Age is more than just a number: The relationship among age, non-chronological age factors, self-perceived driving abilities, and autonomous vehicle acceptance.

Globally, adults aged 65 and older are a rapidly-growing population. Aging is associated with declines in perceptual, cognitive, and physical abilities, which often creates challenges in completing daily activities, such as driving. Autonomous vehicles (AVs) promise to provide older adults one way to maintain their mobility and independence. However, recent surveys of AV acceptance suggest that older adults have a lower AV acceptance compared to younger generations. One challenge is that most of these assessments have not accounted for the various non-chronological age factors that contribute to how older adults perceive their own driving skills and the utility of AVs. To fill this research gap, this study investigated the effects of non-chronological age factors and rated self-perceived driving abilities on AV acceptance across three age groups. An online survey was conducted using Amazon Mechanical Turk (MTurk), for which 438 valid responses were received. Respondents were categorized into a younger (18-40 years), middle-aged (41-64 years), and older (65-79 years) adult age group. Results showed that drivers of a younger age, with higher educational attainment, who rated themselves to have higher social support, and who have lower rated self-perceived driving abilities, report being more willing to accept AVs. Findings from this work can help to inform models of AV technology acceptance and guide in the development of marketing strategies to promote knowledge of AVs.

A User-centered Decision-aiding Process for Selecting Power Analysis Tools.

A priori power analyses have become increasingly popular in scientific communities, but the practice has not been widely discussed by HFE researchers. Given the complexity of conducting such analyses, software tools are essential. We review the emergence and current state of power analysis software tools, and use the concept of User-Centered Design as an analytical lens for examining the relationship between researchers' needs and power analysis tools' characteristics. Centering on users' wants and needs, we propose a decision-aiding process to assist researchers in identifying appropriate power analysis tools. This process consists of five user-centered considerations - two functional criteria (software input; software output) and three usability heuristics (learnability; ease of use; accessibility) - which are organized into a workflow. These criteria and heuristics are translated into questions that researchers should go through during the decision process. We illustrate use of the decision-aiding process with three example scenarios and lay out implications for researchers.

How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking.

In this paper, we would like to investigate how people make sense of unfamiliar information visualizations. In order to achieve the research goal, we conducted a qualitative study by observing 13 participants when they endeavored to make sense of three unfamiliar visualizations (i.e., a parallel-coordinates plot, a chord diagram, and a treemap) that they encountered for the first time. We collected data including audio/video record of think-aloud sessions and semi-structured interview; and analyzed the data using the grounded theory method. The primary result of this study is a grounded model of NOvice's information Vlsualization Sensemaking (NOVIS model), which consists of the five major cognitive activities: 1 encountering visualization, 2 constructing a frame, 3 exploring visualization, 4 questioning the frame, and 5 floundering on visualization. We introduce the NOVIS model by explaining the five activities with representative quotes from our participants. We also explore the dynamics in the model. Lastly, we compare with other existing models and share further research directions that arose from our observations.