Enhancing Consumer Empowerment: Insights into the Role of Rationality When Making Financial Investment Decisions
Bibliographic record
Abstract
With an avalanche of market manipulations and unethical tactics in the Australian financial industry, the empowerment levels of female Australian consumers when making financial investment decisions are highly questionable. Through the theoretical lens of a utilitarian perspective, financial investment decisions are often built on the pillars of trust, security, and assurance, which allow consumers to make decisions rationally and gain empowerment when making these decisions. However, due to the widespread manipulations prevailing in Australian financial markets, the role of rationality and its influence on consumer empowerment remain understudied. Based on this context, this paper uncovers the association between how each stage of rational decision-making (RDM) (i.e., demand identification, information search, and the evaluation of alternatives) influences the consumer power (i.e., consumer resistance and consumer influence) of female Australian consumers when making financial investment decisions. In doing so, this study employs a quantitative approach, whereby the proposed conceptual framework is tested among 357 female Australian consumers to understand their decision-making power in the presence of heightened situations of market manipulation in the financial industry. The results show that information search has a significant positive relationship with consumer influence and consumer resistance when making financial investment decisions. Additionally, the findings suggest that female Australian consumers should not only rely on individual-based sources of power but also have exposure to network-based sources of power to gain empowerment when making financial investment decisions. Lastly, it is suggested that government bodies, financial institutions, and regulatory authorities should not only implement financial literacy programs but also promote gender diversity across organisations to encourage women’s empowerment (i.e., Goal 5 (SDGs)—Achieve Gender Equality and Empower all Women and Girls).
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".