A systematic literature review of financial resilience: antecedents, consequences and future research agenda
Bibliographic record
Abstract
Purpose Following the COVID-19 pandemic, financial resilience has received more academic and societal attention. However, a cohesive understanding, an accepted definition and a consistent measurement scale of this concept do not exist. This study aims to synthesise the literature on financial resilience by examining its research trends, methodologies, designs and themes. Furthermore, a framework outlining the antecedents and consequences of financial resilience is presented, along with an agenda for future research. Design/methodology/approach Using a systematic literature review, the authors identified 155 articles from Scopus, published up until December 2023. This review presents the themes, theories and components of financial resilience, as well as publication trends over time and the countries where financial resilience is researched. Findings This review found three broad research domains: individual, organisational and governmental financial resilience. All three domains define financial resilience as the ability to bounce back from financial shocks, but the government literature also incorporates bouncing forward, illustrating a gap in the individual and organisational literature. Two key aspects of financial resilience are accessing financial resources and developing new capabilities to overcome deficiencies. Originality/value To the best of the authors’ knowledge, this study is the first to review existing research on financial resilience. One of its contributions is to present a universal definition of financial resilience. The authors also examine the antecedents and consequences of financial resilience as outlined in the literature.
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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 | 0.028 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".