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Due to the globalization in the world economy, most multinational organizations have changed the way they conduct business. This change also influenced the structure and working of IT services-providing companies. Due to the contemporary phenomenon of the world being seen as a global village, today organizations have access to a wider talent pool. Different multinational IT services-providing companies follow global software development models, while some of these also call people onshore from distant offices to work on different projects. In this scenario, cross-cultural teams are formed to work together and to fulfill clients’ requirements. The objective of this study is to identify cultural differences that affect the knowledge sharing process in IT services-providing companies. In this study, we present the case of a Swedish IT services-providing company that has extended operations into India. Our intention is to understand employees’ views about their work experience in a cross-cultural team. In this context, the study employs a qualitative approach, which helps to elucidate the role of national culture dimensions on an individual’s behavior within the workplace, as well as the impact national culture dimensions have on knowledge sharing processes. Six cross-cultural team members, who have the experience of working in a cross-cultural environment, were interviewed utilizing a semi-structured interview model. Data collection also includes two and half days of data collected during the observation of a cross-cultural team. The collected data helps to effectively clarify in-depth views about the concerns employees have while working within cross-cultural teams. The data collected was analyzed by applying hermeneutics, through which we mapped the answers to the theory of Hofstede on culture. The latter served as the indicator to focus on cultural dimensions that have decisive influence on knowledge sharing in cross-cultural environments. We conclude by highlighting several specific cultural factors that may affect knowledge sharing in cross-cultural environments.

Intermediary organisations play a distinctive, yet underestimated, role in the learning processes of developing economy clusters. This study situates itself in a new way of thinking about knowledge and innovation; one that emphasises learning as a social process, within communities that emerge through the development of shared practice. It finds that, while previous formulations of intermediaries have emphasised linking and accessing, in some contexts their roles are more fundamental and include community-building and coordinating common strategies. For many agricultural clusters, reflecting a move in developing economies from „import-substitution‟ towards a focus on exports, learning and innovation has become central. Facing challenges in knowledge generation and transfer (Bessant et al, 2003), clustering aids knowledge diffusion amongst producers and stimulates the learning necessary to penetrate international markets (Schmitz and Nadvi, 1999; Humphrey and Schmitz, 2000). While opportunities sometimes exist for learning from global buyers, however, it is more common in natural-resource based clusters for the onus to be on producers to develop their own capabilities (Gomes, 2006). This study examines the contribution a diverse group of actors, categorised as intermediary organisations, make to this process. The practice-based perspective (Amin and Cohendet, 2004) provides a framework through which the intermediary role is conceptualised, alongside insights from the innovation and network literatures (Howells, 2006; Burt, 2005). While these literatures predominantly focus on linking and accessing, however, intermediaries‟ roles are found, in certain developing economy contexts, to stretch wider. Through a case study of a Peruvian agricultural cluster, they are identified as performing a cluster-building role, by providing a platform for inter-firm cooperation. They also, through their ability to coordinate firm actions, facilitate opportunities for value chain learning. In addition, they provide new knowledge inputs to cluster actors, either through their own knowledge creation capabilities or their ability to translate and adapt existing knowledge.

Knowledge acquisition from databases is a research frontier for both data base technology and machine learning (ML) techniques,and has seen sustained research over recent years. It also acts as a link between the two fields,thus offering a dual benefit. Firstly, since database technology has already found wide application in many fields ML research obviously stands to gain from this greater exposure and established technological foundation. Secondly, ML techniques can augment the ability of existing database systems to represent acquire,and process a collection of expertise such as those which form part of the semantics of many advanced applications (e.gCAD/CAM).The major contribution of this thesis is the introduction of an effcient induction algorithm to facilitate the acquisition of such knowledge from databases. There are three typical families of inductive algorithms: the generalisation- specialisation based AQ11-like family, the decision tree based ID3-like family,and the extension matrix based family. A heuristic induction algorithm, HCV based on the newly-developed extension matrix approach is described in this thesis. By dividing the positive examples (PE) of a specific class in a given example set into intersect in groups and adopting a set of strategies to find a heuristic conjunctive rule in each group which covers all the group's positiv examples and none of the negativ examples(NE),HCV can find rules in the form of variable-valued logic for PE against NE in low-order polynomial time. The rules generated in HCV are shown empirically to be more compact than the rules produced by AQ1-like algorithms and the decision trees produced by the ID3-like algorithms. KEshell2, an intelligent learning database system, which makes use of the HCV algorithm and couples ML techniques with database and knowledgebase technology, is also described.

The view that knowledge-yielding single-premise deductive inference must proceed from a known premise is very plausible at first blush. In this thesis I explore in detail the possibility that this view is false. I construct a series of challenging cases against the principle of Counter-Closure, which expresses this view. These cases force theorists endorsing a variety of contemporary views to either (i) abandon Counter-Closure; (ii) admit into their epistemology novel and theory-specific kinds of Gettier cases; or (iii) make significant revisions to their theories. I offer considerations that help would-be deniers of Counter-Closure explain away its prima facie plausibility and suggest a suitable theoretical replacement phrased in terms of justification rather than knowledge. Finally, I connect this discussion with debates in the epistemologies of testimony and memory, where analogue principles to Counter- Closure have been recently subjected to critical scrutiny.