Acheampong, A; Adjei, E K; Asiedu, R O; Atibila, D W and Abu, I M A (2025) Evaluating the factors influencing artificial intelligence technology uptake in health and safety management within the Ghanaian construction industry. Journal of Engineering, Design and Technology, 23(6), pp. 2060-2081. ISSN 1726-0531
Abstract
Purpose – The construction industry in Ghana faces significant challenges in managing health and safety risks, leading to high rates of accidents and fatalities. Despite the potential of artificial intelligence (AI) technologies to improve health and safety management, their adoption in the Ghanaian construction industry remains limited. This paper aims to identify and evaluate key factors influencing the uptake of AI technologies in construction health and safety management within the Ghanaian industry. Design/methodology/approach – The study adopts a rigorous two-step qualitative approach to identify a set of 17 variables. First, an extensive analysis of scholarly publications was conducted to compile an initial variable list. Secondly, a pilot survey involving both academic and industry professionals assisted in refining the identified variables. Subsequently, a questionnaire survey involving 219 Ghanaian construction professionals then collects quantitative assessments of each variable using the purposive sampling technique. Statistical modelling using factor analysis and fuzzy synthetic evaluation (FSE) was applied to process the survey data and determine the criticality of the factor categories. Findings – The factor analysis yielded a three-factor solution underlying the 17 adoption variables: Extensive technological requirements and costs, resistance to change and AI adoption and uncertainty about AI outcomes and value. Subsequently, FSE confirmation showed the Extensive Technological Requirements category as the most critical, with specialized algorithmic demands, infrastructure limitations and expert support needs presenting major obstacles Ghanaian firms face in AI adoption. Originality/value – This research contributes robust empirical evidence and novel factor-based statistical analysis to augment the theoretical discourse surrounding construction safety technology integration and change dynamics. The developed fuzzy quantitative methodology offers a model for assessing complex innovation adoption decisions in the face of uncertainty. The research addresses a gap in existing literature by providing a comprehensive assessment of the technological, organizational and environmental factors shaping AI adoption decisions and offering practical strategies for overcoming adoption barriers.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; construction health and safety management; construction innovation; fuzzy synthetic evaluation; Ghana; technology adoption |
| Index terms: | factor analysis, dynamics, construction professional, technology adoption, artificial intelligence, health and safety management, fatalities, purposive sampling, construction industry, innovation adoption, Ghana, statistical analysis, qualitative approach, evidence, questionnaire, statistical modelling, integration, methodology, construction innovation, publication, environmental factor, construction safety, fuzzy synthetic evaluation, health and safety, survey, strategy, face |
| Subjects: | health risk and incident analysis, management, professional development, organizational analysis, industry analysis, environmental health, statistical analysis, systems engineering, data collection methods, data science, artificial intelligence, research methods, Geography, health safety and environment, building construction, decision-making and optimization, research dissemination and communication, evaluation and assessment methods, innovation studies, psychology, innovation and technology management, environmental science |
| Topics: | Sustainability, Health and Safety, Engineering Principles, Geographical Context, Organizational Design, Digital Applications, Business Strategy, Information Management, Research Practice |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here