Search results

As the world continues to grapple with new and existing challenges every passing day, the need to identify new approaches to maximise impact with given limited resources has become a pressing issue. To this end, overhauling the purchase and delivery of public services by introducing new funding models like commissioning has gained significant attention within the public and policy sector in recent years. Commissioning does not mean merely procurement and purchasing but securing the most appropriate services to address the needs of the population through due diligence and planning to maximise value. Although Commissioning has been practised internationally for many years, it has emerged at scale in New Zealand through the establishment of the Whānau Ora Commissioning Agency. This chapter discusses the Whānau Ora Commissioning model and its development as an indigenous model to commissioning for impact. It highlights the unique characteristics embedded in and exhibited by the model, which enable it to function in an indigenous context and facilitate positive well-being for its population. It also describes the challenges faced to perform and drive momentum forward. Commissioning may not be the only solution to resolving societal challenges. However, it can be instrumental in generating and amplifying value tailored to the context and used in conjunction with other innovative practices.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

In this paper the techniques of zero-non-zero (ZNZ) patterned vector autoregressive modelling are utilized to examine two issues associated with the European single currency – the euro. First, “Granger causality” is employed to examine the causal linkages between the euro exchange rate, the euro area money supply and the gross domestic product (GDP) growth in the euro area. Second, we examine the hypothesis that the euro has become a major influence on international stock markets by testing for the causal relationships between movements in the euro exchange rate, the U.K. pound exchange rate and the London stock market index.

Vector error-correction models (VECMs) have become increasingly important in their application to financial markets. Standard full-order VECM models assume non-zero entries in all their coefficient matrices. However, applications of VECM models to financial market data have revealed that zero entries are often a necessary part of efficient modelling. In such cases, the use of full-order VECM models may lead to incorrect inferences. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of over-parameterised full-order VECM models may weaken the power of statistical inference. In this paper, it is argued that the zero–non-zero (ZNZ) patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. For a ZNZ patterned VECM framework for time series of integrated order two, we provide a new algorithm to select cointegrating and loading vectors that can contain zero entries. Two case studies are used to demonstrate the usefulness of the algorithm in tests of purchasing power parity and a three-variable system involving the stock market.

In this reply, we respond to a series of substantive comments on our work by both Madhavan and Walker. In our response to Madhavan’s comments, we consider three accounts – “weak,” “semi-strong” and “strong” – that clarify how our model “explains” and offers insights that can emerge from our modeling strategy. We also explore ways in which our explanatory strategy might be extended in light of his critique. In our response to Walker’s comments, we adopt the “semi-strong” thesis, which admits variation in network-generating mechanisms, while also recognizing that information needs to be distributed and shared in order for many types of networks to function.

Evaluating pain and discomfort in animals is difficult at best. Veterinarians believe however, that they can establish a proxy for estimating levels of pain and discomfort in canines by observing variations in their activity levels. Sufficient research has been conducted to justify this assertion, but little has been conducted to analyze the volumes of activity data collected. We present the first of a series of analyses aimed at ultimately presenting an effective predictive tool for canine pain and discomfort levels. In this chapter, we perform analyses on a dataset of normal (control) dogs, containing almost 3 million records. The forecasting analyses incorporated multiple polynomial regression models with transcendental transformations and ARIMA models to provide effective determination and prediction of baseline normal canine activity levels.

Colombia has one of the highest levels of inequality in landholding in the world. This inequality has persisted in spite of numerous state-led land reform efforts, which leads to the question: why has it been so difficult to reverse unequal land distribution in Colombia? To answer this question, the chapter examines the role of the state, non-state armed groups, land inequality, land reform efforts, and a history of violence to reveal the relationship between land, inequality and violence in Colombia. This chapter explores the nature of this relationship to understand Colombia’s enduring inequality and to inform theoretical approaches to statehood and power. Rather than reducing state capacity to common Weberian binary constructions of state and statelessness, I explore how state capacity takes on different forms in different regions of Colombia – analyzing how various actors shape land inequality and violence across the territory. Using a comprehensive longitudinal panel data set of displaced persons, I use a negative binomial regression model to demonstrate how land reform, land inequality, and a history of violence have directly affected current displacement of citizens. I argue that several constellations of powerful social actors have at various points converged to control land, through non-state armed groups, to exert a local form of logistical control outside the scope of the federal state, deeply affecting the dynamics violence across different territories. These groups have subsequently engaged in a land grabbing process that has resulted in a reverse form of land reform – leading to persisting inequality in Colombia.

This chapter uses advance order data from an actual manufacturing shop to develop and test a forecast model for total demand. The proposed model made direct use of historical time series data for total demand and time series data for advance orders. Comparison of the proposed model to commonly used approaches showed that the proposed model exhibited greater forecast accuracy.