Why and how does shared language affect subsidiary knowledge inflows? A social identity perspective

We draw on social identity theory to conceptualize a moderated mediation model that examines the relationship between shared language among subsidiary and HQ managers, and subsidiaries’ knowledge inflows from HQ. Specifically, we study (1) whether this relationship is mediated by the extent to which subsidiary managers share HQ goals and vision, and the extent to which HR decisions are centralized; and (2) whether subsidiary type moderates these mediated relationships. Building on a sample of 817 subsidiaries in nine countries/regions, we find support for our model. Implications for research on HQ-subsidiary knowledge flows, social identity theory and international HRM are discussed

Towards a Digital Infrastructure for Illustrated Handwritten Archives

Large and important parts of cultural heritage are stored in archives that are difficult to access, even after digitization. Documents and notes are written in hard-to-read historical handwriting and are often interspersed with illustrations. Such collections are weakly structured and largely inaccessible to a wider public and scholars. Traditionally, humanities researchers treat text and images separately. This separation extends to traditional handwriting recognition systems. Many of them use a segmentation free OCR approach which only allows the resolution of homogenous manuscripts in terms of layout, style and linguistic content. This is in contrast to our infrastructure which aims to resolve heterogeneous handwritten manuscript pages in which different scripts and images are narrowly intertwined. Authors in our use case, a 17,000 page account of exploration of the Indonesian Archipelago between 1820–1850 (“Natuurkundige Commissie voor Nederlands-Indië”) tried to follow a semantic way to record their knowledge and observations, however, this discipline does not exist in the handwriting script. The use of different languages, such as German, Latin, Dutch, Malay, Greek, and French makes interpretation more challenging. Our infrastructure takes the state-of-the-art word retrieval system MONK as starting point. Owing to its visual approach, MONK can handle the diversity of material we encounter in our use case and many other historical collections: text, drawings and images. By combining text and image recognition, we significantly transcend beyond the state-of-the art, and provide meaningful additions to integrated manuscript recognition. This paper describes the infrastructure and presents early results. Keywords: Deep learning · Digital heritage · Natural history Biodiversity heritag