There is a large literature on the economics of crime and punishment, yet surprisingly little attention is paid to the receipt of money for crime. “Contract killing” is surprisingly neglected not only by economists but also by social scientists in general. In this paper, I look at the case not of professional gangster “hitmen” but of individuals who have found themselves in a position where they wish to have a killing carried out. This discussion does not condone the practice any more than an economic analysis of suicide is an inducement to individuals to kill themselves. To the lay reader, the cases where an individual feels the need to pay for killing may seem to be such that rationality is not a likely form of behaviour. However, the economics of crime has adopted the use of the rationality postulate as a heuristic for all types of crime.

Although ads with subtle racist imagery can reinforce negative stereotypes, advertisers can eliminate this problem. After a brief overview of predominantly U.S.-based research on the racial mix of models/actors in ads, a theoretical framework for unmasking subtle racial bias is provided and dimensional qualitative research (DQR) is introduced as a method for identifying and rectifying such ad imagery. Results of a DQR-based study of 622 U.K. television ads with at least one Black actor indicate (1) subtle racially biased imagery now supersedes overt forms, and (2) the most popular ad appeals often mask negative stereotypes. Implications for public policy and advertisers, as well as recommendations for future research, are discussed.

The purpose of this paper is to determine how best to reduce, reuse and dispose of household waste medicines in the National Health Service (NHS) (UK). Through a combination of literature review and empirical work, this research investigates the existing household waste medicines reverse logistics (RL) system and makes recommendations for improvement by benchmarking it against household waste batteries RL. The viability and feasibility of these recommendations are evaluated through in-depth interviews with healthcare professionals and end user surveys. The batteries RL system appears to be a more structured and effective system with more active engagement from actors/stakeholders in instigating RL practices and for this very reason is an excellent comparator for waste medicines RL practices. Appropriate best practices are recommended to be incorporated into the waste medicines RL system, including recapturing product value, revised processing approaches, system cooperation and enforcement, drivers and motivations and system design and facilitation. This study offers academics and professionals an improved insight into the current household waste medicines RL system and provides a step towards reducing an existing gap in this under-researched area. A limitation is that only a small sample of healthcare professionals were involved in subjectively evaluating the feasibility of the recommendations, so the applicability of the recommendations needs to be tested in a wider context and the cost effectiveness of implementing the recommendations needs to be analysed. Reducing, reusing and properly disposing of waste medicines contribute to economic sustainability, environmental protection and personal and community safety. The information retrieved from analysing returned medicines can be used to inform prescribing practice so as to reduce unnecessary medicine waste and meet the medicine optimisation agenda. This paper advocates learning from best practices in batteries RL to improve the waste medicines RL design and execution and supports the current NHS agenda on medicine waste reduction (DoH, 2012). The recommendations made in the paper not only aim to reduce medicine waste but also to use medicines effectively, placing the emphasis on improving health outcomes.

Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders.

This chapter analyses the how, who, where and why of rapid rise in intra-regional investment by companies from ASEAN since 2009. The chapter analyses the push and pull factors of intra-regional investment in ASEAN, the resulting patterns of foreign direct investment (FDI) and the accompanying rise of strong regional players. The region’s FDI landscape is changing in terms of investment sources, players, FDI trends and dynamics of the region. This trend is strongly affected by stepped up efforts by ASEAN governments to encourage their national companies to invest in the region and the influence of the ASEAN Economic Community. Regional integration and emerging business opportunities are providing an impetus not seen before in driving intra-regional investment. As more ASEAN companies position and prepare for AEC 2015, this intra-regional investment wave is likely to gather force. The chapter lists the regional and global ‘footprint’ of the top 50 largest ASEAN companies by revenues. The thus identified companies include companies operating in oil and gas, mining, agri-business, telecommunications, food and beverages, manufacturing, banking, power generation, infrastructure, real estate and healthcare services.

One of the distinctive features of the post-war process of European economic and political integration is the debate about the emergence of a European Social Model (ESM). Advocates and critics have clashed over the precise meaning of the ESM concept, whether it exists in a meaningful and singular form, and whether it challenges or bolsters – by providing some sort of discursive justification – the current neoliberal trajectory of the European Union (EU). While some of the claimed elements of the ESM do exist/have been adopted, this article argues that they do not constitute a coherent alternative to the dominant market liberal model and bias towards negative integration that has underpinned the EU since the 1980s. Furthermore, contemporary developments have served to further entrench these tendencies at the expense of progressive social forces that seek to construct a genuine ESM.

With the increasing complexity of problems in the construction industry, researchers are investigating computationally rigorous intelligent systems with the aim of seeking intelligent solutions. The purpose of this paper is therefore to analyse the research published on ‘intelligent systems in the construction industry’ over the past two decades. This is achieved to observe and understand the historical trends and current patterns in the use of different types of intelligent systems and to exhibit potential directions of further research. Thus, to trace the applications of intelligent systems to research in the construction industry, a profiling approach is employed to analyse 514 publications extracted from the Scopus database. The prime value and uniqueness of this paper lies in analysing and compiling the existing published material by examining variables (such as yearly publications, geographic location of each publication, etc.). This has been achieved by synthesising existing publications using 14 keywords2 ‘Intelligent Systems’, ‘Artificial Intelligence’, ‘Expert Systems’, ‘Fuzzy Systems’, ‘Genetic Algorithms’, ‘Knowledge-Based Systems’, ‘Neural Networks’, ‘Context Aware Applications’, ‘Embedded Systems’, ‘Human–Machine Interface’, ‘Sensing and Multiple Sensor Fusion’, ‘Ubiquitous and Physical Computing’, ‘Case-based Reasoning’ and ‘Construction Industry’. The prime contributions of this research are identified by associating (a) yearly publication and geographic location, (b) yearly publication and the type of intelligent systems employed/discussed, (c) geographic location and the type of research methods employed, and (d) geographic location and the types of intelligent systems employed. These contributions provide a comparison between the two decades and offer insights into the trends in using different intelligent systems types in the construction industry. The analysis presented in this paper has identified intelligent systems studies that have contributed to the development and accumulation of intellectual wealth to the intelligent systems area in the construction industry. This research has implications for researchers, journal editors, practitioners, universities and research institutions. Moreover, it is likely to form the basis and motivation for profiling other database resources and specific types of intelligent systems journals in this area.

This paper investigates the impact of corporate ownership and control on the outcome of financial distress. It is argued that the likelihood of financial distress resulting in insolvency depends on whether firms have controllers, the type of controllers and their cash flow ownership. Using a sample of 484 UK firms, 81 of which filed for insolvency, we show that financially distressed firms with controllers are more likely to be insolvent than widely held firms, where the probability of insolvency is greater when controllers are family or financial institutions. However, the probability of insolvency reduces significantly as the controllers’ cash flow ownership increases beyond 10%