Learning in Context: A Structural Equation Modeling Approach to Analyze Knowledge Acquisition at Trade Fairs

Conceptualizations of trade fairs as temporary clusters have identified important learning processes at such events, particularly at leading international trade fairs-both in developed and developing countries. However, little attention has been paid to the home contexts of participating firms that may affect knowledge acquisition patterns. In particular, it is unclear which contextual factors may influence learning behavior. This paper aims to investigate the role of geographical context conditions at the exhibitors' permanent locations and whether their knowledge acquisition behavior during trade fairs varies systematically with aspects, such as city scale, peripherality, growth dynamics and connectivity. Our analysis is based on a survey of 211 firms conducted between 2014 and 2018 at the China International Industry Fair (CIIF) in Shanghai-one of Asia's most important manufacturing fairs. Using structural equation modeling (SEM), the study identifies significant pathways of knowledge acquisition and how these differ with geographical context.