# Collaborative Use of a Shared System Interface:
## 6 Discussion
### Advantage of Real-Time Synchronized Gaze Recording in Multiuser Human-Computer Interactions Setting  
An advantage of synchronizing participants' gaze recording is that it brings order to the gaze data files and eases later data analyses, allowing the tracking of the exact time and order in which the gaze data were collected [7]. Moreover, synchronizing participants' gaze recording permitted us to develop a measure of gaze convergence using perfectly temporally aligned data, unlike existing measures that are based on Euclidian distance and use temporally asynchronous fixation data. This is a major advantage provided by our synchronized dual eye-tracking setup. Major methods for comparing two eye movement sequences based on Euclidian distance between pairs of eye fixation points [47] were proposed by [PERSON] et al. [49], [PERSON] et al. [59] and [PERSON] et al. [48]. [PERSON] et al. and [PERSON] et al. analyze similarity between two participants' fixation sets by computing their index of similarity based on the linear distance between each fixation point in the first fixation set and its nearest neighbor in the second fixation set, as well as the linear distance between each fixation point in the second fixation set and its nearest neighbor in the first fixation set. As the spatial distance here is always computed against the nearest neighbor in the other fixation set, a problem with this algorithm is that it does not consider the spatial variability in the distribution of the fixation sets [48]. For example, if all fixations in the first set are circumscribed within a very small area of the screen and only one fixation point in the second set is within that same area, then all fixation points in the first set will be compared to only that point from the second set. On the other hand, [PERSON] et al. analyze similarity between two participants' fixation sets as follows. In order to assign each fixation point in the first set to a fixation point in the second set, they analyze all possible assignments of fixation points in the first set to fixations in the second set, to find the unique assignment producing the smallest average deviation. A limitation of these algorithms is that they disregard the ordering of the fixation points of the two participants. Thus, these methods are suitable only when temporal ordering of eye fixations is not important, a condition that does not fit to the definition of the RTGD reflective measure of our SOGC construct (see Table 1). The measure of RTGD in our context of joint use of shared system interface has been facilitated by the synchronized dual eye-tracking paradigm.  
Moreover, in general, despite the advantages provided by existing methods for comparing two eye movement sequences, some are subject to some issues such as not considering the temporal ordering of gaze data (Linear Distance and Edit Distance methods), data loss (MultiMatch method), high sensitivity to small temporal and spatial differences and not accounting for fixation duration (Sample-based measures) [47]. Hence, a straightforward benefit of our study is that we measure the dyad GC construct with data perfectly aligned in time and validate it in terms of content validity and predictive validity. Such measurement is appropriate to IS and HCI contexts. Since the synchronized data are not truncated nor shrunk, it accounts for the natural happening of joint use of shared system interfaces, as it is based on perfectly aligned data and acknowledges the variety of ways user dyads may look together at the screen, including all instants of diverging or converging users' gazes.