E-Marketing Communication and Loyalty in Real Estate Customers Based on Their Income
Bibliographic record
Abstract
The real estate industry is one of the most profitable activities worldwide, for instance, GDP-wise in 2020 it had a share of 5.8% within the Mexican economy, that is above 62 billion dollars (Statista, 2022; The World Bank, 2022). An activity with such relevance should keep up with world trends such as digitalization, e-commerce, and communication especially during the pandemic. COVID-19 accelerated the need of a multichannel integration in order to compete in today’s fierce competition. Digitalization has helped companies and people to find each other allowing them to communicate and exchange value, all of these resulting in long relationships. The problem analyzed within this research is made up of the little knowledge the realtors have regarding digital tools and how they can use them in their daily work activities as well as the effect they may have. Therefore, the purpose of this study is to analyze the influence of e-marketing communication on customer’s loyalty in the real estate business and to find out if there are any variations in terms of people's income. PLS-SEM was used so as to determine the link between each of the constructs in the proposed model, results show that e-marketing communication has a favorable and significant impact on customer loyalty, and that this influence is related to people's income.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".