Gender and the business cycle: an analysis of labour markets in the US and UK

Starting from an improved understanding of the relationship between gender labour market stocks and the business cycle, we analyse the contributing role of flows in the US and UK. Focusing on the post 2008 recession period, the subsequent greater rise in male unemployment can mostly be explained by a less cyclical response of flows between employment and unemployment for women, especially the entry into unemployment. Across gender and country, the inactivity rate is generally not sensitive to the state of the economy. However, a flows based analysis reveals a greater importance of the participation margin over the cycle. Changes in the rates of flow between unemployment and inactivity can each account for around 0.8-1.1 percentage points of the rise in US male and female unemployment rates during the latest downturn. For the UK, although the participation flow to unemployment similarly contributed to the increase of the female unemployment rate, this was not the case for men. The countercyclical flow rate from inactivity to employment was also more significant for women, especially in the US, where it accounted for approximately all of the fall in employment, compared with only 40% for men.

Toward a synergy between simulation and knowledge management for business intelligence

The adoption of simulation as a powerful enabling method for knowledge management is hampered by the relatively high cost of model construction and maintenance. A two-step procedure, based on a divide and conquer strategy, is proposed in this paper. First, a simulation program is partitioned based on a reinterpretation of the model-view-controller architecture. Individual parts are then connected, in terms of abstraction, to guard against possible changes that resulted from shifting user requirements. We explore the applicability of these design principles through a detailed discussion of an industry case study. The knowledge-based perspective guides the design of architecture to accommodate the need of emulation without compromising the integrity of the simulation program. The synergy between simulation and a knowledge management perspective, as shown in the case study, has the potential to achieve the objectives of rapid development of models, with low maintenance cost. This could, in turn, facilitate an extension of the use of simulation in the knowledge management domain.

How to write an unsuccessful entrepreneurial business plan : content analysis of the normative literature reveals a flawed paradigm

The study's aim was to investigate whether an Entrepreneurial Business Planning (EBP) paradigm could be discovered as a body of core, common maxims within the normative EBP literature (works of the 'how-to-write-a-successful-new-venture-business-plan' genre). It employed content analysis techniques adapted mainly from the methodological prescriptions of Krippendorf (1980) and Carney (1972). The textual investigation produced a comprehensive, quantitative data base capable of sufficient interpretative richness to discover that an established Entrepreneurial Business Planning paradigm does exist. Its major elements embrace two key assumptions, four strong mandates and four weaker mandates.

The discovery is significant for two main reasons. First, it provides a formally-researched, explicitly-articulated EBP paradigm. This can replace the anecdotal, unarticulated assumption (implicit in most of the normative EBP literature) that an EBP paradigm 'probably exists'. Second, the research redresses some of the imbalance between entrepreneurship teaching- where Entrepreneurial Business Planning is at the core of international curricula - and entrepreneurship research which has virtually ignored EBP as a topic worthy of serious scrutiny. A firm basis for critical, scholarly exploration of the neglected EBP field is now established. This takes the theory and practice of EBP into a new era beginning with recognition that the discovered EBP paradigm is badly flawed and likely, if blindly applied, to lead to the writing of unsuccessful business plans.

A maturity model of enterprise business intelligence

The implementation of an enterprise-level business intelligence initiative is a large-scale and complex undertaking, involving significant expenditure and multiple stakeholders over a lengthy period. It is therefore imperative to have systematic guidelines for business intelligence stakeholders in referring business intelligence maturity levels. Draw upon the prudent concepts of the Capability Maturity Model, this research proposes a multi-dimensional maturity model with distinct maturity levels for managing enterprise business intelligence initiatives. The maturity model, named Enterprise Business Intelligence Maturiy (EBIM), consists of five core maturity levels and four key dimensions, namely information quality, master data management, warehousing architecture, and analytics. It can be used to assist enterprises in benchmarking their business intelligence maturity level and identifying the critical areas to attain higher level of maturity.

Data quality management in a business intelligence environment : from the lens of metadata

Business Intelligence is becoming more pervasive in many large and medium-sized organisations. Being a long term undertaking Business Intelligence raises many issues that an organisation has to deal with in order to improve its decision making processes. Data quality is one of the main issues exposed by Business Intelligence. Within the organisation data quality can affect attitudes to Business Intelligence itself, especially from the business users group. Comprehensive management of data quality is a crucial part of any Business Intelligence endeavour. It is important to address all types of data quality issues and come up with an all-in-one solution. We believe that extensive metadata infrastructure is the primary technical solution for management of data quality in Business Intelligence. Moreover, metadata has a more broad application for improving the Business Intelligence environment. Upon identifying the sources of data quality issues in Business Intelligence we propose a concept of data quality management by means of metadata framework and discuss the recommended solution.

Making sense of business intelligence : proposing a socio-technical framework for improved decision making in not-for-profit organisations

The authors of this paper argue that human intuition alone cannot be relied upon for strategic decision making in today’s business environment and that quality data intelligence is an imperative. The proposed project described in this paper is research-in-progress, action design research (ADR), to implement an appropriate information systems (IS) enabling enhanced organisational decision making. ADR is a new research method that draws on action research and design research in an organisational setting. In phase 1 of the project, a sociotechnical ‘sense-making’ approach is used to gather and analyse information and decision needs in a not-for-profit (NFP) association, Connections ACT. In phase 2, requirements are designed and modelled to build a conceptual framework that guides NFPs in improving business performance and reporting capability. Phase 3 is the evaluative stage when the framework is reflected upon and refined, with intervention in the organisation’s processes as a promising outcome.

Information quality and governance for business intelligence

This book presents the latest exchange of academic research on all aspects of practicing and managing information using a multidisciplinary approach that examines its quality for organizational growth.

A comprehensive diagnostic framework for evaluating business intelligence and analytics effectiveness

Business intelligence and analytics (BIA) initiatives are costly, complex and experience high failure rates. Organizations require effective approaches to evaluate their BIA capabilities in order to develop strategies for their evolution. In this paper, we employ a design scienceparadigm to develop a comprehensive BIA effectiveness diagnostic (BIAED) framework that can be easily operationalized. We propose that a useful BIAED framework must assess the correct factors, should be deployed in the proper process context and acquire the appropriateinput from different constituencies within an organization. Drawing on the BIAED framework, we further develop an online diagnostic toolkit that includes a comprehensive survey instrument. We subsequently deploy the diagnostic mechanism within three large organizations in North America (involving over 1500 participants) and use the results toinform BIA strategy formulation. Feedback from participating organizations indicates that the BIA diagnostic toolkit provides insights that are essential inputs to strategy development. This work addresses a significant research gap in the area of BIA effectiveness assessment.

Business Intelligence: comparação de ferramentas

Cada vez mais o tempo acaba sendo o diferencial de uma empresa para outra. As empresas, para serem bem sucedidas, precisam da informação certa, no momento certo e para as pessoas certas. Os dados outrora considerados importantes para a sobrevivência das empresas hoje precisam estar em formato de informações para serem utilizados. Essa é a função das ferramentas de “Business Intelligence”, cuja finalidade é modelar os dados para obter informações, de forma que diferencie as ações das empresas e essas consigam ser mais promissoras que as demais. “Business Intelligence” é um processo de coleta, análise e distribuição de dados para melhorar a decisão de negócios, que leva a informação a um número bem maior de usuários dentro da corporação. Existem vários tipos de ferramentas que se propõe a essa finalidade. Esse trabalho tem como objetivo comparar ferramentas através do estudo das técnicas de modelagem dimensional, fundamentais nos projetos de estruturas informacionais, suporte a “Data Warehouses”, “Data Marts”, “Data Mining��� e outros, bem como o mercado, suas vantagens e desvantagens e a arquitetura tecnológica utilizada por estes produtos. Assim sendo, foram selecionados os conjuntos de ferramentas de “Business Intelligence” das empresas Microsoft Corporation e Oracle Corporation, visto as suas magnitudes no mundo da informática.