Rule extraction from technology IPOs in the US stock market

Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.

Factors affecting student attitudes toward flexible online learning in management education

In response to recent technological advances and the trend toward flexible learning in education, the authors examined the factors affecting student satisfaction with flexible online learning. The authors identified 2 key student attributes of student satisfaction: (a) positive perceptions of technology in terms of ease of access and use of online flexible learning material and (b) autonomous and innovative learning styles. The authors derived measures of perceptions of technology from research on the Technology Acceptance Model and used locus of control and innovative attitude as indicators of an autonomous and innovative learning mode. First-year students undertaking an introductory management course completed surveys at the beginning (n = 248) and at the end (n = 256) of course work. The authors analyzed the data by using structural equation modeling. Results suggest that student satisfaction is influenced by positive perceptions toward technology and an autonomous learning mode.

Business education in operations management

This paper examines trends in the practice of Operations Management and in teaching the field in major Business Schools. Operations Management has been defined as the design and management of transformation processes that create value for society. The operations function is the one function directly involved in that transformation, and hence is directly responsible for the activities that justify the existence of the firm, both economically and as a value-creating organization in society. The top rated schools in Operations Management are the top-rated research-intensive Business Schools in the world. Operations Management is an area that has been undergoing rapid change in response to changes in business practices worldwide. It is at the heart of changes of which the AACSB report Management Education at Risk, August 2002 (p 20), comments of Business Schools in general: ‘With regard to global relevance (of Business Schools), the complex opportunities and challenges that emanate from the world scope of operations, outsourcing, supply chains, partnerships, and financial and consumer markets – all linked in real time through the Internet – are not reflected adequately in curricula and learning approaches.’ Products, and even services, depend increasingly on advanced technology. This is true globally and especially so for countries in South East and East Asia, from which Australian Universities draw a significant number of students. Services operations management has become much more important, while there are both educational and industrial needs in management science or operations research.