Second, we obtained 4473 GB of 2573 video files that include the competence behavior. Third, two groups of experts annotated the perceived competence of individuals in the videos. Fourth, we employed machine learning methods, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to predict and understand individual competence. Fifth, to comprehensively understand competence and validate the results of machine learning, ordinary least squares [29] regression was used to provide economic evidence of the impact of 10 traits on competence evaluation. In the data-driven study (study 2), we accurately predicted competence with a cutting-edge multimodal machine learning algorithm, low-rank multimodal fusion (LMF), which exploits the intra- and intermodal interactions among all the visual, vocal, and textual features composing an individual's competence behavior. In total, 43 dimensions of visual features were extracted by OpenFace [30], 24 dimensions of vocal features were extracted by Librosa [31], and 768 dimensions of textual features were extracted by BERT [32]. In study 1, the results of XGBoost prediction indicate that 10 traits contribute to competence evaluation, with a mean absolute error (MAE) value of 7.335 \(\times\) 10\({}^{-6}\), which is highly accurate, and the SHAP results indicate that 8 traits (vocal energy, age, length-to-width ratio, glasses, expression efficiency, capability, intelligence, and skill) contribute positively to competence evaluation, while vocal variation and eye gaze variation contribute negatively. In study 2, the LMF model with 3 modalities (visual, vocal, and textual) has high accuracy, with a value of 87.20%, highlighting the importance of information on individuals' visual, vocal, and textual competence behaviors. In addition, the results indicate that the visual modality contributes the most to individual perceived competence, followed by the vocal modality and then the textual modality. It seems that \"how an individual looks\" and \"how he speaks\" are more important than \"what he says.\"  
This paper offers the following four theoretical contributions: (1) This research extends prior research by integrating a more comprehensive competence evaluation framework with 10 traits, including not only 4 inner traits but also 6 outer traits that can be easily observed, thus helping to clarify the conceptual ambiguity surrounding competence [33]. (2) This study is among the first to directly and empirically predict competence with both 10 knowledge-driven traits and 835 data-driven features extracted from videos, bringing new insights into the evaluation of competence and generating more convincing results. While previous studies used small samples and evaluated competence based on human perception [1; 16; 18], the use of data-driven empirical evidence in this study provides a deeper understanding of an individual's competence evaluation. (3) Methodologically, an advanced deep learning algorithm, LMF, is employed for the analysis of psychology-based issues. Both intra- and intermodal interactions among an individual's visual, vocal, and textual information are mapped, thus leading to a more precise prediction of individual competence, which offers insights into machine learning analysis in psychological research [34; 35; 36]. (4) We provide the Chinese Competence Evaluation Multimodal Dataset (CH-CMD) for individual competence evaluation to the public. This dataset, comprising rich information on visual, vocal, and textual information, along with annotated competence scores, could encourage further studies of the relationship between inner and outer traits and individual competence. The framework of this paper is shown in Figure 1.  
Figure 1: Knowledge-driven study 1 and data-driven study 2.