The Determinants of Online Stock Investment in Malaysia: A Case in Early Phase of COVID-19 Pandemic
Bibliographic record
Abstract
The purpose of this research is to determine the factors that affect the behavioral intention of Malaysians individuals to adopt online stock trading. The primary data is collected with the help of structured questionnaire from 285 participants in the study who are current or potential investors in the Malaysian stock market. The online surveys were distributed from the last quarter of 2019 to the first quarter of 2020. This study uses the structured and self-administered online questionnaire survey tool to collect the primary data from samples. Non-probability convenient sampling method was employed and Partial Least Squares Structural Equation Model (PLS-SEM) is adopted. The results indicate that all constructs, namely performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC) have a direct significant positive relationship toward behavioral intention. In addition, the study shows that PE is the most important factor in determining individuals’ behavioral intention in adopting online stock trading. In conclusion, online stock trading system developer should focus on designing the additional useful features and ensuring the quality of the information to satisfy the demands and desires of the general public and to build features such as prompting traders to avoid the possibility of over trading or with feature enabling users to backtrack and test their trading strategies and to customize different types of analysis to help users making informed investment decisions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".