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This paper aims to document the measures taken by Australian corporate treasurers in the areas of cash, inventory, accounts receivable, accounts payable and risk management to survive the global financial crisis (GFC).

Using qualitative techniques like interviews and a survey questionnaire, this paper summarises the various measures adopted by working capital managers.

The results show that more than half of the participants in the survey altered their working capital management practices during the crisis. Capital expenditure was curtailed, as they aimed at preserving their cash levels while reducing inventory levels. Credit worthiness of institutions became more important, and there was a general decline in credit availability. The results also show that Australian working capital managers exhibit behavioural biases, particularly overconfidence.

It is the first paper that uses open-ended questions to capture the effects of the GFC on working capital management in Australia.

The purpose of this paper is to: first, test if information-adjusted noise model (IANM) can be applied in China; second, quantify noise trader risk, overreaction, underreaction and information pricing errors in that market; and third, explain the relationship between noise trader risk and return.

The authors use a behavioural asset pricing model (BAPM), CAPM, the information-adjusted noise model and model proposed by Ramiah and Davidson (2010).

The findings show that noise traders are active 99.7 per cent of the time on the Shenzhen A-share market. Furthermore, our results suggest that the Shenzhen market overreacts 41 per cent of the time, underreacts 18 per cent of the time and information pricing errors occur 40 per cent of the time.

Various methods have been applied to the Chinese stock market in an effort to measure noise trading activities and all of them failed to account for information arrival. Our study uses a superior and alternative model to detect noise trader risk, overreaction and underreaction in China.

Multinational small-to-medium-sized enterprises (SMEs) are increasingly participating in cross-border digital platforms – especially amid the COVID-19 pandemic. Accordingly, knowledge integration (KI) has become more and more important. In fact, it has been deemed by many as the key to organizational resilience. Given this burgeoning phenomenon, this study aims to explore a path for improving the resilience of multinational SMEs. Through this process, this study also finds a relationship between the KI processes associated with adopting global digital platforms and the resiliency of local–global businesses. Hence, in part, this paper also explores the effectiveness of all these mechanisms.

This study used the stepwise regression method in Stata 16.0 to analyze the direct effects of both horizontal and vertical KI processes on the resilience of local–global businesses. Additionally, t-tests were also used to compare the differences in coefficients between the mechanisms. The sample analyzed comprised data on multinational manufacturing SMEs in the Yangtze River Delta region of China who are using global digital platforms.

The KI processes of these firms, both horizontal and vertical, positively correlate to resilience. Horizontal KI processes more efficiently increase the resilience of global businesses, whereas vertical processes more efficiently increase the resilience of local businesses.

First, this study provides insights into how multinational SMEs can improve their resilience in a crisis. In addition to adding to the knowledge of KI processes, this expands the KM literature on pandemics. Second, by creating two KI processes based on global digital platforms and discussing their influence on resilience, this research deepens the understanding of affordance in the KM literature. Third, focusing on the KI research stream, the results shed light on how KI processes might occur and how firms develop their KI processes.

The number of construction dispute cases has surged in recent years. The effective exploration and management of risks associated with construction contracts helps to directly enhance the overall project performance. The existing approaches to identify the risks associated with construction project contracts have a heavy reliance on manual review techniques, which are inefficient and highly restricted by personnel experience. The existing intelligent approaches only work for the contract query and storage. Hence, it is necessary to improve the intelligence level for contract risk management. This study aims to propose a novel method for the intelligent identification of risks in construction contract clauses based on natural language processing.

This proposed method can formalize the linguistic logic and semantic information of contract clauses into multiple triples and transform the structural processing results of general clauses in a construction contract into rights and interests rules for risk review. In addition, the core semantic information of special clauses in a construction contract, rights and interests rules are used for semantic conflict detection. Finally, this study achieves the intelligent risk identification of construction contract clauses.

The method is verified by selecting several construction contracts that had been applied in engineering contracting as a corpus. The results showed a high level of accuracy and applicability of the proposed method.

This novel method can identify the risks in contract clauses with complex syntactic structures and realize rule extension according to the semantic relation network of the ontology. It can support efficient contract review and assist the decision-making process in contract risk management.

The purpose of this study is to establish a novel airfoil icing prediction model using deep learning with geometrical constraints, called geometrical constraints enhancement neural networks, to improve the prediction accuracy compared to the non-geometrical constraints model.

The model is developed with flight velocity, ambient temperature, liquid water content, median volumetric diameter and icing time taken as inputs and icing thickness given as outputs. To enhance the icing prediction accuracy, the model involves geometrical constraints into the loss function. Then the model is trained according to icing samples of 2D NACA0012 airfoil acquired by numerical simulation.

The results show that the involvement of geometrical constraints effectively enhances the prediction accuracy of ice shape, by weakening the appearance of fluctuation features. After training, the airfoil icing prediction model can be used for quickly predicting airfoil icing.

This work involves geometrical constraints in airfoil icing prediction model. The proposed model has reasonable capability in the fast assessment of aircraft icing.