Financial services experience and consumption in Nigeria
Bibliographic record
Abstract
Purpose To understand the financial services experience and consumption in Nigeria from the perspectives of both the customers and managers. This study aims to explore this under-researched area and contribute towards a transformative financial service in the country. Design/methodology/approach Semi-structured interviews with 26 bank customers and seven top bank executives. Findings A conceptual framework, which has emerged from the analysis of the results, revealed three overarching factors that shape these experiences from the perspective of both consumers and managers – service maintenance, service technology and service dynamics. Practical implications For sustainable service maintenance, bank executives need to increase the overall level of transparency in their operations, particularly regarding bank charges, to ensure that customers are not subjected to hidden and unnecessary charges. The use of technology in service provision and delivery should play a prominent role. Managers should also provide innovative and user-friendly technology, communicating with customers and raising awareness of the benefits. Customers who are reluctant to adopt the technology should be educated and reassured. Recognising the service dynamics, managers should improve customer services and relationships, effectively manage the mobile money agent relationship and market new and relevant products to their target audience. Social implications The understanding of the financial services experience and consumption of citizens and residents in the demonstrate how the appropriate programmes and policies that enhance financial inclusion could be introduced and implemented in the country. It enables financial service managers to improve their services to their customers and policymakers to develop timely, relevant and appropriate policies to address and/or bridge the identified gaps in financial inclusion. The understanding of the financial services experience and consumption of citizens and residents in the demonstrate how the appropriate programmes and policies that enhance financial inclusion could be introduced and implemented in the country. Originality/value Through the sampling, this paper reiterates the need for consumer engagement and collaborative customer-provider relationships in redesigning financial services. This aligns with the transformative research agenda, which aims to increase access to financial services, decrease disparity and ensure consumers’ financial well-being.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".