Success factors in an alliancing contract : a case study in Australia

Many studies have been carried out in relation to construction procurement methods. Evidence shows that there needs to be a change of culture and attitude in the construction industry, moving away from traditional adversarial relationship into cooperative and collaborative relationship. At the same time there is also an increasing concern and discussion on alternative procurement methods, drifting away from traditional procurement systems. Relational contracting approaches have become more popular in recent years, and have appeared in common forms such as partnering, alliancing and relationship management contracts. This paper reports the findings of a survey undertaken with a private organisation based on an alliance project during its design stage, identifying the critical factors that influence the success of the alliance project. Legal aspects focusing on dispute resolution in alliancing are also highlighted.

A model for assessing and correcting construction project health

Abstract Maintaining the health of a construction project can help to achieve the desired outcomes of the project. An analogy is drawn to the medical process of a human health check where it is possible to broadly diagnose health in terms of a number of key areas such as blood pressure or cholesterol level. Similarly it appears possible to diagnose the current health of a construction project in terms of a number of Critical Success Factors (CSFs) and key performance indicators (KPIs). The medical analogy continues into the detailed investigation phase where a number of contributing factors are evaluated to identify possible causes of ill health and through the identification of potential remedies to return the project to the desired level of health. This paper presents the development of a model that diagnoses the immediate health of a construction project, investigates the factors which appear to be causing the ill health and proposes a remedy to return the project to good health. The proposed model uses the well-established continuous improvement management model (Deming, 1986) to adapt the process of human physical health checking to construction project health.

Data mining for lifetime prediction of metallic components

The ability to accurately predict the lifetime of building components is crucial to optimizing building design, material selection and scheduling of required maintenance. This paper discusses a number of possible data mining methods that can be applied to do the lifetime prediction of metallic components and how different sources of service life information could be integrated to form the basis of the lifetime prediction model

An Investigation of Cognitive, Emotional and Negative Behavioural Responses to Service Recovery Strategies

Despite the best intentions of service providers and organisations, service delivery is rarely error-free. While numerous studies have investigated specific cognitive, emotional or behavioural responses to service failure and recovery, these studies do not fully capture the complexity of the services encounter. Consequently, this research develops a more holistic understanding of how specific service recovery strategies affect the responses of customers by combining two existing models—Smith & Bolton’s (2002) model of emotional responses to service performance and Fullerton and Punj’s (1993) structural model of aberrant consumer behaviour—into a conceptual framework. Specific service recovery strategies are proposed to influence consumer cognition, emotion and behaviour. This research was conducted using a 2x2 between-subjects quasi-experimental design that was administered via written survey. The experimental design manipulated two levels of two specific service recovery strategies: compensation and apology. The effect of the four recovery strategies were investigated by collecting data from 18-25 year olds and were analysed using multivariate analysis of covariance and multiple regression analysis. The results suggest that different service recovery strategies are associated with varying scores of satisfaction, perceived distributive justice, positive emotions, negative emotions and negative functional behaviour, but not dysfunctional behaviour. These finding have significant implications for the theory and practice of managing service recovery.

The Second Life of urban planning? Using neogeography tools for community engagement

The majority of the world’s citizens now live in cities. Although urban planning can thus be thought of as a field with significant ramifications on the human condition, many practitioners feel that it has reached the crossroads in thought leadership between traditional practice and a new, more participatory and open approach. Conventional ways to engage people in participatory planning exercises are limited in reach and scope. At the same time, socio-cultural trends and technology innovation offer opportunities to re-think the status quo in urban planning. Neogeography introduces tools and services that allow non-geographers to use advanced geographical information systems. Similarly, is there potential for the emergence of a neo-planning paradigm in which urban planning is carried out through active civic engagement aided by Web 2.0 and new media technologies thus redefining the role of practicing planners? This paper traces a number of evolving links between urban planning, neogeography and information and communication technology. Two significant trends – participation and visualisation – with direct implications for urban planning are discussed. Combining advanced participation and visualisation features, the popular virtual reality environment Second Life is then introduced as a test bed to explore a planning workshop and an integrated software event framework to assist narrative generation. We discuss an approach to harness and analyse narratives using virtual reality logging to make transparent how users understand and interpret proposed urban designs.

SCOOT CBN

SCOOT is a hybrid event combining the web, mobile devices, public displays and cultural artifacts across multiple public parks and museums in an effort to increase the perceived and actual access to cultural participation by everyday people. The research field is locative game design and the context was the re-invigoration of public sites as a means for exposing the underlying histories of sites and events. The key question was how to use game play technologies and processes within everyday places in ways that best promote playful and culturally meaningful experiences whilst shifting the loci of control away from commercial and governmental powers. The research methodology was primarily practice led underpinned by ethnographic and action research methods. In 2004 SCOOT established itself as a national leader in the field by demonstrating innovative methods for stimulating rich interactions across diverse urban places using technically-augmented game play. Despite creating a sophisticated range of software and communication tools SCOOT most dramatically highlighted the role of the ubiquitous mobile phone in facilitating socially beneficial experiences. Through working closely with the SCOOT team, collaborating organisations developed important new knowledge around the potential of new technologies and processes for motivating, sustaining and reinvigorating public engagement. Since 2004, SCOOT has been awarded $600,00 in competitive and community funding as well as countless in kind support from partner organisations such as Arts Victoria, National Gallery of Victoria, Melbourne Museum, Australian Centre for the Moving Image, Federation Square, Art Centre of Victoria, The State Library of Victoria, Brisbane River Festival, State Library of Queensland, Brisbane Maritime Museum, Queensland University of Technology, and Victoria University.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

Framework for measuring success of construction projects

The construction industry is dynamic in nature. The concept of project success has remained ambiguously defined in the construction industry. Project success means different things to different people. While some authors consider time, cost and quality as the predominant targets, others suggest that success is something more complex. The aim of this report is to develop a framework for measuring success of construction projects. A range of Key Performance Indicators (KPIs), measured both objectively and subjectively is developed. The identification of KPIs helps set a benchmark for measuring the performance of a construction project and provides significant insights into developing a general and comprehensive base for further research.

Structural differentiation and corporate venturing : the moderating role of formal and informal integration mechanisms

Research has suggested that corporate venturing is crucial to strategic renewal and firm performance, yet scholars still debate the appropriate organizational configurations to facilitate the creation of new businesses in existing organizations. Our study investigates the effectiveness of combining structural differentiation with formal and informal organizational as well as top management team integration mechanisms in establishing an appropriate context for venturing activities. Our findings suggest that structural differentiation has a positive effect on corporate venturing. In addition, our study indicates that a shared vision has a positive effect on venturing in a structurally differentiated context. Socially integrated senior teams and cross-functional interfaces, however, are ineffective integration mechanisms for establishing linkages across differentiated units and for successfully pursuing corporate venturing.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Improving the management of building life cycle : a data mining approach

The construction industry has adapted information technology in its processes in terms of computer aided design and drafting, construction documentation and maintenance. The data generated within the construction industry has become increasingly overwhelming. Data mining is a sophisticated data search capability that uses classification algorithms to discover patterns and correlations within a large volume of data. This paper presents the selection and application of data mining techniques on maintenance data of buildings. The results of applying such techniques and potential benefits of utilising their results to identify useful patterns of knowledge and correlations to support decision making of improving the management of building life cycle are presented and discussed.

Using Agents for Mining Maintenance Data while interacting in 3D Objectoriented Virtual Environments

This report demonstrates the development of: (a) object-oriented representation to provide 3D interactive environment using data provided by Woods Bagot; (b) establishing basis of agent technology for mining building maintenance data, and (C) 3D interaction in virtual environments using object-oriented representation. Applying data mining over industry maintenance database has been demonstrated in the previous report.