Digital Technology Adoption and Employee Productivity in Real Estate Firms: Mediating and Moderating Effects of Technological Drivers, Barriers, and Operational Knowledge in North-Central Nigeria

The need for the adoption of digital technology is becoming increasingly important for the improvement of the efficiency of operations and employee productivity in the real estate sector. This study explores the effect of the availability of digital technology, employee knowledge of digitalization, and the use of technology on employee productivity in the real estate sector in North-Central Nigeria. It further explores the mediation role of technological drivers and technological barriers, and the moderating role of employees’ working knowledge of the effect of the use and availability of technology on employee productivity. A quantitative research design and methodology were used for this study, and data were collected through structured questionnaires among some real estate businesses’ employees in the states included in North-Central Nigeria, namely Abuja, Benue, Kogi, Kwara, Nasarawa, Niger, and Plateau States. Through partial least squares structural equation model analysis (PLS-SEM), the proposed hypotheses were rigorously tested in this study. Results showed that both the use of available digital technology and employees' know-how of digitalization significantly impact the use of technology and employee productivity positively and negatively, respectively. Technological drivers served as positive mediators, but technological barriers negatively mediate the impact of available technology on employee productivity. Employee working knowledge positively moderated the positiveness of technological use on employee productivity for improved real estate industry performance and recommends formal employee continuous education and technological infrastructure in the industry for increased employee productivity and other beneficial values offered by adequate technological use within the industry. Received: 01 December 2025 / Accepted: 31 December 2025 / Published: January 2026