Dynamics of firm-level upgrading and the role of learning in networks in emerging markets

This paper investigates which external learning mechanisms in networks contribute to various upgrading types in emerging market firms, and how internalisation of externally acquired knowledge complements external learning. It develops a dynamic model of firm-level upgrading for analysis, where learning in networks is emphasized. Methodologically, it applies a new approach through a comprehensive analysis of a firm’s networks embedded not only in production systems (i.e. GVCs/GPNs) but also in knowledge systems. Primary data is collected through in-depth interviews with Polish food-processing and clothing firms. Multinomial logistic regression analysis is applied on a dataset of networks of these firms over 12 years covering the transition period. We find that the key source of process upgrading is learning in knowledge networks as opposed to that of in GVCs. Strikingly, learning-by-interacting in GVCs impedes not functional, but managerial upgrading, a previously unexplored upgrading type, which is also shown to be a prerequisite for functional upgrading. Finally, whilst learning-by-training and research within the firm is a ‘potent’ condition for external learning to contribute to all of the upgrading types, it is a ‘must’ for successful functional upgrading. These findings strongly suggest the importance of an integrative approach to learning in research on upgrading and the complementarity between organisational and technology upgrading. Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Technological Forecasting and Social Change. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Technological Forecasting and Social Change, [Published (2019)] DOI: 10.1016/j.techfore.2018.06.042 © 2019, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0