Building Telecoms Service Quality for Brand Loyalty
Bibliographic record
Abstract
Service firms dependent on high technological infrastructure operate within a different service context from typical professional service firms. As a result, there is a tendency for such service firms to deviate from the existing (customer-centric) schools of service quality definition and measurement. This becomes difficult to operationalise within the context of brand loyalty. This led the researchers to review the extant literature in the relevant subject areas with the aim of finding out whether there has been a shift in the definition of service quality for high-tech dependent service firms. Following this, a pilot study consisting one-on-one interviews and a field survey was conducted. The aim was to gain insight on the understanding of key technical managers of a telecoms operator (MTN) in Lagos, Nigeria. On the other hand, the field survey (of MTN customers) carried out was aimed to juxtapose customer perception, based on the findings in the literature, with the orientations of the managers. The results indicate a disconnect between managers’ orientations and the perception of their customers. While managers believe that their customers are loyal to their brand due to operational efficiency provided, customers indicate very high switch-over tendencies to the firm’s competitors irrespective of operational efficiency. Suggestions on how service quality can be operationalised for brand loyalty were given as well as areas requiring further research.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".