Abstract
This research aims to investigate the contributions of interorganisational relationships and knowledge-management practices as predictors of collaborative commerce (c-commerce) adoption. A non-compensatory adoption decision process was modelled using a neural network approach to examine the predictors of c-commerce adoption. A survey was undertaken in 136 firms for this research. The results showed that both interorganisational relationships and knowledge-management processes played an important role in predicting the adoption of c-commerce. In particular, variables from interorganisational relationships such as trust, communication, collaboration, and information sharing are found to contribute strongly to the predictive power of the model when compared with knowledge-management processes. This study provides insights for firms that would like to improve their supply-chain collaboration through the implementation of c-commerce. The findings lead to an understanding of what attributes of interorganisational relationships and knowledge-management processes can contribute to the improved adoption of c-commerce in the supply chain. Unlike past adoption studies which have tended to focus on technology, and organisational and environmental factors, this research examined interorganisational relationships and knowledge-management processes which are increasingly gaining the attention of researchers and practitioners. This study has also extended the existing literature by examining a non-compensatory model for technology adoption.
Original language | English |
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Pages (from-to) | 2006-2018 |
Number of pages | 13 |
Journal | International Journal of Production Research |
Volume | 51 |
Issue number | 7 |
DOIs | |
Publication status | Published - 1 Apr 2013 |
Keywords
- collaborative commerce
- interorganisational relationships
- knowledge management
- neural network
- technology adoption
ASJC Scopus subject areas
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering