Strategic approaches of corporate codes of ethics in Australia: a framework for classification and empirical illustration

Purpose - Research has so far not approached the contents of corporate code of ethics from a strategic classification point of view. Therefore, the objective of this paper is to introduce and describe a framework of classification and empirical illustration to provide insights into the strategic approaches of corporate code of ethics content within and across contextual business environments.

Design/methodology/approach - The paper summarizes the content analysis of code prescription and the intensity of codification in the contents of 78 corporate codes of ethics in Australia.

Findings - The paper finds that, generally, the studied corporate codes of ethics in Australia are of standardized and replicated strategic approaches. In particular, customized and individualized strategic approaches are far from penetrating the ethos of corporate codes of ethics content.

Research limitations/implications - The research is limited to Australian codes of ethics. Suggestions for further research are provided in terms of the search for best practice of customized and individualized corporate codes of ethics content across countries.

Practical implications - The framework contributes to an identification of four strategic approaches of corporate codes of ethics content, namely standardized, replicated, individualized and customized.

Originality/value - The principal contribution of this paper is a generic framework to identify strategic approaches of corporate codes of ethics content. The framework is derived from two generic dimensions: the context of application and the application of content. The timing of application is also a crucial generic dimension to the success or failure of codes of ethics content. Empirical illustrations based upon corporate codes of ethics in Australia's top companies underpin the topic explored.

Problem and solution trees : a practical approach for identifying potential interventions to improve population nutrition

Population nutrition problems have a diversity of contributory factors and, ideally, multi-sectoral solutions should be developed by the relevant stakeholders, based on a common understanding of these factors. The problem and solution tree approach is a participatory process of working through the layers of determinants and then developing potential interventions for a specific issue, using the available data and expertise. We tailored this approach for non-communicable disease-related nutrition problems in Pacific Islands and applied it in several countries. The process led to the identification of a considerable range of determinants of unhealthy diets and potential interventions to improve the situation. This practical approach also offered the additional benefit of developing stakeholder awareness in the issues. Problem trees are a relatively simple tool to implement, easy to adapt to differing needs, can generate a wealth of information and can be more widely used.

Bayesian networks and decision trees in the diagnosis of female urinary incontinence

This study compares the effectiveness of Bayesian networks versus Decision Trees in modeling the Integral Theory of Female Urinary Incontinence diagnostic algorithm. Bayesian networks and Decision Trees were developed and trained using data from 58 adult women presenting with urinary incontinence symptoms. A Bayesian Network was developed in collaboration with an expert specialist who regularly utilizes a non-automated diagnostic algorithm in clinical practice. The original Bayesian network was later refined using a more connected approach. Diagnoses determined from all automated approaches were compared with the diagnoses of a single human expert. In most cases, Bayesian networks were found to be at least as accurate as the Decision Tree approach. The refined Connected Bayesian Network was found to be more accurate than the Original Bayesian Network accurately discriminated between diagnoses despite the small sample size. In contrast, the Connected and Decision Tree approaches were less able to discriminate between diagnoses. The Original Bayesian Network was found to provide an excellent basis for graphically communicating the correlation between symptoms and laxity defects in a given anatomical zone. Performance measures in both networks indicate that Bayesian networks could provide a potentially useful tool in the management of female pelvic floor dysfunction. Before the technique can be utilized in practice, well-established learning algorithms should be applied to improve network structure. A larger training data set should also improve network accuracy, sensitivity, and specificity.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Improved GART neural network model for pattern classification and rule extraction with application to power systems

Generalized adaptive resonance theory (GART) is a neural network model that is capable of online learning and is effective in tackling pattern classification tasks. In this paper, we propose an improved GART model (IGART), and demonstrate its applicability to power systems. IGART enhances the dynamics of GART in several aspects, which include the use of the Laplacian likelihood function, a new vigilance function, a new match-tracking mechanism, an ordering algorithm for determining the sequence of training data, and a rule extraction capability to elicit if-then rules from the network. To assess the effectiveness of IGART and to compare its performances with those from other methods, three datasets that are related to power systems are employed. The experimental results demonstrate the usefulness of IGART with the rule extraction capability in undertaking classification problems in power systems engineering.

Analysis of the stages of change model of drug and alcohol treatment readiness among prisoners

A classification and regression tree (CART) analysis was applied to data for 237 male participants (M = 31.93 years, SD = 7.64) in a prison-based substance abuse treatment program to study the integrity of the Stages of Change model of treatment readiness. Using the Stages of Change Questionnaire (STOCQ), participants were assigned to Contemplation (102), Action (118), or Maintenance (17) groups. A CART analysis then examined differences in the overall group profiles on the basis of scores on the Psychological Inventory of Criminal Thinking, the Situational Confidence Questionnaire, and the Carlson Psychological Survey. The assumption of discrete stages of change was not supported. Alternative models are suggested: one based on states of change and one on personality characteristics. A focus on equal attention to both cognitive and behavioral aspects of substance abuse treatment readiness is suggested.