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This paper describes the development of a semantic differential test paradigm for evaluating product sound quality by a population of human subjects. The design of this specific experiment was also structured to test for a variety of expected common experimental errors. An investigation on the dependence of presentation order was executed by implementing both a fully balanced and a partially balanced design, and differences in subject populations were tested by independently testing two geographically separate populations. In order to conduct this testing, a general semantic-differentials graphical user interface (GUI) was written in MATLAB to present the specific stimuli to each subject in a unique specific sequence and extract a variety of semantic differential ratings for later statistical analyses. A discussion of the advantages and disadvantages for Semantic Differentials approach is presented as well as a discussion of the confidence in the resulting subject dataset.

This paper describes the development of a model for the predicted quality assessment of power seat mechanisms. This model was based on the statistical correlation of objective popular sound quality metrics (and their associated distribution statistics) to a set of subjective results obtained in a previously reported [1] semantic differentials evaluation experiment. More important then the model itself, the greatest surprises were in the statistical analyses of the subjective response data and the statistical significance and independence of the various commercial software generated sound quality metrics. Step-wise regression was used to identify a sub-set of independent variables that best fit the subjective response data and determine the appropriate model coefficients. This resultant model showed significantly better correlation to this population of subjective response data than other overall sound quality metrics e.g., Loudness, UBA, Pleasantness, etc.

The present paper describes two studies performed with the aim of developing a human factors evaluation technique for the total vehicle based upon users' subjective experience while interacting with automotive controls and displays. In the first study, a total vehicle task analysis was conducted using a card-sort technique to partition users- activities in and around the vehicle into conceptually cohesive groups, or task scenarios. In the second study, each task scenario was evaluated in each of two vehicles using twenty five bipolar attribute rating scales chosen to describe users' experience while interacting with controls and displays. Results of a factor analysis on these data revealed that users' experience could be described along four different dimensions. These data were used to compare and contrast the two different vehicles.

There is evidence that consumers' emotions can affect their behavior and performance when performing tasks using a system [1]. In the automotive industry, it is particularly important to focus on consumers' emotional experience and its influence on vehicle interactions, in addition to traditional usability and performance measures. This paper focuses on a qualitative, user-based usability assessment and design support tool that measures subjective dimensions of consumers' experience when interacting with systems. In addition, this paper describes the development of a predictive model relating subjective experience from interacting with a vehicle system and overall satisfaction with that system. Results from this study revealed that consumers' overall satisfaction can be related to dimensions of ease-of-use, shape, and reach/manipulation.

As automobile manufacturers seek to provide customers with a growing array of in-vehicle information and functionality, information management becomes an important task for the driver. The organization of controls and information in the human/machine interface (HMI) is a major factor determining the efficiency with which users access and process information. This paper describes two studies in which empirical psychological methods are used to identify users' conceptual organization of automotive system functions. Results provide guidance for laying out controls and structuring hierarchical menus in HMIs for two products.