Pedagogical learnings for management education : developing creativity and innovation

This paper argues that management education needs to consider a trend in learning design which advances creative learning through an alliance with art-based pedagogical processes. A shift is required from skills training to facilitating transformational learning through experiences that expand human potential, facilitated by artistic processes. This creative learning focus stems from a qualitative and quantitative analysis of an arts-based intervention for management development, called Management Jazz, conducted over three years at a large Australian University. The paper reviews some of the salient literature in the field, including an ‘Artful Learning Wave Trajectory’ Model. The Model considers four stages of the learning process: capacity, artful event, increased capability, and application/action to produce product. Methodology for the field-based research analysis of the intervention outcomes is presented. Three illustrative examples of arts-based learning are provided from the Management Jazz program. Finally, research findings indicate that artful learning opportunities enhance capacity for awareness of creativity in one’s self and in others, leading, through a transformative process, to enhanced leaders and managers. The authors conclude that arts-based management education can enhance creative capacity and develop managers and leaders for the 21st century business environment.

Determining the informational, navigational and transactional intent of web queries

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.