Identifying key enablers to improve business performance in Taiwanese electronic manufacturing companies

Purpose – Integrated supplier management (ISM), new product development (NPD) and knowledge sharing (KS) practices are three primary business activities utilised to enhance manufacturers' business performance (BP). The purpose of this paper is to empirically investigate the relationships between these three business activities (i.e. ISM, NPD, KS) and BP in a Taiwanese electronics manufacturing context. Design/methodology/approach – A questionnaire survey is first administered to a sample of electronic manufacturing companies operating in Taiwan to elicit the opinions of technical and managerial professionals regarding business activities and BP within their companies. A total of 170 respondents from 83 companies respond to the survey. Factor, correlation and path analysis are undertaken on this quantitative data set to derive the key factors which leverage business outcomes in these companies. Following empirical analysis, six semi-structured interviews are undertaken with manufacturing executives to provide qualitative insights into the underlying reasons why certain business activity factors are the strongest predictors of BP. Findings – The investigation shows that the ISM, NPD and KS constructs all play an important role in the success of company operations and creating business outcomes. Specifically, the key factors within these constructs which influenced BP are: supplier evaluation and selection; design simplification and modular design; information technology infrastructure and systems and open communication. Accordingly, sufficient financial and human resources should be allocated to these important activities to derive accelerated rates of improved BP. These findings are supported by the qualitative interviews with manufacturing executives. Originality/value – The paper depicts the pathways to improved manufacturing BP, through targeting efforts into the above-mentioned factors within the ISM, NPD and KS constructs. Based on the empirical path model, and the specific insights derived from the explanatory interviews with manufacturing executives, the paper also provides a number of practical implications for manufacturing companies seeking to enhance their BP through improved operational activities.

Which cultural values matter to business process management? Results from a global Delphi study

Purpose – Business process management (BPM) requires a holistic perspective that includes managing the culture of an organization to achieve objectives of efficient and effective business processes. Still, the specifics of a BPM-supportive organizational culture have not been examined so far. Thus, the purpose of this paper is to identify the characteristics of a cultural setting supportive of BPM objectives. Design/methodology/approach – The paper examines the constituent values of a BPM-supportive cultural setting through a global Delphi study with BPM experts from academia and practice and explore these values in a cultural value framework. Findings – The paper empirically identifies and defines four key cultural values supporting BPM, viz., customer orientation, excellence, responsibility, and teamwork. The paper discusses the relationships between these values and identifies a particular challenge in managing these seemingly competing values. Research limitations/implications – The identification and definition of these values represents a first step towards the operationalization (and empirical analysis) of what has been identified as the concept of BPM culture, i.e. a culture supportive of achieving BPM objectives. Practical implications – Identifying these cultural values provides the basis for developing an instrument that can measure how far an existing cultural context is supportive of BPM. This, in turn, is fundamental for identifying measures towards achieving a BPM culture as a necessary, yet not sufficient means to obtain BPM success. Originality/value – The paper examines which cultural values create an environment receptive for BPM and, thus, specifies the important theoretical construct BPM culture. In addition, the paper raises awareness for realizing these values in a BPM context.

Land use planning and the airport metropolis

Australian airports have emerged as important urban activity centres over the past decade as a result of privatisation. A range of reciprocal airport and regional impacts now pose considerable challenges for both airport operation and the surrounding urban and regional environment. The airport can no longer be managed solely as a specialised transport entity in isolation from the metropolis that it serves. In 2007 a multidisciplinary Australian Research Council Linkage Project (LP 0775225) was funded to investigate the changing role of airports in Australia. This thesis is but one component of this collaborative research effort. Here the issues surrounding the policy and practice of airport and regional land use planning are explored, analysed and detailed. This research, for the first time, assembles a distinct progression of the wider social, economic, technological and environmental roles of the airport within the Australian airport literature from 1914 – 2011. It recognises that while the list of airport and regional impacts has grown through time, treatment within practice and the literature has largely remained highly specialised and contained within disciplinary paradigms. The first publication of the thesis (Chapter 2) acknowledges that the changing role of airports demands the establishment of new models of airport planning and development. It argues that practice and research requires a better understanding of the reciprocal impacts of airports and their urban catchments. The second publication (Chapter 3) highlights that there is ad hoc examination and media attention of high profile airport and regional conflict, but little empirical analysis or understanding of the extent to which all privatised Australian airports are intending to develop. The conceptual and methodological significance of this research is the development of a national land use classification system for on-airport development. This paper establishes the extent of on-airport development in Australia, providing insight into the changing land use and economic roles of privatised airports. The third publication (Chapter 4) details new and significant interdependencies for airport and regional development in consideration of the progression of airports as activity centres. Here the model of an ‘airport metropolis’ is offered as an organising device and theoretical contribution for comprehending the complexity and planning of airport and regional development. It delivers a conceptual framework for both research and policy, which acknowledges the reciprocal impacts of economic development, land use, infrastructure and governance ‘interfaces’. In a timely and significant concurrence with this research the Australian Government announced and delivered a National Aviation Policy Review (2008 – 2009). As such the fourth publication (Chapter 5) focuses on the airport and urban planning aspects of the review. This paper also highlights the overall policy intention of facilitating broader airport and regional collaborative processes. This communicative turn in airport policy is significant in light of the communicative theoretical framework of the thesis. The fifth paper of the thesis (Chapter 6) examines three Australian case studies (Brisbane, Adelaide and Canberra) to detail the context of airport and regional land use planning and to apply the airport metropolis model as a framework for research. Through the use of Land Use Forums, over 120 airport and regional stakeholders are brought together to detail their perspectives and interactions with airport and regional land use planning. An inductive thematic analysis of the results identifies three significant themes which contribute to the fragmentation of airport and regional and land use planning: 1) inadequate coordination and disjointed decision-making; 2) current legislative and policy frameworks; and 3) competing stakeholder priorities and interests. Building on this new knowledge, Chapter 7 details the perceptions of airport and local, state and territory government stakeholders to land use relationships, processes and outcomes. A series of semi-structured interviews are undertaken in each of the case studies to inform this research. The potential implications for ongoing communicative practice are discussed in conclusion. The following thesis represents an incremental and cumulative research process which delivers new knowledge for the practical understanding and research interpretation of airport and regional land use planning practice and policy. It has developed and applied a robust conceptual framework which delivers significant direction for all stakeholders to better comprehend the relevance of airports in the urban character and design of our cities.

Recommending people in social networks using data mining

This thesis improves the process of recommending people to people in social networks using new clustering algorithms and ranking methods. The proposed system and methods are evaluated on the data collected from a real life social network. The empirical analysis of this research confirms that the proposed system and methods achieved improvements in the accuracy and efficiency of matching and recommending people, and overcome some of the problems that social matching systems usually suffer.

A semi-supervised graph-based algorithm for detecting outliers in online-social-networks

In this paper, we propose a semi-supervised approach of anomaly detection in Online Social Networks. The social network is modeled as a graph and its features are extracted to detect anomaly. A clustering algorithm is then used to group users based on these features and fuzzy logic is applied to assign degree of anomalous behavior to the users of these clusters. Empirical analysis shows effectiveness of this method.

The heterogeneous cluster ensemble method using hubness for clustering text documents

We propose a cluster ensemble method to map the corpus documents into the semantic space embedded in Wikipedia and group them using multiple types of feature space. A heterogeneous cluster ensemble is constructed with multiple types of relations i.e. document-term, document-concept and document-category. A final clustering solution is obtained by exploiting associations between document pairs and hubness of the documents. Empirical analysis with various real data sets reveals that the proposed meth-od outperforms state-of-the-art text clustering approaches.

A concept-based retrieval method for entity-oriented search

Entity-oriented retrieval aims to return a list of relevant entities rather than documents to provide exact answers for user queries. The nature of entity-oriented retrieval requires identifying the semantic intent of user queries, i.e., understanding the semantic role of query terms and determining the semantic categories which indicate the class of target entities. Existing methods are not able to exploit the semantic intent by capturing the semantic relationship between terms in a query and in a document that contains entity related information. To improve the understanding of the semantic intent of user queries, we propose concept-based retrieval method that not only automatically identifies the semantic intent of user queries, i.e., Intent Type and Intent Modifier but introduces concepts represented by Wikipedia articles to user queries. We evaluate our proposed method on entity profile documents annotated by concepts from Wikipedia category and list structure. Empirical analysis reveals that the proposed method outperforms several state-of-the-art approaches.

XML clustering and its application to XML transformation

The continuous growth of the XML data poses a great concern in the area of XML data management. The need for processing large amounts of XML data brings complications to many applications, such as information retrieval, data integration and many others. One way of simplifying this problem is to break the massive amount of data into smaller groups by application of clustering techniques. However, XML clustering is an intricate task that may involve the processing of both the structure and the content of XML data in order to identify similar XML data. This research presents four clustering methods, two methods utilizing the structure of XML documents and the other two utilizing both the structure and the content. The two structural clustering methods have different data models. One is based on a path model and other is based on a tree model. These methods employ rigid similarity measures which aim to identifying corresponding elements between documents with different or similar underlying structure. The two clustering methods that utilize both the structural and content information vary in terms of how the structure and content similarity are combined. One clustering method calculates the document similarity by using a linear weighting combination strategy of structure and content similarities. The content similarity in this clustering method is based on a semantic kernel. The other method calculates the distance between documents by a non-linear combination of the structure and content of XML documents using a semantic kernel. Empirical analysis shows that the structure-only clustering method based on the tree model is more scalable than the structure-only clustering method based on the path model as the tree similarity measure for the tree model does not need to visit the parents of an element many times. Experimental results also show that the clustering methods perform better with the inclusion of the content information on most test document collections. To further the research, the structural clustering method based on tree model is extended and employed in XML transformation. The results from the experiments show that the proposed transformation process is faster than the traditional transformation system that translates and converts the source XML documents sequentially. Also, the schema matching process of XML transformation produces a better matching result in a shorter time.

A novel method for finding similarities between unordered trees using matrix data model

Trees are capable of portraying the semi-structured data which is common in web domain. Finding similarities between trees is mandatory for several applications that deal with semi-structured data. Existing similarity methods examine a pair of trees by comparing through nodes and paths of two trees, and find the similarity between them. However, these methods provide unfavorable results for unordered tree data and result in yielding NP-hard or MAX-SNP hard complexity. In this paper, we present a novel method that encodes a tree with an optimal traversing approach first, and then, utilizes it to model the tree with its equivalent matrix representation for finding similarity between unordered trees efficiently. Empirical analysis shows that the proposed method is able to achieve high accuracy even on the large data sets.

Modelling NPO-government relations : Australian case studies

Non-profit organizations (NPOs) are major providers of services in many fields of endeavour, and often receive financial support from government. This article investigates different forms of government/nonprofit funding relationships, with the viewpoint being mainly, though not exclusively, from the perspective of the non-profit agencies. While there are a number of existing typologies of government/NPO relations, these are dated and in need of further empirical analysis and testing. The article advances an empirically derived extension to current models of government/NPO relations. A future research agenda is outlined based on the constructs that underpin typologies, rather than discrete categorization of relationships.

The ranking based constrained document clustering method and its application to social event detection

With the growing size and variety of social media files on the web, it’s becoming critical to efficiently organize them into clusters for further processing. This paper presents a novel scalable constrained document clustering method that harnesses the power of search engines capable of dealing with large text data. Instead of calculating distance between the documents and all of the clusters’ centroids, a neighborhood of best cluster candidates is chosen using a document ranking scheme. To make the method faster and less memory dependable, the in-memory and in-database processing are combined in a semi-incremental manner. This method has been extensively tested in the social event detection application. Empirical analysis shows that the proposed method is efficient both in computation and memory usage while producing notable accuracy.

Assessing the property market impact of stigma removal : high voltage overhead transmission lines removal in Wellington, NZ

This thesis advances the understanding of the impact of stigma on property values. A case study in Wellington, New Zealand, enabled hedonic modelling and an empirical analysis to determine the impact of the stigma from the high voltage transmission line structure and how long the stigma remained after removal. The results reveal a substantial difference between the discount applied to individual properties while the structure is in place, as compared to the overall increase in neighbourhood value once the structure, which created the stigma, is removed.

Tensor-based item recommendation using probabilistic ranking in social tagging systems

A common problem with the use of tensor modeling in generating quality recommendations for large datasets is scalability. In this paper, we propose the Tensor-based Recommendation using Probabilistic Ranking method that generates the reconstructed tensor using block-striped parallel matrix multiplication and then probabilistically calculates the preferences of user to rank the recommended items. Empirical analysis on two real-world datasets shows that the proposed method is scalable for large tensor datasets and is able to outperform the benchmarking methods in terms of accuracy.

Financial literacy and superannuation investment decision-making in a choice environment : an exploratory study

This thesis examines superannuation fund members' level of financial literacy and its association with investment choice decisions in the setting of the mandatory superannuation system in Australia. It also investigates a range of contextual and socio-demographic factors in explaining financial literacy and investment choice. The empirical analysis shows that while most survey respondents displayed high levels of self-rated and general financial literacy, fewer scored as well in relation to more advanced literacy regarding superannuation investment options. The findings of this study have important implications for policy-makers and the superannuation industry in educating and engaging with fund members.

Mining discriminative Itemsets in data streams

This paper presents a single pass algorithm for mining discriminative Itemsets in data streams using a novel data structure and the tilted-time window model. Discriminative Itemsets are defined as Itemsets that are frequent in one data stream and their frequency in that stream is much higher than the rest of the streams in the dataset. In order to deal with the data structure size, we propose a pruning process that results in the compact tree structure containing discriminative Itemsets. Empirical analysis shows the sound time and space complexity of the proposed method.