Emotional Intelligence and Extended Service Profit Chain in Telecom Industry in Oman – An Empirical Validation
Bibliographic record
Abstract
A research study was initiated to investigate the influence and impact of Emotional Intelligence (EI) on extended Service Profit Chain (SPC) model in a telecom service industry in Oman. The operations management literature frequently exhorts that in addition to quality compliance, customer satisfaction, retention some attention to be devoted to attributes like employees satisfaction and loyalty as well. Accordingly, the SPC integrates EI as a pivotal component and has been in wide use for studying mutual linkages between employees and customers. The present study empirically examines the suitability and usefulness of this model in telecommunications industry, by collecting feedback about various attributes associated with from entities in both upstream and downstream paths viz. Original Equipment Manufacturers (OEM), Service Providers (SP) and Customers. Using a battery of carefully-crafted, inter-linked hypotheses by thorough statistical analysis of the survey data was made to validate the assumptions and the soundness of three-tier architecture of SPC. The proposed research framework demonstrated that Service Quality (SQ) of upstream OEMs increases in proportion to the SQ and employee loyalty of SPs, which in turn generates satisfaction and loyalty among downstream customers. Interestingly, loyalty among downstream customers diffuses or propagates upward, translating into higher sales and performance for upstream OEMs. These findings suggest that EI is a benevolent, binding force and plays an invisible hand, in enhancing internal performance of an organization. By embracing extended SPC model, service industries are bound to gain competitive advantage and unleash firm profitability.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".