Behavioural and Psychological Factors That Influence the Usage of Formal Financial Services Among the Low Income Households
Bibliographic record
Abstract
The emerging economies need to frame and implement effective financial inclusion policies for sustainable development and growth. Recent initiative of India that every Low Income Households (LIHs) has a bank account is a sweeping success; but the flipside is that half of these accounts are either inactive or less active, which raises concern. In this context, this research attempts to identify the behavioural and psychological factors that influence the usage of formal financial services (FFS) among LIHs in India. Theory of Planned Behaviour is used as the base theoretical model, in which ‘Habit’ was introduced as a moderating variable that interacts with Behavioural Intention to influence Actual Usage. Data was collected from 253 respondents and analysed using SmartPLS 3.0. This study revealed that the exogenous variables Attitude, Subjective Norms, Perceived Behavioural Control positively influenced the intention to use FFS; moreover, Habit negatively moderated the BI-AU relationship. Therefore, the policy makers on financial inclusion drive may consider these identified factors in their mission to improve the usage of FFS among LIHs, and to curtail the informal or alternative financial services.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".