| 其他摘要 | The purpose of this paper is to construct an assessment tool to measure perceived security, interactive naturalness and comprehensibility of intelligent connected vehicles through a systematic approach like qualitative research and large-scale questionnaire, and to explore the relationship between these three variables and critical influencing factors in three studies, including eight sub-studies. The method used in this article consists of three main steps: 1) Establish a comprehensive and representative questionnaire set by qualitative research method, dictionary retrieval, literature summary and expert interview; 2) Based on the set of representative items, quantitative research was adopted to investigate the factor structure formed by these items, and the validity was verified by criterion-related variables. 3) We further verified the above findings by using new samples or creating differentiated indicators and traditional usability to further verify the perceived security scale, interactive naturalness scale and the comprehensibility scale of intelligent connected vehicles to ensure the reliability, validity and uniqueness of the structure of the measurement tools developed.
In Study 1, the perceived security scale of intelligent connected vehicles was developed and verified. In Study 1a, users in Beijing (N = 373) were selected as the research objects, and an 8-item perceived security scale was established. Through exploratory factor analysis, a two-dimensional model of cognitive safety and emotional safety was found. Pearson partial correlation analysis showed that these two components may have unique effects on different cause (e.g., social support, familiarity) and effect variables (e.g., purchase intention, driving intention). In Study 1b, using a new sample from Shenzhen (N = 352), confirmatory factor analysis consolidated the validity of the two-dimensional model. Further correlation analysis with the addition variables of perceived government support and recommendation intention suggested that the scale had good criterion-related validity. Based on the results of Study 1a and 1b, the main factors affecting cognitive safety are perceived controllability, tendency to seek out new technologies, intelligent connected vehicles driving experience, perceived government support, drive intention and intention to be other road users. The main factors affecting emotional security are social support, familiarity, perceived benefit, purchase and recommendation intention.
Study 2 explored the structure and function of interactive naturalness through three sub-studies. In Study 2a, dictionary retrieval, literature review and expert interview were carried out to obtain a 9-item interactive naturalness scale of intelligent connected vehicles. In Study 2b, 353 intelligent connected vehicle users were surveyed by questionnaire. Exploratory factor analysis found two factor structures (joyful fluency and universal awareness). Further regression analysis showed that these two factors had significant and unique predictive effects on key indicators such as satisfaction. In Study 2c, a new sample (N = 349) was used to verify the stability of the two-factor model. The regression results also showed that the two interactive natural experience dimensions also significantly predicted the recommendation intention, loyalty and other important variables. We also found that the joyful fluency significantly predicted the recommendation intention, loyalty and other important variables. Universal awareness was more influenced by interaction and intelligence-related functions. Implications for how the scale can be applied to future human-computer interaction research were discussed in Study 2.
In Study 3, the comprehensibility scale of intelligent connected vehicles was developed and verified. In view of the lack of measurement of scene and interpretation requirements in previous studies on the comprehensibility scale, Study 3a obtained the 19-item comprehensibility scale of intelligent connected vehicles Study 3b constructed the comprehensibility scale of intelligent connected vehicles (random grouping 1, N = 1036), and obtains a four-factor scale with good reliability and validity. In Study 3c (random grouping 2, N = 1035), according to existing research conclusion and expert assessment, differentiated explanations were created (state information only, state information and reasons, state information / reasons and forecast information before takeover, state information / reasons and forecast information after takeover). According to literature analysis and industry reports, 8 typical takeover scenarios with time urgency (high urgency, low urgency) were interpreted, and it was found that there was significant difference in comprehensibility scale scores of intelligent connected vehicles under different interpretation levels (p < 0.01). The results showed that it was more accurate in the interpretation quality of distinguishing different temporal urgency (p < 0.001). Meanwhile, the measurement results were more consistent with the objective performance results (p < 0.01), and were significantly correlated with the scores of usage intention, satisfaction and curiosity (p < 0.01). The comprehensibility evaluation system with good reliability and validity has been verified in a specific system.
This study developed a set of psychological measurement tools for measuring safety perception, interaction naturalness and comprehensibility in intelligent connected vehicles, which made up for the lack of qualitative research basis, quantitative research verification and single objective in previous studies. The specific mechanism of psychological factors involved in this tool deserves further attention. The influencing factors identified in this study can be used to conceive new ways to improve people's perception of safety, natural interactive experience of product interaction, and comprehensibility. |
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