A Relevance-Based Technology–Organisation–Environment Model of Critical Success Factors for Digital Procurement Adoption in Chinese Construction Companies

Author:

Luo Guan1ORCID,Serrão Carlos2ORCID,Liang Decui3,Zhou Yang4

Affiliation:

1. Business School, University Institute of Lisbon, 1649-026 Lisbon, Portugal

2. Information Sciences, Technologies and Architecture Research Center (ISTAR), Instituto Universitário de Lisboa (ISCTE-IUL), 1600-189 Lisboa, Portugal

3. School of Economics and Management, University of Electronic Science and Technology of China, Chengdu 611731, China

4. Institute of Finance and Public Management, Anhui University of Finance and Economics, Bengbu 233030, China

Abstract

With the emergence of digital transformation, there is an increasing need for Chinese construction companies to adopt digital procurement (D-procurement). However, there is a lack of theoretical foundation to guide and support the adoption practices. This study aims to fill the research gap through the provision of a model by grouping a set of relevance-based critical success factors (CSFs) into the Technology–Organisation–Environment (TOE) framework for D-procurement adoption success (DAS). A case study approach is applied in the research. We selected H Group as it is one of the most representative D-procurement cases in China. The study includes two parts. In the first part, a systematic literature review was conducted, and 17 CSFs were identified from 12 selected studies. By grouping the 17 CSFs into the TOE framework, we put forward a basic CSF–TOE model. In the second part, an in-depth interview was carried out in H Group, where the 17 selected experts were asked to rank the previously identified CSF. Based on their order of relevance, the 17 CSFs were re-organised in the basic CSF–TOE model, and a relevance-based CSF–TOE model was finally proposed. This study is vital for D-procurement adoption because most existing CSF studies are based on the literature and questionnaire surveys, and there is a lack of actual case studies. In addition, this study significantly contributes to the field of D-procurement adoption for construction companies by providing a theoretical framework for practice and a relevance-based CSF–TOE model for research.

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference103 articles.

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2. Wang, K.W., Woetzel, J., Seong, J., Manyika, J., Chui, M., and Wong, W. (2021, August 01). Digital China: Powering the Economy to Global Competitiveness. Mckinsey. Available online: https://www.mckinsey.com/featured-insights/china/digital-china-powering-the-economy-to-global-competitiveness.

3. Laffont, J.-J., and Tirole, J. (1993). A Theory of Incentives in Procurement and Regulation, MIT Press.

4. Shaham, E., Westerski, A., Kanagasabai, R., Narayanan, A., Ong, S., Wong, J., and Singh, M. (2021, January 2–9). Using unsupervised learning for data-driven procurement demand aggregation. Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, Virtual Conference, Singapore.

5. Nicoletti, B. (2018). Agile Procurement, Springer International Publishing.

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