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This paper investigates the reasons for the differences in customers' acceptance of service robots (CASR) in actual experience and credence service settings for the following two aspects: (1) different antecedents affecting CASR and (2) different customer perceptions of their own characteristics (role clarity and ability) and service robot characteristics (anthropomorphism and ability).

The data were collected using online surveys in an experience service setting (Hotel, N = 426) and a credence service setting (Hospital, N = 406). Differences in experience and credence service settings were examined using two statistical methods, namely, PLS-SEM to test the differences in antecedents affecting CASR and independent-samples t-tests to test the differences in customer perceptions of their own characteristics and service robot characteristics.

The results indicate that customers in an experience (vs credence) service setting have stronger positive attitudes toward and a greater intention to use service robots. Further, this paper finds there are two key reasons for the differences in CASR. The first is different antecedents. Perceived usefulness is positively influenced by the anthropomorphism of a service robot and customer ability in the experience service setting, but is influenced not in the credence service setting. Conversely, service robot autonomy positively relates to perceived ease of use in the credence service setting, but does not in the experience service setting. The second reason for CASR differences is different customer perceptions. Customers' ability and perceived ease of use are higher, while their perception of anthropomorphism of the service robot is lower in the experience (vs credence) service setting.

This study helps explain why there are differences in the CASR in different settings and presents two perspectives: (1) antecedents' affecting CASR and (2) customer perceptions of their own as well as service robot characteristics.

This study aims to integrate and revalidate previously proposed various structural models in understanding residents’ attitudes and behaviors in relation to mega-events before the events.

This study focussed on the 2022 Beijing Winter Olympics and used a questionnaire-based quantitative survey prior these events. A PLS-SEM analysis was run on a sample of 473 residents, in testing relationships between residents’ trust, perceived impacts, support for hosting and subjective well-being.

Results revalidate propositions from previous research, but suggest key contextual differences in light of biosecurity risks. Residents’ perceived positive (cultural) and negative (environmental) impacts affect their support for mega-events, and their perceived positive (economic and cultural) and negative (social) impacts affect their subjective well-being. Variances in the relationships were found for those who perceive a high biosecurity risk.

The data were collected from one mega-event, and thus the findings of this study are highly contextualized.

This research suggest that mega-event organizers should put effort into promoting the benefits of hosting mega-events and work collaboratively with stakeholders to reduce potential negative costs and risks as well as increase resident well-being via bringing in economic and cultural benefits.

This research focusses on social well-being during and post COVID in relation to the hosting of a mega-event.

The data were collected from the 2022 Beijing Winter Olympics, a mega-event that, because of COVID-19 and restricted spectator flows, potentially had characteristics quite different from that of other Winter Olympics or sporting mega-events.

Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers. This paper aims at exploring the effects of dynamic message sign (DMS) of fog warning system on driver performance.

First, a testing platform was established based on driving simulator and driver performance data under DMS were collected. The experiment route was consisted of three different zones (i.e. warning zone, transition zone and heavy fog zone), and mean speed, mean acceleration, mean jerk in the whole zone, ending speed in the warning zone and transition zone, maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected. Next, the one-way analysis of variance was applied to test the significant difference between the metrics. Besides, drivers’ subjective perception was also considered.

The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone. Besides, when drivers enter a heavy fog zone, DMS can reduce the tension of drivers and make drivers operate more smoothly.

This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform. The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.