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This study aims to examine the role of Australian casinos in facilitating money laundering and Chinese capital flight.

The reports and transcripts of evidence from government inquiries into money laundering in Australian casinos are integrated with analyses of Asian transnational crime.

Money laundering in Australian casinos is linked to transnational crime and Chinese capital flight. A central finding is that junket operators play a key role in facilitating money laundering. The casinos are particularly exposed to criminal influences in the Chinese very important person gambling market, since they have used junket operators and underground banks, many of whom are closely linked to major Chinese criminal groups from Hong Kong and Macau.

Very little information is available on money laundering in Australian casinos and this research has relied on the government inquiries that have been conducted over the past two years on the subject.

The author’s focus on money laundering in Australian casinos in the context of Asia-Pacific transnational crime is important for Federal and state government regulators grappling with the rapidly changing money laundering issues. The government inquiries recognised that the money laundering was related to transnational crime, but did not have the time and resources to explore the topic. The paper provides state government casino regulators and financial crime regulators with a broader international perspective to anticipate future money laundering and crime pressures facing Australia’s casinos.

Money laundering in Australian casinos has had devastating social implications on the community. My research helps to focus attention on the problems of transnational crime and money laundering.

Little research has examined the linkages between casinos and transnational crime. This study has found that Australian casinos were used to launder the proceeds of illegal drug trafficking and to facilitate Chinese capital flight. While casinos have been forced by damming government inquiries to tighten anti-money laundering controls, it is likely that there will be pressure to relax these controls in the future because of competitive pressure from other casinos in the Asia-Pacific region.

The study aims to unpack the role of Malawi Library Association (MALA) in developing librarianship in Malawi. It also explores an array of opportunities and challenges that are present for MALA.

The study applies the interpretivist paradigm for the research design. Qualitative data were collected from a purposeful sample totaling 24 practicing librarians and paraprofessionals in different work environments to inform the study phenomenon.

The study gathers that the role of MALA has been in its infancy stage for a long time. However, the apparent developments of MALA manifest in its pro-educational initiatives. Overall, MALA is impeded by a litany of obstacles such as financial constraints and a lack of advocacy strategy.

The study theorizes the role of MALA by triangulating the advocacy coalition framework, institutional theory and professionalization theory in the library and information practice.

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.

Using various proxies for the firms' return on equity (ROE) and retention ratios (b) the authors calculate 36 sustainable growth rates, on a rolling basis, for a comprehensive set of firms over a 52-year period. The authors then assess the ability of these different sustainable growth rates to predict the actual, out-of-sample, five-year growth rates of the firms' earnings.

The authors compare the forecast to determine which method of estimating ROE and b produce the lowest mean-squared-errors and then determine the estimation method that works best for firms with different characteristics and for firms in different industries.

Overall, using the median ROE of all firms in the market and the 5-year average of the specific firm's retention ratio produces the lowest, statistically significant, forecast errors. Variations are documented based on firm characteristics, including dividend payout, level of ROE and industry.

The findings can guide practitioners in using the best earnings forecasting method.

Financial textbooks seem universally to suggest that one method of estimating the growth rate of a firm's earnings is to calculate the “sustainable growth rate” by multiplying the firm's ROE by the firm's b. At the same time, multiple methods of proxying for both ROE and b have been suggested; therefore, it is an interesting and useful empirical question, which, heretofore, has not been addressed in the literature, as to which estimation of the sustainable growth rate best approximates the actual future growth of the firm's earnings. The findings can guide practitioners in using the best earnings forecasting method.

This study aims to examine the value relevance of earnings in terms of predicting the value variables such as cash flow, capital investment (CI), dividend and stock return under the Indian institutional settings.

The study used panel Granger causality tests to examine causality relationships among variables and panel data regression models to check the statistical associations between earnings and value variables.

Based on a data set of 7,280 Bombay Stock Exchange-listed firm-years spanning over ten years from March 2009 to March 2018, the results show higher sensitivity of earnings toward cash flows, CI, divided and stock return and vice-versa. Further, the findings deduced from the empirical results demonstrate that earnings are positively related to value variables. Overall, the results established that earnings are value-relevant and have predictive ability to forecast the value variables that facilitate investors in portfolio valuation. The results are consistent with the predictive view of the value relevance of earnings. Several robustness checks confirm these results.

This study brings new empirical evidence from a distinct capital market, India, and provides a new facet to the value relevance debate in terms of its prediction view. The study is among earlier attempts that jointly measure the ability of earnings in forecasting different value variables by taking a uniform sample of firms at the same period. Hence, the study provides a comprehensive view of the predictive ability of reported earnings.