Longitudinal Student Evaluations of a Postgraduate Unit Using Importance-Performance Analysis

The Australian tourism tertiary education sector operates in a competitive and dynamic environment, which necessitates a market orientation to be successful. Academic staff and management in the sector must regularly assess the perceptions of prospective and current students, and monitor the satisfaction levels of current students. This study is concerned with the setting and monitoring of satisfaction levels of current students, reporting the results of three longitudinal investigations of student satisfaction in a postgraduate unit. The study also addresses a limitation of a university’s generic teaching evaluation instrument. Importance-performance analysis (IPA) has been recommended as a simple but effective tool for overcoming the deficiencies of many student evaluation studies, which have generally measured only attribute importance or importance at the end of a semester. IPA was used to compare student expectations of the unit at the beginning of semester with their perceptions of performance ten weeks later. The first stage documented key benchmarks for which amendments to the unit based on student feedback could be evaluated during subsequent teaching periods.

Knowledge management for ERP success

The business value of Enterprise Resource Planning (ERP) systems and in general large software implementations has been extensively debated in both popular press and academic literature for over three decades. Despite the positive motives for adoption, various organizations have reported negative impacts from these large investments. This ‘disconnect’ between large IS investments and firms’ organizational performance may be attributable to the economic transition from an era of competitive advantage based on information to one that is based on Knowledge. This paper discusses the initial findings of a two-phased study that focuses on empirically assessing the impact of knowledge management on the success of Enterprise Resource Planning systems. The research study uses information gathered from twenty-seven public sector organizations in Queensland, Australia. Validation of the a priori model constructs through factor analysis identified two dimensions of knowledge management. Further analysis assessed the comparative differences in perceptions of knowledge management in ERP, across four employment cohorts.

Key performance indicators for measuring construction success

The construction industry is dynamic in nature. The concept of project success has remained ambiguously defined in the construction industry. Project success is almost the ultimate goal for every project. However, it means different things to different people. While some writers consider time, cost and quality as predominant criteria, others suggest that success is something more complex. The aim of this paper is to develop a framework for measuring success of construction projects. In this paper, a set of key performance indicators (KPIs), measured both objectively and subjectively are developed through a comprehensive literature review. The validity of the proposed KPIs is also tested by three case studies. Then, the limitations of the suggested KPIs are discussed. With the development of KPIs, a benchmark for measuring the performance of a construction project can be set. It also provides significant insights into developing a general and comprehensive base for further research.

A framework for analyzing system intelligence of the building control system : a study of the integrated building management system

The lack of satisfactory consensus for characterizing the system intelligence and structured analytical decision models has inhibited the developers and practitioners to understand and configure optimum intelligent building systems in a fully informed manner. So far, little research has been conducted in this aspect. This research is designed to identify the key intelligent indicators, and develop analytical models for computing the system intelligence score of smart building system in the intelligent building. The integrated building management system (IBMS) was used as an illustrative example to present a framework. The models presented in this study applied the system intelligence theory, and the conceptual analytical framework. A total of 16 key intelligent indicators were first identified from a general survey. Then, two multi-criteria decision making (MCDM) approaches, the analytic hierarchy process (AHP) and analytic network process (ANP), were employed to develop the system intelligence analytical models. Top intelligence indicators of IBMS include: self-diagnostic of operation deviations; adaptive limiting control algorithm; and, year-round time schedule performance. The developed conceptual framework was then transformed to the practical model. The effectiveness of the practical model was evaluated by means of expert validation. The main contribution of this research is to promote understanding of the intelligent indicators, and to set the foundation for a systemic framework that provide developers and building stakeholders a consolidated inclusive tool for the system intelligence evaluation of the proposed components design configurations.

Evaluation of functional performance in commercial buildings

This report fully summarises a project designed to enhance commercial real estate performance within both operational and investment contexts through the development of a model aimed at supporting improved decision-making. The model is based on a risk adjusted discounted cash flow, providing a valuable toolkit for building managers, owners, and potential investors for evaluating individual building performance in terms of financial, social and environmental criteria over the complete life-cycle of the asset. The ‘triple bottom line’ approach to the evaluation of commercial property has much significance for the administrators of public property portfolios in particular. It also has applications more generally for the wider real estate industry given that the advent of ‘green’ construction requires new methods for evaluating both new and existing building stocks. The research is unique in that it focuses on the accuracy of the input variables required for the model. These key variables were largely determined by market-based research and an extensive literature review, and have been fine-tuned with extensive testing. In essence, the project has considered probability-based risk analysis techniques that required market-based assessment. The projections listed in the partner engineers’ building audit reports of the four case study buildings were fed into the property evaluation model developed by the research team. The results are strongly consistent with previously existing, less robust evaluation techniques. And importantly, this model pioneers an approach for taking full account of the triple bottom line, establishing a benchmark for related research to follow. The project’s industry partners expressed a high degree of satisfaction with the project outcomes at a recent demonstration seminar. The project in its existing form has not been geared towards commercial applications but it is anticipated that QDPW and other industry partners will benefit greatly by using this tool for the performance evaluation of property assets. The project met the objectives of the original proposal as well as all the specified milestones. The project has been completed within budget and on time. This research project has achieved the objective by establishing research foci on the model structure, the key input variable identification, the drivers of the relevant property markets, the determinants of the key variables (Research Engine no.1), the examination of risk measurement, the incorporation of risk simulation exercises (Research Engine no.2), the importance of both environmental and social factors and, finally the impact of the triple bottom line measures on the asset (Research Engine no. 3).

Evaluation of separation management algorithms in Class G airspace

Use of Unmanned Aerial Vehicles (UAVs) in support of government applications has already seen significant growth and the potential for use of UAVs in commercial applications is expected to rapidly expand in the near future. However, the issue remains on how such automated or operator-controlled aircraft can be safely integrated into current airspace. If the goal of integration is to be realized, issues regarding safe separation in densely populated airspace must be investigated. This paper investigates automated separation management concepts in uncontrolled airspace that may help prepare for an expected growth of UAVs in Class G airspace. Not only are such investigations helpful for the UAV integration issue, the automated separation management concepts investigated by the authors can also be useful for the development of new or improved Air Traffic Control services in remote regions without any existing infrastructure. The paper will also provide an overview of the Smart Skies program and discuss the corresponding Smart Skies research and development effort to evaluate aircraft separation management algorithms using simulations involving realworld data communication channels, and verified against actual flight trials. This paper presents results from a unique flight test concept that uses real-time flight test data from Australia over existing commercial communication channels to a control center in Seattle for real-time separation management of actual and simulated aircraft. The paper also assesses the performance of an automated aircraft separation manager.