Jiuchang Wei

In high‐risk industries, occupational risks are often manifested through everyday operational disruptions rather than catastrophic events alone. In freight transportation, recurrent incidents such as vehicle breakdowns, cargo problems, and traffic accidents generate substantial demands for assistance and coordination, yet the broader patterns underlying these accident‐related situations remain insufficiently understood. Drawing on 17,696 accident‐related help‐seeking posts contributed by 14,437 truck drivers to China's largest online Q&A platform between 2018 and 2020, this study examines how occupational risks are expressed and organised within digital interaction spaces. Structural topic modelling identifies 50 latent accident‐related topics, which are subsequently organised through network‐based community detection into five structurally distinct accident‐related scenarios spanning traffic accident, emergency mobility restoration, localised situational assistance, roadside operational recovery, and institutional liability and compensation handling. These scenarios characterise how assistance needs and resource requirements are matched under accident‐related conditions, varying along two dimensions: the urgency of support and the temporal stage of problem handling. Multinomial logistic regression shows that engagement with different accident‐related scenarios is systematically associated with driver and vehicle characteristics. Cross‐scenario comparisons further reveal variation in both response volume and response effectiveness, indicating that different accident‐related situations generate distinct patterns of collective response on digital platforms. The findings demonstrate that platform‐generated help‐seeking posts provide a valuable source of occupational risk information beyond conventional accident datasets. By organising fragmented accident‐related incidents into recurring scenarios and examining their associations with driver characteristics and collective response patterns, the study develops a scenario‐based perspective on occupational risk in freight transportation. It further shows that online participation does not necessarily translate into effective assistance, revealing important differences in the problem‐solving capacity of online occupational communities across accident‐related situations.

Purpose: This study aims to examine how firm-level trade decoupling risk (TDR) drives the reconfiguration of cross-border supply chains (CBSCs). Specifically, we investigate whether TDR leads to CBSC relationship termination, and how this effect is moderated by home-host bilateral relations and proactive supply chain risk disclosure (SCRD). Design/methodology/approach: Drawing on host country sentiment (HCS) theory, we conceptualize firm-level TDR as the anticipated institutional pressure borne by firms whose home country faces the prospect of escalating decoupling actions. This pressure is catalyzed by negative host-country public sentiment toward the firm's home country. We proxy for TDR using the intensity of negatively framed decoupling-related media coverage, which we quantify by applying a large language model (LLM). We test our hypotheses using regression models on a sample of 21,796 firm-year observations of CBSC relationships from 1,460 Chinese A-share listed companies during 2017–2022. Findings: Higher TDR significantly increases the likelihood of firms terminating their CBSC cooperation. We find that this effect is mitigated by favorable home-host bilateral relations and proactive SCRD. Furthermore, TDR drives supply chain reconfiguration, leading firms to establish new relationships in countries with better bilateral ties, a phenomenon known as “friend-shoring”. Originality/value: This study systematically quantifies firm-level TDR through an LLM-based textual analysis of media data. By operationalizing public sentiment as a quantifiable institutional pressure, this study provides robust micro-level evidence of how it shapes firms' strategic shifts in global supply chains.

Many consequential life goals, such as weight management and health behavior change, require sustained self-regulation over extended periods and involve recurrent setbacks rather than discrete completion points. Although prior research has examined goal progress and short-term motivation, less is known about how accumulated experience during goal pursuit shapes subsequent decisions following setbacks. In particular, prior effort and prior achievement are often treated as interchangeable signals of progress, despite their potentially distinct psychological implications. This research examines how prior effort and achievement differentially shape decision-making in long-term goal pursuit. Using longitudinal self-monitoring data from 12,439 users of a weight-management application (Boohee), supplemented by a laboratory experiment, we conceptualize goal pursuit as a cyclical self-regulation process organized around a personalized milestone—each participant’s lightest recorded weight. Rather than representing a predefined endpoint, this milestone constitutes a psychologically salient turning point that prompts retrospective self-appraisal. We distinguish two forms of pre-milestone experience: persistence duration (time invested) and achievement magnitude (weight loss attained). Results reveal a systematic dissociation between effort-based and achievement-based experiences. Greater pre-milestone persistence predicts stronger post-milestone behavioral persistence following setbacks, driven by lower performance-related stress and greater goal commitment. In contrast, greater pre-milestone achievement predicts higher tolerance for post-milestone weight regain (i.e., greater outcome endurance), mediated by increased perceived controllability. These findings indicate that accumulated effort and accumulated achievement operate as distinct decision reference points that shape how individuals interpret setbacks and regulate subsequent behavior. Together, this research demonstrates that milestone-based self-appraisal generates feedback loops through which prior experiences differentially influence motivation, emotional responses, and endurance in later stages of goal pursuit. By integrating a temporal perspective with self-regulation and mindset theories, this work advances understanding of dynamic persistence in self-directed, real-world goal pursuit and clarifies why early success does not reliably translate into long-term maintenance.

This study employed the XGBoost model to conduct an in‐depth analysis of consumer complaints and identified the key risk factors predicting vehicle recalls in the US market, providing valuable proactive risk management support for automakers and regulatory agencies. We leveraged the extensive data resources from National Highway Traffic Safety Administration to construct high‐precision recall risk prediction models to predict the risk of recall. The models exhibited exceptional performance across different time windows, particularly maintaining a high level of area under the curve values over a prediction timespan of up to 18 months, demonstrating their predictive accuracy and stability. Our study contributes to risk management theory by addressing the challenges of integrating consumer complaints into predictive models for vehicle recall risk. While prior research has primarily focused on text mining of complaint content, our work systematically incorporates structured complaint data and recall records to enhance predictive accuracy. Also, our research distinguishes the indicators for the initial recall after launch to the market and the indicators for subsequent recalls, and bridges a critical gap in recall risk prediction at different stages of a vehicle's life cycle.