Mobil iletişim teknolojilerinin kurumsal kabulü.
Bibliographic record
Abstract
The purpose of this study is to identify the key adoption factors for mobile communication technologies, specifically smartphones, at private sector organizations. We have proposed a comprehensive research model based on the Diffusion of Innovation theory, Institutional theory, and Technology-Organization-Environment framework. Sequential explanatory design mixed method research strategy, which incorporates quantitative and qualitative approaches was used in this research. A Structural Equation Model was used to assess the model based on the data collected from senior and middle managers at 213 and 141 private sector organizations in Turkey and Canada, respectively. The Constant Comparative Method was used to analyze follow-up data that resulted from transcription of the interviews. In the first part of the study, the research model was applied in Turkish organizations. The results show that expertise, security and the environmental characteristics of competitive pressure, customer expectations, and partner expectations have the most significant influence on adoption in Turkey. The qualitative findings confirmed these results. In the second part of the study, the research model has been applied in Canadian organizations. Results show that security and top management support have the most significant effect on adoption in Canada. The qualitative findings confirmed the quantitative results. As these results suggest that there are significant differences between the two countries in terms of their adoption behavior, in the third part of the study, we investigated the differences in patterns between the adoption behaviors of the two countries and identified the impact of cultural differences on adoption. The results show that national culture has a significant effect on the adoption of smartphones by organizations. The implications of these findings are discussed.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.105 |
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; both teacher heads agree on what is shown here.
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".