Exploring the influence of social media and materialism on impulsive real estate buying decisions among young immigrants in Canada
Bibliographic record
Abstract
Purpose The purpose of this paper is to study the impact of social media and materialism on impulsive buying decisions and real estate. Furthermore, the paper examines whether social media correlates with materialism and provides insights that will facilitate a better economic climate. Design/methodology/approach The data for the study was collected using an online survey circulated among young immigrants in Canada. A five-point Likert scale was used, followed by structure modeling to test the hypothesis. Findings The findings reveal how impulsive buying behaviors are influenced by materialism and social media among young immigrants. The data support two hypotheses since it confirms that social media affects the amount of materialistic wants possessed by respondents and that the higher their levels of materialism, the more likely they are to make impulsive buying decisions, especially when it comes to buying real estate. Research limitations/implications As the data was limited to Canada, the findings are limited to this region and could vary across geographic regions. The age group was not considered as a huge factor as minors do not always have the purchasing power in terms of housing. Practical implications Materialism, social media and impulsive buying may not always lead to purchasing a home spontaneously. However, one must still consider their financial situation before purchasing anything. The findings in this paper will help customers and consumers of social media to understand what truly drives impulsive buying, resulting in unnecessary purchases. Originality/value To the best of the authors’ knowledge, this is the first study to examine the factors affecting impulsive real estate buying decisions among young immigrants in Canada, including social media and materialism.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".