Jian Dai

Human–artificial intelligence (AI) teams are increasingly embedded in risk analysis, yet news reports repeatedly portray meaningful oversight as fragile when review conditions are poorly designed. This study examines how public discourse has portrayed the roles, failure modes, and governance mechanisms of human–AI teaming in risk analysis. Drawing on 184,282 AI‐related Factiva news articles (1956–2025), we use a funnel‐shaped mixed‐methods design to identify 3571 articles describing human–AI interaction in risk‐analytic contexts. Structural topic modeling identifies 20 thematic clusters and their temporal and cross‐domain dynamics, whereas LLM‐assisted inductive thematic analysis produces five mirror‐mapped pairs of failure modes and governance mechanisms: opacity and explainability, bias and auditing, agency erosion and cognitive friction, systemic fragility and oversight and liability, and accountability vacuums and institutional governance. The analysis shows that media discourse often follows an AI‐predicts‐human‐decides paradigm and portrays formal human‐in‐the‐loop requirements as insufficient without deliberate interaction design.

Purpose: This study aims to resolve a key strategic dilemma in acqui-hires: whether to pursue a strategy of “strategic commitment” (immediate integration) or “flexibility” (deferral) to unlock the value of a target's proprietary know-how. It investigates which real options logic yields superior post-acqui-hire performance and adjudicates between them using intellectual capital contingencies. Design/methodology/approach: Drawing on real options theory and the intellectual capital framework, this study frames the integration choice as a contest between exercising a “growth option” and holding a “deferral option.” Using a unique dataset of 208 acqui-hires (2005–2021), ordinary least squares (OLS) regression models test how a target's proprietary know-how (structural capital) influences acquirer performance, and how this relationship is moderated by the founder's entrepreneurial experience (human capital) and venture capital (VC)-backed status (relational capital). Findings: The analysis reveals that while target proprietary know-how is positively associated with acquirer performance, this effect is significantly strengthened by the founder's entrepreneurial experience and the target's VC-backed status. These findings provide strong empirical support for the strategic commitment perspective, suggesting that proactive structural integration to preempt talent flight is superior to a cautious, flexibility-based approach. Originality/value: The study opens the “black box” of acqui-hire value creation by demonstrating that structural integration is critical for converting tacit human capital into firm performance. It refines real options theory for human-centric assets by identifying that the “option to wait” is eroded by asset depreciation risks, and extends entrepreneurship literature by tracing the post-acquisition value of founder capital.

Selecting the right merger and acquisition (M&A) target is a critical yet challenging endeavor, as the success of these strategic initiatives depends mainly on identifying compatible firms. This study draws upon the theoretical perspectives of strategic fit and organizational search to propose and validate a novel, two-stage M&A target recommendation approach designed as a managerial decision support system. Initially, the method facilitates a focused search in which acquirers define explicit criteria for identifying a highly relevant initial target within a knowledge graph (KG). It then employs a similarity-based search expansion using advanced KG embedding models to recommend additional targets that exhibit latent structural similarities. The efficacy of this approach is validated on a large-scale U.S. M&A dataset (2010–2022). Our key findings are threefold. First, our model demonstrates statistically significant superiority over benchmarks, confirmed through robustness checks, including 10-fold cross-validation and temporal validation. Second, in an experiment on deals by experienced acquirers, our model is more effective at identifying these targets, quantitatively demonstrating its superior recommendation quality. Third, our analysis uncovers counterintuitive patterns, revealing that machine-identified structural similarities can be more potent predictors of fit than traditional human-centric filters, such as geography. It further explores the tool's boundary conditions, showing that it is more effective in complex, high-tech sectors. This KG-based methodology offers a more informed, strategically refined, and empirically validated tool to enhance the quality of M&A decisions.

The integration of artificial intelligence (AI) into engineering education is essential for fostering innovation, strategic thinking and interdisciplinary skills in the intelligent era. On this basis, this study aims to track and visually represent the research outputs associated with AI applications in engineering education, providing insights into the current research landscape and identifying areas for further investigation. The analysis offers theoretical and methodological direction for leveraging AI in engineering education. Utilising bibliometric methods, we conducted a comprehensive visualisation analysis of 378 core publications from the Web of Science (WoS) database, spanning from the beginning of the twenty‐first century to the present. Our findings show a consistent rise in publication volume from 2000 to 2017, with a significant surge from 2018 to 2023. The study identifies the International Journal of Engineering Education and Computer Applications in Engineering Education as pivotal journals in the field. The research clusters around two central themes: essential supportive technologies and specific educational applications. Within engineering education, expert systems, data mining, prediction and machine learning are highlighted as key research areas. The field has evolved through distinct phases, starting with an early focus on technology support systems, moving to an emphasis on pedagogical applications, and currently striving for a balance between diverse technologies and practical applications. Context and implications: Rationale for the study Research on AI‐enabled engineering education is necessary because it is a vital measure for cultivating innovative, strategic and interdisciplinary talents in the era of intelligence. The article applies bibliometric techniques to visualise the developmental pulse of AI use in engineering education. Why the new findings matter The new findings firstly help researchers to grasp the development pulse and research priorities in the field and fill the research gaps, and secondly provide theoretical and methodological guidance for the application of artificial intelligence in engineering education through visualisation. Implications for researchers and practitioners The study summarises the current situation of AI in engineering education, breaks down the knowledge map of the field, provides practitioners, especially new researchers, with important references and guidance to understand the development of the field, and triggers the continuous attention of all sectors of society to the field. In addition, school administrators will be able to guide the development and practice of education and teaching based on the findings of the study, and frontline teachers will be able to practise the integration and application of AI and engineering education, which will effectively improve the learning efficiency of engineering education.

This study leverages machine learning (ML) techniques to assess the impact of CEO characteristics on the international performance of firms. Analyzing data from Chinese listed companies between 2008 and 2021, this study evaluates 14 ML algorithms and identifies the random forest model as the most effective. Additionally, the SHapley Additive exPlanations (SHAP) algorithm is employed for result interpretation and visualization. The findings indicate that most CEO traits can predict a firm's international success. Notably, international experience, age, and CEO duality emerge as the top predictors. Specifically, both international experience and CEO duality positively influence performance, while the CEO's age exhibits a complex, non‐linear relationship with performance. This study provides a nuanced perspective on how CEO characteristics influence a firm's international success.