Barriers to achieving digital twin maintenance management of constructed facilities in the Nigerian health-care sector
David Ojimaojo Ebiloma & Clinton Aigbavboa
What the paper says
Purpose Digital twin technology offers multiple possibilities for efficient facility maintenance. However, some challenges hinder their usage for managing constructed facilities in the health-care sector. Therefore, this study aims to use the principal component analysis (PCA) to investigate the barriers to digital twin maintenance management of health-care facilities in Nigeria. Design/methodology/approach The post-positivist philosophical perspective adopted in this study informed a quantitative research approach using a questionnaire survey. Using a purposive sample, 442 respondents were selected from the Nigerian health-care industry. Maintenance personnel, top management staff and heads of departments in Nigerian hospitals formulated the respondents. Descriptive analysis and PCA were used for data analysis. Findings The analysis revealed that all the identified barriers ranked above the average mean, with Insufficient research, corruption and budget allocation not systematically done, ranking the top three. PCA clustered the variables into technical skill, management and financial barriers. Practical implications The study recommends conducting adequate research on digital twin usage within developing countries, which will boost the readiness to implement the technology to maintain constructed facilities. Due to its high cost, there is also a need for transparency and efficiency in allocating finances to fund the uptake of digital twin technology. Originality/value The innovativeness and significance of digital twin technology portend the need to explore the challenges that could hinder its easy adoption in managing constructed facilities.
3 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.32 × 0.4 = 0.13 |
| M · momentum | 0.57 × 0.15 = 0.09 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.