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Despite its flourishing development since first proposed, job crafting literature has provided limited insights into why people craft their jobs. This study theoretically develops a two-dimensional integrative framework for the motives of job crafting, including orientation (self-oriented vs work-oriented vs other-oriented) and self-determination (autonomous vs introjected vs external) dimensions. We further investigate the specific motives of job crafting from actor and observer perspectives.

We conducted two critical-incident recall surveys among 120 and 100 employees from varied sectors and organizations, who responded from the actor and observer perspective respectively. 395 and 299 valid open-ended responses were then collected and coded following the steps for content analysis.

Drawing from the proposed two-dimensional theoretical framework, we identified 16 specific job crafting motives from actor and observer perspectives.

Our findings remind managers to pay attention to employees' motives of job crafting and take appropriate managerial actions according to their varied motives.

By incorporating job crafting from the motivation literature and identifying diversified motives that drive employees to engage in job crafting, this qualitative study contributes to both the job crafting literature and the broader application of self-determination theory in the field of organizational behavior.

Taking attribution theory as an overarching framework, the study aims to examine how employees attribute and respond to a colleague's approach crafting.

Two complementary studies, including a scenario experiment (Study 1; N = 114) and an online survey (Study 2; N = 220), were conducted to test the hypothesized model.

Study 1 found support for the attribution of a prosocial motive to approach crafting, which in turn led to more social support and less social undermining among observers. This mediation was stronger when the job crafter was perceived as less other-oriented. Study 2 replicated the findings of Study 1 and further showed that when observers attributed both high impression management and prosocial motives to approach crafting, the positive relationship between their prosocial motive attribution and social support for the job crafter got weakened, while the negative relationship between their prosocial motive attribution and social undermining of the job crafter was strengthened.

The findings demonstrate that approach crafting gives rise to specific attributions and reactions toward the job crafter, which enrich the understanding of the social consequences of job crafting in the workplace.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

Leader–member exchange (LMX) relationship is a crucial context for individuals’ generation of creative ideas. Unlike the fruit research between LMX quality and employee creativity, the relationship between LMX ambivalence and employee creativity is scarce. This study thus aims to examine the effect of LMX ambivalence on employee creativity.

This study conducted an on-site survey of research and development teams in China and distributed paired questionnaires with a time lag of one month. Data from 116 leaders and 484 subordinates were collected and analyzed.

The results showed that LMX ambivalence was directly negatively or indirectly negatively related to employee creativity via self-efficacy. Employee cynicism acted as a potential personal moderator of the effect of LMX ambivalence, specifically, employee cynicism attenuated the negative influence of LMX ambivalence on employee creativity via self-efficacy.

This study extends our knowledge of the complex effects of LMX relationships by empirically exploring whether and how LMX ambivalence influences employee creativity, with self-efficacy introduced as one crucial underlying mechanism. Meanwhile, this study enriches the existing cynicism literature by demonstrating the role of employee cynicism as a buffer in the relationship between LMX ambivalence and employee creativity.

With the rapid development of the pipeline transportation and exploitation of mineral resources, it is urgent requirement for the high-performance polymer matrix composites with low friction and wear to meet the needs of solid material transportation. This paper aims to prepare high-performance ultrahigh molecular weight polyethylene (UHMWPE) matrix composites and investigate the effect of service condition on frictional behavior for composite.

In this study, UHMWPE matrix composites with different content of MoS₂ were prepared and the tribological performance of the GCr15/composites friction pair in various sliding speeds (0.025–0.125 m/s) under dry friction conditions were studied by ball-on-disk tribology experiments.

Results show that the frictional behavior was shown to be sensitive to MoS2 concentration and sliding velocity. As the MoS2 content is 2 Wt.%, composites presented the best overall tribological performance. Besides, the friction coefficient fluctuates around 0.21 from 0.025 to 0.125 m/s sliding speed, while the wear rate increases gradually. Scanning electron microscopy images, energy-dispersive spectroscopy and Raman Spectrum analysis present that the main wear mechanisms were abrasive and fatigue wear.

The knowledge obtained herein will facilitate the design of UHMWPE matrix composites with promising self-lubrication performances which used in slag transport engineering field.

Megaprojects provide an ideal context for exploring the dynamic characteristics of stakeholders within a collaborative innovation system. This research aims to examine the changes in stakeholder salience and functional roles during the evolution of such a system.

This study is empirically grounded on the Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject in China, analyzed with the stakeholder salience, stakeholder functional role, stakeholder dynamics and collaborative innovation system theoretical lens.

The megaproject collaborative innovation system can be divided into four stages: the birth stage, development stage, mature stage and re-innovation stage. Stakeholder salience generally remains unchanged throughout the lifecycle of the collaborative innovation system, except for engineering consulting firms (ECF). ECF transitioned from a definitive stakeholder to an expectant stakeholder upon project completion. The number of definitive stakeholders during the first three stages increases gradually. Besides, stakeholder functional roles shift in eight different directions throughout the lifecycle of the megaproject collaborative innovation system because they possess different core resources necessary for implementing innovations and are positioned differently within the collaborative innovation system.

This study contributes to the theory and practice of collaborative innovation in megaprojects. First, it offers insights into the evolution of megaproject collaborative innovation systems from the perspective of stakeholder interactions. Second, it has significant implications for managing stakeholder relationships based on their salience and functional roles at different stages of the collaborative innovation system.

New generation of construction workers (NGCWs) who were born in the 1980s and later have gradually become the main workforce of Chinese construction industry. They may behave differently when dealing with knowledge-related activities due to divergent characteristics caused by generational discrepancy. To provide a theoretical foundation for construction companies and safety managers to improve safety management, this research explores the factors and paths impacting the NGCWs' ability to share their safety knowledge.

Based on literature review, main factors that influence the safety knowledge sharing of the NGCWs were identified. Decision-Making Trial and Evaluation Laboratory and Interpretive Structural Modeling were applied to identify the hierarchical and contextual relations among the factors influencing the safety knowledge sharing of the NGCWs.

The results showed that sharing atmosphere ranked first in centrality and had a high degree of influence and being influenced, indicating itself an extremely important influencing factor of safety knowledge sharing of NGCWs. Six root influencing factors were identified, including individual characteristics, work pressure, sharing platform, incentive mechanism, leadership support and safety management system.

The number of influencing factors of safety knowledge sharing of the NGCWs identified in this study is limited, and the data obtained by the expert scoring method is subjective. In future studies, the model should be further developed and validated by incorporating experts from different fields to improve its integrity and applicability.

The influencing factors identified in this paper can provide a basis for construction companies and safety managers to improve productivity and safety management by taking relevant measures to promote safety knowledge sharing. The research contributes to the understanding knowledge management in the context of the emerging market. It helps to answer the question of how the market can maintain the economic growth success through effective knowledge management.

This paper investigates the influencing factors of NGCWs' safety knowledge sharing from the perspective of intergenerational differences, and the 13 influencing factor index system established expands the scope of research on factors influencing safety knowledge sharing among construction workers and fills the gap in safety knowledge sharing research on young construction workers. Furthermore, this paper establishes a multi-layer recursive structure model to clarify the influence path of the influencing factors and contributes to the understanding of safety knowledge sharing mechanism.

Green innovation (GI) in megaprojects has become a significant research topic that attracts both megaproject management scholars' and practitioners' attention. Green transformational leadership (GTL) is acknowledged as an important antecedent to GI in the permanent context. However, limited research investigates the mechanism and condition of how GTL effectively affects GI in the temporary (i.e. megaproject) context. This study seeks to examine the mechanism and condition of GTL in improving GI by assessing the mediating role of green knowledge sharing (GKS) and the moderating effect of innovation climate (IC).

Regression analysis was performed on data obtained from 303 experts who have been involved in megaprojects.

GTL has a significant positive impact on two aspects of GI, including green product innovation (GPDI) and green process innovation (GPCI). Besides, GKS mediates the relationship between GTL and the two aspects of GI. Moreover, IC plays a significantly positive moderating role in the relationship between GTL and GKS and the relationship between GKS and the two aspects of GI.

This study adds knowledge to the theory and practice by unveiling the “black box” between GTL and GI in the temporary (i.e. megaproject) context. First, this study extends the continuing discussion on the direct effect of GTL on GI to the temporary (i.e. megaproject) context. Second, this study facilitates the understanding of the mechanism to generate better GI performance considering the mediating role of GKS and the moderating effect of IC in the temporary (i.e. megaproject) context. The results can illuminate megaproject practitioners on generating better GI performance.

The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.

This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.

This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.

This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.

The purpose of this paper is to identify the Nomoto ship model parameters accurately, in order to produce a very close match between the predictions based on the model and the full‐scale trials.

Various ship maneuvering mathematical models have been used when describing the ship dynamics behavior. The Nomoto ship model is a class of simplified hydrodynamic derivative type models which are the most widely used, accepted and perhaps well developed. To determine the model parameters accurately, particle swarm optimization (PSO) is chosen as an evolution algorithm in this paper. This arithmetic can guarantee the convergence and global optimization ability, and avoid sinking into a local optimal solution.

The process of PSO for identifying the Nomoto ship model parameters is given.

Availability of the full‐scale trial data are the main limitations.

The ship model parameters provide very useful advice in ship's autopilot process.

The paper presents a new parameter identification method for the second‐order Nomoto ship model based on PSO.