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The purpose of this paper is to investigate how boundary-spanning search affects sustainable competitive advantage under the conditions that competitors also search and to test the moderating role of knowledge integration capability.

This paper classifies boundary-spanning search into proactive search and responsive search by considering competition and develops a theoretical model in which knowledge integration capability moderates the effects of proactive and responsive searches on sustainable competitive advantage. Empirical analyses were conducted on the data of 245 Chinese advanced manufacturing firms.

The results show that proactive and responsive searches have inverted U-shaped relationships with sustainable competitive advantage. Moreover, the relationships between proactive and responsive searches and sustainable competitive advantage are moderated by knowledge integration capability. Specifically, as knowledge integration capability increases, the inverted U-shaped relationship between proactive search and sustainable competitive advantage becomes flatter, whereas the inverted U-shaped relationship between responsive search and sustainable competitive advantage becomes almost linear.

This paper enriches the research of boundary-spanning search by considering competition and uncovers how boundary-spanning search affects sustainable competitive advantage under the conditions that competitors also search. Furthermore, this paper sheds light on that the effects of proactive and responsive searches on sustainable competitive advantage are even more complex than inverted U-shaped patterns and provides a contingent viewpoint to deeply understand the relationship between boundary-spanning search and sustainable competitive advantage.

Although interorganizational learning has attracted substantial attention, research about its effects on green innovation is still rare. Combining theories of organizational learning and absorptive capacity, this study explores the relationships among interorganizational learning, green knowledge integration capability (GKIC) and green innovation (GI), and analyzes the moderating role of green absorptive capacity (GAC). Based on resource-based and ambidexterity theories, this study focuses on vertical exploitative (VEL) and lateral explorative learning (LEL). This study expands the research of GI by proposing two different interorganizational learning mechanisms and uncovering the intricate relationship between them and GI.

Based on a sample of 203 Chinese manufacturing firms, the authors used a hierarchical regression analysis and bootstrap method to test the theoretical framework and research hypotheses of this paper.

Results show that VEL and LEL have positive effects on GI. GKIC partially mediates the relationship between VEL and GI and completely mediates the relationship between LEL and GI. Moreover, GAC plays a moderating role between LEL and GKIC and moderates the effect of LEL on GI via GKIC, such that the effect is stronger when GAC increases. However, it does not moderate the relationship between VEL and GKIC.

First, founded on resource-based and ambidexterity theories, this study considers two dimensions of interorganizational learning, VEL and LEL. Second, by testing the mediating role of GKIC, the authors provide a theoretical lens to understand the relationship between interorganizational learning and GI. Third, by examining boundary conditions of GAC, the authors enrich organizational learning and absorptive capacity theory in the context of green development.

The purpose of this paper is to investigate the effect of subjective well-being on individual innovation behavior. The authors propose that such effect is mediated by knowledge sharing, and individual absorptive capacity moderates the relationship between knowledge sharing and innovation behavior.

A research model was developed. Survey data were based on a sample of 220 employees from 20 enterprises in China. Hierarchical multiple regression analyses and bootstrap approach were applied to test the research model.

Subjective well-being has significantly positive influence on individual innovation behavior, and this effect is mediated by knowledge sharing. Furthermore, absorptive capacity moderates the mediated relationships between subject well-being and individual innovation behavior via knowledge sharing.

This research sheds light on an effective role to promote individual innovation behavior in management practices. It suggests that harmonious atmosphere, communication platform, and well-established learning plans can be cultivated to enhance individual innovation ability.

This research discusses the antecedent of innovation behavior from the viewpoint of individual psychology, and identifies the different roles of knowledge sharing and absorptive capacity on individual innovation behavior.

The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.

Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The purpose of this paper is to propose a new deep learning-based model to carry out better maintenance for naval propulsion system.

This model is constructed by integrating different deep learning algorithms. The basic idea is to change the connection structure of the deep neural network by introducing a residual module, to limit the prediction output to a reasonable range. Then, connect the Deep Residual Network (DRN) with a Generative Adversarial Network (GAN), which helps achieve data expansion during the training process to improve the accuracy of the assessment model.

Study results show that the proposed model achieves a better prediction effect on the dataset. The average performance and accuracy of the proposed model outperform the traditional models and the basic deep learning models tested in the paper.

The proposed model proved to be better performed naval propulsion system maintenance than the traditional models and the basic deep learning models. Therefore, our model may provide better maintenance advice for the naval propulsion system and will lead to a more reliable environment for offshore operations.