Voice-based digital assistants are growing in popularity and have been acknowledged as a crucial part of in-car interaction. Currently, academic attention is being paid to various voice assistant scenarios. However, sparse literature focuses on the adoption of voice assistants within the in-vehicle context. The objective of this paper is to examine key factors influencing people’s willingness to use voice assistance in electric cars. First, eight general variables were identified based on the literature review, as well as four demographic variables. These factors were then integrated to construct a hypothetical research model. After that, we carried out an empirical study to examine the structural relationships in the model based on the questionnaire survey results (N = 427). The hypothesis testing results indicated that most path relationships among variables were validated. Finally, we discussed the research findings and developed corresponding design strategies to enhance user acceptance towards in-car voice assistants, both from designers’ and car enterprises’ viewpoints. This article offers valuable theoretical and practical implications for the development of such technologies.

As augmented reality (AR) technology is increasingly permeating the automotive industry, this study investigates users’ attitudes towards AR automotive assistants and their impact on usage behavior. Using the theory of reasoned action (TRA) and integrating insights from the Kano model, critical factors driving user acceptance and engagement were identified. The research reveals that trust in AR technology, perceived utility, and ease of interaction are prioritized by users. Clustering analysis identified three distinct user groups: a ‘Safety-Conscious Group’, a ‘Technology Enthusiast Group’, and an ‘Experience-Seeking Group’, each displaying unique preferences towards AR features. Additionally, a support vector machine (SVM) model effectively predicted user behavior with a training set accuracy of 89.96%. These findings provide valuable insights for the design and marketing of AR automotive assistants, acknowledging both essential features and delighters identified through the Kano model. By understanding user preferences and expectations, tailored AR solutions can be developed to enhance user satisfaction and adoption rates in the automotive sector. Moreover, this research contributes to the sustainable development goals related to the automotive industry by fostering innovation in vehicle technology, promoting eco-friendly driving practices, and enhancing overall mobility efficiency.

This study aims to optimize the visual interaction design of AR-HUD and reduce cognitive load in complex driving situations. An immersive driving simulation incorporating eye-tracking technology was utilized to analyze objective physiological indices and measure subjective cognitive load using the NASA-TLX. Additionally, a visual cognitive load index was integrated into a BP-GA neural network model for load prediction, enabling the derivation of an optimal solution for AR-HUD design. The optimized AR-HUD interface demonstrated a significant reduction in cognitive load compared to the previous prototype. The experimental group achieved a mean total score of 25.63 on the WP scale, whereas the control group scored 43.53, indicating a remarkable improvement of 41.1%. This study presents an innovative approach to optimizing AR-HUD design, effectively reducing cognitive load in complex driving situations. The findings demonstrate the potential of the proposed algorithm to enhance user experience and performance.

Objective to solve the puzzle of the lack of guiding theory of regional culture public goods service design in the field of service design, and to explore the psychological and emotional representation and association connotation of public goods service under specific regional culture. Methods By symbolic semantics, the cultural semantics, folk semantics and regional environment semantics of regional cultural public goods service design were characterized and analyzed from three levels: personal emotion, group affiliation and historical folklore. Combining symbolic semantic analysis with user behavior model, the historical intention of regional culture is abstracted and decomposed into multidimensional design factors layer by layer, and the framework of service design factors is further established. Results Taking the product service system of "Maritime Silk Road in western Guangdong" as an example, with the help of image scale map and semantic style analysis of symbolic semantics, the common feelings on the cultural level of "Maritime Silk Road Culture" were analyzed, and the folk journey scenarios and service blueprints were constructed, which were presented in the form of the design of regional cultural service system in western Guangdong. Conclusion It promotes the emotional resonance and cultural identity experience of users in the design of public goods and services by regional culture, and provides theoretical guidance for the design of regional culture products and services.

The appearance of products plays a crucial role in determining consumers’ preferences and purchase intentions. However, there is sparse literature focusing on the modelling design or related approaches for agriculture machines. To address the issue, this article proposed an innovative design model integrating the Analytic Network Process (ANP) and Quality Function Deployment (QFD) to improve the appearance of a baler, coordinating customer requirements and engineering technical requirements in the design process. First, three customer requirements (CRs) and eight design requirements (DRs) were identified via several qualitative research methods, including literature review, market research and user interviews. Second, the integrated ANP-QFD model was employed to obtain the weights of DRs considering the inner relationship among them and was then compared with the results derived from a single QFD model. Finally, the theoretical findings in the present study were applied to the appearance design of a baler. The researcher then conducted a specific analysis on the aspects of shape, colour, material, and surface decoration. It can be derived that an integrated ANP-QFD model can overcome the shortcomings of the single QFD method and clarify the weight of demand indicators, providing valuable insights for different stakeholders with regard to balers’ appearance design.

In order to optimize the design of intelligent agricultural harvester, improve the backward situation of agricultural machinery design in western Guangdong, and explore new research methods to guide the design of modern agricultural harvester. Using QFD (Quality Function Deployment Principle), this paper analyzes the form and functional characteristics of the intelligent agricultural harvester, and from the aspects of optimizing the product appearance, product function, product quality and man-machine optimization of the intelligent agricultural harvester, establishes a house of quality matrix suitable for agricultural harvesters in western Guangdong. This paper analyzes the mapping relationship between the quality function and the design features of the intelligent agricultural harvester, determines the design feature factor framework and weight of the intelligent agricultural harvester, and obtains the importance values of the function features and design elements, so as to establish the priority of the design resources and function configuration optimization, and then guide the example design of the intelligent agricultural harvester. AHP (Analytic Hierarchy Process) is used to verify that the characteristic factors of QFD design are consistent with the user evaluation data, which proves that the design example scheme of intelligent agricultural harvester is feasible to some extent, and provides a new research idea for the design of the same type of agricultural harvesting equipment.