Visualising London

This research investigates how photographs can be analysed to extract meaning. Two methodologies, visual anthropology and social semiotics, are used to analyse a collection of images and accompanying texts generated by a group of first year tourism students in London. Photographs are categorised into subject areas including iconic buildings, street scenes, people and analysed according to how they relate to the photographers’ characteristics, such as age and nationality. A group of images of Big Ben are then analysed using a social semiotics approach, considering both compositional and contextual information to extract meanings. Results and techniques are then contrasted and compared, noting how the complexity of the image makers’ experience of the city they are documenting lead to their images having multi-layered meanings, and that combining analytic methods can fruitfully reveal a range of these meanings.

Hierarchical structures: conducive or barrier to knowledge management?

This study examines managers‟ perceptions of Knowledge Management (KM) prior to implementation of KM-systems in a global insurance company and investigates whether Hierarchical structures are conducive to KM. Mixed methods are used, combining large scale surveying and case study using content analysis to organize the data into themes that provide the basis for arguments. Evidence suggests that managers strongly align their perception of KM with communication. Despite a multi-layered, hierarchical structure and strong middle management presence, organizational structure was not viewed as an issue. These factors are usually barriers to communication and organizational flexibility, yet managers believe that they may not inhibit KM becoming fully embedded. This evidence is contradicted by the results of a global KM study where silos, stovepipe and hierarchical structures were commonly cited as barriers. This contributes to the understanding of managerial mis-conceptions of knowledge as opposed to communication, and how organizations effectively share knowledge.