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Despite the dramatic increase in people's use of social media, relatively few studies have examined its effect on careers. Drawing upon social comparison theory and self-regulation theory, this study aims to investigate how career-oriented social media usage interacts with social comparison orientation (SCO) to influence the career exploration of university students.

Three waves of survey data are collected from 482 university students in China. Hypotheses are tested through ordinary least squares analysis.

Results show that career-oriented social media usage increases career anxiety, which in turn promotes career exploration. Furthermore, SCO strengthens such influence of career-oriented social media usage.

Given the limited attention paid to the effects of social media in career contexts, this study distinguishes career-oriented social media usage and proposes insights into its effect on career exploration. In doing so, this study extends social media literature and provides implications for the transition of university students from school to work in the digital era.

This study aims to provide a salt ready-mix to instant fried noodles manufacturers.

Response surface methodology was used to get optimized salt ready-mix based on carbonate salt, disodium phosphate, tripotassium phospahte, sodium hexametaphosphate and sodium chloride. Peak viscosity of flour and yellowness, cooking loss and hardness of noodles were considered as response factors for finding optimized salt formulation.

The results showed that salts have an important role in governing quality of noodles. Optimum levels of five independent variables of salts, namely, carbonate salt (1:1 mixture of sodium to potassium carbonate), disodium phosphate, sodium hexametaphosphate, tripotassium phosphate and sodium chloride were 0.64%, 0.29%, 0.25%, 0.46% and 0.78% on flour weight basis, respectively.

To the best of the authors’ knowledge, this is the first study to assess the effect of different combinations of different salts on the quality of noodles. These findings will also benefit noodle manufacturers, assisting in production of superior quality noodles.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.

The purpose of this paper is to develop a coupled lattice Boltzmann model for the simulation of the freezing process in unsaturated porous media.

In the developed model, the porous structure with complexity and disorder was generated by using a stochastic growth method, and then the Shan-Chen multiphase model and enthalpy-based phase change model were coupled by introducing a freezing interface force to describe the variation of phase interface. The pore size of porous media in freezing process was considered as an influential factor to phase transition temperature, and the variation of the interfacial force formed with phase change on the interface was described.

The larger porosity (0.2 and 0.8) will enlarge the unfrozen area from 42 mm to 70 mm, and the rest space of porous medium was occupied by the solid particles. The larger specific surface area (0.168 and 0.315) has a more fluctuated volume fraction distribution.

The concept of interfacial force was first introduced in the solid–liquid phase transition to describe the freezing process of frozen soil, enabling the formulation of a distribution equation based on enthalpy to depict the changes in the water film. The increased interfacial force serves to diminish ice formation and effectively absorb air during the freezing process. A greater surface area enhances the ability to counteract liquid migration.

Drawing upon the theory of communication visibility, this research intends to investigate the direct effect of enterprise social media (ESM) usage on team members’ knowledge creation capability (KCC) and the mediating effects of psychological safety and team identification. In addition, it aims to untangle how the efficacy of ESM usage varies between pre- and post-COVID-19 periods.

Using two-wave survey data from 240 members nested within 60 teams, this study utilizes a multilevel approach to test the proposed hypotheses.

We discover that ESM usage enhances team members’ KCC. More importantly, the results show that psychological safety and team identification mediate the ESM–KCC linkage. Interestingly, we further find that the impacts of ESM usage on team members’ KCC, psychological safety, and team identification are stronger in the pre-COVID-19 period than those in the post-COVID-19 period.

This research sheds light on the ESM literature by unraveling the mechanisms of psychological safety and team identification underlying the linkage between ESM usage and team members’ KCC. Moreover, it advances our understanding of the differential efficacy of ESM usage in pre- and post-COVID-19 periods.

The use of social media is an integral part of modern life, yet the impact of social media on creativity is a paradox. Drawing on the conservation of resources theory, the authors propose that social media, as an ecological condition, both nurture and deplete resources. Accordingly, the authors investigated two inconsistent mechanisms: creative self-efficacy and ego depletion.

Study 1 established the within-person effects of social media use on creativity by tracking 64 college students for five working days. Using a sample of 493 employee–leader-matched dyads in a national bank, Study 2 tested the entire model. Study 3 is a follow-up experiment based on a sample of 160 participants.

The results consistently showed that: (1) social media use had a positive impact on creativity in general; (2) social media use increased ego depletion and creative self-efficacy, which were two inconsistent mediators; (3) hedonic use of social media reduced the negative impact of cognitive use of social media on ego depletion.

This research sheds new light on the paradox between social media use and creativity and highlights the benefits of the balanced use of social media features. This research has implications for creative stimulation and job design in digital contexts.

From a psychosocial perspective, this study aims to understand the impact of psychosocial safety climate (PSC) on the intent to stay of construction workers and provides practical recommendations for construction enterprises to retain construction workers.

This study proposes the conceptual framework explained by the conservation of resources (COR) theory and develops a mediation model of “PSC – job satisfaction – intent to stay” within the framework supported by the stimulus–organism–response (SOR) model. Then, a questionnaire survey of 489 construction workers in Guangzhou was conducted and structural equation modeling (SEM) analysis was performed on the data collected.

Results show that PSC has a significant and positive effect on job satisfaction and intent to stay. In addition, job satisfaction partially mediates the effect of PSC on intent to stay. Hence, the theoretical model of “PSC – job satisfaction – intent to stay” has been empirically tested and supported.

This study is the first to investigate the effect of PSC on intent to stay and enriches the research on the retention of construction workers. The COR theory explains well the mechanism of PSC influence on intent to stay, thus expanding its application to the construction field. Moreover, this study provides practical recommendations for construction enterprises to retain workers so as to build a stable and productive workforce.

Technology is the lifeline for the logistics industry, and it has been immensely disrupted by the emerging blockchain technology. This paper has two main objectives. The first is to explore how the current blockchain technology can be implemented in the logistics industry with the aim of improving logistic services amongst the network of logistics service providers (LSPs). The second is to propose the development of a blockchain model for the small and medium logistics service supply chain.

A prototype blockchain-based logistics system has been created and tested in a case study with a real logistics company. The primary technologies for developing a blockchain model on the Hyperledger platform as well as how the system is designed based on the logistics service flow are explained.

The study has resulted in the successful implementation of the proposed prototype blockchain-based logistics system. In particular, the case company has managed to fully utilise the developed tracking and tracing system. Whilst utilising the prototype, the participants have been able to fulfil their responsibilities in an effective manner. The performance of LSPs has improved following the World Bank Logistics Performance Index (LPI) criteria.

This paper contributes to current research in the application of blockchain technologies in the domain of logistics and the supply chain to progress LSPs towards Logistics 4.0. The current frameworks for Logistics 4.0 and how blockchain as a disruptive technology revolutionises logistic services are reviewed. In addition, this paper highlights the benefits of blockchain technology that LSPs can leverage to further improve their performance based on the LPI criteria.