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Encountering work failures is not uncommon for employees. Employee learning from work failures is critical to knowledge management and employee development. This study aims to examine leaders’ role in facilitating employee learning from work failures.

A two-wave survey featuring full-time employees in China was conducted to test the hypotheses. Linear regression analyses were adopted.

Leader consideration and leader structure initiation are positively related to employee learning from work failures. Leader structure initiation strengthens the positive relationship between leader consideration and employee learning from work failures. Employee intrinsic motivation mediates the interactive effect of leader consideration and leader structure initiation on employee learning from work failures, such that leader structure initiation strengthens the positive relationship between leader consideration and employee learning from work failures through employee intrinsic motivation.

Despite the importance of employee learning from work failures, little is known about leaders’ role in facilitating such behavior. Whereas leader consideration and structure initiation are distinct and sometimes even competing, this study suggests that they complement each other to exert positive influences on employee learning from work failures. In addition, this study identifies an underlying influence mechanism.

Shared leadership has received significant empirical and theoretical attention in the project management literature. However, a dearth of studies reveals how shared leadership promotes project performance. Drawing on the theory of conservation of resources, this research proposes a serial mediation model that investigates the relationship between shared leadership and project performance through team failure learning and team resilience.

A field study was conducted that surveyed 79 project teams in various industries (comprising 380 project team members and 79 project managers) using a multisource, time-lagged survey design.

Our findings show that shared leadership has a positive impact on project performance. More importantly, team failure learning and team resilience play sequential mediating roles in the relationship between shared leadership and project performance.

This research offers new ways for project managers to manage project performance effectively. Project managers are encouraged to recognize the benefits of shared leadership. To do this, they should facilitate team failure learning and improve team resilience, which serves to boost project performance.

This research provides a novel perspective on how shared leadership influences project performance. To the best of our knowledge, we are among the first to explore the serial mediating effects of team failure learning and team resilience on the relationship between shared leadership and project performance.

Shared leadership is an effective mechanism for managing project teams. Its performance-enhancing benefits have been demonstrated in many studies. Nonetheless, there is an obvious silence about how to promote shared leadership in Lean Six Sigma (LSS) project teams. To address this deficit, the purposes of this study are to investigate the influence of shared leadership on LSS project success and to explore how team psychological safety, project task complexity and project task interdependence influence shared leadership.

A multi-source, time-lagged survey design with a four-month interval was conducted. To do this, the authors collected data from 71 project teams (comprising 71 project managers and 352 project members) using LSS approaches in the manufacturing and service industries.

The findings show that shared leadership positively influences LSS project success. The authors also found that team psychological safety fosters the development of shared leadership and, more importantly, these effects are stronger when the tasks are more complex and more interdependent.

These findings advance our understanding of the factors that enable shared leadership and equip LSS project managers with practical techniques to improve shared leadership for the success of their projects.

This study extends the theory of shared leadership to the context of LSS project management and is among the first, to the best of the authors’ knowledge, to theoretically propose and empirically validate how to promote shared leadership in LSS project teams.

Ambidexterity is the source of organizational sustainable development and long-term success. However, understanding the role of individual ambidexterity in organizations remains underdeveloped. Recently, scholars have increasingly emphasized the importance of individual ambidexterity, calling for more research on the topic. This study aims to explore the factors influencing individual ambidexterity. It proposed that goal orientation would be related to individual ambidexterity, and perceived cooperative goal interdependence and constructive controversy would play moderating roles in this relationship.

The paper opted for a survey study. Questionnaires were distributed to enterprise managers from a part-time MBA program at a university located in Beijing, China. They were also asked to bring copies to their colleagues, so they could fill them out. The authors obtained 229 valid questionnaires and used hierarchical regression analysis to test the relationships.

The results revealed that both learning goal orientation and performance goal orientation were significantly and positively related to individual ambidexterity. Perceived cooperative goal interdependence and constructive controversy positively moderated the relationship between learning goal orientation/ performance goal orientation and individual ambidexterity.

The paper provides beneficial suggestions for both managers and employees. It offers a reference for managers regarding how to promote employee ambidexterity. It also provides suggestions for employee career development.

The paper explored the factors influencing ambidextrous activities at the individual level, a very scare approach in extant studies. It also constructed a systematic process mechanism of individual ambidexterity, integrating both internal and external factors.

Based on a review of previous research of organizational learning from experience, this paper aims to point out the notable gaps and unresolved issues in the research area and proposes a “multilevel integrated model of learning from experience”, which could integrate current research findings and serve as the theoretical framework for further investigation.

This paper is a theoretical review.

From the individual, team, organizational and multiple levels, in an order of the outcome of success and failure, this study reviews previous research about organizational learning from experience down to the last detail and points out some of their limitations, including relative fragmented-wise, lack of grope about the underlying motivations, lack of overall framework, etc. Then, this study proposes the “multilevel integrated model of learning from experience”, which provides a systematic and fine-grained framework for studies in this field.

This paper emphasizes that true underlying motivations impelling learning from experience shall be identified and exploration for the antecedents shall be further deepened. Besides, this study figures out that various factors played their parts in the process and outcome of learning from experience through both subjective perception and objective experience. Thus, future research shall distinguish the influence of learning from experience, respectively, into “knowing” and “doing”.

This study is an attempt to review and integrate current research of learning from experience in multiple levels and further differentiates the influences of different experience outcomes (success vs failure). The proposed theoretical model provides clear suggestions of where future research should be directed.

Sailing toward sustainability is becoming the strategic focus of shipping firms. Drawing on organizational information processing theory (OIPT) and the theory of planned behavior (TPB), we investigated the impact of digital transformation (DT) on shipping firms’ sustainable management performance and the boundary conditions guiding this relationship.

The authors examined the hypotheses by employing hierarchical linear modeling on two-wave time-lagged data from 189 shipping firm employees in China.

The results suggest that a shipping firm’s DT is positively associated with its sustainable management performance and that the relationship is strengthened by having better cross-functional and customer coordination mechanisms. Furthermore, our three-way interaction analyses show that while injunctive norms in a shipping firm’s networks can strengthen the contingency roles of both cross-functional and customer coordination mechanisms, descriptive norms alone significantly influence customer coordination.

Drawing on organizational information processing and planned behavior theories, the present research provides new insights into leveraging DT for sailing toward sustainable success. Moreover, this study extends the current understandings of the boundary conditions of the relationship between DT and sustainable management performance by showing the two-way and three-way interaction effects of coordination mechanisms and subjective norms. The findings of the present research can be utilized as effective strategies for promoting sustainable management performance.

The purpose of this paper is to explore and examine discrepancies of food aesthetics portrayed on social media across different types of restaurants using a large-scale data set of food images.

A neural food aesthetic assessment model using computer vision and deep learning techniques is proposed, applied and evaluated on the food images data set. In addition, a set of photographic attributes drawn from food services and cognitive science research, including color, composition and figure–ground relationship attributes is implemented and compared with aesthetic scores for each food image.

This study finds that restaurants with different rating levels, cuisine types and chain status have different aesthetic scores. Moreover, the authors study the difference in the aesthetic scores between two groups of image posters: customers and restaurant owners, showing that the latter group tends to post more aesthetically appealing food images about the restaurant on social media than the former.

Restaurant owners may consider performing more proactive social media marketing strategies by posting high-quality food images. Likewise, social media platforms should incentivize their users to share high-quality food images.

The main contribution of this paper is to provide a novel methodological framework to assess the aesthetics of food images. Instead of relying on a multitude of standard attributes stemming from food photography, this method yields a unique one-take-all score, which is more straightforward to understand and more accessible to correlate with other target variables.

The purpose of this paper is to study the effect of current-carrying condition on the electrochemical process and atmospheric corrosion behavior of the commercial aluminum alloys.

Potentiodynamic polarization tests were performed to study the electrochemical process of the aluminum alloys. Salt spray tests and weight loss tests were carried out to study the atmospheric corrosion behavior. The corrosion morphology of the alloys was observed, and the products were analyzed.

The corrosion process of four aluminum alloys was accelerated in the current-carrying condition. Moreover, the acceleration effect on A2024 and A7075 was much stronger than that on A1050 and A5052. The main factors would be the differences in microstructure and corrosion resistance between these alloys. As the carried current increased, the corrosion rate and corrosion current density of the aluminum alloys gradually increased, with the protection of the corrosion product film decreasing linearly.

This is a recent study on the corrosion behavior of conductors under current-carrying condition, which truly understands the corrosion status of power grid materials. Relevant results provide support for the corrosion protection and safe service of aluminum alloy in power systems.

During the design of a new product, the generation of assembly sequences plans (ASPs) has become one of the most important problems taken into account by researchers. In fact, a good mounting order allows the time decrease of the assembly process which leads to the reduction of production costs. In this context, researchers developed several methods to generate and optimize ASP based on various criteria. Although this paper aims to improve the quality of ASP it is necessary to increase the number of criteria which must be taken into account when generating ASPs.

In this paper, an ASP generation approach, which is based on three main algorithms, is proposed. The first one generates a set of assembly sequences based on stability criteria. The obtained results are treated by the second algorithm which is based on assembly tools (ATs) workspace criterion. An illustrative example is used to explain the different steps of this proposed approach. Moreover, a comparative study is done to highlight its advantages.

The proposed algorithm verifies, for each assembly sequence, the minimal required workspace of used AT and eliminates the ASPs non-respecting this criterion. Finally, the remaining assembly sequences are treated by the third algorithm to reduce the AT change during the mounting operation.

The proposed approach introduces the concept of AT workspace to simulate and select ASPs that respect this criterion. The dynamic interference process allows the eventual collision detection between tool and component and avoids it. The proposed approach reduces the AT change during the mounting operations.

This study aims to explain the state-of-the-art machine learning models that are used in the intrusion detection problem for human-being understandable and study the relationship between the explainability and the performance of the models.

The authors study a recent intrusion data set collected from real-world scenarios and use state-of-the-art machine learning algorithms to detect the intrusion. The authors apply several novel techniques to explain the models, then evaluate manually the explanation. The authors then compare the performance of model post- and prior-explainability-based feature selection.

The authors confirm our hypothesis above and claim that by forcing the explainability, the model becomes more robust, requires less computational power but achieves a better predictive performance.

The authors draw our conclusions based on their own research and experimental works.