Smart functions in vehicles have led to an increase in the complexity of control interfaces. This study aims to develop a model for evaluating in-vehicle controller complexity and to investigate the relationship between complexity and task performance. A research framework consisting of three complexity dimensions (functional, behavioral, and structural dimensions) and controller-related variables was developed based on previous literature. A user experiment was conducted using 10 vehicles and 91 participants. A regression analysis was used to examine the relationship between the measurement variables and perceived controller complexity, and the results indicated correlations between them. An increase in functional dimension variables caused an increase in the perceived complexity level, while behavioral dimension variables are not a statistically significant predictor. Structural dimension variables showed different results depending on the characteristics of the variables. The results of the control task experiment showed a negative correlation between task performance and the perceived complexity level. In addition, satisfaction decreased with increasing levels of complexity. These results provide insights for managing in-vehicle controller complexity.
|Number of pages||10|
|Journal||International Journal of Human-Computer Interaction|
|Publication status||Published - 2019 Jan 2|
Bibliographical noteFunding Information:
This research was supported by the Graduate School of YONSEI University Research Scholarship Grants in 2017.
© 2018, © 2018 Taylor & Francis Group, LLC.
All Science Journal Classification (ASJC) codes
- Human Factors and Ergonomics
- Human-Computer Interaction
- Computer Science Applications