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The purpose of this study is to contribute to empirical research on individual ambidexterity drivers. This paper analyzes the relationships between inclusive leadership, team knowledge acquisition, team knowledge sharing, digital tools usage and individual ambidexterity.

This study conducted a questionnaire survey of high-tech and manufacturing enterprises in China and obtained 75 leader questionnaires and 365 employee questionnaires. The hypotheses were tested using hierarchical and cross-level regressions.

The research indicates that inclusive leadership improves team knowledge acquisition and sharing. However, only team knowledge sharing significantly boosts individual ambidexterity, and not team knowledge acquisition. Thus, inclusive leadership fosters individual ambidexterity primarily through team knowledge sharing. Digital tools usage strengthens the impact of inclusive leadership on team knowledge sharing, thereby intensifying its effect on individual ambidexterity. However, digital tools usage weakens the effect of inclusive leadership on team knowledge acquisition.

First, this study addresses the call for research on ambidexterity at different levels, revealing the heterogeneous impact of team knowledge acquisition and sharing on individual ambidexterity. Second, this study developed a theoretical model to explore how leadership affects individual ambidexterity. Third, this study responds to the question that digitalization has won, but has leadership lost by investigating the role of digital tools usage in the relationship between inclusive leadership and team knowledge integration.

To assist firefighters in extinguishing fires more safely and efficiently, this paper aims to present an eversion-growing firefighting robot that overcomes the maneuverability constraints of existing firefighting robots, which hinder their ability to approach fire sources effectively.

The robot uses an everting tube as the body, with the cavity formed during eversion to deliver water to the tip. The loading capacity models are extended by considering the mechanical reinforcement provided by the contact between the everting tube and obstacles. Then the authors design a steering mechanism based on the Yoshimura-ori structure to ensure the robot’s steering capabilities. Furthermore, the authors present a new tip mount for the eversion-growing robot in fire conditions.

Experiments demonstrate the robot’s ability to navigate obstacles effectively using the Yoshimura-ori steering mechanism and perform firefighting tasks successfully.

The robot innovatively overcomes traditional firefighting limitations and integrates the eversion-growing structure with the Yoshimura-ori steering mechanism, demonstrating effective obstacle-traversing capabilities in performing tasks.

This study aims to examine the effects of the characteristics of apparel professionals on their attitude toward sustainability-related technology in the context of a developing country, Bangladesh.

A quantitative approach was used to investigate the apparel professionals’ perception of sustainability-related technology. A survey was conducted, and 204 valid responses were used in data analysis. The structural equation modeling technique was used to analyze the data.

The findings demonstrate that apparel professionals’ personal innovativeness positively impacts their knowledge of apparel technology. Knowledge of apparel technology and environmental issues in apparel manufacturing both significantly and positively impact their level of awareness of sustainability-related technology in apparel manufacturing. The findings also suggest that managers’ level of awareness of sustainability-related technology has a significant positive impact on their attitude toward sustainability-related technology.

Fishbein’s attitude theory was applied to examine how the various characteristics of apparel professionals (i.e. personal innovativeness in technology, knowledge of apparel technology, knowledge of environmental issues of apparel manufacturing) affect their awareness of and attitude toward sustainability-related technology. This study expands our understanding of the causal flow among cognitive variables of apparel professionals, including their innovativeness, knowledge, awareness and attitudes. The findings of the study can be helpful to the apparel industry to improve apparel professionals’ adoption of sustainable technology.

This paper aims to investigate the sentiment connectedness among 10 European stock markets between January 2020 and July 2022, associating such connectedness with the level of the geopolitical risk index.

For this purpose, a time-varying parameter vector autoregressive connectedness framework is used.

Results show a high degree of sentiment connectedness. Overall, the sentiments of Portugal, France, the Netherlands, Spain, Germany and Italy are net transmitters of shocks while those of Poland, Sweden, Norway and Romania are net receivers. Additional evidence indicates that when geopolitical risks increase, the sentiment connectedness tends to decrease. However, the reverse holds under extremely high levels of geopolitical risks.

Overall, this study provides some significant contributions to the literature. First, to the best of the authors’ knowledge, this is among the first few studies to examine the dynamic connectedness among stock market sentiment across countries. This issue needs special consideration for European countries because of their close geographical distance and strong integration due to the European Union’s co-development strategies. Second, the association of sentiment connectedness with geopolitical risk is examined for the first time. This is even more meaningful in the context of growing geopolitical risks stemming from the Ukraine war, which could affect international financial markets.

This paper aims to study the bibliometric analysis of resource orchestration research in management and businessareas.

The research method is based on a three-step protocol of dataset setting, dataset refining and analyzing the data. First, the 202 published articles in the resource orchestration field were collected from the Web of Science database. Second, the document information was refined. Finally, we used the bibliometric analysis toolbox to investigate the documents. Also, bibliometric analysis in this research has been conducted using VOSviewer and SPSS software.

The findings of this research examined the evolution of the resource orchestration theory. Also, this paper has identified five major themes in this field: resource orchestration for entrepreneurship and innovation, orchestrating supply chain, orchestrating information technology resources, orchestrating intangible resources and the role of resource orchestration in small firm performance. Finally, emerging trends in the resource orchestration area include sustainable development, innovation and information technology.

This paper is the first to use bibliometric analysis to examine resource orchestration theory. Accordingly, this research contributes to the resource orchestration area through the theoretical evolution, underlying streams of research and emerging themes in research topics and methodologies.

Previous studies have rarely integrated the financing modes of a capital-constrained manufacturer with the choices of online sales strategies. To address this gap, the authors study how a manufacturer selects optimal financing modes under different sales strategies in three dual-channel supply chains.

This paper considers three sales strategies, namely, combining a traditional retailer channel with one of the direct selling, reselling and agency selling channels, and two common financing modes, namely, bank financing and retailer financing. The authors obtain equilibrium outcomes of the manufacturer and traditional retailer and then provide the conditions for them to select optimal financing modes under three sales strategies.

The results indicate that the manufacturer’s financing decisions rely on the initial capital and interest rates, and the manufacturer selects retailer financing only if the initial capital is relatively larger. In terms of financing mode options, the retailer financing mode is more beneficial for the manufacturer under the three sales strategies. From the perspective of sales strategies, the direct selling model is more beneficial. In addition, the higher the consumer acceptance of the online channel, the more profits the manufacturer obtains.

This paper provides suggestions on how the capital-constrained manufacturer chooses financing modes and sales strategies.

This paper integrates the financing mode and different sales strategies to investigate the manufacturer’s optimal operational decisions. These sales strategies allow us to investigate the manufacturer’s optimal financing modes in the presence of both different financing modes and sales strategies.

Promoting technological innovation is important to address the complexity of major engineering challenges. Technological innovations include short-term innovations at the project level and long-term innovations that can enhance competitive advantages. The purpose of this study is to develop an incentive mechanism for the public sector that considers short-term and long-term efforts from the private sector, aiming to promote technological innovation in major engineering projects.

This study constructs an incentive model considering the differences in short-term and long-term innovation efforts from the private sector. This model emphasizes the spillover effect of long-term efforts on current projects and the cost synergy effect between short-term and long-term efforts. It also explores the factors influencing the optimal incentive strategies for the public sector and innovation strategies for the private sector.

The results indicate that increasing the output coefficient of short-term and long-term efforts and reducing the cost coefficient not only enhance the innovation efforts of the private sector but also prompt the public sector to increase the incentive coefficient. The spillover effect of long-term innovation efforts and the synergy effect of the two efforts are positively related to the incentive coefficient for the public sector.

This research addresses the existing gap in understanding how the public sector should devise incentive mechanisms for technological innovation when contractors acting as the private sector are responsible for construction within a public-private partnership (PPP) model. In constructing the incentive mechanism model, this study incorporates the private sector's short-term efforts at the project level and their long-term efforts for sustained corporate development, thus adding considerable practical significance.

Flooding is China’s most frequent and catastrophic natural hazard, causing extensive damage. The aim of this study is to develop a comprehensive assessment of urban flood risk in the Hubei Province of China, focusing on the following three issues: (1) What are the factors that cause floods? (2) To what extent do these factors affect flood risk management? (3) How to build an effective comprehensive assessment system that can be used to reduce flood risk?

This study combines expert opinion and evidence from the extent literature to identify flood risk indicators across four dimensions: disaster risk, susceptibility, exposure and prevention and mitigation. The Criteria Importance Through Intercriteria Correlation (CRITIC) and the Grey Relational Analysis (RA)-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision-making approach were applied to calculate the weighting of factors and develop a model of urban flood risk. Then, ArcGIS software visualizes risk levels and spatial distribution in the cities of Hubei Province; uncertainty analysis verified method accuracy.

The results show that there are significant differences in the level of urban flood risk in Hubei Province, with cities such as Tianmen, Qianjiang, Xiantao and Ezhou being at high risk, while cities such as Shiyan, Xiangyang, Shennongjia, Yichang, Wuhan and Huanggang are at lower flood risk.

The innovative method of combining CRITIC-GRA-TOPSIS reduces the presence of subjective bias found in many other flood risk assessment frameworks. Regional data extraction and uncertainty analysis enhance result reliability, supporting long-term decision-making and urban planning. Overall, the methodological approach developed provides an advanced, highly effective and efficient analysis and visualization of flood risk. This study deepens the understanding of flood risk assessment mechanisms and more broadly supports the development of resilient cities.

This paper aims to provide a full introduction, new classification, comparison and investigation of the challenges as well as applications of layerless 3D printing, which is one of the industry 4.0 pioneers.

Given the significance and novelty of uniform 3D printing, more than 250 publications were collected and reviewed in an unbiased and clear manner.

As a result, the majority of uniform parts printed in polymer form are known up to this point. In a novel division for better researchers’ comprehension, uniform printing systems were classified into three categories: oxygen inhibition (OI), liquid lubrication (LL) and photon penetration (PP), and each was thoroughly investigated. Furthermore, these three approaches were evaluated in terms of printing speed, precision and accuracy, manufacturing scale and cost.

The parameters of each approach were compared independently, and then a practical comparison was conducted among these three approaches. Finally, a variety of technologies, opportunities, challenges and advantages of each significant method, as well as a future outlook for layerless rapid prototyping, are presented.

In laser powder bed fusion (L-PBF) additive manufacturing, spatter particles are ejected from the melt pool and can be detrimental to material performance and powder recycling. Quantifying spatter generation with respect to processing conditions is a step toward mitigating spatter and better understanding the phenomenon. This paper reveals process insights of spatter phenomena by automatically annotating spatter particles in high-speed video observations using machine learning.

A high-speed camera was used to observe the L-PBF process while varying laser power, laser scan speed and scan strategy on a constant geometry on an EOSM290 using Ti-6Al-4V powder. Two separate convolutional neural networks were trained to segment and track spatter particles in captured high-speed videos for spatter characterization.

Spatter generation and ejection angle significantly differ between keyhole and conduction mode melting. High laser powers lead to large ejections at the beginning of scan lines. Slow and fast build rates produce more spatter than moderate build rates at constant energy density. Scan strategies with more scan vectors lead to more spatter. The presence of powder significantly increases the amount of spatter generated during the process.

With the ability to automatically annotate a large volume of high-speed video data sets with high accuracy, an experimental design of observed parameter changes reveals quantitively stark changes in spatter morphology that can aid process development to mitigate spatter occurrence and impacts on material performance.