A measure of 'high commitment organisation' performance values in sport management professionals and sport organisations

Many organizations have realized the growing importance of adopting a 'High Commitment Organization' (HCO) approach with a focus on shared values to assist them in meeting their competitive challenges. A survey questionnaire based on the McDonald and Gandz (1992) list of values, employing confirmatory and principal components analyses was used to create scales to (a) explore the importance the sport management professional placed on those values, (b) explore the individual's perception of the importance placed on those values by their employing organization, (c) to compare these hierarchies with the values of the HCO, and (d) to measure the extent of value congruence. Three clear sets of values emerged: Development / Adhocracy (D/A) Values, Humanistic / Clan (H/C) Values and Conformity / Hierarchy (C/H) Values. Findings indicate significant differences between sport management professionals' values and those of their organizations. Employees placed higher importance on (D/A) and (H/C) Values than their organizations, while Sport organizations placed higher importance on (C/H) Values than their employees. There is stronger support by individuals than organizations for the values underpinning the HCO approach. These levels of individual - organizational value incongruence have implications for individual job satisfaction, motivation and organizational effectiveness.

Global optimisation of neural networks using a deterministic hybrid approach

Selection of the topology of a neural network and correct parameters for the learning algorithm is a tedious task for designing an optimal artificial neural network, which is smaller, faster and with a better generalization performance. In this paper we introduce a recently developed cutting angle method (a deterministic technique) for global optimization of connection weights. Neural networks are initially trained using the cutting angle method and later the learning is fine-tuned (meta-learning) using conventional gradient descent or other optimization techniques. Experiments were carried out on three time series benchmarks and a comparison was done using evolutionary neural networks. Our preliminary experimentation results show that the proposed deterministic approach could provide near optimal results much faster than the evolutionary approach.

Building agent-based hybrid intelligent systems

Many complex problems including financial investment planning, foreign exchange trading, knowledge discovery from large/multiple databases require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. In this paper, it is argued that agent technology is well snited for constructing hybrid intelligent systems (especially loosely coupled hybrid intelligent systems) through a successful case study. A great number of heterogeneous computing techniques/packages are easily integlated into the experimental system under a unifying agent framework, which implies that agent technology can greatly facilitate the construction of hybrid intelligent systems.

Mining sequential causal patterns with user-specified skeletons in multi-sequence of event data

The approaches proposed in the past for discovering sequential patterns mainly focused on single sequential data. In the real world, however, some sequential patterns hide their essences among multi-sequential event data. It has been noted that knowledge discovery with either user-specified constraints, or templates, or skeletons is receiving wide attention because it is more efficient and avoids the tedious selection of useful patterns from the mass-produced results. In this paper, a novel pattern in multi-sequential event data that are correlated and its mining approach are presented. We call this pattern sequential causal pattern. A group of skeletons of sequential causal patterns, which may be specified by the user or generated by the program, are verified or mined by embedding them into the mining engine. Experiments show that this method, when applied to discovering the occurring regularities of a crop pest in a region, is successful in mining sequential causal patterns with user-specified skeletons in multi-sequential event data.