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Exploring the Lean and Green relationship goes back to the beginnings of Lean manufacturing. Most cases established that companies implementing Lean have Green results. However, there are Lean practices with a higher impact on Green, but others with less impact. Therefore, this paper presents research that explores the relationship between Lean and Green in manufacturing companies and aims to determine whether Lean practices have a higher association with Green aspects.

A survey was conducted amongst manufacturing firms to determine their Lean Index (LI). The internally related elements of the Lean construct determined each firm’s LI, whilst Cronbach alpha determined internal LI consistency. The survey also identified firms developing six Green aspects: International Organisation for Standardisation (ISO) 14001, ISO 50001, general Green aspects and the specific aspects of materials, energy and water. An individual sample t-test shows different LI levels of association for each Green aspect. Binomial logistic regression shows the LI element association for each Green aspect.

LI is higher at firms reporting the inclusion of Green aspects. More than half of LI components have a statistically relevant association with the six Green aspects. In general, Ishikawa diagrams had the highest association with Green aspects whilst the lowest was seen in workers as improvement initiators. By grouping the LI elements into their categories, the Lean practices related to controlling processes have a higher association, whilst the involvement of employees has the lowest.

Further research found in this paper identifies the possibilities for investigating the specificities of each Lean tool to develop Green aspects in companies.

Practitioners learn that Lean and Green are not separate issues in business. This article provides evidence that Lean practices in place at companies are already associated with Green aspects, so integration may already be happening.

This paper provides specifics on the relationship between each Lean practice and developing Green aspects. Thus, this paper specifies the Lean practices that contribute most to Green efficiency to support the joint development of both themes.

Graded honeycombs are materials that exhibit better energy absorption performance compared to uniform honeycombs without adding additional weight. This paper introduces a novel modularized graded honeycomb into a commercial crash box to improve its crashworthiness.

A modularized graded honeycomb is inserted into a commercial crash box to develop a novel crash box. Finite element analyses are conducted to investigate the crashworthiness. Pareto cumulative influence analysis is conducted to rank the effects of design parameters on crashworthiness. A surrogate model-based multi-objective optimization is carried out to improve energy absorption while limiting the impact peak force. An optimal Pareto solution set is obtained.

Modularized honeycomb-filled crash box outperforms that of its corresponding uniform honeycomb-filled crash box and empty crash box in resisting impact. Pareto cumulative influence analysis reveals that for most crashworthiness indicators, cell-wall thicknesses of crash box tube contribute the most, followed by average relative density and graded coefficient of modularized honeycomb (MH). Graded coefficient contributes nearly 10% on mean force and maximum displacement, but it has insignificant influence on peak force and weight. Optimization results show that the optimal designs can not only absorb more energy but also limit the peak force compared with those of uniform honeycomb-filled crash box.

This paper fills a MH into a commercial crash box to propose a novel crash box and demonstrates the positive impact of modularized design on crashworthiness compared with that of uniform honeycomb-filled crash box. Moreover, modularizing honeycomb does not change the weight of the filler, and thus, the novel crash box would benefit development of crash box with lightweight and excellent energy absorption capacity.

ESG issues are gaining increasing attention from investors, but the environmental, social and governance (ESG) rating disagreement caused by different standards of rating agencies misleads investors' investment decisions. This can lead to an increased risk of stock price crashes, causing turbulence in the financial markets and reducing investors' confidence. The paper investigates whether ESG rating disagreement of the current period increases stock price crash risk and the mechanism to mitigate this impact.

With the sample of the listed companies of Shanghai and Shenzhen Stock Exchanges from 2010 to 2022 this paper examines the impact of ESG rating disagreement itself on stock price crash risk. Moreover, this paper examines the mechanisms by analyzing the moderating effect of distraction of investors; digital economy and corporate intelligence maturity.

This paper finds that ESG rating disagreement itself would amplify the stock price crash risk. When exploring the moderating effect of institutional investors' distraction, digital economic development level and corporate intelligence, the paper found that they would mitigate the impact of ESG rating disagreement on stock price crash risk. The relationship between ESG rating disagreement and stock price crash risk is more pronounced in the context of heavily-polluted, state-owned enterprises (SOEs) and enterprises with star analysts.

Currently, few articles discuss ESG rating disagreement, especially the impact of current ESG rating disagreement on stock price crash risk. This paper focuses on this topic and provides strategies to mitigate the impact of current ESG rating divergence on stock price crash risk.

Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

The purpose of this paper is to provide a critical historical analysis of the business (mis)behaviors and influencing factors that discourage enduring cooperation between principals and agents, to introduce strategies that embrace the social values, economic motivation and institutional designs historically adopted to curtail dishonest acts in international business and to inform an improved principal–agent theory that reflects principal–agent reciprocity as shaped by social, political, cultural, economic, strategic and ideological forces

The critical historical research method is used to analyze Chinese compradors and the foreign companies they served in pre-1949 China.

Business practitioners can extend orthodox principal–agent theory by scrutinizing the complex interactions between local agents and foreign companies. Instead of agents pursuing their economic interests exclusively, as posited by principal–agent theory, they also may pursue principal-shared interests (as suggested by stewardship theory) because of social norms and cultural values that can affect business-related choices and the social bonds built between principals and agents.

The behaviors of compradors and foreign companies in pre-1949 China suggest international business practices for shaping social bonds between principals and agents and foreign principals’ creative efforts to enhance shared interests with local agents.

Understanding principal–agent theory’s limitations can help international management scholars and practitioners mitigate transaction partners’ dishonest acts.

A critical historical analysis of intermediary businesspeople’s (mis)behavior in pre-1949 (1840–1949) China can inform the generalizability of principal–agent theory and contemporary business strategies for minimizing agents’ dishonest acts.

A successful process of design concept evaluation has positive influence on subsequent processes. This study aims to consider the evaluation information at multiple stages and the interaction among evaluators and improve the credibility of evaluation results.

This paper proposes a multi-stage approach for design concept evaluation based on complex network and bounded confidence. First, a network is constructed according to the evaluation data. Depending on the consensus degree of evaluation opinions, the number of evaluation rounds is determined. Then, bounded confidence rules are applied for the modification of preference information. Last, a planning function is constructed to calculate the weight of each stage and aggregate information at multiple evaluation stages.

The results indicate that the opinions of the evaluators tend to be consistent after multiple stages of interactive adjustment, and the ordering of design concept alternatives tends to be stable with the progress of the evaluation.

Updating preferences according to the bounded confidence rules, only the opinions within the trust threshold are considered. The attribute information of the node itself is inadequately considered.

This method addresses the need for considering the evaluation information at each stage and minimizes the impact of disagreements within the evaluation group on the evaluation results.

Despite a growing number of manufacturing firms actively collaborating with ecosystem members aiming for advanced service solutions, it remains unclear how digital collaboration capability (DCC) affects incremental service innovation (ISI) and radical service innovation (RSI) in the context of ecosystems. Drawing on dynamic capabilities theory and resource orchestration theory, this study aims to investigate the impacts of DCC on ISI/RSI and examine the mediating role of organizational agility (OA).

In this empirical paper, the authors obtained questionnaire data from 238 Chinese manufacturing firms embedded in the ecosystems. Then, the authors examined all the hypotheses through hierarchical regression analyses.

The empirical evidence indicates that DCC has a distinct effect on ISI and RSI. DCC positively affects ISI, whereas DCC shows an inverted U-shaped effect on RSI. In addition, OA mediates the positive effect of DCC on ISI within a certain range, whereas OA always mediates the inverted U-shaped effect of DCC on RSI.

This study enriches and extends existing research on DCC and service innovation by elucidating the heterogeneous effects of DCC on ISI/RSI and the complicated mediating role of OA. This study also has practical implications for manufacturing firms to revisit their collaboration capability and agility to boost ISI/RSI in the context of ecosystems.

The purpose of this paper is to investigate the fault estimation issue of nonlinear dynamical systems via distributed sensor networks. Furthermore, based on the communication topology of sensor networks, the nonfragile design strategy considering the gain fluctuation is also adopted for distributed fault estimators.

By means of intensive dynamical model transformation, sufficient conditions with disturbance attenuation performance are established to design desired fault estimator gains with the help of convex optimization.

A novel distributed fault estimation framework for a class of nonlinear dynamical systems is established over a set of distributed sensor networks, where sampled data of sensor nodes via local information exchanges can be used for more efficiency.

The proposed distributed fault estimator gain fluctuations are taken into account for the nonfragile strategy, such that the distributed fault estimators are more applicable for practical sensor networks implementations. In addition, an illustrative example with simulation results are provided to validate the effectiveness and applicableness of the developed distributed fault estimation technique.

Given the complexity and increasing interdependence of supply chain networks, a broader perspective beyond a simple binary relationship is needed to analyse the impact of supply chain networks on firms. Based on social network theory, this study aims to explore the relationship between firms’ network position in the supply chain network and digital transformation, as well as the moderating effects of structural holes and network partners’ digitalisation.

This study analyses a sample of Chinese A-share companies listed on the Shanghai and Shenzhen Stock Exchanges for the period 2011–2020 using regression analysis to test hypothesised relationships.

A firm’s centrality in the supply chain network is positively related to its digital transformation, and that the firm’s structural holes and the level of network partners’ digitalisation can both strengthen the positive influence of network centrality on digital transformation. Moreover, the heterogeneity analysis reveals that the positive effect of firms’ network centrality on digital transformation is more pronounced in non-SOEs and upstream firms.

In the pursuit of digital transformation, firms should recognise the significance of their position in the supply chain network and enhance their capability for information and resource acquisition by proactively strengthening their business cooperation with other network members. Moreover, firms should emphasise the value of open networks and their partners in supply chain networks.

This study applies social network theory to investigate the role of a firm’s external supply chain network in its digital transformation, clarifying the context and boundaries in which a firm’s position in the supply chain network influences digital transformation. The results extend the research and theoretical perspective on digital transformation and the consequences of supply chain networks.

Given that a prerequisite for COVID-19 transmission is the interaction between individuals, it is reasonable to suspect that transportation networks may have contributed to the spread of COVID-19. This study uses the air transportation network to quantify the risk of COVID-19 spread in the United States. The proposed model is applied at the county level and identifies the risk of importing COVID-19-infected passengers into a given county. We also undertake an examination of the factors influencing the spread of COVID-19 in relation to air travel. Utilizing an extensive dataset encompassing various socioeconomic, demographic, and healthcare-related variables, our results indicate a positive relationship between these factors and the relative risk of COVID-19 spread, highlighting the pronounced impact of population density, air travel volume, and larger household sizes on increasing travel-related risk. Conversely, greater healthcare capacity, particularly in terms of hospital and intensive care unit (ICU) beds, is associated with reduced risk. We provide estimates of expected relative risk for each county and a ranking that can be useful for informing public health policies to stem the spread of the virus by devoting resources such as screening and enhanced travel protocols to airports located in at-risk counties.