Exploring the adoption and implementation of discrete and integrated IT systems: A Framework for analysis

Despite the substantial organisational benefits of integrated IT, the implementation of such systems – and particularly Enterprise Resource Planning (ERP) systems – has tended to be problematic, stimulating an extensive body of research into ERP implementation. This research has remained largely separate from the main IT implementation literature. At the same time, studies of IT implementation have generally adopted either a factor or process approach; both have major limitations. To address these imitations, factor and process perspectives are combined here in a unique model of IT implementation. We argue that • the organisational factors which determine successful implementation differ for integrated and traditional, discrete IT • failure to manage these differences is a major source of integrated IT failure. The factor/process model is used as a framework for proposing differences between discrete and integrated IT.

A computational intelligent approach to multi-factor analysis of violent crime information systems

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors.