Virtual reality (VR) is receiving attention enough to be considered as its revival age in both industrial and academic field. Since VR systems have various types of interaction with users and new types of interaction are constantly being developed, various studies investigating user experience (UX) of VR systems are continuously needed. However, there is still a lack of research on the taxonomy that can recognize the main characteristics of VR system at a glance by reflecting the influencing factors of UX. Therefore, we collected and reviewed the research related to the UX evaluation of the VR system in order to identify the current research status and to suggest future research direction. To achieve this, a systematic review was conducted on UX studies for VR, and taxonomies of VR system including influencing factors of UX were proposed. A total of 393 unique articles were collected, and 65 articles were selected to be reviewed via Systematic Reviews and Meta-Analyses methodology. The selected articles were analyzed according to predefined taxonomies. As a result, current status of research can be identified base on the proposed taxonomies. Besides, issues related to VR devices and technology, and research method for future research directions can be suggested.

Eye mouse devices are operated by ocular movements, which have proven to be useful for disabled people who find eye control to be the easiest input method. However, there are few studies comparing the two typical click methods for eye mouse (blink and dwell) in identical experimental conditions for general object selection operations. Especially, investigation on task performance (effectiveness and efficiency) of selecting objects is not sufficient. Therefore, this study aims to investigate the relationships between task performance of selecting a stimulus and independent variables with comparing the two different click methods. To investigate the relationship, movement distance, eye input method, stimulus position, and stimulus size were considered as independent variables. Results of the blink method revealed a higher efficiency than the dwell method, particularly as the stimulus size increased. Also, both efficiency and effectiveness of stimuli in upper parts of screen was higher than for those in lower ones. Additionally, efficiency was remarkably higher when the stimulus size increased. The results of this study may be used as guidelines to design interfaces using an eye mouse device. Especially, it can be applied to various conditions of selecting an object in the working environment using the eye mouse.

This study aims to analyze and categorize user experience (UX) of smartphones by utilizing social media data (Twitter). Social media (e.g., Facebook, Twitter) can be helpful for observing natural UX through the users’ words. It is a potentially valuable source of data that can be used to examine the thoughts of millions of people. To gather UXs of smartphones, mining social media data (Twitter) techniques were used. Collected UXs were categorized according to the product smartness and the relationship between the product smartness and UXs were identified. A total of 19,288 tweets involving the term “smartphone” were collected from 2014.06.01 to 2014.08.31. Among these, a total of 699 tweets were related to UXs of smartphones. In addition, 478 tweets were categorized according to the five dimensions of product smartness (Autonomy, Adaptability, Multi-functionality, Connectivity, and Personalization). Results found that many satisfactory experiences for all dimensions, but there were unsatisfactory experiences associated with the multi-functionality and connectivity. Findings suggest that mining techniques can be used to gather and analyze UX effectively and efficiently. Also, the results of this study can be helpful for understanding user’s implicit needs concerning smartphones, and provide valuable insights for developing smartphones.

This article reviewed both studies on general smart car technologies and human–computer interaction (HCI)/human–vehicle interaction studies that were published in journals and conferences so that the current status of research can be identified and future research directions can be suggested. Furthermore, previous studies on elderly drivers were reviewed, as these drivers could be the most vulnerable social group in terms of new technology acceptance. A total of 257 articles for HCI research and 45 articles for elderly drivers were selected and reviewed from 11,267 collected articles (2010–2014). According to the results, most articles were mainly related to safety and adaptive features (e.g., driver’s state recognition, vehicle surrounding monitoring, driver action-suggestion), and infotainment research in terms of HCI (e.g., information technology devices–vehicle interaction, vehicle–vehicle interaction) was relatively insufficient despite its high research demand. According to the results of the literature review and technological trends analysis based on previous technical road maps, from HCI/human factors engineering (HFE) perspectives, research related to “Assistance systems,” “Physiological & mental state recognition,” “Position sensor technology,” “Behavior recognition,” and “Infotainment” was suggested to HCI/HFE researchers for further research. In particular, HCI/HFE researchers need to focus on research on acceptable levels of automation, observing new driving behaviors, investigation of driver characteristics to develop personalized services, and new technology acceptance to develop and improve smart cars in the future.