The continuous recombination of codification and personalisation km strategies: A retrospective study

It is increasingly considered important to understand how companies plan their Knowledge Management (KM) strategy. The literature provides evidence that there may be different possible approaches to KM strategy. A significant distinction has been made between "codification" and "personalization". Sometimes, these two approaches have been seen to be alternative to one another. In other cases scholars argued that a company can follow a strategy that mixes the two approaches depending on diverse intertwined factors. Still, on this topic, the literature provides various and sometimes contrasting results that need clarification and confirmation. Especially, there is the need to understand if changes in internal and external conditions may induce modifications in a firm's KM strategy.The goal of the study is to analyse how the mix of codification and personalisation can vary over time in the same company, due to changing organizational and environmental conditions. With this purpose, the evolution of KM initiatives of a multinational company was investigated. The findings of the study confirm that the strategic mix can change over the years due to modifications in the factors of the company's internal and external context. Furthermore, the case shows that the different factors have different weight and play a different role in influencing such changes. Specifically, in the investigated case, the factors related to the competitive context affected the evolution of the KM strategy more significantly than internal factors (which were just enablers or constraints of the evolutionary path). In addition, the study shows that this classic distinction between codification and personalization may not be easy to use in practical terms, due to the complexity of KM activities and needs in a company: This point can represent a fresh start of a future research agenda

Efficient large-scale airborne LiDAR data classification via fully convolutional network

Nowadays, we are witnessing an increasing availability of large-scale airborne LiDAR (Light Detection and Ranging) data, that greatly improve our knowledge of urban areas and natural environment. In order to extract useful information from these massive point clouds, appropriate data processing is required, including point cloud classification. In this paper we present a deep learning method to efficiently perform the classification of large-scale LiDAR data, ensuring a good trade-off between speed and accuracy. The algorithm employs the projection of the point cloud into a two-dimensional image, where every pixel stores height, intensity, and echo information of the point falling in the pixel. The image is then segmented by a Fully Convolutional Network (FCN), assigning a label to each pixel and, consequently, to the corresponding point. In particular, the proposed approach is applied to process a dataset of 7700\u2009km2 that covers the entire Friuli Venezia Giulia region (Italy), allowing to distinguish among five classes (i ground, vegetation, roof, overground and power line/i), with an overall accuracy of 92.9%