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In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

The purpose of this paper is to develop a model considering synergy effect for prefabricated construction service combination selection.

This research defines prefabricated construction service as a service-led construction method that meets the specific requirements of clients. Based on network theory, the multi-dimensional collaborative relationships of the prefabricated construction inter-services are formulated. The synergy effect is quantitatively calculated through the linear weighting of the strengths of collaborative relationships. Further, a weighted synergy network (WSN) is developed, from which a service composition selection model considering the synergy effect is established. Then, a genetic algorithm is employed to implement the model.

The results showed that (1) when the number of prefabricated construction services is increased, the synergy effect of combination options is enhanced; (2) The finer-grained prefabricated construction services, the stronger the synergy effect of service combination; (3) Clients have heterogeneous preferences for collaborative relationships, and there are differences in the synergy effect of service combination.

The contribution of this research includes proposed a method to quantify the synergy effect from the perspective of collaborative relationships, explored the specific procedure for the prefabricated construction service combination selection under the service-led construction, and provided a reference for promoting the development in construction. Besides, the model proposed could be applied to prefabricated construction service composition selection with diverse research boundaries or client preferences by executing the same procedure.

In the face of frequent supply chain disruptions, this study aims to identify the relationships between human capital (HC), digital supply chain practice (DSCP) and supply chain robustness and resilience (i.e. SCRO and SCRE).

To examine the proposed conceptual model based on the resource-based view and organizational information processing theory, an empirical study was conducted using a sample of Chinese manufacturers. Hierarchical regression is applied to understand the direct effect, mediating effect and moderating effect among HC, DSCP, SCRO, SCRE and digital culture.

The results reveal that HC positively influences digital maturity and supply chain resilience. DSCP positively affects both SCRO and SCRE. However, HC does not significantly influence the adoption of digital tools, and only employees’ capital has a positive relationship with SCRO. In addition, the moderating effect of digital culture on the relationship between HC and DSCP was examined.

As an early exploration of the relationships among HC, DSCP, SCRO and SCRE, this study enriches the literature by offering new insights and responding to the call for more empirical research and theoretical application. It emphasizes the importance of HC and DSCP as strategic resources and highlights the need for a digital culture to implement DSCP.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

Environmental sustainability orientation has emerged to drive firms into eco-friendly production. Yet, the consequence of this new strategic thinking on firms’ green innovations, especially small- and medium-scale enterprises (SMEs), remains unresolved. Recognizing that the connection between environmental sustainability orientation and green innovation may not always be direct, the study theorizes that dynamic capability and entrepreneurial orientation may form part of the boundary conditions that strengthen its effect on small enterprises’ green innovation. The study adjoins the dynamic capability theory with the entrepreneurial orientation theory to test this relationship among small businesses within a developing economy. Results from the partial least squares–structural equation modeling (PLS-SEM) suggest that environmental sustainability orientation will result in green innovation when the SME’s dynamic capability can develop a creative reconfiguration of knowledge and new distinctive resources to support this new strategic direction. Similarly, findings from the study suggest that environmental sustainability orientation will translate into better green innovation outcomes when the SME entrepreneurial orientation has a solid attraction to protect the ecosystem and does not perceive green innovation as a risky enterprise.

Big data challenges and opportunities on the Internet of Vehicles (IoV) have emerged as a transformative paradigm to change intelligent transportation systems. With the growth of data-driven applications and the advances in data analysis techniques, the potential for data-adaptive innovation in IoV applications becomes an outstanding development in future IoV. Therefore, this paper aims to focus on big data in IoV and to provide an analysis of the current state of research.

This review paper uses a systematic literature review methodology. It conducts a thorough search of academic databases to identify relevant scientific articles. By reviewing and analyzing the primary articles found in the big data in the IoV domain, 45 research articles from 2019 to 2023 were selected for detailed analysis.

This paper discovers the main applications, use cases and primary contexts considered for big data in IoV. Next, it documents challenges, opportunities, future research directions and open issues.

This paper is based on academic articles published from 2019 to 2023. Therefore, scientific outputs published before 2019 are omitted.

This paper provides a thorough analysis of big data in IoV and considers distinct research questions corresponding to big data challenges and opportunities in IoV. It also provides valuable insights for researchers and practitioners in evolving this field by examining the existing fields and future directions for big data in the IoV ecosystem.

This paper examines the evolving landscape of accounting and finance research on the Chinese capital market, building on a previous study published at Abacus in 2018.

By incorporating data from 1999 to 2023, our analysis offers a detailed examination of shifts in academic focus, methodological advancements and thematic expansions over the last quarter-century.

The study reveals a substantial increase in accounting and finance publications related to the Chinese capital market in both Tier 1 and Asia-Pacific journals. The dynamic growth of the Chinese capital market during this period reflects profound economic transformations, characterized by technological innovations, sustainability commitments and regulatory reforms.

We conclude that the globally important Chinese capital market has attracted increasing academic attention, significantly advancing the understanding of accounting and finance research in China’s capital market.

We examine how superstition shapes corporate tax avoidance and do so by taking a risk perspective and focusing on the zodiac-year belief prevalent in China.

We adopt a difference-in-differences research design to compare the degree of corporate tax avoidance in the CEOs’ zodiac year with that in the adjacent years. We do propensity-score matching to form a sample of Chinese listed firms for the regression analysis.

We find causal evidence that firms exhibit a greater magnitude of tax avoidance in the CEOs’ zodiac years, a result attributable to relatively weak tax enforcement in the Chinese context. We also find that the zodiac-year effect on corporate tax avoidance is more pronounced for firms with tight financial constraints, firms with high business risk, firms headquartered in regions with a high degree of superstition and non-state-owned firms.

This study is the first to show that superstition is a determinant factor of tax avoidance and contributes to the tax literature by shedding light on the behavioral risk factors that shape corporate tax avoidance. We take the perspective of CEOs’ risk appetite to analyze how tax avoidance is influenced by the CEOs’ trade-off between the costs and benefits of avoiding taxes. Our results suggest that, when CEOs are more risk-averse, they attach more importance to financial risk than the risk of reputational losses and litigation associated with corporate tax avoidance. The findings imply that tax avoidance can be curbed by increasing (or decreasing) the tax (financial) risk confronting the CEOs.

The COVID-19 pandemic has significantly impacted global tourism, with international travel bearing the burden of restrictions. Domestic tourism has also faced substantial challenges. This paper aims to analyse the impact of the COVID-19 pandemic on domestic tourism in Spain, focusing on travel from Madrid (the country’s capital) to other tourist destinations.

Mobile phone data has been used to study the evolution of tourist trips over the summers of 2019, 2020 and 2021. Regression models are used to explain the number of visitors at destinations.

The pandemic not only caused a drastic drop in tourist flows but also disrupted the overall pattern of the domestic flow system. Winning destinations were typically areas in proximity to Madrid and less densely populated destinations, while urban destinations were major losers. The preferences of domestic tourists varied notably by income group, but the decrease in trip volumes showed only marginal differences.

The paper demonstrates the potential of mobile phone data analysis to study the uneven impact of external shocks, such as the COVID-19 pandemic, on tourist destinations. This approach considers spatial resilience heterogeneity within regions or provinces. By incorporating income information, the analysis introduces a social dimension to highly detailed spatial data, surpassing traditional studies conducted at the regional or national levels.

COVID-19大流行对全球旅游业产生了重大影响，国际旅行受到了限制的影响最为严重。国内旅游也面临着重大挑战。本文分析了COVID-19大流行对西班牙国内旅游的影响，重点关注从马德里（该国首都）到其他旅游目的地的旅行。

本研究使用移动电话数据研究了2019年、2020年和2021年夏季旅游出行的演变。采用回归模型解释了各目的地游客数量。

大流行不仅导致了旅游流量急剧下降，还扰乱了国内流动系统的总体模式。获胜的目的地通常是马德里附近的地区和人口较稀少的目的地，而城市目的地是主要的输家。国内游客的偏好在收入群体之间有明显差异，但旅行量的减少只显示出边际差异。

本文展示了使用移动电话数据分析研究外部冲击（如COVID-19大流行）对旅游目的地的不均匀影响的潜力。该方法考虑了区域或省份内的空间弹性异质性。通过整合收入信息，该分析为高度详细的空间数据引入了社会维度，超越了传统在区域或国家水平进行的研究。

The purpose of this paper is to clarify the influence of low earth orbit space environment on the wear mechanism of TC4 alloy material and crank rocker mechanism.

In this study, friction experiments were carried out on TC4 alloy friction discs and crank rocker mechanisms, both before and after exposure to atomic oxygen and proton irradiation. Nanoindentation, grazing incidence X-ray diffraction (GIXRD), and X-ray photoelectron spectroscopy were employed to systematically characterize alterations in mechanical properties, surface phase, and chemical composition.

The results show that the wear mechanism of TC4 alloy friction disc is mainly adhesive wear in vacuum environment, while the wear mechanism of crank rocker mechanism includes not only adhesive wear but also abrasive wear. Atomic oxygen exposure leads to the formation of more oxides on the surface of TC4 alloy, which form abrasive particles during the friction process. Proton irradiation will lead to a decrease in fatigue performance and an increase in hardness on the surface of TC4 alloy, thus causing fatigue wear on the surface of TC4 alloy, and more furrows appear on the crank rocker mechanism after proton irradiation. In the three environments, the characteristics of abrasive wear of the crank rocker mechanism are more obvious than those of the TC4 alloy friction disc.

These results highlight the importance of understanding the subtle effects of atomic oxygen and proton irradiation on the wear behavior of TC4 alloy and provide some insights for optimizing its performance in space applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0051/