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The purpose of this paper is to apply an analysis of complex networks to empirically research international agricultural commodity trade and countries’ trading relations. The structure of global agricultural commodity trade is quantitatively described and analysed.

Based on statistical physics and graph theory, the research paradigm of a complex network, which has sprung up in the last decade, provides us with new global perspective to discuss the topic of international trade, especially agricultural commodity trade. In this paper, the authors engage in the issue of countries’ positions in international agricultural commodity trade using the latest complex network theories. The authors at first time introduce the improved bootstrap percolation to simulate cascading influences following the breaking down of bilateral agricultural commodity trade relations.

On a mid-level structure, countries are classified into three communities that reflect the structure of the “core/periphery” using the weighted extremal optimisation algorithm and the coarse graining process. On a micro-level, countries’ rankings are provided with the aid of network’s node centralities, which presents world agricultural commodity trade as a closed, imbalanced, diversified and multi-polar development.

The authors at first time introduce the improved bootstrap percolation to simulate cascading influences following the breaking down of bilateral agricultural commodity trade relations.

In this paper, the authors use the balance sheet data to investigate the interconnectedness and risk contagion effects in China's banking sector. They firstly study the network structure and centrality of the interbank network. Then, they investigate how and to what extent the credit shock and liquidity shock can lead to the risk propagation in the banking network.

Referring to the theoretical framework by Haldane and May (2011), this paper uses the network topology theory to analyze the contagion mechanism of credit shock and liquidity shock. Centrality measures and log-log plot are used to evaluate the interconnectedness of China's banking network.

The network topology has shown clustering effects of large banks in China's financial network. If the Industrial and Commercial Bank of China (ICBC) is in distress, the credit shock has little impact on the Chinese banking sector. However, the liquidity shock has shown more substantial effects than that of the credit shock. The discount rate and the rollover ratio play significant roles in determining the contagion effects. If the credit shock and liquidity shock coincide, the contagion effects will be amplified.

The results of this paper reveal the network structure of China's interbank market and the resilience of banking system to the adverse shock. The findings are valuable for regulators to make policies and supervise the systemic important banks.

The balance sheet data of different types of banks are used to construct a bilateral exposure matrix. Based on the matrix, this paper investigates the knock-on effects of credit shock triggered by the debt default in the interbank market, the knock-on effects of liquidity effects, which is featured by “fire sale” of bank assets, and the contagion effects of combined shocks.

Fat‐tailed distributions have been found in many financial and economic variables ranging from forecasting returns on financial assets to modeling recovery distributions in bankruptcies. They have also been found in numerous insurance applications such as catastrophic insurance claims and in value‐at‐risk measures employed by risk managers. Financial applications include:

The purpose of this article is to understand the current research status and future development trends in the field of numerical simulation on rock mass grouting.

This article first searched the literature database (EI, Web of Science, CNKI, etc.) for keywords related to the numerical simulation of rock mass grouting to obtain the initial literature database. Then, from the initial database, several documents with strong relevance to the numerical simulation theme of rock mass grouting and high citation rate were selected; some documents from the references were selected as supplements, forming the sample database of this review study (a total of 90 articles). Finally, through sorting out the relationship among the literature, this literature review was carried out.

The numerical simulation of rock mass grouting is mainly based on the porous media model and the fractured media model. It has experienced the development process from Newtonian fluid to non-Newtonian fluid, from time-invariant viscosity to time-varying viscosity, and from generalized theoretical model to engineering application model. Based on this, this article summarizes four scientific problems that need to be solved in the future in this research field: the law of grout distribution at the cross fissures, the grout diffusion mechanism under multi-field coupling, more accurate grouting theoretical model and simulation technology with strong engineering applicability.

This research systematically analyzes the current research status and shortcomings of numerical simulation on rock mass grouting, summarizes four key issues in the future development of this research field and provides new ideas for the future research on numerical simulation on rock mass grouting.

Efficient numerical assessment of performance is particularly important in digital material design of porous materials. This study aims to present an up-scaled approach to virtually investigate permeation of fluids through a real porous filter membrane with a heterogeneous micro-structure.

The method of asymptotic homogenization is applied. The structural parameters of the micro-structure are directly obtained from structural equation modeling image analysis of a commercial filter membrane without fitting procedures. The simulation results are compared to permeation experiments of gaseous nitrogen and liquid water.

The authors found that variations in the pressure gradients across the membrane, resulting from the heterogeneity of pore structure, need to be considered. Remarkable agreement between simulations and experiments is observed.

Despite some research in the field of filtration, no studies on filter membranes have been published yet, although they represent a large segment of filtration technology.

This paper aims to assess the readiness of retail workers to use blockchain technology (BCT) to improve supply chain performance. The assessment was made via a quantitative approach taken using a theoretical framework based on Keller’s motivation model and self-determination theory in the BCT context.

The authors collected data from 567 retail workers from an emerging country through a structured survey questionnaire. The authors tested the hypotheses of the proposed model using Warp PLS 7.0 and controlled firm age, industry type and technological intensity.

Our findings may help firms in making the process of digital transformation inclusive. The authors found that supplier-based attention and motivation through BCT lead to supply chain performance, and that supplier-based satisfaction and trust achieved through BCT positively impact supply chain performance. Further, supplier-based relevance on raw material selection with the higher trust and motivation levels achieved through BCT was found to have a positive impact on supply chain performance.

IT supply chain applications are referred to as “lean” rather than “rich” because they still rely mainly on written and numerical means to present data. When the environment is less ambiguous, then less rich media can be used to facilitate communication. IT supply chain applications allow suppliers to spend time building relationships with other suppliers instead of focusing on administrative tasks, thus enhancing such relationships.

This study can be considered the first to assess retailer readiness to use BCT to improve supply chain performance through the theoretical lens of Keller’s motivation model and self-determination theory.

The purpose of this paper is to examine employees’ knowledge creation processes by leveraging a conceptual framework based on the socialisation, externalisation, combination and internalisation (SECI) model introduced by Nonaka and Takeuchi (1995). Given that many employees work within teams, in the current study, the authors examine the impact that team-level trust and intrinsic motivation have on an employee’s SECI model and, in turn, the relationship between SECI model and individual creativity. As such, this work represents one of the first works to examine team-level factors that shape individual knowledge creation and creativity. Additionally, building on and extending previous SECI research, the authors develop a scale to measure SECI models that uses peer-rated assessments.

Data were collected from 431 employees who worked in 59 teams drawn from 51 companies in a variety of industry sectors, both SME’s and corporate. To minimise common method bias, the SECI model questionnaire was adapted to the individual level through peer ratings instead of self-ratings (each employee rated three peers). To assess the hypotheses, hierarchical linear models using IBM SPSS were applied. The questionnaires were completed using both paper and online versions.

Results showed that SECI mediates the relationships between individual-level creativity and both team-level intrinsic motivation and trust. Furthermore, findings suggest that the scale developed is a reliable measure of SECI.

Knowledge creation and sharing practices should take into account both, a team’s trust and its intrinsic motivation, which would result in creativity.

This paper examines the impact that team-level factors (i.e. team trust and team intrinsic motivation) have on individual SECI and creativity across a variety of industries. As such, this work is one of the first to examine the impact of team-level factors in shaping individual knowledge creation and creativity. Given the support that the study found for this hypothesis, this work demonstrates that team trust and intrinsic motivation are salient factors in shaping individual employee knowledge creation and creativity. Given the novelty of this work, the authors hope is that this study will be the foundation upon future cross-level studies of individual-level SECI and individual creativity can be built so as to improve SECI models.

Team learning is critical to interdisciplinary research teams (IDR teams) to use heterogeneous knowledge effectively. Nevertheless, team learning is rarely addressed in the IDR team literature. Also, few studies investigate the antecedents and consequences of team learning in IDR teams, leading to a lack of guidance for management practices. This study aims to investigate how team learning can be developed and how team learning influences team outcomes in IDR teams.

A questionnaire survey on 304 members of 37 IDR teams in a research university in China is conducted. Data are analyzed using a partial least square structural equation modeling.

The results support most hypotheses in general. For the antecedent variables, task interdependence, trust and constructive conflict positively affect team learning. For the outcome variables, team learning improves shared mental models, coordination quality and team performance significantly. Additionally, task uncertainty positively moderates the team learning-coordination quality relation and team learning-team performance relation. However, this paper does not find support for the moderating role of task uncertainty on the team learning-shared mental models relation.

To the best of the knowledge, this is the first study investigating the antecedents and consequences of team learning in IDR teams. A multidimensional measurement of team learning for the IDR team context is developed. This study investigates how team behavioral factors influence team learning and the effect of team learning on shared mental models, coordination quality and team performance. This study also explores the contingency role of task uncertainty in the effects of team learning.

The purpose of this paper is to promote social network analysis (SNA) methodology within the humanitarian research community, surveying its current state of the art and demonstrating its utility in analyzing humanitarian operations.

A comprehensive survey of the related literature motivates a proposed agenda for interested researchers. Analysis of two humanitarian networks in Afghanistan demonstrates the use and utility of SNA, based on secondary data. In the second case study, the use of random graphs to detect network motifs is demonstrated using Monte Carlo simulation to create the benchmark null sets.

SNA is an adaptable and highly useful methodology in humanitarian research, quantifying patterns of community structure and collaboration among humanitarian organizations. Network motifs suggesting distinct affinity between particular agencies within humanitarian clusters are observed.

The authors summarize common challenges of using SNA in humanitarian research and discuss ways to alleviate them.

Practitioners can use SNA as readily as researchers, to visualize existing networks, identify areas of concern and better communicate observations.

By making SNA more accessible to a humanitarian research audience, the authors hope its ability to capture complex, dynamic relationships will advance understanding of effective humanitarian relief systems.

To the best of knowledge, it is the first study to conduct a systematic analysis of the application of SNA in empirical humanitarian research and outline a concrete SNA-based research agenda. This is also a currently rare instance of a humanitarian study using random graphs to assess observed SNA measures.