Mindfulness metacognitive practice that can be performed in the workplace. Drawing on the theory of conservation of resources, we test a moderated mediating model of how and when employee mindfulness has a positive effect on work engagement. Via analysis of data from 311 employees from 83 teams at different times, this study investigates the relationship between employee mindfulness and work engagement as well as the moderating effect of team mindfulness and the mediating effect of recovery level. The results from this multi-wave field study show that the mindfulness of the individual employee has a positive influence on work engagement and that recovery level plays a mediating role. Team mindfulness positively moderates the relationship between individual mindfulness and work engagement. This conclusion may bridge the relationship between mindfulness and work engagement theory.

In the context of a progressively intricate and uncertain global economic landscape, the credit risk businesses encounter is intensifying. This study seeks to analyze whether intelligent transformation, a significant trend in current organization development, might serve as a novel method for mitigating credit risk. We employ panel data from 1533 listed enterprises in China’s manufacturing sector to investigate how intelligent transformation influences credit risk empirically. This research indicates that intelligent transformation can mitigate business credit risk. The production, management, and financing effects are the primary mechanisms via which intelligent transformation mitigates credit risk. Heterogeneity analysis indicated that the credit risk reduction effect of the intelligent transformation of traditional manufacturing firms surpassed that of intelligent manufacturing enterprises. In contrast to high-growth firms, low-growth enterprises exhibited more robust credit risk mitigation benefits from intelligent transformation. Subsequent analysis indicated that enhancing supply chain finance can facilitate intelligent transformation and, hence, more effectively mitigate credit risk.

The unreliable service and the unstable operation of a high frequency bus line are shown as bus bunching and the uneven distribution of headways along the bus line. Although many control strategies, such as the static and dynamic holding strategies, have been proposed to solve the above problems, many of them take on some oversimplified assumptions about the real bus line operation. So it is hard for them to continuously adapt to the evolving complex system. In view of this dynamic setting, we present an adaptive holding method which combines the classic approximate dynamic programming (ADP) with the multi-stage look-ahead mechanism. The holding time, that is the only control means used in this study, will be determined by estimating its impact on the operation stability of the bus line system in the remained observation period. The multi-stage look-ahead mechanism introduced into the classic Q-learning algorithm of the ADP model makes the algorithm get through its earlier unstable phase more quickly and easily. During the implementation of the new holding approach, the past experiences of holding operations can be cumulated effectively into an artificial neural network used to approximate the unavailable Q-factor. The use of a detailed simulation system in the new approach makes it possible to take into accounts most of the possible causes of instability. The numerical experiments show that the new holding approach can stabilize the system by producing evenly distributed headway and removing bus bunching thoroughly. Comparing with the terminal station holding strategies, the new method brings a more reliable bus line with shorter waiting times for passengers.