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The paper aims to present an experimental and numerical investigation of the load–settlement behavior of soil reinforced by stone column, as well as to evaluate the plane strain unit cell model for the analysis of stone columns.

The numerical analysis was done using both axisymmetric and plane strain models. The elastic perfectly plastic behavior of Mohr–Coulomb was adopted for both soil and column material. The numerical results of this study were validated by the comparison with the in-situ measurements of a full-scale loading test on a stone column. This study also evaluated the effect of different parameters involved in the design of a stone column, including Young’s modulus of the column material, column diameter, spacing between the stone columns and Poisson’s ratio of the column material.

After the numerical simulation, the results from both axisymmetric and plane strain models are quite comparable. In addition, the numerical results revealed that the stone column with low spacing, a large diameter and a high Young’s modulus indicated better behavior against the settlement.

The axisymmetric unit cell model was used in many numerical studies on the behavior of stone columns. In the present work, a field load test on stone column was simulated using a plane strain unit cell model. This research adds that the plane strain unit cell model can be used to predict the settlement of reinforced soil with stone columns.

This study explores the economic impact of the COVID-19 crisis on herding behaviour in the Australian equity market by considering liquidity, government interventions and sentiment contagion.

This study utilizes a daily dataset of the top 500 stocks in the Australian market from January 2009 to December 2021. Both predictive regression and portfolio approaches are employed to consider the impact of COVID-19 on herding intention.

This study confirms that herding propensity is more pronounced at the beginning of the crisis and becomes less significant towards later phases when reverse herding is more visible. Investors herd more toward sectors with less available information on financial support from the government during the financial meltdown. Conditioning the stock liquidity, herding is only detectable during highly liquid periods and high-liquid stocks, which is more observable during the initial phases of the crisis. Further, the mood contagion from the United States (US) market to Australian market and asymmetric herding intention are evident during the pandemic.

This is the first study to shed further light on the impact of a health crisis on the trading behaviour of Australian investors, which is driven by liquidity, public information and sentiment. Notwithstanding the theoretical contributions to the prior literature, several practical implications are proposed for businesses, policymakers and investors during uncertainty periods.

The main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.

The authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.

Innovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.

This paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.