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Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.

The purpose of this paper is to explore how process management (PM) can support small- and medium-sized enterprises (SMEs) in pursuing sustained competitive advantage. For this purpose, a dynamic capabilities (DC) lens was used.

A narrative literature review and a multiple case study with an action research approach at two road freight transport companies were used.

PM provides structure and system thinking to support the development of competitive advantage. Concerning PM, management of knowledge, management style and process orientation are key factors for the generation of competitive advantage for SMEs.

This study contributes to PM literature by studying its support for and implementation at SMEs. Furthermore, the study contributes to the literature on DC by providing concrete examples of activities linked to such capabilities.

This study contributes to practitioners by providing examples of implementing PM and identifying competitive advantage, connected with PM elements.

This study has social and environmental implications for the quality of life of the Swedish people.

This paper contributes to clarifying the connection between the research fields of quality management and DC to explore how PM can support SMEs in pursuing sustained competitive advantage.