern University (China).

Wen-Yu Chao

Wen-Yu Chao is a doctoral student in the School of Industrial Engineering at Purdue University. She received her MS in human factor engineering in 2010 from National Tsing Hua University (Taiwan).

Vincent G. Duffy

Vincent G. Duffy is an associate professor of Industrial Engineering and Agriculture & Biological Engineering at Purdue University. He obtained his Ph.D. in Human Factors from Purdue University.