A self-assessment tool for knowledge management implementation in the construction industry

Knowledge management (KM) provides a structured process to establish the link between knowledgebased assets within an organisation and its desired business objectives. Although KM issues are becoming increasingly important to the construction industry, there is currently no measurement tool for assessing the implementation of KM programmes. This paper reports on the development of such a tool which can be used as both a means of self-assessment and also for benchmarking purposes. Important practices needed for successful KM implementation were identified from the literature and via a self-administered survey targeting large and medium construction organisations in Hong Kong. Survey findings demonstrate the potential of the proposed self-assessment tool to measure the individual’s perception of the relative importance of KM antecedents and practices, also providing early insight of KM implementation by highlighting the negative gaps between what “is” and “should be” happening, thus identifying areas that need re alignment of KM strategies and tactics. The paper also suggests this tool could be further developed to help organisations to formulate and modify their KM programmes according to their own specific internal business environment, and the nature of their projects.

Conceptual model linking knowledge management with organisational performance

Better management of knowledge assets has the potential to improve business processes and increase productivity. This fact has led to considerable interest in recent years in the knowledge management (KM) phenomenon, and in the main dimensions that can impact on its application in construction. However, a lack of a systematic way of assessing KM initia-tives’ contribution towards achieving organisational business objectives is evident. This paper describes the first stage of a research project intended to develop, and empirically test, a KM input-process-output framework comprising unique and well-defined theoretical constructs representing the KM process and its internal and external determinants in the context of con-struction. The paper presents the underlying principles used in operationally defining each construct through the use of extant KM literature. The KM process itself is explicitly mod-elled via a number of clearly articulated phases that ultimately lead to knowledge utilisation and capitalisation, which in turn adds value or otherwise to meeting defined business objec-tives. The main objective of the model is to reduce the impact of subjectivity in assessing the contribution made by KM practices and initiatives toward achieving performance improvements.

Empirical modelling of the relationship between bus and car speeds on signalised urban networks

Vehicle speed is an important attribute for the utility of a transport mode. The speed relationship between multiple modes of transport is of interest to the traffic planners and operators. This paper quantifies the relationship between bus speed and average car speed by integrating Bluetooth data and Transit Signal Priority data from the urban network in Brisbane, Australia. The method proposed in this paper is the first of its kind to relate bus speed and average car speed by integrating multi-source traffic data in a corridor-based method. Three transferable regression models relating not-in-service bus; in-service bus during peak; and in-service bus during off peak periods with average car are proposed. The models are cross-validated and the interrelationships are significant

Visual front-end wars : Viola-Jones face detector vs Fourier Lucas-Kanade

The performance of visual speech recognition (VSR) systems are significantly influenced by the accuracy of the visual front-end. The current state-of-the-art VSR systems use off-the-shelf face detectors such as Viola- Jones (VJ) which has limited reliability for changes in illumination and head poses. For a VSR system to perform well under these conditions, an accurate visual front end is required. This is an important problem to be solved in many practical implementations of audio visual speech recognition systems, for example in automotive environments for an efficient human-vehicle computer interface. In this paper, we re-examine the current state-of-the-art VSR by comparing off-the-shelf face detectors with the recently developed Fourier Lucas-Kanade (FLK) image alignment technique. A variety of image alignment and visual speech recognition experiments are performed on a clean dataset as well as with a challenging automotive audio-visual speech dataset. Our results indicate that the FLK image alignment technique can significantly outperform off-the shelf face detectors, but requires frequent fine-tuning.