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Many previous studies have identified that physiological responses of a driver are significantly associated with driving stress. However, research is limited to identifying the effects of traffic conditions (low vs. high traffic) and road types (highway vs. city) on driving stress. The objective of this study is to quantify the relationship between driving stress and traffic conditions, and driving stress and road types, respectively. In this study, electrodermal activity (EDA) signals for a male driver were collected in real road driving conditions for 60 min a day for 21 days. To classify the levels of driving stress (low vs. high), two separate models were developed by incorporating the statistical features of the EDA signals, one for traffic conditions and the other for road types. Both models were based on the application of EDA features with the logistic regression analysis. City driving turned out to be more stressful than highway driving. Traffic conditions, defined as traffic jam also significantly affected the stress level of the driver, when using the criteria of the vehicle speed of 40 km/h and standard deviation of the speed of 20 km/h. Relevance to industry: The classification results of the two models indicate that the traffic conditions and the road types are important features for driving stress and its related applications.

Artificial intelligence (AI) is becoming increasingly prevalent in all spheres of society. Still, the perception of AI from users and customers remains the main barrier for its widespread adoption. Previous studies showed that the acceptance of new technologies in society depends on perceived characteristics. This study examined users’ perception of trust, the difficulty of the task, and application performance when using an AI‐based technology. These factors help us to elucidate the mechanisms for building trust in AI technology from the users’ perspective. A total of 18 participants took part in the experiment with the Google AutoDraw software as an AI tool. As a result, the difficulty of the task, perceived performance, and success/failure of the task can be regarded as the influential factors for the perceived trust evaluation. The perceived trust of users in new AI products would be increased by improving product performance and the successful implementation of the tasks. The obtained results and insights can serve AI product developers to increase the level of users’ trust and attraction towards their technologies and applications.

With the continuous technological enhancement of banking services, customers can avail of better, more secure services which present improved opportunities and convenience. Of the many methods available to perform banking operations, customers commonly use traditional banking, online banking, and mobile banking. Each of these existing methods has advantages and limitations that affect customer experience, trust, satisfaction, and continued intention to use such services. In this study, an attempt was made to develop and fit a model to evaluate and measure the effect of perceived characteristics on banking services. To this end, a questionnaire was administered to 91 participants in Korea to investigate their experiences in the three types of services: offline banking (traditional banking), online banking, and automated teller machines (ATM). The factor design for evaluating the user experience through the perceived characteristics of the banking system was performed by conducting exploratory and confirmatory factor analyses. The proposed model exhibited validity and reliability to evaluate the user experience in the banking system. The results obtained can help banking specialists and professionals increase the level of customers’ trust, loyalty, and intention to use their services.

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