Bibliographic record
Abstract
Financial Services Marketing presents a communication-centered perspective on how service organizations build credibility, trust, and performance advantage through strategic messaging and stakeholder engagement. Anticipating the digital and structural evolution of the financial sector, this work integrates foundational theories from strategic communication, organizational communication, consumer behavior, and managerial sciences to demonstrate how communication functions as both a relational and governance mechanism. Central to the book is the argument that communication shapes service delivery, customer experience, organizational coordination, and brand legitimacy. The work applies key frameworks — including Market Segmentation, the STP Model (Segmentation, Targeting, Positioning), Relationship Marketing, the Marketing Mix, and Decision-Making Models — to show how audience analysis informs persuasion strategies, identity positioning, message tailoring, and trust formation within high-stakes service environments. The theoretical grounding additionally draws from the Resource-Based View (RBV), Knowledge-Based View (KBV), Service-Dominant Logic (SDL), Market Orientation Theory, and emerging communication governance perspectives. These lenses together highlight how intangible communicative resources — expertise, digital infrastructures, information flows, and cultural alignment — shape organizational performance and stakeholder relationships. Importantly, the work introduces early insights into digital adoption, online behavior, and e-service communication — including trust, usability, perceived value, and risk communication — which have since become central themes in digital communication and fintech research. As such, the book provides an early academic contribution to understanding how segmentation-driven governance, communication infrastructure, and decentralization of information support more responsive and participatory service systems.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.359 | 0.129 |
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".