The impact of digital marketing on the adoption of building information modeling system in Jordanian interior design companies: The moderating role of credibility
Bibliographic record
Abstract
Failure in digital marketing strategies implementation in building information modeling (BIM) adoption has been observed among some interior design firms. The impact of digital marketing on BIM system adoption among interior design companies was examined in this descriptive analytical research. The research also examined the credibility of BIM system adoption and how the system moderates credibility in the relationship between digital marketing strategies and BIM system adoption. A conceptual model comprising digital marketing strategies (content marketing, social media, and search engines) as independent variables impacting information modeling system adoption as a dependent variable was proposed. The moderating effect of credibility in the relationship between digital marketing strategies and BIM system adoption was examined. Data were gathered through questionnaires delivered online to 300 selected study participants via Google Forms. Usable data from 250 participants and the study hypotheses were analyzed using Structural Equation Modeling (SEM) with Partial Least Square (PLS). The results showed a significant impact of digital marketing strategies (content marketing, social media, and search engines) on BIM system adoption. It also emphasized the moderation of the role of credibility in the relationship between digital marketing strategies (content marketing, social media and search engines). This study can help Jordanian interior design companies to make optimal use of digital marketing strategies (content marketing, social media, and search engines) to adopt the BIM system to increase profits.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".