Intelligent negotiation behaviour model for an open railway access market

In an open railway access market, the provisions of railway infrastructures and train services are separated and independent. Negotiations between the track owner and train service providers are thus required for the allocation of the track capacity and the formulation of the services timetables, in which each party, i.e. a stakeholder, exhibits intelligence from the previous negotiation experience to obtain the favourable terms and conditions for the track access. In order to analyse the realistic interacting behaviour among the stakeholders in the open railway access market schedule negotiations, intelligent learning capability should be included in the behaviour modelling. This paper presents a reinforcement learning approach on modelling the intelligent negotiation behaviour. The effectiveness of incorporating learning capability in the stakeholder negotiation behaviour is then demonstrated through simulation.

Portfolio selection using artificial intelligence

The application of artificial intelligence in finance is relatively new area of research. This project employed artificial neural networks (ANNs) that use both fundamental and technical inputs to predict future prices of widely held Australian stocks and use these predicted prices for stock portfolio selection over a long investment horizon. The research involved the creation and testing of a large number of possible network configurations and draws conclusions about ANN architectures and their overall suitability for the purpose of stock portfolio selection.

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

The Impact of Internal Market Orientation on Staff Service Behaviours

The impact of service direction, service training and staff behaviours on perceptions of service delivery are examined. The impact of managerial behaviour in the form of internal market orientation (IMO) on the attitudes of frontline staff towards the firm and its consequent influence on their customer oriented behaviours is also examined. Frontline service staff working in the consumer transport industry were surveyed to provide subjective data about the constructs of interest in this study, and the data were analysed using structural equations modelling employing partial least squares estimation. The data indicate significant relationships between internal market orientation (IMO), the attitudes of the employees to the firm and their consequent behaviour towards customers. Customer orientation, service direction and service training are all identified as antecedents to high levels of service delivery. The study contributes to marketing theory by providing quantitative evidence to support assumptions that internal marketing has an impact on services success. For marketing practitioners, the research findings offer additional information about the management, training and motivation of service staff towards service excellence.