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Construction organisations cannot underestimate the improvement in public–private partnership (PPP) projects’ implementation. At the same time, construction organisations cannot overlook the risk arising from engaging in PPP construction projects. Hence, this study aims to establish the influence of risk resource management (RRM) in managing PPP risk in the construction industry in Ghana.

The researchers adopted qualitative and quantitative research methods to achieve the aim of the study, in which Delphi questions and a close-ended questionnaire were developed. A total of 650 construction specialists, including procurement officers, consultants, project managers, quantity surveyors, site engineers and planning officers were chosen using random and purposive sampling techniques. Recovered data were analysed using descriptive statistics and confirmatory factor analysis (CFA). The CFA maximum likelihood estimation extractor compresses 19 variables into 3 pattern matrices.

The results of the study revealed three factors that measure RRM in Ghana’s PPP construction industry, including financial resource management which was influenced by communicating the budget to project team members and project partners understanding the budget, and material resource management which was influenced by the provision of materials transportation and provision of delivery programs and labour resource management which was impacted by a commitment to pay social security and taxes and provision of good salaries, to address RRM in PPP construction organisations.

To incessantly improve the PPP risk management (RM) in construction through RRM, there should be a strong liaison between the universities, government agencies and the construction industry, and such collaboration will assist the industry to obtain first-hand information regarding the study findings and how they can be implemented to help the development of RM in the construction industry. This study is limited to Ghana and CFA and further study should explore structural equation model to determine the structure and measurement model of the risk resource variables.

The study may be valuable to industry stakeholders looking for new approaches to improve RM in their construction activities, particularly in PPP projects. Also, to assist reduce PPP risk, construction companies should use RRM in their organisations.

The research paper’s purpose is to contribute to the literature by analysing the essential resources and processes required for successful commercialisation, the contemporary challenges and opportunities of artificial intelligence initiatives in Türkiye, and the diverse models and methods employed by these initiatives.

Within the scope of the research, interviews were conducted with 10 entrepreneurs who established artificial intelligence-oriented enterprises in technoparks in Istanbul and Antalya. All 10 interviews were analysed using the MAXQDA20 software tool. Structured qualitative content analysis was used for the data analysis procedure.

Based on the research, external factors have a significant impact on the future growth opportunities of the market. Expanding the client base, gaining international recognition, and securing financing are crucial for success. However, the findings reveal challenges in the relatively young local ecosystem. One major criticism is the lack of support in marketing and sales activities for refined products. To address this, providing financial incentives and knowledge transfer to those in need is vital.

Since the research was conducted only with entrepreneurs who established and successfully commercialised artificial intelligence-oriented enterprises, it is recommended that future studies be performed with a widespread sample group, considering this limited situation. Furthermore, to overcome survivorship bias, it is recommended that posterior studies include failed commercialisation attempts in AI ventures.

It can be argued that there is no deliberate approach or model for commercialization. Entrepreneurs often draw from their own prior experiences or observe industry trends. Given the limited financial resources available in the domestic market and the challenge of attracting foreign investors to Turkish brands, entrepreneurs tend to rely on internal approaches for commercialisation.

This research delves into the commercialisation prospects and obstacles encountered by AI start-ups in Türkiye. It comprises qualitative insights into business models, commercialisation approaches, opportunities, and challenges. The data were obtained from interviews with entrepreneurs operating in the industry.