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An emerging body of literature has pinpointed the role of supply chain structure in influencing the extent to which supply chain members disclose information about their internal practices and performance. Nevertheless, empirical research investigating the effects of firm-level relational embeddedness on network-level transparency still lags. Drawing on social network analysis, this research examines the effect of relational embeddedness on supply chain transparency and the contingent role of digitalization in the context of environmental, social and governance (ESG) information disclosure.

In their empirical analysis, the authors collected secondary data from the Bloomberg database about 2,229 firms and 14,007 ties organized in 107 extended supply chains. The authors employed supplier and customer concentration metrics to measure relational embeddedness and performed multiple econometric models to test the hypothesis.

The authors found a positive effect of supplier concentration on supply chain transparency, but the effect of customer concentration was not significant. Additionally, the digitalization of focal firms reinforced the impact of supplier concentration on supply chain transparency.

The study findings contribute by underscoring the critical effect of relational embeddedness on supply chain transparency, extending prior literature on social network analysis, providing compelling evidence for the intersection of digitalization and supply chain management, and drawing important implications for practices.

This paper aims to improve the manufacturability of additive manufacturing (AM) for topology-optimized (TO) structures. Enhancement of manufacturability focuses on modifying geometric constraints and classifying the building orientation (BO) of AM parts to reduce stresses and support structures (SSs). To this end, artificial intelligence (AI) networks are being developed to automate design for additive manufacturing (DfAM).

This study considers three geometric constraints for their correction by convolutional autoencoders (CAEs) and transfer learning (TL). Furthermore, BOs of AM parts are classified using generative adversarial (GAN) and classification networks to reduce the SS. To verify the results, finite element analysis (FEA) is performed to compare the stresses of modified components with the original ones. Moreover, one sample is produced by the laser-based powder bed fusion (LB-PBF) in the BO predicted by the AI to observe its SSs.

CAE and TL resulted in promoting the manufacturability of TO components. FEA demonstrated that enhancing manufacturability leads to a 50% reduction in stresses. Additionally, training GAN and pre-training the ResNet-18 resulted in 80%, 95% and 96% accuracy for training, validation and testing. The production of a sample with LB-PBF demonstrated that the predicted BO by ResNet-18 does not require SSs.

This paper provides an automatic platform for DfAM of TO parts. Consequently, complex TO parts can be designed most feasibly and manufactured by AM technologies with minimal material usage, residual stresses and distortions.

We aim to empirically investigate the effect of affiliated banker directors (ABDs) on corporate tax avoidance. Furthermore, we conduct cross-sectional analyses on the impact of ABDs and explore the underlying mechanisms through which ABDs might influence corporate tax avoidance.

Using a large sample between 1999 and 2016, we empirically examine the impact of ABDs on corporate tax avoidance. We address the endogeneity concerns through an instrumental variable approach and robustness tests with alternative measures of ABDs and corporate tax avoidance.

Our results demonstrate that firms with ABDs exhibit lower levels of corporate tax avoidance. This negative association persists after controlling for potential endogeneity issues and is robust to alternative measures. We further document that the negative effect is stronger when firms are more bank-dependent and financially constrained. Our results indicate that ABDs limit corporate tax avoidance by strengthening corporate governance, mitigating information risks and protecting their reputational capital.

This research extends the existing literature by exploring the influence of ABDs on corporate accounting policies, particularly tax avoidance. These findings enhance our understanding of how directors’ banking experience bolsters corporate governance, information transparency and reputation, ultimately safeguarding stakeholder interests. This paper offers valuable implications for both financial practitioners and policymakers.

This study aims to deepen our understanding of how chatbots’ empathy influences humans–AI relationship in frontline service encounters. The authors investigate the underlying mechanisms, including perceived anthropomorphism, perceived intelligence and psychological empowerment, while also considering variations between different stages of the customer journey (before and after purchase).

Data collection was conducted through an online survey distributed among 301 customers who had experience using AI-based service chatbot in frontline service encounters in China. The hypotheses were examined through structural equation modeling and multi-group analysis.

The findings of this study revealed the positive impacts of emotional and cognitive empathy on humans–AI relationship through perceived anthropomorphism, perceived intelligence and psychological empowerment. Furthermore, this study verified the moderating effect of the customer journey stages, such that the impacts of anthropomorphism and intelligence on humans–AI relationship displayed more strength during the pre- and post-purchase phases, respectively.

This research offers practical implications for companies: recognize and enhance empathy dimensions in AI-based service chatbot to empower human–AI relationships; boost customer empowerment in human–AI interactions; and tailor anthropomorphic features in the pre-purchase stage and improve problem-solving capability in the post-purchase stage to enrich user experiences.

This study extends relationship marketing theory and human–AI interaction frameworks by investigating the underlying mechanisms of the effect of two-dimensional empathy on human–AI relationship. This study also enriches service design theories by revealing the moderating effect of customer journey stages.

The authors revisit the traditional OLI paradigm with the objective to allocate politics within the set of internationalization advantages by building on the political strategy literature. The authors outline the specific role of political advantage that facilitates and propels the international expansion of state-owned multinational enterprises (SOMNEs) from emerging markets.

A conceptual paper which explains the role of political advantage in the internationalization of SOMNEs. The authors expand the scope of the OLI to capture the impact of firms' home governments' policies and relationships with host countries which are leveraged by SOMNEs in their internationalization.

The authors define political advantage as a new type of advantage which depends on and is sourced from external actors. The authors argue that P-advantage is a multifaceted and unstable part of POLI composition, which is contingent on political shifts and may be leveraged by various firms. The authors also assert that political capabilities have limitations in sustaining political advantage, which may be compensated via enhancing the political activity of firms.

The authors conceptualize the POLI-advantages paradigm for the internationalization of SOMNEs by proposing that in addition to the traditional ownership, location, and internalization advantages, firms can capitalize on their political advantage to enter markets where internationalization might have been difficult without their political connections.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

China is a significant energy consumer with increasingly severe resource constraints and environmental problems, requiring low-carbon energy transformation and encouraging high-quality energy development (HED). Green finance significantly affects the effect on HED as a cutting-edge financial strategy to support environmental improvement and encourage green development.

Using panel data from 30 provinces from 2007 to 2019 and the system-generalized method of moments method, this paper investigates the impact of green finance on HED, and further explores their threshold effect, heterogeneous and asymmetry analysis.

The main results indicate that: (1) green finance positively affects HED in China; in other words, a 1% increase in the green finance index will boost HED by an average of 0.767%; (2) as the economy improves, the positive impact of green finance on HED will be even more significant and (3) the contribution of green finance to HED is more significant in the northern provinces and areas with lower HED levels.

This paper puts forward relevant policy suggestions to further improve the construction of the green financial system.

Ultracapacitors find extensive applications in various fields because of their high energy density and long cycling periods. However, due to the movement of ions and the arrangement patterns on rough/irregular electrode surfaces during the charge and discharge process of ultracapacitors, the parameters of ultracapacitors usually change with the variation of operating conditions. The purpose of this study is to accurately and quickly identify the parameters of ultracapacitors.

A variable forgetting factor recursive least square (VFFRLS) algorithm is proposed in this paper for online identifying the equivalent series resistance and capacitance C of ultracapacitors. In this work, a real-time error-based strategy is developed to adaptively regulate the value of the forgetting factor of traditional forgetting factor recursive least square (FFRLS) algorithm. The strategy uses the square of the average time autocorrelation estimation of the prior error and the posterior error between the predicted output and the actual output as the adjustment basis of forgetting factors.

Experiments were conducted using the proposed scheme, and the results were compared with the estimation results obtained by the recursive least squares (RLS) algorithm and the traditional FFRLS algorithm. The maximum root mean square error between the estimated values and actual values for VFFRLS is 3.63%, whereas for FFRLS it is 9.61%, and for RLS it is 19.33%.

By using the proposed VFFRLS algorithm, a relatively high precision can be achieved for the online parameter estimation of ultracapacitors. Besides, the dynamic balance between parameter stability and tracking performance can be validated by dynamically adjusting the forgetting factor.

User value co-creation behaviors are crucial for the sustainable development of Virtual Brand Communities. This research, grounded in social exchange theory, investigates the impact of community satisfaction and identification on customer value co-creation behaviors and further explores how the reciprocity norm moderates these relationships.

Our research data were collected from users across multiple brand communities, totaling 481 survey responses. Structural equation modeling was performed to test the research hypotheses.

These results provide in-depth insights into the nexus between user-community relationships and customer value co-creation behaviors. While community satisfaction and identification positively influence co-creation, their effects vary across different value co-creation behaviors. Notably, the reciprocity norm within the community dampens the relationship between community satisfaction and value co-creation behaviors.

Unlike previous studies focusing on customer value co-creation behaviors, our research emphasizes social exchange, unveiling the mechanisms behind customer value co-creation. Our findings not only enrich the body of knowledge on customer value co-creation but also deepen our understanding of online collective behavior and knowledge sharing, offering valuable insights for the development of virtual communities.

This paper aims to systematically investigate the effect of laser remelting on the surface morphology and mechanical properties of laser deposition manufactured thin-walled Ti-6Al-4V alloy.

Thin-walled Ti-6Al-4V samples were prepared by laser deposition manufacturing (LDM) method and subsequently surface-treated by laser remelting in a controlled environment. By experiments, the surface qualities and mechanical properties of LDM Ti-6Al-4V alloy before and after laser remelting were investigated.

After laser remelting, the surface roughness of LDM Ti-6Al-4V alloy decreases from 15.316 to 1.813 µm, hard and brittle martensite presents in the microstructure of the remelted layer, and the microhardness of the laser remelted layer increases by 11.39%. Compared with the machined LDM specimen, the strength of the specimen including the remelted layer improves by about 5%, while the elongation and fatigue life decrease by about 72.17% and 64.60%, respectively.

The results establish foundational data for the application of laser remelting to LDM thin-walled Ti-6Al-4V parts, and may provide an opportunity for laser remelting to process the nonfitting surfaces of LDM parts.