Inflation Targeting: Lessons from Four Countries

In recent years, a number of central banks have announced numerical inflation targets as the basis for their monetary strategies. After outlining the reasons why such strategies might be adopted in the pursuit of price stability, this study examines the adoption, operational design, and experience of inflation targeting as a framework for monetary policy in the first three countries to undertake such strategies New Zealand, Canada, and the United Kingdom. It also analyzes the operation of the long-standing German monetary targeting regime, which incorporated many of the same features as later inflation-targeting regimes. The key challenge for all these monetary" frameworks has been the appropriate balancing of transparency and flexibility in policymaking. The study finds that all of the targeting countries examined have maintained low rates of inflation and increased the transparency of monetary policymaking without harming the real economy through policy rigidity in the face of economic developments. A convergence of design choices on the part of targeting countries with regard to operational questions emerges from this comparative study, suggesting some lines of best practice for inflation-targeting frameworks.

Towards a Model for Evaluating Organizational Readiness for ERP and Data Warehousing Projects

The need for an integrated enterprise-wide set of management information pronounced Data Warehousing the ‘hot topic’ of the early-to-mid 1990’s, however, it became unfashionable through the mid-to-late 1990s, with the approach of Y2K and the widespread implementation of ERP (Enterprise Resource Planning) systems. However, in recent times, the re-emergence of Data Warehousing, coupled with ERP implementations, to address the limitations and unrealised benefits of ERP systems in the area of reporting, provides researchers and managers with new challenges. This paper lays the foundation for a model of organisational prerequisites for enterprise-wide integration projects. The model is aimed at the ‘Intelligence’ phase of managerial decision making for such projects and should help managers assess their organisation’s readiness for ERP and data warehousing projects .The main objective of the paper is to present a literature-based model which lists the key prerequisites that organisations should assess prior to undertaking these expensive projects

Justifying an ERP Investment: Critical Success Factors for Transformation Investments

Enterprise Resource Planning (ERP) projects often lead to disappointing outcomes, even downright failures, which is not in keeping with the vast investments they require. It is argued in this paper that a lack of managerial focus on the Critical Success Factors (CSFs) for ERP implementation at the outset of their project (however well captured they have been in the literature to date) is the root cause of organisations not achieving the expected outcomes from their Transformation investment. This paper examines the ERP project implementations of four organisations where data and observations were collected over a two year period. From this rich body of empirical evidence, we propose to contribute to our collective understanding of the CSFs for ERP implementation that play a key role in ensuring that investments in ERP packages (Transformation investments) have a greater likelihood of producing desired project outcomes. This research paper also contributes to increasing the perceived usefulness of CSFs to managers and practitioners and illustrates the fact that organisations should undertake self-assessment exercises to improve their preparations for ERP project implementations

A Framework for Designing Digital Health Interventions

The only sustainable way to provide more effective healthcare and at the same time to reduce soaring healthcare costs is by keeping people healthier. Digitally Based Change Interventions (DBCI) are interventions that utilise digital technologies to promote and maintain health and wellbeing through monitoring, managing and preventing personal health problems. DBCIs are typically automated, interactive, and personalized ‘just-in-time’ adaptive interventions (JITAIs) that provide real time support to individuals especially during moments when they have the greatest opportunity to engage in a healthier behaviour (or are most vulnerable to engaging in a negative behaviour). To date, the potential of DBCIs has scarcely been realized, partly because of difficulties in generating an accumulating knowledge base for guiding their design. As a result, most designers do not use theory as a basis for developing new interventions or for analysing why some interventions fail and others succeed. In this paper, we bring together insights from a number of theories in order to bridge this gap and to produce a “theory-based” framework for assisting with their design. In turn, we demonstrate the power of this framework by using it to review the design of a digital programme previously described in a well cited paper

Decision Making in the ERP Community

This paper, although bibliographical in nature, stresses the importance of researching ERP from the perspective of the ERP community, defined here as a triadic group composed of (1) an ERP vendor, (2) an ERP consultant and (3) an implementing organisation; and focuses on understanding the relationships and inter-dependencies that exist between these actors. In this novel perspective, the second contribution of this paper is to introduce the concept of category manipulation – that is to say non-decision making - to the area of IS in general and more specifically to ERP research. As far as the authors are aware, this paper is the first to examine the ERP decision making process under this light. The researchers\u27 objective is to structure their ideas in an effort to lay the foundations for a model of ERP decision making that can inform both the practice and investigation of ERP implementation

Estimation of channelized hotelling observer performance with known class means or known difference of class means

Journal ArticleThis paper concerns task-based image quality assessment for the task of discriminating between two classes of images. We address the problem of estimating two widely-used detection performance measures, SNR and AUC, from a finite number of images, assuming that the class discrimination is performed with a channelized Hotelling observer. In particular, we investigate the advantage that can be gained when either 1) the means of the signal-absent and signal-present classes are both known, or 2) when the difference of class means is known. For these two scenarios, we propose uniformly minimum variance unbiased estimators of S N R?, derive the corresponding sampling distributions and provide variance expressions. In addition, we demonstrate how the bias and variance for the related AUC estimators may be calculated numerically by using the sampling distributions for the S N R? estimators. We find that for both S N R? and AUC, the new estimators have significantly lower bias and mean-square error than the traditional estimator, which assumes that the class means, and their difference, are unknown

Magic-State Functional Units: Mapping and Scheduling Multi-Level Distillation Circuits for Fault-Tolerant Quantum Architectures

Quantum computers have recently made great strides and are on a long-term path towards useful fault-tolerant computation. A dominant overhead in fault-tolerant quantum computation is the production of high-fidelity encoded qubits, called magic states, which enable reliable error-corrected computation. We present the first detailed designs of hardware functional units that implement space-time optimized magic-state factories for surface code error-corrected machines. Interactions among distant qubits require surface code braids (physical pathways on chip) which must be routed. Magic-state factories are circuits comprised of a complex set of braids that is more difficult to route than quantum circuits considered in previous work [1]. This paper explores the impact of scheduling techniques, such as gate reordering and qubit renaming, and we propose two novel mapping techniques: braid repulsion and dipole moment braid rotation. We combine these techniques with graph partitioning and community detection algorithms, and further introduce a stitching algorithm for mapping subgraphs onto a physical machine. Our results show a factor of 5.64 reduction in space-time volume compared to the best-known previous designs for magic-state factories.Comment: 13 pages, 10 figure