Modeling the Determinants of Stock Market Investment Intention and Behavior Among Studying Adults: Evidence from University Students Using PLS-SEM

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Thank you for the opportunity to review the manuscript. Overall, the manuscript is well-structured in terms of research design, methodology, and result analysis, providing valuable insights into the investment behavior of university students in Uzbekistan's stock market. However, there are several issues that need to be addressed:

1. It is recommended to reduce the number of keywords.
2. The introduction needs to be reorganized.Specifically, it should clearly review the existing issues, identify the research gaps, articulate the motivation for the study, summarize the research methods employed, highlight the innovations in one or two sentences, summarize the key conclusions, and outline the contributions of the work.
3. The citation format for the references throughout the manuscript must adhere to the journal's guidelines.
4. Please standardize the representation of decimal points in all tables.
5. In the discussion section, some paragraphs are overly lengthy and should be appropriately divided.

Author Response

Please see the attachment

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This paper studies  the main determinants of stock market investment intentions using a sample of students from Uzbekistan.

The paper does use a good number of observations and selected a relevant number of variables that the students are questioned about. At the same time, there are some issues:

1) The authors found a negative role for digital literacy and not a significant one for financial literacy. Maybe this depends on the specialization the students follow. Perhaps, given the low interest for financial investment in Uzbekistan, people in general are not interested in investing. Thus, an analysis according to different study fields would be welcome.

2) It would be interesting if the authors could say something about what motivates people NOT to invest.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

I am pleased to review the article titled “Modeling the Determinants of Stock Market Investment Intention and Behavior among Studying Adults: Evidence from University Students Using PLS-SEM” submitted to the International Journal of Financial Studies. The study addresses an important and timely issue: the low participation of the public, specifically university students in the Uzbekistan stock market, despite structural reforms aimed at enhancing capital market activity. It investigates the cognitive and behavioural determinants of stock market investment intention and actual participation using the Theory of Planned Behaviour (TPB) and Partial Least Squares Structural Equation Modelling (PLS-SEM).

The central research question, i.e. what factors influence investment intention and behaviour among university students, is both relevant and original in the context of emerging markets. The authors appropriately identify and fill a significant empirical gap by focusing on youth investors in Uzbekistan, where limited financial participation is a known issue despite government-driven market reforms. The inclusion of constructs such as digital literacy, financial well-being, and behavioural biases (e.g., overconfidence and herding) adds nuance and timeliness to the inquiry, especially given the digital shift in financial services and investment platforms.

This paper contributes to the literature by providing empirical evidence from a unique population in a largely under-researched geography. Compared to other published works, the use of PLS-SEM enables robust testing of both direct and mediating relationships, thereby enhancing our understanding of the behavioural mechanisms underlying financial engagement. Furthermore, the negative effect of digital literacy on investment intention stands out as a counterintuitive and thought-provoking finding, challenging assumptions made in studies from more developed contexts.

The methodology is largely sound. The study employs a clearly justified sample size (n = 369), supported by G*Power analysis, a comprehensive measurement model with rigorous testing for reliability, validity, and multicollinearity, and an appropriate application of PLS-SEM for a complex path model. Nonetheless, the paper could be improved in several respects. First, the justification for selecting specific measurement scales, particularly for financial well-being and risk tolerance, would benefit from deeper critical reflection and cross-validation against other studies in similar socio-economic settings. The authors acknowledge the single-item operationalisation of financial literacy, which may oversimplify a multidimensional construct. A broader inclusion of psychometrically validated subscales could enhance robustness.

The conclusions are generally consistent with the presented evidence and do answer the research question. The significant positive effects of risk tolerance, overconfidence bias, and herding behaviour on investment intention, and the mediating role of intention on actual participation, align with TPB expectations. However, the explanation for the unexpected negative relationship between digital literacy and investment intention, while plausible, is speculative and would benefit from triangulation with qualitative data or longitudinal analysis.

The references cited are generally appropriate and include up-to-date, peer-reviewed literature. However, certain key works in behavioural finance and digital inclusion, especially those addressing low-income or low-trust environments could have been integrated to deepen the discussion of unexpected results.

Tables and figures are well-presented, informative, and align closely with the text. The research framework diagram is helpful for guiding readers through the model’s structure, while tables presenting cross-loadings, discriminant validity, and path coefficients demonstrate a strong commitment to methodological transparency.

The article fits well with the journal’s international scope and would be of interest to readers concerned with financial inclusion, youth behaviour, and capital market development in emerging economies. The authors offer clear social and economic implications by advocating for integrated financial and digital literacy programs, which may inform policy and curriculum design in similar contexts.

In terms of originality, the article does bring new and significant insights, particularly by contextualising established behavioural theories within the socio-economic environment of Uzbekistan. It bridges theory and practice effectively by identifying policy-relevant educational interventions and highlighting subgroup differences across gender and academic disciplines.

The quality of communication is high. The writing is clear, technically precise, and accessible to a global academic audience. The introduction adequately outlines the rationale and importance of the study. The research design is replicable and described in sufficient detail. The results are presented clearly, with proper statistical treatment and appropriate interpretations. The discussion section draws meaningful implications, although the paper could strengthen its acknowledgement of limitations, especially those related to cross-sectional design and potential cultural bias in psychometric items.

Recommendation

I recommend acceptance after minor revisions. These revisions should focus on improving the theoretical justification for construct operationalisation, expanding the discussion of limitations, and elaborating on the policy and practical implications of the negative relationship between digital literacy and investment intention. However, I believe this is a well-conceived and competently executed study that makes a meaningful contribution to the field.

• The use of a composite score treated as a single observed item lacks nuance. Since financial literacy is multidimensional (encompassing knowledge, behaviour, and attitude), a more robust modelling of its components as separate constructs or indicators would improve construct validity and reflect actual variance in respondents’ skills and awareness.
• The paper should explain in more depth why specific measurement instruments were chosen for constructs such as digital literacy, overconfidence bias, and financial well-being. Reference to cultural or contextual suitability (e.g., Uzbek context) would strengthen methodological transparency.
• The AVE for financial well-being falls below the acceptable 0.50 threshold (reported at 0.432), signalling weak convergent validity. Authors should either refine or remove problematic items or acknowledge this limitation and justify their retention based on theory.
• The negative relationship between digital literacy and investment intention is intriguing, but it may be context-dependent. The authors should moderate the strength of causal claims and explicitly label this finding as exploratory or speculative, given the absence of longitudinal or qualitative follow-up.
• The study’s cross-sectional design, reliance on self-reported data, lack of qualitative triangulation, and use of convenience sampling should be more explicitly acknowledged to prevent overgeneralization.
• Some citations are inconsistently formatted (e.g., “Sarwar & and Afaf”), and others are missing publication details. The reference list should be reviewed thoroughly for compliance with the journal’s style guide.

Further Suggestions:

1. Add a Table of Hypotheses
• For reader clarity, especially given the complex conceptual framework, a summarising table listing all hypotheses (H1 to H9) and their expected relationships would improve navigation through the study.
2. Include a Brief Thematic Summary in the Abstract
• While the abstract is generally strong, consider including a brief line summarising the most impactful practical implication (e.g., "Digital literacy was negatively associated with investment intention, suggesting caution in assuming technology readiness equates to investment readiness").
3. Elaborate on the Implications for Curriculum Development
• The conclusion touches on policy and education, but could be expanded. The authors should suggest how universities in Uzbekistan might integrate behavioural finance, digital literacy, and risk awareness into their curricula to stimulate financial engagement.
4. Discuss Gender and Academic Field Differences More Deeply
• The Multi-Group Analysis reveals interesting subgroup differences. These should be discussed more fully in the discussion section to enrich theoretical contributions and practical recommendations (e.g., tailoring programs by field of study or gender).

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The presented manuscript is a near-excellent study of the determinants of financial market participation among university students in Uzbekistan. While focusing on young adults may not be ideal for a whole-country generalization, the topic is scientifically valid and fits the journal’s profile. The paper comprises all the necessary sections: a detailed literature review, which justifies the hypotheses and helps construct the SEM model; a detailed description of the used methodology, which is standard with ample references; a solid description of the obtained results; and a lengthy and deep discussion. The selected estimator is PLS-SEM, given the number of variables and the results of pre-estimation “checks”, which allows the authors to examine mediating effects.

What can be improved in the paper is the following. First of all, the literature review. The significance of TPB (the theory of planned behavior) for the topic is never exemplified in the cited literature. Are there any similar studies that have employed TPB to analyze financial-market participation in a similar context? If not, the choice of TPB must be well-grounded via reasoning and/or evidence from data. The same applies to previous studies on the topic using SEM. Why was structural modeling chosen for the analysis? Was it based on prior literature? Addressing these issues would significantly improve the paper.

Finally, Table 2 shows 30 items for the DL variable, but in the text and Annex B, in contradiction to the table, it is demonstrated that only 19 were left because of the high VIF. A comment on which state (before or after the filtering out) Table 2 represents is required. Moreover, presenting a variant of Figure 1 with path values on the graph in the results section would make the results more transparent. Seeing paths in tables is more taxing on the reader.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have answered all issues.