Assessment of country-of-origin-related and -neutral elements of mobile communication service offers: An empirical study of consumers with a Turkish migration background in Germany
Bibliographic record
Abstract
Due to more than three million people in Germany with a Turkish migration background country-of-origin (COO)-sensitive, designs of offers directed at this customer segment have been implemented by various corporations and discussed in the management literature for quite a while.Unfortunately, to date most publications have a weak empirical foundation and refrain from simultaneously investigating preference effects of several country-of-origin-sensitive and -neutral offer characteristics among Turkish migrants living in Germany.Therefore, the present paper explores the relative impacts of three COO-sensitive offer characteristics and one COO-neutral attribute of bundled mobile communication offers on preference statements derived from a conjoint-analysis of questionnaire responses of 249 consumers in Germany with Turkish roots.The results suggest that for the offering category in question a COO-neutral feature (cell phone type/brand) shapes the preferences of Turkish migrants almost to the same extent as the three remaining price-and communication-related characteristics investigated.Furthermore, we found that Turkish consumers in Germany encompass four subsegments with distinct preferences with respect to the design of mobile communication offerings.The members of these subsegments in turn differ primarily in terms of their age and gender structures as well as their level of accommodation to the German culture.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".