## 5 Discussion of Findings and Recommendations for Future AD Studies
### Data Collection Methods  
Concerning collection methods, the review results show that a vast majority of studies collected self-report data. In comparison to that, behavioral data were only reported in every second publication. From there, the question arises regarding what the reasons for this observation are. One obvious reason is that survey approaches [31; 137] might focus more on technology readiness and deliberately collect atttitudinal measures only. For these approaches, to date, there is no available behavioral criterion, such as buy/usage rates. Studies operationalizing acceptance via the TAM [35] do not provide the possibility to provide insights into behavioral measures. As soon as the functions are available, however, research needs to investigate whether predictions made by these studies hold true. With commercial availability of L2 automation, such a study could have already been conducted, but, to our knowledge, this is still missing. One positive aspect of self-report measures is that psychometrically validated scales have been applied frequently. This shows the professionalism of research of the reviewed venues and deliberate preparation of study design. Another factor for the imbalance might be that self-report measures are much easier to collect. It does not take comparably much effort to hand out a questionnaire or interviewing participants. In contrast, the collection of behavioral data is much more complex. For example, dynamic vehicle data require extensive pre-processing before descriptive and inferential analyses can be run. The collection of eye-tracking data requires even more resources due to the need for manual calibration to ensure data quality, although such data provide the possibility to make direct inferences about cognitive processes [138]. For example, prior research has suggested that the number of gaze switches (i.e., monitoring behavior) can serve as an indicator for trust/reliance [18], or interface understanding [29]. One solution to the difficulty and extensive effort of collecting behavioral data might be experimenters' single-item ratings of interaction performance [139; 140; 141]. However, this approach requires well-trained raters and, ideally, ratings are given single-blind, so that the rater is not aware of assigned experimental conditions for the participant. In addition, there should be more than one rater to ensure inter-rater reliability. Despite requiring additional time and cost efforts, researchers should consider the collection and analysis of behavioral data should in the planning of a user study, since it can provide additional valuable insights about the tested interface or feature. It is not for nothing that the usability ISO-9241 [34] includes effectiveness and efficiency as behavioral components and satisfaction as an atttitudinal component. These sources of data do not always align well [11; 118], which is not necessarily a bad study outcome. It rather supports the assumption that both sources of data are necessary to derive a holistic impression of an interface. This has also been emphasized by [PERSON] et al. [8; 142], who expressed the urgency of triangulation in user studies. We appeal to researchers in the field of human-automation interaction to always consider additional behavioral observations. The importance of collecting behavioral measures of course depends on the respective research question. For example, there are instances where researchers are interested specifically in the public opinion of a large sample of respondents [25]. As explicitly mentioned in the Introduction of this work, we do not see the recommendations as mandatory but rather as thought-provoking. Thus, it might guide exclusive attitude research towards future work on the behavioral consequences. \(\Rightarrow\)_REC 4: Researchers should consider the collection of more than a single source of data (i.e., triangulating behavioral, psychophysiological, and self-reported data).