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This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

The objectives of this study are to examine the impacts of CSR on purchase intentions either directly or indirectly and to investigate the moderating effects of consumer ethnocentrism on the relationship between CSR and purchase intentions.

This study utilized an online self-administered survey for data collection. All the measurement items were adopted or adapted from prior research concerning international marketing (Churchill, 1979). Concerning rating scales, the items of CSR, brand attitudes, and consumer ethnocentrism were measured from 1 (strongly disagree) to 7 (strongly agree), and the items of purchase intentions were measured from 1 (strongly disagree) to 5 (strongly agree).

The empirical findings suggest that CSR significantly influences purchase intentions directly and indirectly via brand attitudes. Besides, consumer ethnocentrism strengthens the positive effects of CSR on brand attitudes and purchase intentions.

This research sheds light on young Chinese consumers' purchase behaviors, which could facilitate Western brands implementing effective and efficient marketing strategies in the Chinese market.

This study aims to investigate whether gender diversity in audit committees affects the purchase of nonaudit services in China. Results from family and nonfamily firms are compared and the critical mass participation of females are further examined.

The sample comprises 1,834 Chinese listed companies from 2012 to 2021, among which 910 are family firms. The Heckman (1979) two-stage model is used to mitigate the potential endogeneity issue in the selection of gender diversity. Propensity score matching is also used to further alleviate the endogeneity problem in relation to family firms.

Results show a significant and negative correlation between the gender diversity in audit committees and nonaudit service fees. This association is more apparent in nonfamily than in family firms. Findings are consistent and robust to endogeneity tests and sensitivity analyses. The analysis of critical mass and symbolic participation shows that three female directors can more significantly restrain nonaudit fees than one to two females on the board.

This study contributes to literature on resource dependence theory, which posits that audit committees help enterprises establish contact with auditors, improve the company legitimacy, assist in communication and provide relevant expertise. This study also relates to agency theory, which holds that differences in the severity of types I and II agency problems between family and nonfamily firms lead to differences in auditor selection and related costs.

Extending from previous research on the relation between the gender diversity in audit committees and nonaudit fees, the present study delves into this connection within the context of China, an emerging economy. As a result, this investigation offers novel insights and expands upon current knowledge. In addition, the correlation between the gender diversity of audit committees and nonaudit fees is explored for family and nonfamily firms.

This paper aims to investigate the bonding of the photonic integrated circuit (PIC) chip with the heat sink using the AlNi self-propagating soldering method.

Compared to industrial optical modules, optical modules for aerospace applications require better reliability and stability, which is hard to achieve via the dispensing adhesive process that is used for traditional industrial optical modules. In this paper, 25 µm SAC305 solder foils and the AlNi nanofoil heat source were used to bond the back of the PIC chip with the heat sink. The temperature field and temperature history were analyzed by the finite element analysis (FEA) method. The junction-to-case thermal resistance is 0.0353°C/W and reduced by 85% compared with the UV hybrid epoxy joint.

The self-propagating reaction ends within 2.82 ms. The maximum temperature in the PIC operating area during the process is 368.5°C. The maximum heating and cooling rates of the solder were 1.39 × 107°C/s and −5.15 × 106°C/s, respectively. The microstructure of SAC305 under self-propagating reaction heating is more refined than the microstructure of SAC305 under reflow. The porosity of the heat sink-SAC305-PIC chip self-propagating joint is only 4.7%. Several metastable phases appear as AuSn3.4 and AgSn3.

A new bonding technology was used to form the bonding between the PIC chip with the heat sink for the aerospace optical module. The reliability and thermal resistance of the joint are better than that of the UV hybrid epoxy joint.

There have been limited investigations on the mechanical characteristics of tunnels supported by corrugated plate structures during fault dislocation. The authors obtained circumferential and axial deformations of the spiral corrugated pipe at various fault displacements. Lastly, the authors examined the impact of reinforced spiral stiffness and soil constraints on the support performance of corrugated plate tunnels under fault displacement.

By employing the theory of similarity ratios, the authors conducted model tests on spiral corrugated plate support using loose sand and PVC (polyvinyl chloride) spiral corrugated PE pipes for cross-fault tunnels. Subsequently, the soil spring coefficient for tunnel–soil interaction was determined in accordance with ASCE (American Society of Civil Engineers) specifications. Numerical simulations were performed on spiral corrugated pipes with fault dislocation, and the results were compared with the experimental data, enabling the determination of the variation pattern of the soil spring coefficient.

The findings indicate that the maximum axial tensile and compressive strains occur on both sides of the fault. As the reinforced spiral stiffness reaches a certain threshold, the deformation of the corrugated plate tunnel and the maximum fault displacement stabilize. Furthermore, a stronger soil constraint leads to a lower maximum fault displacement that the tunnel can withstand.

In this study, the calculation formula for density similarity ratio cannot be taken into account due to the limitations of the helical corrugated tube process and the focus on the deformation pattern of helical corrugated tubes under fault action.

This study provides a basis for the mechanical properties of helical corrugated tube tunnels under fault misalignment and offers optimization solutions.

The role of corporate social responsibility (CSR) fulfillment is critical when building resilience of project-based organizations (PBOs). However, fulfilling CSR to build a highly resilient PBO remains a black box problem. This study explores the different CSR combinations that enhance PBO resilience.

This study defines CSR in terms of shareholder, employee, and social CSR, and analyzes corporate characteristics in terms of corporate scale and nature. Data are collected from Hexun.com and the China Stock Market and Accounting Research Database (CSMAR). The qualitative comparative analysis (QCA) method is used to analyze 48 listed construction and engineering companies from China to explore the CSR configurations for PBOs in enhancing organizational resilience.

A large firm size is a necessary condition for high organizational resilience. We find six paths to build high and non-high resilience in PBOs, and the driving mechanisms of high and non-high resilience exhibit an asymmetric relationship.

This study cracks the black box of CSR fulfillment and PBO resilience. It reveals the CSR configurations that enhance or inhibit the resilience of PBOs. It also provides scientific basis for PBOs in their fulfillment of CSR in response to crises, and the enhancement of organizational resilience. Future research can be expanded to other industries, as the study sample is only limited to civil engineering construction companies. Since this study uses cross-sectional data, time series can be introduced in the future to further explore the relationship between CSR and organizational resilience.

This study provides targeted suggestions that can help decision-makers of construction companies to determine how they can fulfill CSR to enhance organizational resilience. At the same time, it can provide intellectual support for PBOs to cope with systemic crises and promote the fulfillment of CSR.

In terms of theoretical value, on the one hand, this study verifies the relationship between CSR fulfillment and PBO resilience, revealing its mechanism of action and multiple paths; on the other hand, it provides a new way of thinking for management research methods and enriches the theoretical study of organizational resilience.

Deglobalization and the coronavirus disease 2019 (COVID-19) pandemic have severely hindered multinational enterprise (MNE) investment. At the same time, digital technology is seriously challenging it with traditional production factor flows. Few studies have realized that the impact of digitalization is not limited to either transaction costs or the location-boundness of firm-specific advantages (FSAs), but extends to profound changes in the fundamental essence of MNEs. There is still limited understanding of this body of knowledge as a whole, including how its subtopics are interrelated. This study took the production factor change perspective to review MNE theory in the digital era. Therefore, this study aims to identify any upcoming and undeveloped themes in order to provide a platform suited to direct future research.

This paper presents a summary and a review of 151 articles published between 2007 and 2020. Such review was conducted to systematically explain the connotations and influential mechanisms of digital empowerment on MNE theory. This was achieved by using the CiteSpace citation visualization tool to build a keyword co-occurrence network.

The research findings pertain to how digitalization expands, breaks through, and even reshapes traditional MNE theory from four distinctive angles: the influential factors of internationalization, the process of internationalization, competitive advantage, and location choice. The findings are followed by the presentation of future research directions.

This paper presents an examination of MNE theory in the digital era from the perspective of production factor change. In doing so, it identifies significant theoretical innovation opportunities for future scholarly research priorities.

With the development of social media, live-streaming has become an indispensable marketing activity for firms, especially in China. From the initial cooperation with the influencer, firms begin to create their own live-streaming channel, namely, the brands' self-built live-streaming. The purpose of this study is to explore the process of consumer engagement in the brands' self-built live-streaming.

This research comprises two experimental studies. Study 1 examined the effect of streamer types (CEO vs. celebrity) on consumer engagement. Study 2 investigated the moderating effects of product innovativeness.

Results showed that CEO streamers could enhance consumer engagement by increasing consumers' cognitive trust, and celebrity streamers could enhance consumer engagement by increasing consumers' emotional trust. In addition, consumer engagement was higher for really new products (vs. incremental new products) in CEO streamers' (vs. celebrity streamers') live-streaming.

Compared with previous studies that focused on streamers based on the influencer marketing, this study expands the scope of research on the live-streaming ecosystem by exploring the effect of different streamer types on the brands' self-built live-streaming. By investigating consumer engagement, this study gives implications for the sustainable traffic issue in live-streaming e-commerce.

Although previous studies have studied the impact of spiritual leadership (SL) on employees’ innovation, the research on mechanisms and the boundary conditions for stimulating this relationship is scant. This paper aims to follow the idea of social capital theory (SCT), which contends that social relationships are resources that lead toward the development of intellectual capital, important for innovative work behavior (IWB) of employees; the mediating role of knowledge sharing self-efficacy (KSSE) and moderating role of innovation climate (IC) are considered.

The authors collected the data from the foreign and local employees working in multinational companies in China. The quantitative analysis was performed using Smart-PLS 3.0.

The results indicated that employee high-ranking of SL is positively related to KSSE. Moreover, SL is significant to enhance IWB, whereas KSSE explained this relationship. The authors also suggest that an employee’s KSSE is significant to form important behavior at work (IWB). However, IC did not play its moderating role in the SL – IWB link.

This study explores the influence of the leadership style (SL) on employees’ KSSE and the effect of KSSE on IWB, which have not been studied previously. The current study confirms the relationship between SL and IWB in the multicultural workplace and reveals the deeper influence of an individual’s belief (KSSE) mechanism between them. SCT was applied to explain the proposed relationships.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.