Managing knowledge in tourism planning: How to assess your capability

This paper explores the theme of strategic planning in a State Tourism Organization (STO) from a knowledge management perspective. It highlights the value of knowledge in strategy making and the importance of an organisation's knowledge management agenda in facilitating a strategic planning process. In particular, it considers the capability of an STO to implement knowledge management as the key to a successful strategic planning exercise. In order to develop greater insight into the factors that impact on planning competence, the key aim of this paper is to develop a framework on which the capability of a STO to implement a knowledge-based agenda in strategic planning can be assessed. Research on knowledge management in the field of tourism is limited and there is little practical account of the application of knowledge management principles in tourism planning. Further, there is no apparent tool or instrument that allows for the assessment of an STO's capability to implement knowledge management in planning initiatives. Based on a literature review, a three-point framework of assessment is developed. The three elements of the framework are identified as: 1. Integration of knowledge management objectives with strategic imperatives; 2. A planning approach that balances top-down (outcome focused) with bottom-up (process focused) planning processes; and 3. Organisational capacity, including leadership, people and culture, process, technology, content and continuous improvement. The framework is tested through application to a practical case study - a planning initiative undertaken by a leading tourism STO in Australia. The results demonstrate that the framework is a useful means to evaluate organisational capability in knowledge-led strategic planning exercises and would be of practical value as a point of reference for future knowledge- based strategic planning projects. Copyright © by The Haworth Press, Inc. All rights reserved.

Systemic absorptive capacity: creating early-to-market returns through R&D alliances

For most complex emergent technologies, product-market success depends on efficient linkages between changing lead innovators within the R&D process. In this paper, our unit of analysis is a complex high technology product and the system of alliance linkages formed to progress a product through R&D milestones. We present a model and evidence for advancing our understanding of how achieving early-to-market returns depends on systemic absorptive capacity. This systemic absorptive capacity is the cumulative efficiency in the use of absorptive capacity to link changing lead innovators across successive milestones in R&D product development. We advance propositions of how systemic absorptive capacity can explain performance differences between rival product development systems competing for early-to-market returns with similar products through accelerating speed to market, cost and quality advantages. These explanations are contrasted with the conclusions of previous studies that have focused on absorptive capacity of single firms or single alliances in RD.

The effects of consumer knowledge on responses to event sponsorships

Research into consumer responses to event sponsorships has grown in recent years. However, the effects of consumer knowledge on sponsorship response have received little consideration. Consumers' event knowledge is examined to determine whether experts and novices differ in information processing of sponsorships and whether a sponsor's brand equity influences perceptions of sponsor-event fit. Six sponsors (three high equity/three low equity) were paired with six events. Results of hypothesis testing indicate that experts generate more total thoughts about a sponsor-event combination. Experts and novices do not differ in sponsor-event congruence for high-brand-equity sponsors, but event experts perceive less of a match between sponsor and event for low-brand-equity sponsors. (C) 2004 Wiley Periodicals, Inc.

The market for preferences

Learning processes are widely held to be the mechanism by which boundedly rational agents adapt to environmental changes. We argue that this same outcome might also be achieved by a different mechanism, namely specialisation and the division of knowledge, which we here extend to the consumer side of the economy. We distinguish between high-level preferences and low-level preferences as nested systems of rules used to solve particular choice problems. We argue that agents, while sovereign in high-level preferences, may often find it expedient to acquire, in a pseudo-market, the low-level preferences in order to make good choices when purchasing complex commodities about which they have little or no experience. A market for preferences arises when environmental complexity overwhelms learning possibilities and leads agents to make use of other people's specialised knowledge and decision rules.

Industry-specific human capital, knowledge labour, and industry wage structure in Taiwan

This paper investigates the role of industry-specific human capital (ISHC) in determining industry wage structure. The model presented in this paper distinguishes between knowledge labour and physical labour. Knowledge labour is physical labour embodied with ISHC. It is postulated that more ISHC-intensive industries, such as high-tech industries, pay higher wages and the wage premiums increase with workers' experience. The hypothesis is tested using a merged sample of 1997 - 1999 manpower utilization survey data from a newly industrialized economy - Taiwan. The findings show support for the effect of ISHC.

Load profiling and data mining techniques in electricity deregulated market

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.