Reshaping marketing through immersive technologies – an exploratory study in the Egyptian real estate sector
Bibliographic record
Abstract
Purpose This paper aims to explore the role of immersive technologies in reshaping the marketing techniques for the real estate industry and their effect on the consumer purchase decision process. Design/methodology/approach The research methodology is based on a qualitative approach, utilizing the case study research method. Data collection was conducted in two phases. The first phase involved general interviews with chief executive officers from Canada and Egypt. The second phase included a site visit to the company’s location in Egypt, where observations of the technology implementation were made, along with semi-structured interviews with all involved departments. The classical model of the consumer purchase decision-making process served as a solid theoretical framework. Findings Immersive technologies are transforming real estate marketing. It provides the buyers with an innovative customer journey, enabling a smooth customer experience and easy purchase decision, and the sellers with a simple sales journey focusing on customer preferences. The study also reveals that immersive technologies have environmental, economic and social impacts. Originality/value There is still a gap in marketing studies regarding the effects of immersive technologies on consumer behavior in the real estate industry, particularly in Africa. Using the classical model of consumer purchase decision, this in-depth case study provides a novel exploration of how real estate agents use immersive technologies to promote properties and how these technologies influence consumer decisions at each stage. The paper makes an empirical contribution by examining this sector in Egypt. This contributes to advancing our understanding of consumer behavior models and improving marketing practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".