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Since limit attention has been paid on debt collection calls and collectors' voice metrics, this study explores how collectors' voice metrics influence borrowers' delinquent behavior in debt collection calls. Results of survival analysis show that collectors' voice with higher pitch, higher speed, lower loudness, and lower entropy of energy prolongs borrowers' delinquent time. Mediation analysis indicates that borrowers' voice energy and call duration mediate the effects of collectors' voice metrics on delinquent time. Additional analysis on heterogeneity suggests that short‐term and long‐term repayment borrowers respond to collectors' voice metrics differently.

PurposeAlthough phone calls are widely used by debt collection services to persuade delinquent customers to repay, few financial services studies have analyzed the unstructured voice and text data to investigate how debt collection call strategies drive customers to repay. Moreover, extant research opens the “black box” mainly through psychological theories without hard behavioral data of customers. The purpose of our study is to address this research gap.Design/methodology/approachThe authors randomly sampled 3,204 debt collection calls from a large consumer finance company in East Asia. To rule out alternative explanations for the findings, such as consumers' previous experience of being persuaded by debt collectors or repeated calls, the authors selected calls made to delinquent customers who had not been delinquent before and were being called by the company for the first time. The authors transformed the unstructured voice and textual data into structured data through automatic speech recognition (ASR), voice mining, natural language processing (NLP) and machine learning analyses.FindingsThe findings revealed that (1) both moral appeal (carrot) and social warning (stick) strategies decrease repayment time because they arouse mainly happy emotion and fear emotion, respectively; (2) the legal warning (stick) strategy backfires because of decreasing the happy emotion and triggering the anger emotion, which impedes customers' compliance; and (3) in contrast to traditional wisdom, the combination of carrot and stick fails to decrease the repayment time.Originality/valueThe findings provide a valuable and systematic understanding of the effect of carrot strategies, stick strategies and the combinations of them on repayment time. This study is among the first to empirically analyze the effectiveness of carrot strategies, stick strategies and their joint strategies on repayment time through unstructured vocal and textual data analysis. What's more, the previous studies open the “black box” through psychological mechanism. The authors firstly elucidate a behavioral mechanism for why consumers behave differently under varying debt collection strategies by utilizing ASR, NLP and vocal emotion analyses.

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In social interactions, people who are perceived as competent win more chances, tend to have more opportunities, and perform better in both personal and professional aspects of their lives. However, the process of evaluating competence is still poorly understood. To fill this gap, we developed a two-step empirical study to propose a competence evaluation framework and a predictor of individual competence based on multimodal data using machine learning and computer vision methods. In study 1, from a knowledge-driven perspective, we first proposed a competence evaluation framework composed of 4 inner traits (skill, expression efficiency, intelligence, and capability) and 6 outer traits (age, eye gaze variation, glasses, length-to-width ratio, vocal energy, and vocal variation). Then, eXtreme Gradient Boosting (XGBoost) and Shapley Additive exPlanations (SHAP) were utilized to predict and interpret individual competence, respectively. The results indicate that 8 (4 inner and 4 outer) traits (in descending order: vocal energy, age, length-to-width ratio, glasses, expression efficiency, capability, intelligence, and skill) contribute positively to competence evaluation, while 2 outer traits (vocal variation and eye gaze variation) contribute negatively. In study 2, from a data-driven perspective, 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 of an individual’s competence behavior. The results indicate that vocal and visual features contribute most to competence evaluation. In addition, we provided a Chinese Competence Evaluation Multimodal Dataset (CH-CMD) for individual competence analysis. This paper provides a systemic competence framework with empirical consolidation and an effective multimodal machine learning method for competence evaluation, offering novel insights into the study of individual affective traits, quality, personality, etc.