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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 paper aims to investigate the impact of quality loss in transit on e-commerce supply chain pricing, production and financing decisions.

The authors consider a Stackelberg game model with a supplier, logistics firm and e-commerce platform. The logistics firm is capital-constrained and obtains funding from the e-commerce platform by debt financing or equity financing. Through backward induction, this paper first solves the equilibrium results under the two financing schemes and then reveals the financing preferences of all parties.

The results demonstrate that equity financing reduces financing costs and promotes production significantly. However, it may also lead to overproduction, particularly in markets with poor profitability and high cost factors. When the percentage of product quality loss is large, equity financing is preferable. With the increasing of transportation level, the benefits of debt finance are steadily growing. In addition, equity financing is the Pareto dominant scheme for all firms under certain circumstances. The extensions consider hybrid financing and another quality loss type.

The paper derives the equilibrium solutions and financing preferences, then specifies the threshold for applying financing schemes. Provide guidance for logistics firms’ finance model innovation and core enterprise involvement in the logistics industry.

The paper investigates how logistics firms’ financing strategies are impacted by product quality loss.

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.

The purposes of this paper are optimization of high speed reducer in electric vehicles based on the analysis of lubrication and verification of simulation accuracy and optimization results.

The traditional CFD method presents poor applicability to complex geometric problems due to grid deformity. Therefore, moving particle semi-implicit (MPS) method is applied in this study to simulate lubrication of the reducer and analyze the influence of input speed and lubrication system design on the distribution. According to the results, the reducer is optimized. Meanwhile, the experiments for lubrication and churning power loss is carried out to verify the accuracy of simulation and optimization effects.

The flow field of lubricant inside the reducer is obtained. The lubrication system of reducer needs to be improved. Simulation and experiment show that the optimization is sufficient and efficient.

According to the simulation of lubrication, the reducer is optimized. The lubrication experimental setup is established. The conclusion of paper can provide the method and tool for reducer in electric vehicle.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2024-0123/

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.

This study aims to reveal the mechanism of CEO overconfidence in the digital transformation of specialized, refined, distinctive and innovative (SRDI) enterprises, thereby enriching research related to upper echelons theory and corporate digital transformation.

This study uses listed SRDI companies in China from 2017 to 2022 as a sample and adopts a fixed-effects regression model to analyze the direct, mediating, and moderating effects of CEO overconfidence on corporate digital transformation.

First, CEO overconfidence significantly promotes SRDI enterprises' digital transformation. Second, according to the “cognition-behavior-outcome” model, we found that entrepreneurial orientation plays a mediating role. Third, based on the principle of procedural rationality and the interaction perspective between the CEO and the executive team, we introduce the heterogeneity of the executive team as a moderating variable. Our findings indicate that age heterogeneity within the executive team has a negative moderating effect, whereas educational and occupational heterogeneities have positive moderating effects.

This study expands on earlier research that focuses primarily on CEO demographic characteristics. It enriches the analytical perspective of upper echelons theory on corporate digital transformation by analyzing the psychological characteristics of CEOs, that is, overconfidence and its mediating pathways. Moreover, this study goes beyond the previous literature that does not differentiate between CEOs and executive teams by introducing the concept of CEOs' interactions with the executive team and including the heterogeneity of the executive team as a moderating variable in the literature. Thus, continuing to deepen the application of upper echelons theory to corporate digital transformation. Additionally, this study contributes to the literature on the positive consequences of overconfidence.

Employee readiness for change is essential for long-term organizational success. However, organizations often struggle to generate employee support for change as they fail to mitigate associated uncertainties. Studies exploring possible antecedents of employee readiness for change primarily focus on internal organizational practices, while external practices have been overlooked in the discussion. Drawing from uncertainty reduction theory, we examine how external organizational practices in terms of external CSR positively affect readiness for change.

In a survey of 377 employees from 29 German companies, we test the hypothesized chain of effects between external CSR, perceived organizational support, perceived uncertainty, and readiness for change by using structural equation modeling.

Results from structural equation modeling demonstrate a positive relationship between external CSR and readiness for change, which is sequentially mediated by perceptions of organizational support and uncertainty.

By highlighting the role of external organizational practices in promoting change readiness, the paper offers new insights into the mechanisms of effective change management.

We developed and tested a theoretical model that measures the importance of social media and dynamic capabilities with regard to improving innovation performance in startups from the agribusiness sector (AgTech), that is, technology-based companies with scalability potential that operate in the agribusiness sector.

This model analyzed three dynamic capabilities: internal collaboration capacity, absorptive capacity, and organizational agility. To test this theoretical model, we employed structural equation modeling with partial least squares estimation (PLS-SEM) analyzing 237 AgTech in Brazil.

Our results show that the use of social media positively affects innovation performance and contributes to internal collaboration capability (a second-order construct that encompasses commitment, communication, and trust). Internal collaboration is an antecedent of absorptive capacity and organizational agility. These two dynamic capabilities positively impact innovation performance. Our results also indicate that these dynamic capabilities serially mediate the relationship between social media and innovation performance.

While some literature describes the dark side of social media, our study sheds light on the importance of these platforms to improve internal collaboration in AgTech, a pivotal capability that is necessary to develop other relevant capabilities.

This study presents results from one of most large AgTech survey found in literature. Furthermore, this study also shows that there are dynamic capabilities essential to develop others relevant dynamic capabilities, indicating that innovation performance involves a sequential integration of capabilities.