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This study aims to compare the impact of Australian onshore and offshore immigration detention centres (IDCs) on detainees’ health and health-care events.

It uses data extracted from the Australian Government’s quarterly health reports from 2014 to 2017. These reports contain a range of data about the health and well-being of detainees, including complaints/presenting symptoms and number of appointments and hospitalisations. To compare onshore and offshore data sets, the authors calculated the rate of health events per quarter against the estimated quarterly onshore and offshore detention population. They ran a series of two-proportion z-tests for each matched quarter to calculate median z- and p-values for all quarters. These were used as an indicator as to whether the observed differences between onshore and offshore events were statistically significant.

The results suggest that adults detained onshore and offshore have substantial health needs, however, almost all rates were far higher in offshore detention, with people more likely to raise a health-related complaint, access health services and be prescribed medications, often at two to three times the rate of those onshore.

This paper adds to a modest body of literature that explains the health of people detained in Australian IDCs. To the best of the authors’ knowledge, this is the first paper to explore health service utilisation and a range of other variables found in the Australian Government’s quarterly health reports. These findings bolster the evidence which suggests that detention, and particularly offshore detention is particularly harmful to health.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

This study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.

This study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.

This study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.

These findings can potentially help market participants, policymakers, stock exchanges and regulators.

Unlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

The purpose of this paper is to understand the financial opaqueness established through offshore businesses and financial secrecy through the requirements of information exchanges, and their deadly combination for facilitating money-laundering activities and tax evasion. It also puts into light some key recommendations for a country like Nepal that has been struggling to put adequate efforts into understanding financial opacity and secrecy.

This paper navigates through global issues on layering through opaque corporate structures, and mechanisms required for information exchange so as to figure out solutions and challenges to address them by developing countries like Nepal, with specific actions pertaining to Nepal.

Understanding financial opacity and secrecy is a prerequisite to tackling financial crimes. While focusing on global solutions and inherent challenges regarding such issues, concerted efforts are required to capacitate a country on contextual matters.

This work is an original work with an analysis of a global issue in an interconnected world with solutions catered to the local contexts of Nepal.

To address the absence of Lean in transportation logistics in the digital era, this study aims to investigate the application of Lean transportation (LT) tools to reduce waste and facilitate the digital transformation of dedicated road transportation in the offshore industry.

The study adopts action research with a multimethod approach, including a scoping review, focus groups (FG) and participant observation. The research is conducted within the offshore supply chain of a major oil and gas company.

Implementing LT’s continuous improvement tools, particularly value stream mapping (VSM), reduces offshore transportation waste and provides empirical evidence about the intersection of Lean and digital technologies. Applying techniques drawn from organisational learning theory (OLT), stakeholders involved in VSM mapping and FGs engage in problem-solving and develop action plans, driving digital transformation. Waste reduction in loading and unloading stages leads to control actions, automation and process improvements, significantly reducing downtime. This results in an annual monetary gain of US$1.3m. The study also identifies waste related to human effort and underutilised digital resources.

This study contributes to theory and practice by using action research and LT techniques in a real intervention case. From the lens of OLT, it highlights the potential of LT tools for digital transformation and demonstrates the convergence of waste reduction through Lean and Industry 4.0 technologies in the offshore supply chain. Practical outputs, including a benchmarking questionnaire and a plan-do-check-act cycle, are provided for other companies in the same industry segment.

Innovation ecosystems consist of independent and yet interdependent enterprises and actors. This constitutes a challenge for innovation leadership to set the direction to pursue UN SDGs. Enhanced insights and understandings of how collaborative innovation leadership can be conducted in practice are revealed in two cases conducted using a participatory action research approach. Hereby theory and practice are linked by the ecosystem participant actions to reach competitiveness of the offshore wind energy ecosystem compared to traditional/other renewable energy sources pursuing UN SDG number 7 of ‘affordable and clean energy’. The two cases are situated in ports in offshore wind energy innovation ecosystems, respectively in the Baltic Sea and in the North Sea. The first case contains complementary ecosystem participants with both leadership and expert positions. The second case contains both complementary and competitive ecosystem participants with leadership positions. The findings note that both cases can use collaborative innovation leadership through the point of origin in the guiding star of the value proposition at ecosystem level to go beyond their own enterprise self-interest. Next, collaborative innovation leadership is based in heterogeneities of both knowledge domains and individual and organisational behaviours for the innovative imagination of future needed initiatives for collaborative design-based sensemaking. A short-term challenge is present from reduced revenue in own enterprise from future needed initiatives. This short-term impact needs to be addressed for enhanced long-term robustness. The findings are summarised in a model for dissemination of collaborative innovation leadership in ecosystems to pursue UN SDGs in practice.

The purpose of this paper is to understand the impending relationship between the impact of the US–China trade war on Taiwanese firms' spending on R&D and their offshore investment in technologically advanced countries (TAC), the authors examined if changes in these firms' R&D ratios and the growing presence of skilled workers in Taiwan's labour market during the trade war have affected their offshore investments in TAC.

Using a model built on pooled cross-sectional time-series data from 2012–2019, the authors examined whether a change in R&D ratios of domestic firms in Taiwan and the growing presence of skilled workers in Taiwan's labour market have affected the offshore investment by these firms during the trade war. Using data from the Manpower Utilisation Survey, the authors applied differences–in–differences–in–differences and differences–in–differences–in–differences–in–differences estimation methods and found that the trade war indeed gave a boost to Taiwan's job market, particularly for skilled workers.

From the estimation results, the authors noticed a rise in employment opportunities alongside a decline in the earnings of skilled workers in industries where more firms have spent on R&D as well as invested in offshore operations. However, firms in Taiwan that had not heavily spent on R&D from industries where investment in foreign operations was otherwise high have also attracted skilled workers during the trade war.

An in-depth analysis of the impact of the trade war on domestic firms' spending on R&D and their investment in offshore operations in TAC should be helpful to policymakers interested in understanding the effects of the trade war and subsequent changes in firms' spending on R&D on labour market outcomes. If changes in the R&D ratios and a steady supply of skilled workers influenced the outflow of Foreign Direct Investment (FDI) to TAC, this insight could be helpful for those devising policies and measures to curb the impact of the trade war on domestic spending on R&D.

The study findings not only provide broad lessons to policymakers in Taiwan, but the country case study can guide growing economies that are equally careful while perceiving trade war as a significant deterrent to domestic R&D spending and the outflow of FDI.