Abstract
One of the challenges in property valuation is the lack of up-to-date and reliable data to arrive at a justifiable estimate. Concerns around the data collection process and how it can be improved using emerging technologies to ensure more reliable data and credible valuations motivated the study. Thus, the study investigated the adoption of technology in the property valuation data collection process in South Africa. To achieve this, sub-objectives were formulated to establish the technologies predominantly used by valuers and identify the factors (drivers, barriers, and strategies) influencing technology use. After grouping the findings into empirical factors, the study examined the relationships among these factors and their influence on the technologies being used.
A conceptual framework was developed to illustrate the relationships between the factors and their influence on technology use. The study employed a quantitative research approach, collecting data through a questionnaire survey that was electronically distributed. The study yielded 103 responses from registered valuers in South Africa, identified using purposive sampling. Descriptive and inferential statistics were used to analyse the sample data. Furthermore, Pearson’s correlations, Spearman’s rank-order correlations, and Chi-square tests were used to test the hypothesised relationships between the factors and their influence on the technologies.
Descriptive statistics revealed that the most frequently used technologies were mobile technology, Internet of Things, and geographic information systems. Key drivers were ‘using technology enhances efficiency’, ‘using technology will increase productivity’, and ‘ability to use technology’, while the main barriers included ‘the expensive cost of hardware and software’, ‘limited knowledge or lack of awareness’, and ‘high training costs’. The most effective strategies were identified as ‘provision of education and training on technology use’, ‘creating awareness’, and ‘collaboration with professional bodies’.
The findings of the exploratory factor analysis revealed that ‘technological benefits and market area’, ‘technology advancement and development’, and ‘organisational and individual attributes’ were the most important factors. The correlation tests revealed that
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while most factors correlated, there were some key exceptions. ‘Resistance to innovation’ did not correlate with the following: ‘technological benefits and market area’, ‘collaborative partnerships with industry players’, ‘motivation to use the system’, and ‘governmental and organisational strategies’. Furthermore, ‘collaborative partnerships with industry players’ did not correlate with ‘technical barriers’. Additionally, the Chi-square test highlighted thirteen significant relationships.
The findings of this study contribute to the theoretical framework for technology adoption within the property valuation industry by identifying key factors influencing the use of technology. This research provides valuable insights into current technologies, the factors collectively influencing their adoption, and strategies for targeted improvement, helping valuers mitigate challenges and implement the identified technologies during the data collection process, for more up-to-date and reliable data. The study recommends further research with a broader sample and alternative research approaches to explore technology adoption in other valuation phases, such as data analysis.