BlockChain (BC) Upending Customer Experience: Promoting a New Customer Relationship Management (CRM) Structure Using Blockchain Technology (BCT)
Bibliographic record
Abstract
This study aims to explore the role of BC and its impact on CRM by suggesting an extended CRM on the basis of BC capabilities thru developing an analytic hierarchy planning-based framework to establish criteria weights developing a new self-assessment model to determine the most critical factors impacting the BC investment in CRM to enhance customer experience and to enable parties to work together in a trusted technology environment. An analytical hierarchical process (AHP) approach was utilized to prioritize and weigh the factors affecting the BC investment in modern CRM in the service industry based on the extant literature and its interpretation. This approach resulted in a ranking of 19 sub-factors based on experienced customer service professionals and technologists’ evaluations. Findings revealed a significant insight into proposing a new generation of CRM based on BCT, focusing on using the powerful BC platform considering all factors influencing the BC investment in modern CRM from a business perspective. Understanding the new combination of BC and CRM can solve the challenges and dilemmas linked to the untrusted environment of handling CRM data in the information systems field. This study provides valuable information and critical analysis of BC regarding CRM integration. Directions for future research are also included.
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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 | 0.005 |
| 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 | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".