Artificial intelligence-powered coding assistants (AI-CAs) have become essential tools in programming; however, there is limited understanding of the mechanisms driving programmers’ adoption of these tools in their daily coding tasks. This study aims to examine the role of utilitarian and hedonic values in the adoption of AI-CAs by extending the Technology Acceptance Model (TAM). The data gathered from an online survey of 283 Korean programmers is analyzed using structural equation modeling. The results showed that both perceived enjoyment and perceived usefulness positively influence the attitudes and usage intentions toward AI-CAs. Interestingly, perceived enjoyment has a stronger influence on the intention to use than perceived usefulness, suggesting that recognizing the intrinsic motivation for using AI-CAs is crucial for fully leveraging their benefits. The model also confirms that the compatibility and relative advantages of AI-CAs enhance their adoption. This research enriches the current knowledge base by incorporating hedonic values into the TAM, offering new insights on the design of AI-CAs aimed at enhancing their adoption among developers.

Four-wheel independent steering (4WIS) vehicles refer to vehicles designed to be able to move in multi-orientation by eliminating the mechanical linkage among the wheels. This study aims to evaluate and compare the usability of previously proposed 4WIS vehicle controllers. A usability test with 33 drivers was conducted to compare the five control interfaces (steering wheel buttons, touchscreen buttons, center console buttons, joystick, and paddle shifters). In order to identify what factors contributes to the usability of 4WIS control interfaces, we utilized a mixed-method approach that collects and analyzes both quantitative and qualitative data within the study. The quantitative results indicate that button-type interfaces shorten task completion time and reduce workload than joystick or paddle shifters. Qualitative analysis implies that touchscreen buttons reduce the perceived safety of drivers due to the lack of tactile feedback. Design recommendations for 4WIS control interfaces were provided from the usability testing results.

Industry 4.0 is potentially innovative in the workers’ role, which is becoming increasingly involved in smart activities. In this situation, it is necessary to improve highly repetitive uncomfortable working postures to reduce physical risks. This study intends to assess physical risks during VR interaction for manufacturing work. Posture-related physical risk levels were calculated using ergonomic risk assessment tools RULA, REBA, and OWAS. Three task conditions were considered for the experiment in a VR-based car-assembly environment. An analysis of variance was applied to investigate significant differences between task conditions, and it suggested that a higher risk level was obtained while working in the overhead position for RULA and REBA, whereas the squatting position obtained a higher risk level for OWAS. Sensitivity analysis identified that the upper arm and neck were responsible for the highest risk level for RULA, the upper arm, neck, and trunk for REBA, and the back posture parameter for OWAS.

This study aimed to investigate the effects of non-driving-related tasks (NDRTs) on takeover quality in the context of automated driving. Specifically, we examined the effects of three categories of NDRT attributes (i.e., physical, cognitive, and visual) on longitudinal and lateral driving measures when the drivers resumed control. We designed a driving simulator study where the participants experienced automated driving journeys and takeover situations. When the automated mode was activated, drivers engaged in one of the nine NDRTs. The results showed that the cognitive load of NDRTs had a significant negative correlation with both longitudinal and lateral control measures. However, the effects of two attributes in the physical category and one attribute in the visual category on driving performance did not show statistical significance. Overall, the findings indicated that the influence of cognitive attributes on takeover quality is more salient than that of the physical and visual attributes, which provides insights into the understanding of takeover situations to improve driving safety.

Advancements in technology have spurred the development of new in-vehicle applications. Drivers are faced with different driving contexts due to an increase in the number of devices that provide a wealth of diverse information. However, such a scenario can cause drivers to become distracted. Therefore, research on how the presentation of visual information can affect drivers’ performance is important. In this study, an analysis of quantifiable measurements that affect drivers’ perception of visual complexity and visual search performance was conducted. A questionnaire was administered to assess subjective perception of visual complexity, and a user experiment using eye tracking was designed to explore participants’ visual search performance. The results of subjective visual complexity perception and visual search performance suggested that some objective measurement variables were significantly related only to perceived visual complexity, whereas others affected both subjective and behavioral measurements. Thus it is possible to predict which quantifiable measurement variables affect subjective perception of visual complexity and which affect visual search performance. Therefore, this study allows understanding and explaining of perception of visual complexity by quantifiable measurements and the different ways by which these measurements affect visual search performance.

The number of global smartphone users is rapidly increasing. However, the proportion of elderly persons using smartphones is lower than that of other age groups because they feel it is difficult to use touch screens. There have only been a few studies about usability and elderly smartphone users or designs for them. Based on this background, we studied the pointing action of elderly users, which is a basic skill required to use touch screens on smartphones. We reviewed previous works to determine specific research methods and categorized them into three groups: (a) effect of target size and spacing on touch screen pointing performance, (b) effect of age on pointing performance, and (c) feedback of touch screens. To investigate the touch screen pointing performance of elderly, we conducted two experiments. In the first experiment, 3 target sizes (5 mm, 8 mm, and 12 mm) and 2 target spacings (1 mm, 3 mm) were evaluated. Adding to that, we analyzed whether touch screen pointing performance is dependent on the location of the target. In the second experiment, 3 types of feedback (auditory, tactile, and audiotactile) were evaluated. The results show that (a) pointing performance of elderly was significantly influenced by size, spacing, and location of target, and (b) the performance was higher in audiotactile feedback condition. We expected that these results can contribute to the design of smartphone applications for elderly users.

This research focuses on identifying continuous usage intentions before and after the product adoption phase. A theoretical model was developed, specifying continuous usage intentions and user behavior throughout the product adoption life cycle. The proposed model considers users' characteristics, product quality, sociocultural factors, and continuous usage intention factors, which were obtained from previous studies related to technology acceptance models and continuous usage. The relationship between these factors was analyzed, hypotheses tests to verify the relationships were conducted, and the proposed model was confirmed. Quantitative data were collected using a survey targeted at smart phone users. Data analysis verified the continuous usage model using structural equation modeling. The results from before and after the product adoption phase showed that previous usage experience affects users' characteristics, perceived usefulness, and social factors. The data proved causality between user satisfaction, attachment, commitment, and continuous usage intention after the adoption phase. The relationship between the factors and the model itself was verified statistically, providing insight into the adoption–diffusion–usage perspective with regard to the interaction between user and high-tech product.