Bad apples in bad barrels revisited: Cognitive moral development, just world beliefs, rewards, and ethical decision-making

In this study, we test the interactive effect on ethical decision-making of (1) personal characteristics, and (2) personal expectancies based on perceptions of organizational rewards and punishments. Personal characteristics studied were cognitive moral development and belief in a just world. Using an in-basket simulation, we found that exposure to reward system information influenced managers' outcome expectancies. Further, outcome expectancies and belief in a just world interacted with managers' cognitive moral development to influence managers' ethical decision-making. In particular, low-cognitive moral development managers who expected that their organization condoned unethical behavior made less ethical decisions while high cognitive moral development managers became more ethical in this environment. Low cognitive moral development managers also behaved less ethically when their belief in a just world was high.

Hanging Basket, C House, Coorparoo, Brisbane

Timber alcove located under skylight within outdoor room.

Hanging Basket, C House, Coorparoo, Brisbane

Timber alcove located under skylight within outdoor room.

Hanging Basket, C House, Coorparoo, Brisbane

Timber alcove located under skylight within outdoor room.

Hanging Basket, C House, Coorparoo, Brisbane

Timber alcove located under skylight within outdoor room.

Making Fedora easier to implement with Fez: A free open source content model and workflow management front-end to Fedora

The University of Queensland, Australia has developed Fez, a world-leading user-interface and management system for Fedora-based institutional repositories, which bridges the gap between a repository and users. Christiaan Kortekaas, Andrew Bennett and Keith Webster will review this open source software that gives institutions the power to create a comprehensive repository solution without the hassle..

Intuition: Myth or a Decision-making Tool?

Faced with today’s ill-structured business environment of fast-paced change and rising uncertainty, organizations have been searching for management tools that will perform satisfactorily under such ambiguous conditions. In the arena of managerial decision making, one of the approaches being assessed is the use of intuition. Based on our definition of intuition as a non-sequential information-processing mode, which comprises both cognitive and affective elements and results in direct knowing without any use of conscious reasoning, we develop a testable model of integrated analytical and intuitive decision making and propose ways to measure the use of intuition.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).