Exploring Factors Affecting Consumers’ Adoption of Shopping via Mobile Applications in Turkey
Bibliographic record
Abstract
The aim of this study is to identify the factors behind consumers’ adoption of shopping via mobile applications and to develop a new model that explains this situation. The related literature was examined for this purpose. Delphi technique was preferred to determine factors in the study. Data was collected through questionnaires. Exploratory Factor Analysis (EFA) was conducted with SPSS. A research model based on an integration of various theoretical fields was developed. As a result of EFA, ten new dimensions emerged in the study. And then, in order to statistically analyze the measurement and structural models, this study used Smart PLS for Structural Equation Modeling (SEM) technique. After path analysis with Smart PLS, a new conceptual model was developed to explain adoption of shopping via mobile applications by consumers in Turkey. Structures such as Personalization, Word of Mouth Communication and Perceived Mobility used in the model developed within the scope of this research, but rarely used in this field of studies, were verified to be determinants of shopping behavior via mobile applications in Turkey. The model developed within the study is both valid and reliable in terms of its structure and all relations established within the scope of the model are significant.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".