Does BLE technology contribute towards improving marketing strategies, customers’ satisfaction and loyalty? The role of open innovation
Bibliographic record
Abstract
The purpose of this study is to explore the marketing strategies for the introduction of Beacons technology applications (BLE) technology in businesses and how it can convert potential clients into satisfactory and loyal customers. Open innovation is the pathway to taking businesses to the next level with the help of and introduction to BLE technology contributions. Companies are flexible towards adopting new technologies to make sure that their marketing strategies are working positively. Global Village Dubai was the market that we targeted to study the customers’ needs and wants. This was done through 138 questionnaires distributed among 159 stores that met our research criteria. We used ANOVA through the SPSS program for analysis of the data. The results supported all our hypothesis of the study, which means there is a positive and strong relationship of adopting BLE technology on customers’ satisfaction which leads towards customers’ loyalty, making them stick to the brand for long term. The results of our research revealed that Beacons technology applications have positively influenced marketing strategies which in turn have impacted customer satisfaction and loyalty. The proximity marketing is the best strategy developed in lieu of open innovation. Future studies are welcomed for analyzing the same model in perspective of other markets for clearer advocacy of the hypothesis that proximity marketing is the marketing strategy that helps in customers’ happiness leading towards their loyalty.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".