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Entrepreneurship of universities has a critical role due to its important effects. However, there is a significant gap in the literature regarding the methodology for evaluating university entrepreneurship. Evaluating university entrepreneurship provides valuable information to policymakers and governments. This research presents an evaluation methodology for assessing university entrepreneurship using a hybrid multi-criteria decision-making (MCDM) approach.

For this purpose, entrepreneurship data of 50 universities from Türkiye were used. The evaluation framework consists of 4 main criteria and 23 sub-criteria. The importance level was determined by weighting the main criterion and sub-criteria with the subjective Best-Worst Method (BWM) based on expert opinions and the objective CRiteria Importance Through Intercriteria Correlation (CRITIC) method. The importance levels were determined by weighting the main and sub-criteria using the subjective BWM based on expert opinions and the objective CRITIC method. The final rankings were obtained by integrating the different ranking values obtained with the Borda function.

Findings revealed that the most important main criterion in the entrepreneurship of universities is economic and social contribution activities, and the most important sub-criterion is international patents. The proposed method ranked all universities according to entrepreneurship. Sensitivity and validation analyses proved that the method is robust, stable and consistent.

This study also provides a new perspective to policymakers and decision-makers in evaluating university entrepreneurship.

This study, which uses a hybrid approach of MCDM methods to evaluate the entrepreneurship of universities, can be adapted to different national and regional higher education systems, and this study provides guidance to researchers in this regard.

This study aims to understand how female globally mobile employees (GMEs), a group that is gaining a stronger presence in global work, exert their career agency within the context of structural constraints.

Using theoretical perspectives of career-agency theory and person-environment fit as frames of reference, this study collected 113 blog posts written by 19 female GMEs and conducted semi-structured interviews with 11 female GMEs who are currently living and working in Japan.

This study found that the informants exercised career agency as a response to challenges related to foreignness and gender. Through career-related agentic behaviors, the informants strove to increase their fit with their environments while interacting with different aspects of structural constraints. This study also found that people in different stages of global mobility exhibited different mindsets toward their foreignness, consequently influencing their career agency.

This study highlights the dynamic interaction between structural constraints and the agency of female GMEs, advancing the understanding of career agency in women’s global work. In addition, it recognizes the presence of expatriate residents as a distinct subgroup within the GME population, shedding light on the evolving ambiguous boundaries between self-initiated expatriates and other talent categories.

Existing studies have been conducted to explain the process of digital transformation. This work aims to identify the paradoxes encountered by companies in undertaking digital transformation and the role of digital affordances in overcoming these paradoxes.

This study uses rich empirical data from four traditional Chinese manufacturers that have successfully achieved digital transformation to explain how companies can overcome the digital transformation paradox with the help of digital affordances.

The authors identify the paradoxes that traditional companies encounter when carrying out data transformation based on the experience of four Chinese traditional manufacturing enterprises that have successfully achieved digital transformation – the paradox of flexibility and stability of organization structure, the paradox of cost and profit and the paradox of perception between executives and employees. Based on this, we propose three digital affordances that play an important role in overcoming the digital transformation paradoxes – digital decentralization, digital agility and digital citizenship.

This study identifies three forms of critical digital affordances and introduces citizenship into digital transformation contexts.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

In recent years, augmented reality (AR) emerged as a notable technology within the fashion industry, enhancing consumers’ shopping experience by offering virtual try-on (VTO). To comprehensively understand female apparel consumers’ adoption intentions of this novel shopping tool, this study expanded the Technology Acceptance Model by incorporating constructs from Innovation Diffusion Theory and two psychological factors – fit confidence (FC) and body esteem (BE).

This study used a scenario-based design where participants imagined dress shopping, watched a video of a woman using an AR-based VTO app and then completed a survey. The sample included a total of 388 U.S. female participants.

The findings demonstrated the explanatory power of the research model and highlighted important factors affecting adoption decisions. Among the five innovative dimensions, relative advantage, complexity and trialability significantly impacted perceived ease of use (PEOU) and/or perceived usefulness (PU). PEOU, PU, FC and BE all significantly affected attitude and purchase intention, with attitude partially mediating the effects on intention.

Findings highlight that retailers can enhance AR-based VTO adoption by promoting its relative advantages, such as convenience and personalization, and by offering free trials or demos to increase the PEOU. Furthermore, designing AR-VTO features with inclusivity and body positivity can address consumer concerns about fit and body image, improving engagement and satisfaction. These insights provide actionable strategies for technology developers and retailers aiming to create consumer-centric shopping experiences.

This study examined both technological and psychological antecedents, providing researchers and marketers with a comprehensive understanding of fashion consumers’ shopping experiences using AR-based VTO.

The purpose of this paper is to analyze the symbiotic evolution decisions of digital innovation enterprises, research institutes and the government in the digital innovation ecosystem.

Based on innovation ecosystem theory and an evolutionary game model, this study constructs a tripartite symbiotic evolution game model of digital innovation ecosystems with digital innovation enterprises, research institutes and the government as the main bodies and analyzes the influencing factors as well as the evolution paths of the different behavioral strategies of each subject through numerical simulation.

The research shows that the digital innovation ecosystem has the characteristic of self-organization, which requires the symbiotic cooperation of each subject. The government plays an active role in any stage of symbiotic evolution, and the system cannot enter symbiosis under a low level of subsidies and penalties. Only when the initial willingness to cooperate of digital innovation enterprises and scientific research institutes is at a medium or high level is the system likely to become symbiotic. While digital innovation enterprises are more sensitive to government subsidies and punishments, scientific research institutes are more sensitive to the distribution proportion of cooperation income.

This study includes government regulation into the research scope, expands the research mode of the digital innovation ecosystem and overcomes the difficulties of empirical research in collecting dynamic large sample data. It vividly and systematically simulates the symbiotic evolution process of the digital innovation ecosystem, which provides a theoretical and practical reference for digital innovation ecosystem governance.

The application of industrial robots in modern production is becoming increasingly widespread. In the context of flexible production lines, quickly and accurately identifying and grasping specified workpieces is particularly important. This study aims to propose a grasping scheme that combines traditional methods with deep learning to improve grasping accuracy and efficiency.

First, a dataset generation method is proposed, which constructs a point cloud dataset close to the real scene without the need for extensive data collection. Then, the 3D object detection algorithm PointPillars is improved based on the features of the scene point cloud, allowing for the analysis of part poses to achieve grasping. Finally, a grasp detection strategy is proposed to match the optimal grasp pose.

Experimental results show that the proposed method can quickly and easily construct high-quality datasets, significantly reducing the time required for preliminary preparation. Additionally, it can effectively grasp specified workpieces, significantly improving grasping accuracy and reducing computation time.

The main contribution of this paper is the integration of a novel dataset generation method, improvements to the PointPillars algorithm for 3D object detection and the development of an optimal grasp detection strategy. These advancements enable the grasping system to handle real-world scenarios efficiently and accurately, demonstrating significant improvements over traditional methods.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”

We explore the driving forces behind the channel choices of the manufacturer and the platform by considering asymmetric selling cost and demand information.

This paper develops game-theoretical models to study different channel strategies for an E-commerce supply chain, in which a manufacturer distributes products through a platform that may operate in either the marketplace channel or the reseller channel.

Three primary models are built and analyzed. The comparison results show that the platform would share demand information in the reseller channel only if the service cost performance is relatively high. Besides, with an increasing selling cost, the equilibrium channel might shift from the marketplace to the reseller. With increasing information accuracy, the manufacturer tends to select the marketplace channel, while the platform tends to select the reseller channel if the service cost performance is low and tends to select the marketplace channel otherwise.

All these results have been numerically verified in the experiments. At last, we also resort to numerical study and find that as the service cost performance increases, the equilibrium channel may shift from the reseller channel to the marketplace channel. These results provide managerial guidance to online platforms and manufacturers regarding strategic decisions on channel management.

Although prior research has paid extensive attention to the driving forces behind the online channel choice between marketplace and reseller, there is at present few study considering the case where a manufacturer selling through an online platform faces a demand information disadvantage in the reseller channel and sales inefficiency in the marketplace channel. To fill this research gap, our work illustrates the interaction between demand information asymmetry and selling cost asymmetry to identify the equilibrium channel strategy and provides useful managerial guidelines for both online platforms and manufacturers.

As the scale of mega transportation infrastructure projects (MTIs) continues to expand, the complexity of engineering construction sharply increases and decision-making sustainability faces severe challenges. Decision-making for mega transportation infrastructure projects unveils the knowledge-intensive characteristic, requiring collaborative decisions by cross-domain decision-makers. However, the exploration of heterogeneous knowledge fusion-driven decision-making problems is limited. This study aims to improve the deficiencies of existing decision-making by constructing a knowledge fusion-driven multi-attribute group decision model under fuzzy context to improve the sustainability of MTIs decision-making.

This study utilizes intuitionistic fuzzy information to handle uncertain information; calculates decision-makers and indicators weights by hesitation, fuzziness and intuitionistic fuzzy entropy; applies the intuitionistic fuzzy weighted averaging (IFWA) operator to fuse knowledge and uses consensus to measure the level of knowledge fusion. Finally, a calculation example is given to verify the rationality and effectiveness of the model.

This research finally constructs a two-level decision model driven by knowledge fusion, which alleviates the uncertainty and fuzziness of decision knowledge, promotes knowledge fusion among cross-domain decision-makers and can be effectively applied in practical applications.

This study provides an effective decision-making model for mega transportation infrastructure projects and guides policymakers.