Multivariate hybrid pathways for creating exceptional customer experiences
Bibliographic record
Abstract
Purpose The purpose of this paper is to focus on the evolving field of hybrid services within the customer service domain. The distinguishing characteristic of hybrid services is its rapid advancements and intersection of technology innovations mixed with customer service approaches. Design/methodology/approach Extensive research and analysis has identified numerous models to measure service quality and most of these models are derived from the SERVQUAL. Since SERVQUAL is not clearly focused to analyze the customer’s experience, the authors have used mixed methods of data collection. The two sources of data are both primary and secondary data. Primary source of research is semi-structured feedback with key operations manager and front line employees involved in the business process outsourcing industry. Secondary source of data is based on case studies of organizations engaged in information technology and ecommerce. Findings In this study, the author suggests multivariate hybrid pathways to streamline and deliver exceptional customer experience, which enhances the customer retention and firm’s competitive advantage. This study emphasizes on the imminent growth of hybrid services within the customer service domain. The distinguishing characteristic of hybrid services is its rapid advancements and intersection of technology innovations mixed with customer service approaches. The customers’ interactions with a firm are gaining proportional complexity due to the intercourse of human and technology interactions. Originality/value This study integrates the diverging but distinct pathways that influence customer experience. The study is centralized on the theme that there is a progressive dependence of human interactions with technological developments. It highlights the advent of new digital technologies that are the catalyst for personalized customer experiences.
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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 | 0.003 | 0.008 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".