Down with MNE-centric theories! Market entry and expansion as the bundling of MNE and local assets

Both Anderson and Gatignon and the Uppsala internationalization model see the initial mode of foreign market entry and subsequent modes of operation as unilaterally determined by multinational enterprises (MNEs) arbitraging control and risk and increasing their commitment as they gain experience in the target market. OLI and internalization models do recognize that foreign market entry requires the bundling of MNE and complementary local assets, which they call location or country-specific advantages, but implicitly assume that those assets are freely accessible to MNEs. In contrast to both of these MNE-centric views, I explicitly consider the transactional characteristics of complementary local assets and model foreign market entry as the optimal assignment of equity between their owners and MNEs. By looking at the relative efficiency of the different markets in which MNE and complementary local assets are traded, and at how these two categories of assets match, I am able to predict whether equity will be held by MNEs or by local firms, or shared between them, and whether MNEs will enter through greenfields, brownfields, or acquisitions. The bundling model I propose has interesting implications for the evolution of the MNE footprint in host countries, and for the reasons behind the emergence of Dragon MNEs.

Human figure segmentation using independent component analysis

In this paper, we present a Statistical Shape Model for Human Figure Segmentation in gait sequences. Point Distribution Models (PDM) generally use Principal Component analysis (PCA) to describe the main directions of variation in the training set. However, PCA assumes a number of restrictions on the data that do not always hold. In this work, we explore the potential of Independent Component Analysis (ICA) as an alternative shape decomposition to the PDM-based Human Figure Segmentation. The shape model obtained enables accurate estimation of human figures despite segmentation errors in the input silhouettes and has really good convergence qualities.