Online Customer Experience : What is the Experience and how is it Difference for Frequent and Infrequent Purchasers, Age Cohorts and Gender Groups?

What do we know? • Customer Experience is increasingly becoming the new standard for differentiation in both offline and online retailing, and offers a sustainable competitive advantage. o The economic value of a company’s offering has been observed to increase when the customer has a fulfilling shopping experience (Pine & Gilmore, 1998) o Crafting engaging and customer experience is a known method of generating loyalty, advocacy and word of mouth (Tynan & McKechnie, 2009). o A good experience can entice consumers to shop for longer and spend more (Kim, 2001). • The customer’s experience is made up of diverse elements occurring before, during and after the purchase itself. (Discussed further on page 5). It is cumulative over time and can be influenced by touch points across multiple channels. What remains unclear? • How do Coles customers respond to the elements of online customer experience? • How does the online customer experience differ for frequent and infrequent purchasers? • Do differences between genders and age cohorts for online customer experience exist?

Aspect-based opinion extraction from customer reviews

Text is the main method of communicating information in the digital age. Messages, blogs, news articles, reviews, and opinionated information abounds on the Internet. People commonly purchase products online and post their opinions about purchased items. This feedback is displayed publicly to assist others with their purchasing decisions, creating the need for a mechanism with which to extract and summarize useful information for enhancing the decision-making process. Our contribution is to improve the accuracy of extraction by combining different techniques from three major areas, named Data Mining, Natural Language Processing techniques and Ontologies. The proposed framework sequentially mines product’s aspects and users’ opinions, groups representative aspects by similarity, and generates an output summary. This paper focuses on the task of extracting product aspects and users’ opinions by extracting all possible aspects and opinions from reviews using natural language, ontology, and frequent “tag” sets. The proposed framework, when compared with an existing baseline model, yielded promising results.

The emergent imperatives from a design-led innovation engagement within the mining industry

This research seeks a better and more detailed understanding of the processes of implementing design-led innovation in the mining equipment technology services (METS) sector, in a context where the traditional approach to manufacturing is being challenged. This longitudinal research specifically investigated the barriers to design-led innovation and opportunities that developed throughout this research, to understand how the organisation and culture of a METS company evolved, in order to progress towards design-led change. The significance of these findings is shown in the combined implementation of design imperatives leading towards design-led change at all business levels of an organisational structure.

Association hierarchy mining and its application for network traffic characterisation

This thesis presents an association rule mining approach, association hierarchy mining (AHM). Different to the traditional two-step bottom-up rule mining, AHM adopts one-step top-down rule mining strategy to improve the efficiency and effectiveness of mining association rules from datasets. The thesis also presents a novel approach to evaluate the quality of knowledge discovered by AHM, which focuses on evaluating information difference between the discovered knowledge and the original datasets. Experiments performed on the real application, characterizing network traffic behaviour, have shown that AHM achieves encouraging performance.

Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

The civic virtue of developmentalism : on the mining industry’s social licence to develop Western Australia

This paper examines the social licence to operate (SLO) of Western Australia's (WA's) mining industry in the context of the state's ‘developmentalist’ agenda. We draw on the findings of a multi-disciplinary body of new research on the risks and challenges posed byWA's mining industry for environmental, social and economic sustainability. We synthesise the findings of this work against the backdrop of the broader debates on corporate social responsibility (CSR) and resource governance. In light of the data presented, this paper takes issue with the mining sector's SLO and its assessment of social and environmental impacts in WA for three inter-related reasons. A state government ideologically wedded to resource-led growth is seen to offer the resource sector a political licence to operate and to give insufficient attention to its potential social and environmental impacts. As a result, the resource sector can adopt a self-serving CSR agenda built on a limited win–win logic and operate with a ‘quasi social licence’ that is restricted to mere economic legitimacy. Overall, this paper problematises the political-cum-commercial construction and neoliberalisation of the SLO and raises questions about the impact of mining in WA.

Bang and bust: almost everything you wanted to know about sex and the mining boom (but were afraid to ask)

Recent growth and expansion of the fly-in/fly-out (FIFO) model of mining in remote rural Australia has led to concerns about the health and well-being of those employed by the mines and those in the small rural communities where they are based. A particular concern has been the potential disruption to sexual norms in mining towns and increases in sexually transmitted infections (STIs) and HIV.

Mining, sex work and STIs: why force a connection?

Can the mining boom be blamed for the rising rates of sexually transmitted infections (STIs) in some states? The Australian Medical Association thinks so, with its Queensland president Dr Richard Kidd attributing rising rates of gonorrhoea, syphilis and chlamydia in Queensland and Western Australia to bored and cashed-up miners.

Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering

This project is a step forward in the study of text mining where enhanced text representation with semantic information plays a significant role. It develops effective methods of entity-oriented retrieval, semantic relation identification and text clustering utilizing semantically annotated data. These methods are based on enriched text representation generated by introducing semantic information extracted from Wikipedia into the input text data. The proposed methods are evaluated against several start-of-art benchmarking methods on real-life data-sets. In particular, this thesis improves the performance of entity-oriented retrieval, identifies different lexical forms for an entity relation and handles clustering documents with multiple feature spaces.

An investigation into the effect of infrastructure on fly-in fly-out mining workers

Objective: To explore fly-in fly-out (FIFO) mining workers' attitudes towards the leisure time they spend in mining camps, the recreational and social aspects of mining camp culture, the camps' communal and recreational infrastructure and activities, and implications for health. Design: In-depth semistructured interviews. Setting: Individual interviews at locations convenient for each participant. Participants: A total of seven participants, one female and six males. The age group varied within 20–59 years. Marital status varied across participants. Main outcome measures: A qualitative approach was used to interview participants, with responses thematically analysed. Findings highlight how the recreational infrastructure and activities at mining camps impact participants' enjoyment of the camps and their feelings of community and social inclusion. Results: Three main areas of need were identified in the interviews, as follows: (i) on-site facilities and activities; (ii) the role of infrastructure in facilitating a sense of community; and (iii) barriers to social interaction. Conclusion: Recreational infrastructure and activities enhance the experience of FIFO workers at mining camps. The availability of quality recreational facilities helps promote social interaction, provides for greater social inclusion and improves the experience of mining camps for their temporary FIFO residents. The infrastructure also needs to allow for privacy and individual recreational activities, which participants identified as important emotional needs. Developing appropriate recreational infrastructure at mining camps would enhance social interactions among FIFO workers, improve their well-being and foster a sense of community. Introducing infrastructure to promote social and recreational activities could also reduce alcohol-related social exclusion.

A data mining based method for discovery of web services and their compositions

Due to the availability of huge number of web services, finding an appropriate Web service according to the requirements of a service consumer is still a challenge. Moreover, sometimes a single web service is unable to fully satisfy the requirements of the service consumer. In such cases, combinations of multiple inter-related web services can be utilised. This paper proposes a method that first utilises a semantic kernel model to find related services and then models these related Web services as nodes of a graph. An all-pair shortest-path algorithm is applied to find the best compositions of Web services that are semantically related to the service consumer requirement. The recommendation of individual and composite Web services composition for a service request is finally made. Empirical evaluation confirms that the proposed method significantly improves the accuracy of service discovery in comparison to traditional keyword-based discovery methods.

Relevance feature discovery for text mining

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of large scale terms and data patterns. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences; yet, how to effectively use large scale patterns remains a hard problem in text mining. To make a breakthrough in this challenging issue, this paper presents an innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using this model on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-art term-based methods and the pattern based methods.

Mining resource profiles from event logs

Human resources are often responsible for the execution of business processes. In order to evaluate resource performance and identify best practices as well as opportunities for improvement, managers need objective information about resource behaviours. Companies often use information systems to support their processes and these systems record information about process execution in event logs. We present a framework for analysing and evaluating resource behaviour through mining such event logs. The framework provides a method for extracting descriptive information about resource skills, utilisation, preferences, productivity and collaboration patterns; a method for analysing relationships between different resource behaviours and outcomes; and a method for evaluating the overall resource productivity, tracking its changes over time and comparing it with the productivity of other resources. To demonstrate the applicability of our framework we apply it to analyse behaviours of employees in an Australian company and evaluate its usefulness by a survey among managers in industry.

The BAD project: data mining, database and prediction of protein adsorption on surfaces

Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.