Ethical decision making and health care managers : developing managerial profiles based on ethical frameworks and other influencing factors

Whether the community is looking for “scapegoats” to blame, or seeking more radical and deeper causes, health care managers are in the firing line whenever there are woes in the health care sector. The public has a right to question whether ethics have much influence on the everyday decision making of health care managers. This thesis explores, through a series of published papers, the influence of ethics and other factors on the decision making of health care managers in Australia. Critical review of over 40 years of research on ethical decision making has revealed a large number of influencing factors, but there is a demonstrable lack of a multidimensional approach that measures the combined influences of these factors on managers. This thesis has developed an instrument, the Managerial Ethical Profile (MEP) scale, based on a multidimensional model combining a large number of influencing factors. The MEP scale measures the range of influences on individual managers, and describes the major tendencies by developing a number of empirical profiles derived from a hierarchical cluster analysis. The instrument was developed and refined through a process of pilot studies on academics and students (n=41) and small-business managers (n=41), and then was administered to the larger sample of health care managers (n=441). Results from this study indicate that Australian health care managers draw on a range of ethical frameworks in their everyday decision making, forming the basis of five MEPs (Knights, Guardian Angels, Duty Followers, Defenders, and Chameleons). Results from the study also indicate that the range of individual, organisational, and external factors that influence decision making can be grouped into three major clusters or functions. Cross referencing these functions and other demographic data to the MEPs provides analytical insight into the characteristics of the MEPs. These five profiles summarise existing strengths and weaknesses in managerial ethical decision making. Therefore identifying these profiles not only can contribute to increasing organisational knowledge and self-awareness, but also has clear implications for the design and implementation of ethics education and training in large scale organisations in the health care industry.

To bound or not to bound: preliminary Theory of Time Bounds Specification in strategic alliances

This article augments Resource Dependence Theory with Real Options reasoning in order to explain time bounds specification in strategic alliances. Whereas prior work has found about a 50/50 split between alliances that are time bound and those that are open-ended, their substantive differences and antecedents are ill understood. To address this, we suggest that the two alliance modes present different real options trade-offs in adaptation to environmental uncertainty: ceteris paribus, time-bound alliances are likely to provide abandonment options over open-ended alliances, but require additional investments to extend the alliance when this turns out to be desirable after formation. Open-ended alliances are likely to provide growth options over open-ended alliances, but they demand additional effort to abandon the alliance if post-formation circumstances so desire. Therefore, we expect time bounds specification to be a function of environmental uncertainty: organizations in more uncertain environments will be relatively more likely to place time bounds on their strategic alliances. Longitudinal archival and survey data collected amongst 39 industry clusters provides empirical support for our claims, which contribute to the recent renaissance of resource dependence theory by specifying the conditions under which organizations choose different time windows in strategic partnering.

Stop whining and make the best of it: a cross-national comparison of response to regional disadvantage in the wine industry

Our cross-national field study of wine entrepreneurship in the “wrong” places provides some redress to the focus of the “regional advantage” literature on places that have already won and on the firms that benefit from “clusters” and other centers of industry advantage. Regional “disadvantage” is at best a shadowy afterthought to this literature. By poking around in these shadows, we help to synthesize and extend the incipient yet burgeoning literature on entrepreneurial “resourcefulness” and we contribute to the developing body of insights and theory pertinent to the numerous but often ignored firms and startups that mostly need to worry about how they will compete at all now if they are ever to have of chance of “winning” in the future. The core of our findings suggests that understandable – though contested – processes of ingenuity underlie entrepreneurial responses to regional disadvantage. Because we study entrepreneurship that from many angles simply does not make sense, we are also able to proffer a novel perspective on entrepreneurial sensemaking.

An investigation of unique challenges in National Historic relics and scenic sites (NHRSSs) in Chinese research literature 1982-2008 using co-word analysis

National Housing Relics and Scenic Sites (NHRSSs) in China are the equivalent of National Parks in the West but have contrasting features and broader roles when compared to their Western counterparts. By reviewing and analysing more than 370 academic sources, this paper identifies 6 major issue clusters and future challenges that will influence the management of NHRSSs over time. It also provides a number of cases to illustrate the particular features of NHRSSs. Identifying the hot issues and important challenges in Chinese NHRSSs will provide valuable insights into priorities now being discussed in highly populated areas of the World.

Editorial : International Journal of Knowledge-Based Development

The International Journal of Knowledge Based Development is planned to serve as a platform for the Global Knowledge Based Development Community to exchange academic and professional knowledge and experience, and adopt the learnings in different corners of the globe to achieve a sustainable knowledge-based development. The journal is put together by the executive team of an international think tank (The World Capital Institute – www.worldcapitalinstitute.org). As an international non-profit organization, The World Capital Institute aims to further advance the understanding and application of knowledge capital as the most powerful leverage for development in micro (i.e. individuals-neighborhoods-firms) , mezzo (i.e. communities-cities-clusters), macro (i.e. societies-nations), and supra-macro (supranational-global) levels.

How long is a tweet? Mapping dynamic conversation networks on Twitter using Gawk and Gephi

Twitter is now well established as the world’s second most important social media platform, after Facebook. Its 140-character updates are designed for brief messaging, and its network structures are kept relatively flat and simple: messages from users are either public and visible to all (even to unregistered visitors using the Twitter website), or private and visible only to approved ‘followers’ of the sender; there are no more complex definitions of degrees of connection (family, friends, friends of friends) as they are available in other social networks. Over time, Twitter users have developed simple, but effective mechanisms for working around these limitations: ‘#hashtags’, which enable the manual or automatic collation of all tweets containing the same #hashtag, as well allowing users to subscribe to content feeds that contain only those tweets which feature specific #hashtags; and ‘@replies’, which allow senders to direct public messages even to users whom they do not already follow. This paper documents a methodology for extracting public Twitter activity data around specific #hashtags, and for processing these data in order to analyse and visualize the @reply networks existing between participating users – both overall, as a static network, and over time, to highlight the dynamic structure of @reply conversations. Such visualizations enable us to highlight the shifting roles played by individual participants, as well as the response of the overall #hashtag community to new stimuli – such as the entry of new participants or the availability of new information. Over longer timeframes, it is also possible to identify different phases in the overall discussion, or the formation of distinct clusters of preferentially interacting participants.

Classifying the user intent of web queries using k-means clustering

Purpose: Web search engines are frequently used by people to locate information on the Internet. However, not all queries have an informational goal. Instead of information, some people may be looking for specific web sites or may wish to conduct transactions with web services. This paper aims to focus on automatically classifying the different user intents behind web queries. Design/methodology/approach: For the research reported in this paper, 130,000 web search engine queries are categorized as informational, navigational, or transactional using a k-means clustering approach based on a variety of query traits. Findings: The research findings show that more than 75 percent of web queries (clustered into eight classifications) are informational in nature, with about 12 percent each for navigational and transactional. Results also show that web queries fall into eight clusters, six primarily informational, and one each of primarily transactional and navigational. Research limitations/implications: This study provides an important contribution to web search literature because it provides information about the goals of searchers and a method for automatically classifying the intents of the user queries. Automatic classification of user intent can lead to improved web search engines by tailoring results to specific user needs. Practical implications: The paper discusses how web search engines can use automatically classified user queries to provide more targeted and relevant results in web searching by implementing a real time classification method as presented in this research. Originality/value: This research investigates a new application of a method for automatically classifying the intent of user queries. There has been limited research to date on automatically classifying the user intent of web queries, even though the pay-off for web search engines can be quite beneficial. © Emerald Group Publishing Limited.

The art of re-industrialisation in Shanghai

This paper deals with the development of ‘art clusters’ and their relocation in the city of Shanghai. It first looks at the revival of the city’s old inner city industrial area (along banks of Suzhou River) through ‘organic’ or ‘alternative’ artist-led cultural production; second, it describes the impact on these activities of the industrial restructuring of the wider city, reliant on large-scale real estate development, business services and global finance; and finally, outlines the relocation of these arts (and related) cultural industries to dispersed CBD locations as a result of those spatial, industrial and policy changes.

Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Bayesian mixtures for modelling complex medical data : a case study in Parkinson’s disease

Mixture models are a flexible tool for unsupervised clustering that have found popularity in a vast array of research areas. In studies of medicine, the use of mixtures holds the potential to greatly enhance our understanding of patient responses through the identification of clinically meaningful clusters that, given the complexity of many data sources, may otherwise by intangible. Furthermore, when developed in the Bayesian framework, mixture models provide a natural means for capturing and propagating uncertainty in different aspects of a clustering solution, arguably resulting in richer analyses of the population under study. This thesis aims to investigate the use of Bayesian mixture models in analysing varied and detailed sources of patient information collected in the study of complex disease. The first aim of this thesis is to showcase the flexibility of mixture models in modelling markedly different types of data. In particular, we examine three common variants on the mixture model, namely, finite mixtures, Dirichlet Process mixtures and hidden Markov models. Beyond the development and application of these models to different sources of data, this thesis also focuses on modelling different aspects relating to uncertainty in clustering. Examples of clustering uncertainty considered are uncertainty in a patient’s true cluster membership and accounting for uncertainty in the true number of clusters present. Finally, this thesis aims to address and propose solutions to the task of comparing clustering solutions, whether this be comparing patients or observations assigned to different subgroups or comparing clustering solutions over multiple datasets. To address these aims, we consider a case study in Parkinson’s disease (PD), a complex and commonly diagnosed neurodegenerative disorder. In particular, two commonly collected sources of patient information are considered. The first source of data are on symptoms associated with PD, recorded using the Unified Parkinson’s Disease Rating Scale (UPDRS) and constitutes the first half of this thesis. The second half of this thesis is dedicated to the analysis of microelectrode recordings collected during Deep Brain Stimulation (DBS), a popular palliative treatment for advanced PD. Analysis of this second source of data centers on the problems of unsupervised detection and sorting of action potentials or "spikes" in recordings of multiple cell activity, providing valuable information on real time neural activity in the brain.

Enriching XML documents clustering by using concise structure and content

With the growing number of XML documents on theWeb it becomes essential to effectively organise these XML documents in order to retrieve useful information from them. A possible solution is to apply clustering on the XML documents to discover knowledge that promotes effective data management, information retrieval and query processing. However, many issues arise in discovering knowledge from these types of semi-structured documents due to their heterogeneity and structural irregularity. Most of the existing research on clustering techniques focuses only on one feature of the XML documents, this being either their structure or their content due to scalability and complexity problems. The knowledge gained in the form of clusters based on the structure or the content is not suitable for reallife datasets. It therefore becomes essential to include both the structure and content of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both these kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. The overall objective of this thesis is to address these issues by: (1) proposing methods to utilise frequent pattern mining techniques to reduce the dimension; (2) developing models to effectively combine the structure and content of XML documents; and (3) utilising the proposed models in clustering. This research first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. A clustering framework with two types of models, implicit and explicit, is developed. The implicit model uses a Vector Space Model (VSM) to combine the structure and the content information. The explicit model uses a higher order model, namely a 3- order Tensor Space Model (TSM), to explicitly combine the structure and the content information. This thesis also proposes a novel incremental technique to decompose largesized tensor models to utilise the decomposed solution for clustering the XML documents. The proposed framework and its components were extensively evaluated on several real-life datasets exhibiting extreme characteristics to understand the usefulness of the proposed framework in real-life situations. Additionally, this research evaluates the outcome of the clustering process on the collection selection problem in the information retrieval on the Wikipedia dataset. The experimental results demonstrate that the proposed frequent pattern mining and clustering methods outperform the related state-of-the-art approaches. In particular, the proposed framework of utilising frequent structures for constraining the content shows an improvement in accuracy over content-only and structure-only clustering results. The scalability evaluation experiments conducted on large scaled datasets clearly show the strengths of the proposed methods over state-of-the-art methods. In particular, this thesis work contributes to effectively combining the structure and the content of XML documents for clustering, in order to improve the accuracy of the clustering solution. In addition, it also contributes by addressing the research gaps in frequent pattern mining to generate efficient and concise frequent subtrees with various node relationships that could be used in clustering.

Operations research for mining : a classification and literature review

In this paper, we describe the main processes and operations in mining industries and present a comprehensive survey of operations research methodologies that have been applied over the last several decades. The literature review is classified into four main categories: mine design; mine production; mine transportation; and mine evaluation. Mining design models are further separated according to two main mining methods: open-pit and underground. Moreover, mine production models are subcategorised into two groups: ore mining and coal mining. Mine transportation models are further partitioned in accordance with fleet management, truck haulage and train scheduling. Mine evaluation models are further subdivided into four clusters in terms of mining method selection, quality control, financial risks and environmental protection. The main characteristics of four Australian commercial mining software are addressed and compared. This paper bridges the gaps in the literature and motivates researchers to develop more applicable, realistic and comprehensive operations research models and solution techniques that are directly linked with mining industries.

Stop whining and make the best of it : a cross-national comparison of responses to regional disadvantage in the wine industry

Our cross-national field study of wine entrepreneurship in the “wrong” places provides some redress to the focus of the “regional advantage” literature on places that have already won and on the firms that benefit from “clusters” and other centers of industry advantage. Regional “disadvantage” is at best a shadowy afterthought to this literature. By poking around in these shadows, we help to synthesize and extend the incipient yet burgeoning literature on entrepreneurial “resourcefulness” and we contribute to the developing body of insights and theory pertinent to the numerous but often ignored firms and startups that mostly need to worry about how they will compete at all now if they are ever to have of chance of “winning” in the future. The core of our findings suggests that understandable – though contested – processes of ingenuity underlie entrepreneurial responses to regional disadvantage. Because we study entrepreneurship that from many angles simply does not make sense, we are also able to proffer a novel perspective on entrepreneurial sensemaking.

Determinants of China’s regional innovation capacity

This paper investigates the determinants of China’s regional innovation capacity (RIC) and variations in these determinants between different types of regions. Based on the framework of national innovation capacity (NIC) and research on innovation system, this paper develops a framework of RIC in the Chinese context. Using panel data from 1991 to 2009, clustering analysis is first employed to classify regions according to their innovation development path. Panel data regressions with fixed effect model are conducted to explore the determinants of RIC and how these vary across the different regional clusters. We find that the 30 regions can be clustered into three groups, and there are considerable differences in the drivers of RIC between these different regional groups.