Assessing the Impact of Digital Platforms on Commercial Property Leasing Practices

Thesis Abstract

The rapid proliferation of digital platforms has transformed commercial property leasing practices, fundamentally altering the interaction between landlords, tenants, and intermediaries in the property market. Despite the widespread adoption of online listing portals, mobile applications, and virtual viewing tools, there remains limited empirical understanding of how these digital innovations influence leasing decisions, market transparency, transaction efficiency, and long-term tenant-landlord relationships. This study aims to assess the impact of digital platforms on commercial property leasing practices, with specific objectives to identify the extent of platform utilization, evaluate the influence on leasing transaction efficiency, examine changes in lease negotiation processes, and analyze perceptions of market transparency among stakeholders. Employing a mixed-methods research design, this study integrates quantitative surveys and qualitative interviews to generate comprehensive insights. The quantitative component involves a structured questionnaire administered to a stratified sample of 250 commercial property agents, landlords, and tenants across the metropolitan city of Lagos, Nigeria, selected through simple random sampling methods. The qualitative component includes 20 in-depth interviews with key stakeholders to contextualize quantitative findings and explore nuanced perceptions. Data collection instruments comprise a structured survey questionnaire validated through pilot testing and thematic interview guides developed through literature review and expert consultation. Quantitative data will be analyzed using descriptive statistics, correlation analysis, and multiple regression techniques to determine the relationships between digital platform usage and leasing outcomes. The qualitative data will undergo thematic analysis to identify recurring patterns, stakeholder perceptions, and barriers to platform integration. The research also adopts the Technology Acceptance Model (TAM) and the Diffusion of Innovations Theory to underpin hypotheses regarding factors influencing platform adoption and their effects on leasing practices. Expected key findings include a high prevalence of digital platform usage among property agents and landlords, with evidence suggesting significant improvements in transaction speed and reduced leasing process costs. The study anticipates uncovering that digital platforms positively influence lease negotiation efficiency and market transparency but concurrently raise concerns about data security, stakeholder trust, and the digital divide. Furthermore, the analysis is expected to reveal that perceived ease of use and perceived usefulness significantly influence stakeholder willingness to adopt digital leasing tools. This research will contribute to knowledge by providing empirical evidence on the operational impact of digital platforms within a developing country context, expanding theoretical understanding of technology adoption in real estate markets. It advances previous studies by integrating multiple stakeholder perspectives and employing a comprehensive analytical framework to elucidate the mechanisms through which digital platforms influence leasing practices. The main conclusion underscores the transformative potential of digital platforms to optimize commercial leasing processes, enhance market transparency, and foster innovation in property management. Based on the findings, recommendations will include strategies for increasing stakeholder engagement, enhancing digital literacy among users, establishing data protection policies, and encouraging policy frameworks to support digital integration in commercial real estate markets. It is also proposed that future research explore longitudinal impacts of emerging digital technologies such as blockchain and artificial intelligence on lease management and dispute resolution, thereby broadening the scope of understanding in this evolving field.