TY - JOUR
T1 - Sectoral resilience through learning in networks and GVCs: A historical perspective on the food-processing and clothing industries in Poland
T2 - A historical perspective on the food-processing and clothing industries in Poland
AU - Yoruk, Deniz E.
AU - Yoruk, Esin
AU - Figueiredo, Paulo N.
AU - Johnston, Andrew
N1 - Funding Information:
The first author gratefully acknowledges that the primary data used in this research was collected during her research fellowship in ESRC-funded project 'The Emerging Industrial Architecture of the Wider Europe: the co-evolution of economic and political structures' (Award No.L213252037) at University College London , UK.
Publisher Copyright:
© 2023 The Authors
PY - 2023/4/5
Y1 - 2023/4/5
N2 - This paper investigates how inter-organizational learning in networks and global value chains (GVCs) has contributed to resilience in Poland's food processing and clothing industries. The Polish economy has been widely accepted as resilient since Poland's transition from a planned to a market economy. Through drawing on the regional resilience literature, this paper develops a network-oriented framework of sectoral resilience that integrates network evolution, inter-organizational learning in networks, and the role of history. It uses unique primary data from the period of Poland's abovementioned transition (1989?2001), which is complemented with secondary data on the networking activities of Polish firms in the two abovementioned sectors between 2004 and 2018. In turn, the firms' interactive learning is found to function as an important contributor to their path-dependent network trajectories and resilience. Moreover, knowledge networks and GVCs present different dynamics in terms of their effects on learning and result in uneven sectoral resilience. Learning from knowledge spillovers and by interacting with the co-existence of adaptation- and adaptability-related network characteristics has guided both the studied sectors towards developing short-term adaptive capacity for path-extension and sustainability. Learning from advanced science and technology (S&T) and education regarding exclusive adaptability-related network characteristics has driven Poland's food-processing industry's path-evolving long-term capability to be fully resilient.
AB - This paper investigates how inter-organizational learning in networks and global value chains (GVCs) has contributed to resilience in Poland's food processing and clothing industries. The Polish economy has been widely accepted as resilient since Poland's transition from a planned to a market economy. Through drawing on the regional resilience literature, this paper develops a network-oriented framework of sectoral resilience that integrates network evolution, inter-organizational learning in networks, and the role of history. It uses unique primary data from the period of Poland's abovementioned transition (1989?2001), which is complemented with secondary data on the networking activities of Polish firms in the two abovementioned sectors between 2004 and 2018. In turn, the firms' interactive learning is found to function as an important contributor to their path-dependent network trajectories and resilience. Moreover, knowledge networks and GVCs present different dynamics in terms of their effects on learning and result in uneven sectoral resilience. Learning from knowledge spillovers and by interacting with the co-existence of adaptation- and adaptability-related network characteristics has guided both the studied sectors towards developing short-term adaptive capacity for path-extension and sustainability. Learning from advanced science and technology (S&T) and education regarding exclusive adaptability-related network characteristics has driven Poland's food-processing industry's path-evolving long-term capability to be fully resilient.
KW - Networks
KW - Global value chains
KW - Inter-organizational learning
KW - Resilience
KW - Adaptability
KW - Adaptation
KW - Low and medium technology sectors
UR - http://www.scopus.com/inward/record.url?scp=85151543017&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85151543017&partnerID=8YFLogxK
U2 - 10.1016/j.techfore.2023.122535
DO - 10.1016/j.techfore.2023.122535
M3 - Article
SN - 0040-1625
VL - 192
JO - Technological Forecasting and Social Change
JF - Technological Forecasting and Social Change
M1 - 122535
ER -