As technological development is driven by artificial intelligence, many automotive manufacturers have integrated intelligent agents into in-vehicle information systems (IVIS) to create more meaningful interactions. One of the most important decisions in developing agents is how to embody them, because the different ways of embodying agents will significantly affect user perception and performance. This study addressed the issue by investigating the influences of agent embodiments on users in driving contexts. Through a factorial experiment (N = 116), the effects of anthropomorphism level (low vs. high) and physicality (virtual vs. physical presence) on users' trust, perceived control, and driving performance were examined. Results revealed an interaction effect between anthropomorphism level and physicality on both users' perceived control and cognitive trust. Specifically, when encountering high-level anthropomorphized agents, consumers reported lower ratings of trust toward the physically present agent than toward the virtually present one, and this interaction effect was mediated by perceived control. Although no main effects of anthropomorphism level or physicality were found, additional analyses showed that anthropomorphism level significantly improved users' cognitive trust for those unfamiliar with IVIS. No significant differences were found in terms of driving performances. These results indicate the influences of in-vehicle agents' embodiments on drivers' experience.

Objectives: The objectives of this study were to (a) use data from the 2007 National Roadside Survey (NRS) to determine the characteristics of weekend nighttime drivers with positive blood alcohol concentrations (BACs) on U.S. roads in 2007; (b) determine the relationship of the driving environment and trip characteristics associated with drinking drivers; and (c) compare the findings for the 2007 NRS with those for the 1996 NRS. Methods: Like the 1996 NRS, the 2007 NRS used a stratified random national roadside survey sample of the contiguous 48 states and collected nighttime data on Fridays and Saturdays between 10 p.m. and 3 a.m. Officers directed 8384 drivers into off-road parking areas where our research team asked them to participate in the survey. Results: Of those approached, 7159 (85.4%) provided a breath test. Results revealed that 12 percent of the nighttime drivers had positive BACs, and of those, 2 percent were higher than the 0.08 BAC illegal limit in the United States. Since the 1996 NRS, we found significant reductions in the percentage of BAC-positive drivers across different demographic groups. Age was among the most significant factors associated with a weekend driver having a positive BAC. The probability that a driver would be drinking peaked in the 21- to 25-year-old age group. Male drivers were more likely than female drivers to be drinking, and Asian and Hispanic drivers were less likely than white drivers to be drinking. Drinking drivers were more likely to be driving short distances (5 or fewer miles) late at night (between 1 and 3 a.m.) and to be coming from a bar or restaurant. Finally, 26 percent of the drivers who reported that they would drive less than 5 miles on the night of the survey had positive BACs, compared to only 16 percent who indicated that they would drive between 6 and 20 miles and 10 percent who planned to drive more than 20 miles. Conclusions: The 2007 NRS provides another benchmark in the 4-decade record of drinking drivers on American roads and provides a basis for measuring progress in combating driving under the influence during the coming decade.

Purpose: Using Generation Z consumers from China as an example and focusing on the nostalgia-driven design of brand spokes-character, this study sought to update research on the causal relationship between nostalgia and brand attitude for younger consumers. Two types of nostalgic brand spokes-characters (i.e., eliciting personal nostalgia and historical nostalgia) were examined separately and compared to verify their contributions to more positive brand attitude, as well as related mechanisms, that is, whether consumer trust in the spokes-character mediated the relationship between nostalgic spokes-characters and brand attitude.Design/methodology/approach: An experiment was first conducted to test the causal effects of brand spokes-characters designed to elicit two types of nostalgic feelings (i.e., personal nostalgia and historical nostalgia). Then, the authors investigated the influencing mechanism of nostalgic brand spokes-characters based on bootstrap mediation models.Findings: The results revealed that for less familiar brand spokes-characters, either type of nostalgia-driven design would enhance consumers' brand attitude. Moreover, consumer trust in the spokes-character mediated the relationship between personal-nostalgic brand spokes-characters and brand attitude.Originality/value: This study was the first to examine personal nostalgia and historical nostalgia separately regarding the effects of nostalgic spokes-characters and related mechanisms. By combining methods of experimental design and bootstrap mediation modeling, it provided a more robust evaluation of nostalgia-driven design, and supported using certain nostalgic styles for designing brand spokes-characters, which can help modern brands draw more interest from young consumers and promote more positive brand attitude.

Background: Smartwatch-based gamification holds great promise for enhancing fitness apps and promoting physical exercise; however, empirical evidence on its effectiveness remains inconclusive, partly due to “one-size-fits-all” design approaches that overlook individual differences. While the emerging research area of tailored gamification calls for more accurate user modeling and better customization of game elements, existing studies have relied primarily on rating scale–based measures and correlational analyses with methodological limitations. Objective: This study aimed to improve smartwatch-based gamification through an innovative user modeling approach to better motivate physical exercise among different user groups with tailored solutions. It incorporated both individual preferences and needs for game elements into the user segmentation process and used the maximum difference scaling (MaxDiff) technique, which can overcome the limitations of traditional methods. Methods: With data collected from 2 MaxDiff experiments involving 378 smartwatch users and latent class statistical models, the relative power of each of the 16 popular game elements was examined in terms of what users liked and what motivated them to exercise based on which distinct user segments were identified. Prediction models were also proposed for quickly classifying future users into the right segments to provide them with tailored gamification solutions on smartwatch fitness apps. Results: We identified 3 segments of smartwatch users based on their preferences for gamification. More importantly, we uncovered 4 segments motivated by goals, immersive experiences, rewards, or social comparison. Such user heterogeneity confirmed the susceptibility of the effects of gamification and indicated the necessity of accurately matching gamified solutions with user characteristics to better change health behaviors through different mechanisms for different targets. Important differences were also observed between the 2 sets of user segments (ie, those based on preferences for game elements vs those based on the motivational effects of the elements), indicating the gap between what people enjoy using on smartwatches and what can motivate them for physical exercise engagement. Conclusions: To our knowledge, this study is the first to investigate MaxDiff-based user segmentation for tailored gamification on smartwatches promoting physical exercise and contributes to a detailed understanding of preferences for, and the effectiveness of, different game elements among different groups of smartwatch users. As existing tailored gamification studies continue to explore ways of user modeling with mostly surveys and questionnaires, this study supported the adoption of MaxDiff experiments as an alternative method to better capture user heterogeneity in the health domain and inform the design of tailored solutions for more application types beyond smartphones.