Oct 2026· Journal of construction engineering and management· 0 citations· 25 references
TL;DR
This study proposes a dual-incentive mechanism that integrates model training performance with worker satisfaction, encouraging providers to allocate more resources to safety knowledge recommendation and shows that the dual-incentive mechanism yields stronger incentive intensity, higher provider effort, and greater requester benefits versus single-model training incentives, while remaining robust across cost and risk variations.
Abstract
Despite the effectiveness of personalized safety knowledge enhancement training, existing research overlooks the challenges posed by fragmented worker data and strict privacy regulations. To overcome these challenges, this study proposes a novel FedCSKR framework aimed at achieving privacy-preserving cross-organizational construction safety knowledge recommendation (CSKR). Nonetheless, the effectiveness of CSKR depends on the decisions of both requesters and providers. We develop a game model to capture their interactions and show, through evolutionary analysis, that spontaneous collaboration is unsustainable, leading the system to a noncooperative equilibrium without external incentives. To promote cooperation, we propose a dual-incentive mechanism that integrates model training performance with worker satisfaction, encouraging providers to allocate more resources to safety knowledge recommendation. Modeling and simulations show that the dual-incentive mechanism yields stronger incentive intensity, higher provider effort, and greater requester benefits versus single-model training incentives, while remaining robust across cost and risk variations. Specifically, incentive intensity can be increased by over 98.3% and provider effort levels by more than 197.4%, while simultaneously helping to mitigate the requester’s exposure to uncertainty arising from model performance variability. Theoretical contributions of this study include the integration of federated learning with incentive-driven contract design in CSKR, representing the first effort, to our knowledge, that incorporates worker satisfaction into the incentive mechanism of the FedCSKR. Practically, this study offers a structured decision-making framework to assist construction managers in implementing privacy-compliant, effective, and collaborative CSKR. Overall, this study facilitates cross-organizational collaboration in CSKR and contributes to advancing personalized safety training.
Despite extensive research on knowledge spillovers, existing literature has paid limited attention to the formation mechanism of innovation chains. To address this gap, this paper constructs a multi-party evolutionary game model including two innovators and one supporter, incorporating knowledge spillover effects, and empirically tests the theoretical predictions using data from Chinese listed companies (2016–2025). The results show that the final equilibrium is (cooperation, cooperation, support). Empirically, we find a nonlinear relationship between knowledge spillover and the formation mechanism of an innovation chain with a double threshold effect of absorptive capacity: the positive impact of spillover increases as capacity moves from low to moderate, but diminishes-while remaining positive-when capacity becomes excessively high, revealing an S-shaped pattern. The originality lies in two aspects. Theoretically, our multi-player model extends the conventional bilateral framework to better reflect real-world parallel collaboration. Empirically, our firm-level analysis of Chinese listed companies is among the first to identify a double threshold effect, moving beyond regional-level or single-threshold studies. Practically, our findings suggest that governments should tailor subsidy intensities to firms’ absorptive capacity levels—allocating more to moderate-capacity firms where spillover gains are maximized—offering a more precise strategy for promoting sustainable innovation chain development.
Min Zhang, Xin Jin, Yinan Yu· Systems· 0 citations
Prior research suggests that similarity in pay-performance sensitivities (PPS) among top management team (TMT) members enhances their collaboration. Building on this, the authors aim to use PPS similarity as a proxy for TMT collaboration to investigate its impact on corporate underinvestment.
Using a sample of Chinese listed firms from 2007 to 2022, the authors test the relationship between similar PPS and underinvestment.
The authors find that TMT collaboration is significantly and negatively associated with underinvestment. The authors’ mechanism tests suggest that this relationship is driven by two key mechanisms: improved managerial efficiency and alleviated financing constraints. Furthermore, the authors’ cross-sectional analyses reveal that the mitigating effect of TMT collaboration on underinvestment is more pronounced in environments with higher social trust, in firms with higher accounting information quality and in teams with longer tenure.
Overall, the authors’ study moves beyond the traditional focus on individual executive incentives to demonstrate that the consistency of a team’s incentive structure is a key governance mechanism, offering novel insights from a collective perspective for understanding and mitigating corporate underinvestment.
Anting Li, Yong Ye, Lin Xiao· Accounting Research Journal· 0 citations
Frontline production units in high‐hazard industries face a persistent “knowing‐doing gap” in process safety management: despite formal hazard identification procedures, high reporting volumes coexist with persistent major risks. Through a mixed‐methods case study in a PetroChina Tarim Oilfield unit (survey
N
= 219; interviews
n
= 75), we integrate behavioral safety theory with incentive compatibility principles to develop a diagnostic typology of frontline employees. Cluster analysis reveals four archetypes—Reward‐Seekers, Expert Contributors, Disengaged Compliers, and Alienated Experts—each responding differently to the existing appraisal system. Our analysis demonstrates that a homogeneous, metrics‐driven appraisal system creates perverse incentives across archetypes, undermining genuine risk control. We propose differentiated governance principles that tailor communication, incentives, and support mechanisms to align each archetype's rational behavior with the collective goal of meaningful process hazard identification. This study contributes a micro‐level, human‐centric lens linking workforce heterogeneity to system effectiveness, offering a pathway from procedural compliance to substantive risk reduction.
Xing Chen, Dongxu Gao, Fanzeng Yang et al.· Process safety progress· 0 citations
The entry of JD.com into the food delivery sector and the ensuing subsidy competition have resulted in irrational competition, merchant profit squeezes, and food safety risks in China. This study therefore investigates the collaborative governance mechanisms for food delivery platforms under involutionary competition driven by traffic contestation. A two-agent evolutionary game model between platforms and merchants is developed, and Q-learning simulations are conducted to capture dynamic learning behaviors. The analysis examines the effects of coupon face value, cost-sharing mechanisms, traffic incentives, and government incentive-penalty policies on the strategic choices of both agents. Key findings reveal that merchants are more sensitive than platforms to traffic incentives and government penalties. Traffic-dependent merchants and traffic-independent merchants exhibit significantly different responses to government interventions. The coupon face value demonstrates a threshold effect, where only a reasonable range encourages compliant behavior among both parties. Based on these results, a collaborative governance framework is proposed. For traffic-dependent merchants, the government should focus on regulating platform behaviors and supervising coupon value controls, while platforms should establish a reward-oriented, penalty-supported incentive mechanism. For traffic-independent merchants, the government should strengthen consumer-reporting penalty mechanisms and strictly control collusion risks between platforms and merchants. Platforms should increase inspection frequency and reinforce penalties to prevent, at the source, the decline in product quality and market disorder induced by involutionary competition. This study provides strategic insights for achieving collaborative governance of involutionary competition in platform economies under intense traffic contestation.
As the global focus shifts toward sustainability, the demand for credible Environmental, Social and Governance (ESG) information has accelerated. In emerging economies like Vietnam, following the national commitment to Net Zero by 2050, the role of independent assurance has become critical for ensuring financial transparency and investor confidence. While existing literature heavily explores the demand for ESG disclosures, research on the "supply side"- specifically the factors driving audit firms to provide these specialized services-remains remarkably scarce. This study addresses this gap by applying the Theory of Planned Behavior (TPB) to investigate the psychological and organizational determinants influencing the willingness of audit professionals to offer ESG assurance services. A quantitative research design was employed, utilizing a structured survey to collect data from 235 audit professionals across various firms in Vietnam. The conceptual model was tested using Structural Equation Modeling (SEM) to evaluate the relationships between the TPB constructs. The empirical findings confirm that a positive Attitude, strong Subjective Norms and high Perceived Behavioral Control all significantly and positively increase an auditor's intention to supply ESG assurance. Notably, Subjective Norms-perceived pressure from clients, industry competitors and regulatory bodies-emerged as the most influential driver in the Vietnamese context. Furthermore, the results indicate that behavioral intention is a robust predictor of the actual provision of these services. This research offers critical insights for audit firms in building internal capacity and for policymakers seeking to cultivate a reliable sustainable finance ecosystem in emerging markets.
N. Thu, Hoai· International Research Journ...· 0 citations
This paper investigates the dynamic coopetition and capacity-sharing strategies between an Integrated Manufacturer and a Developer under R&D uncertainty, focusing on the governance of strategically scarce capacity. By constructing a two-stage game model, we analyze how government intervention and risk-hedging mechanisms influence the allocation of idle strategically scarce capacity in innovation-driven industries. The findings reveal a two-sided paradoxical behavioral pattern: in the low-probability R&D interval, rather than relying on safe contract manufacturing, the Integrated Manufacturer counter-intuitively reduces collaborative duration to aggressively gamble on its immature product. Conversely, in the high-probability R&D interval, where conventional wisdom predicts an aggressive pivot to self-production, the manufacturer paradoxically extends or maintains the contract manufacturing duration, driven by the partner’s full cost-sharing incentive mechanism. Furthermore, To maximize the total supply output of strategically scarce resources during collaboration, we uncover a non-linear ‘counterproductive subsidy trap’ and propose a binary ‘critical mass’ policy rule: governments should either withhold subsidies entirely or commit sufficient funding to bypass the supply deficit zone. This framework provides a theoretical foundation for managing scarcity in capital-intensive sectors such as biopharmaceuticals and semiconductors.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
