Towards a Conceptual Framework for Customer Intelligence in the Era of Big Data
Bibliographic record
Abstract
The dominance of services and service-based products in today's economy highlights the significance of customer intelligence for service offerings. Furthermore, the revolution of big data has generated a vast amount of customer data and reshaped the dimensions of organization, management, and technology within enterprises. The big data era also acknowledges the role of customers for value co-creation. Therefore, the objective of this paper is to propose a service-based framework for customer intelligence in the age of big data, hereafter called the SBCI framework, from the design science and service science approach. It laid the groundwork upon design science; the SBCI framework is proposed with the detailed artefacts, including construct, model, method, and instantiation. The framework also reflects service science through the three levels: 1) the network of service systems level for service proposal, 2) the service system level for service creation, and 3) the service level for service operation.
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 | 0.016 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".