The Relationship Between Psychological Factors and Retirement Financial Plan and Its Gender Difference

Abstract

As China’s population ages and the sustainability of the public pension system is at risk, personal savings become crucial. As such, the quality of financial planning for retirement (FPR) has been recognized as a key to safeguarding financial well-being during retirement. This study examines the relationships of two predictors (future time perspective and risk tolerance) and a mediator (subjective financial literacy) in shaping financial planning for retirement, with particular attention to potential gender differences. Using survey data retrieved from respondents aged between 23 and 60 years old, overall sample and gender-based multigroup analysis were used to examine whether gender moderates these relationships. The results reveal that both future time perspective and subjective financial literacy positively influence financial planning for retirement across all gender groups. Notably, we found no significant gender gap in retirement planning behavior. Subjective financial literacy serves as a significant mediator linking both future time perspective and risk tolerance to retirement planning, though the indirect effect of risk tolerance through financial literacy differs significantly between genders. Academically, theoretical propositions related to retirement planning can be accounted for by both genders. Practically, standardized policy can be tailored to address retirement issues across genders. The study emphasizes that financial planning for retirement in China shows no gender gap, and this provides meaningful guidance to policymakers and financial institutions to develop measures to encourage individuals to take financial actions in retirement planning. Finally, the combined interpretation of a strong effect of subjective financial literacy and an insignificant effect of risk tolerance raises concern that adult income earners in China are affected by financial literacy bias when practicing financial retirement planning.

1. Introduction

High longevity and low fertility are shifting the population structure in many countries (Bazzana 2020; O’Driscoll et al. 2021). Clements et al. (2018) estimated that the percentage of people aged 65 and older globally will rise from 12% to 38% by the end of the century. Due to continued aging, concerns over the long-term sustainability of public pension schemes are on the rise (Hinrichs 2021). A direct association between demographic, economic, and social factors and public pension schemes has left many countries facing challenges in addressing the sustainability of their public pension systems (Guardiancich 2012).

The aging population in China is anticipated to surpass the working-age population, thereby widening the labor gap, which will subsequently exacerbate the challenges confronting the public pension system. Zhang et al. (2023) estimated that from 2016 to 2039, China’s cumulative labor gap will reach 153 million, accounting for 11% of the current total population. In other words, maintaining the public pension system will be difficult (Lou 2021). Prompted by the concern of insufficient public pension funds to cover living costs after retirement, Tucker (2009) and Wu et al. (2017) echoed the importance of maximizing weighted income from private savings to attain optimal retirement. Unfortunately, numerous individuals face challenges in saving for retirement, often due to a prevailing belief that effective retirement planning is beyond their reach (Mu 2020). Government initiatives to encourage participation in retirement savings schemes are not yielding favorable results, as evidenced by low participation rates (China Aging Financial Survey 2022). Analysis of individuals’ projected retirement savings against the reality of their savings assets reveals a substantial shortfall in the average amount saved for retirement. This underscores the need for comprehensive research to explore the factors influencing retirement planning behavior.

The growing interest in retirement planning research has resulted in a surge of literature since 2010 (Ingale and Paluri 2023). While some studies highlighted how global structural, economic, and cultural differences exert unique pressures on individuals to save for retirement, others focused on the role of social and behavioral biases, personality traits, and psychological factors in shaping financial literacy and in influencing financial planning for retirement. Nevertheless, these studies lack gender effects. Examining the gender gap in the relationship between psychological factors and financial planning for retirement clarifies how risk tolerance and time-orientation perspective may cause different effects. Women tend to be more risk-averse than men, leading to more conservative investment strategies that may not maximize retirement savings. Traditional gender roles and societal expectations can shape how men and women think about retirement. Women are often socialized to prioritize caregiving and family needs over personal financial security, which can affect their retirement planning behavior (Friedemann and Buckwalter 2014).

Formal and informal social participation among women in China has shifted quite significantly, questioning the relevance of conventional perspectives on gender roles and societal expectations among modern women. The Outline of Women’s Development in China (2021) reveals that the proportion of female employees in the total employment has increased to 43.5%. For another perspective, that does not mean that females have more opportunities for decision-making. In 2022, for example, the proportion of women with decision-making power in government departments, enterprise staff boards, and social organizations was 11.2%, 37.1%, and 26.7%, respectively (National Bureau of Statistics 2025). Given the structural shifts in aging and gender participation, the study examines whether gender plays an important role in the association between psychological factors and retirement planning behavior. Understanding these gender dynamics facilitates the development of inclusive financial strategies, reduces gender disparities in retirement outcomes, and promotes long-term financial well-being for all.

2. Theoretical Background and Hypotheses

The term ‘financial planning for retirement’ (FPR) encompasses a series of activities aimed at accumulating wealth to meet post-retirement life needs (Topa et al. 2018). FPR explains decision-making concerning financial planning activities and saving tendencies. Financial goals for retirement are deeply connected to individual values, and psychological factors such as risk tolerance and future time perspective play a significant role in shaping financial behavior. Individuals may also be influenced by cognitive biases, including overestimating their financial literacy, which can affect their decision-making process. By examining these psychological factors, research can offer a more comprehensive understanding of financial behavior and decision-making in retirement planning.

The Capacity-Willingness-Opportunity (CWO) model by Hershey et al. (2013) attempted to further comprehend the influence of psychological factors on retirement planning. Although it initially suggested that psychological factors associated with retirement planning would remain relatively stable over time, recent evidence indicates that this continuity might not be absolute, as gender disparities could introduce changes. In contrast to other retirement planning models that consider psychological variables as an additive, the CWO model acknowledges the potential interactions among different psychological dimensions. Subjective financial literacy is one of the cognitive factors and skills necessary for retirement planning and savings in the CWO model (Topa et al. 2018). On the other hand, motivational variables that impact retirement planning and saving tendencies make up willingness. Willingness can be linked to factors such as future-time perspective and risk tolerance. The effective achievement of financial goals within the CWO model is influenced by the opportunity dimension, which recognizes the impact of external factors, including environmental factors and constraints. In general, the CWO model is a psycho-motivational model particularly designed to clarify retirement planning. Although the CWO model focuses on understanding the influence of psychological factors on retirement planning, the interaction relationship among different dimensions should be considered when examining retirement planning behavior and saving tendency (Wang 2007). To acquire deep insights into the retirement planning decision-making process, it is reasonable to combine the explanatory model of retirement planning with theories and models related to retirement savings (Taylor and Doverspike 2003). This study integrated the CWO model with the decision-making theory of the 3M model to explain factors related to financial planning for retirement.

Moven’s 3M Model assumes that individuals’ personality traits are followed by a four-level hierarchy based on their abstractness. Arranged from the most abstract to the most concrete levels, the four levels of traits are known as elemental, compound, situational, and surface traits. The model proposes partial mediation at the levels of the hierarchy and was employed in this study as the theoretical foundation, especially the mediation relationship. In the context of financial retirement planning, Hershey and Mowen (2000) proposed that individual differences in personality traits (e.g., future time perspective) were predictive of subjective financial knowledge and retirement planning tendencies. Future time perspective, which influences retirement planning, was also supported by empirical evidence advanced in Tomar et al. (2021). Based on theoretical support, the framework of this study is shown in Figure 1.

2.1. Future Time Perspective and FPR

Hershey et al. (2007) proposed that future-time perspective (FTP) refers to one’s preference for taking a long-term view (as opposed to focusing on the past or present) and a long-term planning orientation. A person who thinks about the future is more likely to take proactive steps. In the context of financial planning for retirement, this involves taking more concrete steps, such as saving and investing. Research shows that people with high FTP are motivated to work toward future goals and rewards, often at the expense of current enjoyment. Rabinovich et al. (2010) determined that FTP can positively influence people’s attitudes toward certain behaviors. In the Hershey model (2003), FTP, a psychological factor, is posited to have a direct, positive relationship with retirement planning. Evidently, Kerry (2018) examined the factors influencing retirement planning and concluded that FTP is a highly related concept in the field of financial retirement planning. These findings collectively indicate that an individual’s FTP is likely to exert a substantial influence on saving behaviors. In addition, gender inequalities have been noted, with men reporting thoughts that extend further into the future, but women reporting more future goals (Greene and DeBacker 2004). In line with sex role differences, men’s future goals tend to focus more on career-related issues, whereas women have more diverse goals, such as retirement planning. Consequently, Hypothesis 1 (H1) posits that future-time perspective is positively associated with FPR.

2.2. Risk Tolerance and FPR

The individual’s risk tolerance (RT) mainly depends on their personality traits, which will determine their risk appetite for investment or savings (Kusairi et al. 2019). When a person has a higher risk tolerance, they tend to be more inclined to invest in high-risk assets. Therefore, individuals’ financial planning for retirement is directly influenced by their financial risk tolerance (Rahies et al. 2022). Previous results show that the relationship between risk tolerance and FPR is mixed. Grable (2016) suggested that a relationship exists between risk tolerance and financial planning or investment decisions. Researchers such as Park and Martin (2022) found that those who are willing to invest in potential high-risk assets are more willing to develop effective plans. Thus, it can be considered that there is a positive relationship between financial risk tolerance and retirement planning. On the contrary, Park and Martin (2022) suggest that a higher level of risk tolerance weakens the relationship with retirement planning. In Chinese culture, people are encouraged to plan in case of a sudden loss. Thus, people who can tolerate higher risk levels prefer a better retirement plan. There are also significant differences in risk tolerance between genders. Women have a smaller risk tolerance than men, and less risky investments will account for a larger proportion in retirement planning (Agunsoye et al. 2022). Compared to men, women’s lower risk tolerance may be determined by their lower income, which is the main reason why women are expected to have lower retirement well-being (James and Agunsoye 2022). Consequently, Hypothesis 2 (H2) postulates that risk tolerance is positively associated with FPR.

2.3. Subjective Financial Literacy and FPR

Subjective Financial Literacy (SFL) refers to an individual’s self-beliefs regarding his/her financial skills and knowledge, i.e., what a person thinks they know (Hadar et al. 2013) about financial matters. Normally, people with subjective financial literacy tend to pay more attention to saving a portion of their income to prevent insufficient savings when income declines, especially after retirement (Lusardi and Mitchell 2022). Hauff et al. (2020) also proposed that subjective financial literacy has a significant impact on retirement planning behavior. On the contrary, Nguyen et al. (2022) found that subjective financial literacy can exert negative effects on retirement saving intention and behaviors, i.e., financial overconfidence led by subjective financial literacy makes individuals save less for retirement. In China, however, probably due to differences in culture, research consistently found a positive correlation between personal financial literacy and retirement planning (Niu and Zhou 2018). As such, Hypothesis 3 (H3) anticipates that people who believe they have higher financial literacy demonstrate higher confidence in carrying out retirement planning activities.

2.4. Mediating Role of Subjective Financial Literacy

Investigation into psychological traits such as SFL and other psychological factors presents an intriguing avenue for further investigation (Murphy 2013). As SFL differs from actual financial literacy, it can shape how individuals interpret and act on their psychological predispositions in the context of retirement planning. Rolison et al. (2017) revealed that individuals exhibiting a high level of FTP tend to have a positive impact on subjective financial knowledge. Based on the psychological perspective, Kooij et al. (2018) suggested that a positive relationship exists between FTP and self-assessed financial knowledge. Integrating the proposal by Mowen (2000) that subjective financial knowledge can be predicted by FTP, this provides a theoretical support suggesting that subjective financial literacy potentially exhibits a central role in the association between FTP and financial planning for retirement. It means, other than an expected direct association between FTP and FPR (H1), there is a possibility of an indirect path. When people believe they have high financial literacy, they have a tendency to think ahead, especially in terms of long-term goals like retirement planning. As such, future-oriented individuals are more likely to engage in financial planning for retirement because they feel capable of dealing with financial matters. Accordingly, the study postulates Hypothesis 4 (H4), where future-time perspectives will have a positive association with subjective financial literacy, which subsequently will have a positive association with financial planning for retirement.

Risk tolerance impacts financial planning for retirement, particularly related to investment activities. The inconsistent association between risk tolerance and financial planning for retirement from prior studies prompted suggestions to include the examination of the indirect effect of risk tolerance on retirement planning. SFL can impact the extent to which risk tolerance is associated with financial planning for retirement. People who do not tolerate risk may often find financial planning stressful. They may feel incapable of making good financial decisions, which consequently leads to postponement or even avoidance in financial planning for retirement. Hermansson and Jonsson (2021) indicated a significant correlation between risk tolerance and financial knowledge. Meanwhile, Mahdzan et al. (2017) argue that individuals with low risk tolerance and higher financial knowledge are more inclined to hold risky assets in retirement investment portfolios. This study proposes Hypothesis 5 (H5) that individuals with high risk tolerance, SFL, can further enhance their confidence in taking on riskier investment strategies, leading to more engagement in retirement planning.

2.5. Gender Gap

Gender gaps persist in how men and women engage in retirement planning, driven by economic, social, and cultural factors. Almenberg and Dreber (2015) argued that gender differences in risk attitudes affect the relationship with financial knowledge, while Cupák et al. (2021) emphasized that gender differences in financial knowledge can affect its relationship with risk tolerance. Moreover, there are significant gender differences in the relationship between SFL and RT. It is interesting to note that gender differences also exist in SFL. Several studies have found that women have lower levels of financial literacy than men (Bucher-Koenen et al. 2017; Fletschner and Mesbah 2011).

Gender disparity in these aspects can mold the way psychological factors affect financial planning for retirement. In financial confidence, for instance, men are often higher than women, making them more likely to convert their confidence into action, such as financial planning for retirement. Additionally, women who generally experience more financial apprehension than men can negatively impact their willingness to engage in investment activities for retirement planning. Thus, we expect psychological factors to have differentiated effects on retirement planning behaviors across genders, with stronger positive effects observed among men.

This difference is grounded in the Chinese context, where women face structural disadvantages. In China, women are less likely than men to be in the highest income quintiles and have lower labor force participation than men (National Bureau of Statistics 2025). This may be due to a more heterogeneous work history because of caring responsibilities among women in China. Empirically, Hershey et al. (2003) found that men tend to have specific retirement goals and possess more financial capabilities, while women tend to have abstract goals, such as happiness or freedom. Davis (2003) also found that due to bridge employment, male retirees are not in a fully retired state when retired, resulting in gender differences in financial ability, with males generally outperforming females (Peng et al. 2007). This pattern persists in China, where male retirees commonly continue working through re-employment arrangements after formal retirement, further amplifying gender differences in retirement preparedness.

3. Data Source

The study employed the survey method to source primary data. In total, nine items for Financial Planning for Retirement were used, of which four items measured retirement planning activities (Hershey et al. 2010) and five items measured the intention to save for retirement (Jacobs-Lawson and Hershey 2005). The items for psychological factors of Risk Tolerance, Future Time Perspective, and Subjective Financial Literacy were drawn from existing literature. The measurement of future time perspective involves a five-item scale (Koposko and Hershey 2014) to assess individuals’ enthusiasm for contemplating and preparing for the future. Five items for Risk Tolerance are adapted from Jacobs-Lawson and Hershey (2003), covering respondents’ attitudes toward risk in the context of retirement financial investments. Meanwhile, six items from Hershey and Mowen (2000) were employed to gauge the Subjective Financial Literacy of respondents. All items applied an even six-point scale, with a rating of 1 indicating “strongly disagree” and 6 indicating “strongly agree.” The measurement items of all variables are shown in Appendix A. The final version of the research instrument was in Chinese. It underwent a meticulous review process to ensure instrument reliability and data validity. A back-to-back translation was also conducted. Before the actual survey, the translated version was rigorously examined using qualitative pretesting methods, expert reviews, and cognitive interviews.

Several criteria are preset for respondents of the study. First, they must be adult income earners for their financial capability and suitability to provide feedback on retirement behavioral relevance. The age of the targeted respondents is between 23 and 60 years old, as they are considered sufficiently established in life/career stage and still within the savings period. As such, random sampling cannot be applied. Alternatively, the study employed a purposive sampling approach, particularly the judgment-based sampling method.

China is a large and diverse country. In order to capture a good geographical spread and diversity, the study covered the Eastern, Central, Western, and Northeast regions of the country. These four major regional divisions constitute the officially recognized economic geographical divisions by the National Bureau of Statistics (NBS) (National Bureau of Statistics of China 2011). The questionnaire was distributed in four major cities, namely Shenzhen, Wuhan, Chongqing, and Dalian. Shenzhen, Wuhan, Chongqing, and Dalian are four major cities located in the Eastern, Central, Western, and Northeast regions of China, respectively, and were selected based on their GDP rankings within each region. Furthermore, Beijing and Shanghai were also included as they are the two super major cities in China and are important due to their administrative and business hubs. These six cities are appropriate as the locations are scattered in different parts of China. In addition, as major cities normally attract many job seekers and entrepreneurs from other districts and states, data collected from these major cities could ensure diversity in terms of respondents’ backgrounds, demographically and geographically. As such, the data includes a good mix of sub-population groups, economic levels, cultural diversity, and different situational exposures.

The required minimum sample size was 77, quantified a priori employing the G*Power 3.1 tool (effect size f² = 015 (medium), with 80% power, at a significance level of α = 0.05) (Kang 2021). A platform called Wenjuanxing (China’s survey-questionnaire platform) was used for data collection. The survey took three months and successfully garnered 271 responses, of which 246 were usable. The final sample size (N = 246) is more than three times the required sample size of 77. Thus, the study’s data are sufficiently representative and statistically powerful for hypothesis testing.

Regional diversity is covered with data from the northern, eastern, southern, and western parts of China, as well as the two supermajor cities, e.g., Beijing and Shanghai. The study’s sample size of 300 exceeds the minimum. Thus, the results have sufficient statistical power. Therefore, it strengthens the argument for their generalizability to similar urban contexts in China. Derived data supporting the findings of this study are available from the corresponding author on request.

4. Results

A total of 246 Chinese between the ages of 23 and 60 participated in the study (mean age 37.9 years, SD = 10.7). Female respondents outnumbered male respondents by nearly 3:2 (42% male). Out of the sample, 13% had a secondary degree, 8% had a college diploma, 59% had a bachelor’s degree, 19% had a master’s degree, and 1% had a doctoral degree. Regarding marital status, 27% are single, 66% are married, 5% are divorced, and 2% are widowed.

4.1. Measurement Model Assessment

The measurement model was evaluated using partial least squares structural equation modeling (PLS-SEM) for its ability to deal with complex models. It offers the added advantage of estimating the measurement model and is suitable for conducting multigroup analysis (MGA) (Hair et al. 2017). Several aspects were assessed, including internal consistency and reliability, convergent validity, indicator reliability, and discriminant validity. The results for the complete group, the male and female samples, are shown in

Table 1. Results for Reliability and Convergent Validity of Measurement Model.

Items	Factor Loading (>0.5)			CR (>0.7)			AVE (>0.5)
	Complete Sample	Female	Male	Complete Sample	Female	Male	Complete Sample	Female	Male
Financial Planning for Retirement 1	0.671	0.618	0.710	0.907	0.899	0.915	0.585	0.564	0.607
Financial Planning for Retirement 2	0.719	0.715	0.725
Financial Planning for Retirement 3	0.713	0.615	0.782
Financial Planning for Retirement 4	0.790	0.742	0.829
Financial Planning for Retirement 5	0.806	0.834	0.788
Financial Planning for Retirement 6	0.853	0.857	0.855
Financial Planning for Retirement 7	0.785	0.836	0.751
Future Time Perspective 1	0.739	0.652	0.786	0.880	0.877	0.879	0.595	0.590	0.592
Future Time Perspective 2	0.765	0.808	0.730
Future Time Perspective 3	0.816	0.818	0.813
Future Time Perspective 4	0.724	0.713	0.723
Future Time Perspective 5	0.810	0.834	0.791
Risk Tolerance 1	0.704	0.672	0.706	0.905	0.915	0.892	0.658	0.686	0.624
Risk Tolerance2	0.871	0.877	0.858
Risk Tolerance 3	0.870	0.882	0.858
Risk Tolerance 4	0.860	0.911	0.813
Risk Tolerance 5	0.735	0.775	0.699
Subjective Financial Literacy 1	0.859	0.838	0.878	0.896	0.896	0.900	0.637	0.634	0.647
Subjective Financial Literacy 2	0.872	0.880	0.872
Subjective Financial Literacy 3	0.845	0.820	0.865
Subjective Financial Literacy 4	0.646	0.715	0.603
Subjective Financial Literacy 5	0.745	0.715	0.769

.

Hulland (1999) recommended a factor loading threshold of 0.5 for indicator reliability. The factor loadings ranged from 0.603 to 1, indicating the indicators are adequately loaded to their respective constructs. The composite reliability (CR) values for the split samples and complete samples all exceeded the 0.70 threshold, confirming the establishment of internal consistency and reliability for all the constructs. Convergent validity was supported, as evidenced by the average variance extracted (AVE), which exceeded 0.50 for the female, male, and complete samples.

The discriminant validity was evaluated using Henseler et al.’s (2015) heterotrait-monotrait (HTMT) ratio of correlation. It examines whether the indicators are cross-loaded onto unintended constructs. An HTMT value less than 0.85 (Hair et al. 2017) indicates establishment of discriminant validity. As evidenced in

Table 2. Discriminant Validity of the Measurement Model.

Dataset	Constructs	Financial Planning for Retirement	Future Time Perspective	Risk Tolerance	Subjective Financial Literacy
Complete Group	Financial planning for retirement
	Future time perspective	0.535
	Risk tolerance	0.355	0.450
	Subjective financial literacy	0.786	0.562	0.481
Female Group	Financial planning for retirement
	Future time perspective	0.496
	Risk tolerance	0.311	0.487
	Subjective financial literacy	0.759	0.557	0.301
Male Group	Financial planning for retirement
	Future time perspective	0.568
	Risk tolerance	0.411	0.407
	Subjective financial literacy	0.805	0.568	0.637

, the model satisfies the HTMT criterion, confirming the presence of discriminant validity.

4.2. Assessment of Measurement Invariance

The permutation test was conducted to assess whether construct measurements demonstrated invariance across the two gender groups (Henseler et al. 2016). Using the Measurement Invariance of Composite Models (MICOM) approach, Step 1 established that the indicators of all constructs were identical between the two gender groups (Table 1 and Table 2). In Step 2, the permutation test confirmed that the correlations of all constructs between the groups equaled 1, with p-values exceeding 0.05, indicating insignificance. As shown in

Table 3. Measurement Invariance Test using MICOM.

Construct	Configural Invariance	Compositional Invariance Assessment				Full Measurement Model Invariance Assessment
		Original Correlation	5.00%	Permutation p Value	Compositional Invariance (Partial Measurement Invariance)	Mean-Original Difference	Confidence Interval	Equality of Means	Variance-Original Difference	Confidence Interval	Equality of Variances	Full Measurement Invariance
Financial Planning for Retirement	Established	0.998	0.997	0.078	Established	0.000	[−0.261, 0.259]	Equal	0.025	[−0.341, 0.306]	Equal	Not Established
Future Time Perspective	Established	0.999	0.989	0.923	Established	0.270	[−0.257, 0.248]	Not equal	−0.075	[−0.409, 0.361]	Equal	Not Established
Risk Tolerance	Established	0.996	0.988	0.373	Established	0.253	[−0.253, 0.255]	Not equal	0.317	[−0.347, 0.337]	Equal	Not Established
Subjective Financial Literacy	Established	0.999	0.996	0.687	Established	0.059	[−0.243, 0.253]	Equal	−0.053	[−0.357, 0.319]	Equal	Not Established

Note: Results based on a two-tailed permutation test at 5% confidence level [2.50%, 97.50%].

, all permutation p-values were insignificant, thereby establishing compositional invariance within the research model. This finding supported partial measurement invariance.

To evaluate full measurement invariance, Step 3 examined the equality of mean values and variances of constructs across the two groups. The results revealed that not all composite mean values and variances were equal, confirming that full measurement invariance was not achieved. Consequently, only partial measurement invariance was supported. With partial measurement invariance established, the study proceeded to multigroup analysis, enabling the comparison of standardized coefficients within the structural model across the two gender groups.

4.3. Structural Model Assessment

The assessment of collinearity through the variance inflation factor (VIF) is the first step in the evaluation of the structural model. There were no collinearity issues observed, as indicated by the VIF values in

Table 4. Path analysis Results.

	Complete					Female					Male
Hypothesis	VIF	B	T	p	Results	VIF	B	T	p	Results	VIF	B	T	p	Results
H1: FTP→FPR	1.418	0.181	3.298	0.001	Supported	1.504	0.161	1.969	0.049	Supported	1.368	0.188	2.492	0.013	Supported
H2: RT→FPR	0.000	0.388	0.007	0.994	Not Supported	1.236	0.063	0.698	0.485	Not Supported	1.456	−0.057	0.759	0.448	Not Supported
H3: SFL→FPR	1.445	0.612	12.081	0.000	Supported	1.333	0.575	7.251	0.000	Supported	1.688	0.668	8.918	0.000	Supported
	R2	Q2				R2	Q2				R2	Q2
FPR	0.518	0.241				0.481	0.186				0.563	0.268
SFL	0.308	0.289				0.250	0.196				0.408	0.376

Note: B = Beta Coefficient, T = t-Statistics, p = Probability (p) value.

, which are below the recommended threshold of 3.3 in both the overall sample and the gender samples (Hair et al. 2021).

Bootstrap computation was employed to assess the association of constructs in the inner model. Three direct hypotheses (H1, H2, and H3) were examined. Following the guideline by Cohen (1988), R² values of 0.26, 0.13, and 0.02 in the PLS path model were considered as substantial, moderate, and weak, respectively. The three independent constructs explain 51.8% of the variance in financial planning for retirement (Figure 2). Hence, the R² value in this study is substantial. The results of the complete sample revealed that FTP was positively and significantly associated with FPR (t = 3.298, p = 0.001). Hence, H1 was supported. The positive and significant findings are also observed in the male and female sample groups. Hypothesis 2, which examines whether risk tolerance is positively associated with FPR, is not supported for the complete sample (β = 0.388, t = 0.007, p = 0.994). H3 hypothesized a positive and significant relationship between SFL and FPR. Evidently, H3 is supported in the complete sample (β = 0.612, t = 12.081, p = 0.000), as well as in both the gender subsamples. Further examination of hypothesis tests on the male and female subsamples revealed similar results as the complete sample, apart from the negative coefficient in H2 for the male subsample.

The explanatory power of the model is assessed using the R² values. Cohen (1988) recommends the R² threshold of at least 0.26, 0.13, and 0.02, respectively, in the PLS path model for substantial, moderate, and weak levels. Across the complete and subsamples (complete, male, and female), all but one of the R² values for the endogenous variables were above 0.26 (the lowest being 0.250 detected in the female sample). Accordingly, it is fair to conclude that most of the explanatory powers of the models are substantial, except for one at a moderate level.

The assessment of predictive relevance was conducted using the Q² value. As per Hair et al. (2017), the model’s predictive relevance is confirmed by the Q² value being larger than 0. Furthermore, the degrees of predictive relevance for each effect are represented by the values of 0.02, 0.15, and 0.35, indicating weak, moderate, and strong degrees, respectively. The Q² values ranging from 0.186 to 0.376 were observed for the endogenous constructs. Thus, Q² values in this study can be described as moderate to substantial.

4.4. Mediation Analysis

The examination for mediation was further extended to an analysis of coefficients. The indirect effect of the mediator in a simple mediation model (Figure 3) comprises a product of a and b coefficients, while the direct effect is the c coefficient.

Accordingly, as shown in

Table 5. Mediation Analysis Results.

	Complete				Female				Male
Hypothesis	B	T	p	Results	B	T	p	Results	B	T	p	Results
H4: FTP→SFL→FPR	0.243	6.495	0.000	Supported	0.262	4.442	0.000	Supported	0.235	4.641	0.000	Supported
H5: RT→SFL→FPR	0.159	3.674	0.000	Supported	0.050	0.814	0.416	Not Supported	0.280	4.775	0.000	Supported

, the indirect path coefficients for H4: FTP→SFL→FPR for the complete sample resulted in an indirect effect of 0.243 (a × b). With a direct effect of 0.181 (c’), the overall effects for this mediation model amounted to 0.424 (0.196 + 0.101), which indicated that the indirect effect accounted for a substantial portion, which amounted to 57% (0.243/0.424) of the total effects. Overall, the results indicated there is a partial mediating role of Subjective Financial Literacy in the relationship between FTP and FPR. Within the gender context, the indirect effect and direct effect are significant at the same time for the female subgroup (indirect effect = 0.262, direct effect = 0.161) and the male subgroup (indirect effect = 0.235, direct effect = 0.188), which also indicates the partial mediation of SFL in the relationship between FTP and FPR for the sub-sample. Hence, H4 was supported.

Similarly, the indirect path coefficients for H5: RT→SFL→FPR for the complete sample resulted in an indirect effect of 0.159 (a × b). However, the direct effect is not significant, which indicates a full mediating role of Subjective Financial Literacy in the relationship between RT and FPR. This full mediation relationship is also supported in the male group. As for the female group, the mediating role of SFL in the relationship between RT and FPR is not supported due to the insignificant indirect effect. Hence, H5 was supported for the complete and male sample, but not supported for the female sample.

4.5. Multigroup Analysis

Multigroup analysis is employed to examine the gender gap. The model’s validity and reliability were confirmed for both the female and male groups, as indicated in Table 1 and Table 2. Table 4 presents the results of testing the predictive relevance of the structural model and examining the relationships among the constructs.

The path analysis in the female subgroup yielded similar results to the complete sample. Nevertheless, there were differences in the strength of the effects compared to the complete sample. The path with the highest significance was observed between SFL and FPR, with β = 0.575 and a p-value of 0.000. Following Figure 4, financial planning for retirement exhibited the lowest R² value (0.481).

The relationships in the male subgroup also show results similar to those in the complete sample. Shown in Table 4, the path with the strongest significance was also found between SFL and FPR (β = 0.668, p = 0.000). According to Figure 5, the highest R² value was observed for financial planning for retirement (R² = 0.563) and SFL (R² = 0.408).

Once measurement invariance was established (see Table 3), this study continued by examining group comparisons using multigroup analysis. This study assessed permutation test values (

Table 6. Multigroup Analysis.

Relationships	Difference (Female-Male)	p Value (Female vs. Male)	Results
FTP→FPR	−0.027	0.826	Not Supported
FTP→SFL→FPR	0.106	0.368	Not Supported
RT→FPR	0.120	0.281	Not Supported
RT→SFL→FPR	−0.332	0.012	Supported
SFL→FPR	−0.094	0.398	Not Supported

) to examine significant differences between females and males in their effects on FPR. The results showed that only one relationship differed between females and males: risk tolerance and subjective financial literacy (p < 0.05).

5. Discussion

The effectiveness of financial retirement planning is highly dependent on savings and investment decisions. These decisions are influenced by psychological characteristics. The collectivist culture of Chinese individuals, which contrasts with the individualistic culture in Western countries, may limit the applicability of existing literature in explaining the impact of psychological factors on FPR in China’s context. This study examined the interactions among FTP, RT, and SFL and their effects on FPR, with a focus on gender-based differences. This is crucial, as having a clear understanding of the factors influencing retirement planning, particularly if the gender gap persists, can lead to better, gender-specific measures to address inadequate retirement savings.

The study found that the future time perspective had a significant positive relationship with FPR. The study found a significant positive impact of FTP on FPR in the overall, female, and male samples. It means individuals who have a higher level of FTP prefer savings and investing instead of spending or consuming immediately. It indicates that they are willing to engage in retirement planning and savings to maintain their living standard after retirement. The findings are consistent with Tomar et al. (2021) and Clark et al. (2019), who proposed that the relationship between FTP and saving tendency is positive. Thus, drives to shape the perspective of prioritizing future needs can be strengthened to encourage adult income earners to save for retirement. A retirement-planning advocate can exploit the wide coverage of digital platforms in China’s highly digitalized society by using short-video content on retirement savings to instill and elevate the sense of importance of accumulating adequate savings to maintain a high quality of life after retirement.

Although the study posited that risk tolerance positively affects financial planning for retirement, it fails to show that risk tolerance significantly affects individuals’ FPR across all samples (complete, female, and male). The sample was drawn from different wide geographical parts of China, and the implications of risk tolerance may be different across different places. The diversity of respondents may further affect the cause of risk tolerance as they are from various economic levels and subcultural backgrounds. The collectivist culture of the Chinese may also have altered risk appetite and its relationship to retirement planning. Perhaps the future research agenda could consider including these aspects.

Subjective financial literacy demonstrated the highest direct effect on financial planning for retirement across the complete sample (direct effect = 0.612), the female sample (direct effect = 0.575), and the male sample (direct effect = 0.668). This finding is consistent with previous research (Zhu 2021; Hershey et al. 2007). That is, factors that enhance their perceived financial literacy would foster retirement planning behavior among income earners. This finding raises a major concern when synthesizing with unsupported results for the RT-FPR relationship in H2. Integrating the findings of H2 and H3 indicates the presence of a financial literacy bias, where overconfidence or underconfidence may be a stronger behavioral driver than risk tolerance. Overreliance on subjective literacy in financial decision-making may be counterproductive. As such, the study raises a potential issue that both academics and policymakers need to address. Meanwhile, practitioners can use AI tools to address customers’ financial knowledge gaps, enhancing their financial literacy at minimal cost to financial institutions. The improved sense of financial literacy may assist individuals in making rational investment decisions and, subsequently, in better retirement planning.

Subjective financial literacy indirectly mediated the relationships between FTP and FPR and between RT and FPR. The mediating role of subjective financial literacy in the relationship between FTP and FPR was supported in all samples. This finding is in line with the previous literature. In the other mediating test, the mediating role of subjective financial literacy in the relationship between RT and FPR was absent in the female group. This may be due to Chinese women generally managing family asset allocation, which makes them more rational in choosing investment products regardless of their risk tolerance, ensuring stable returns after retirement. Thus, it is interesting to note that risk tolerance, whether directly or indirectly, does not have any impact on financial planning for retirement in females. Based on the findings of the mediating role, the government and financial institutions can increase individuals’ expectations for the future, enhance FTP, and improve their confidence in financial literacy through lectures and publicity. In addition, FTP and SFL can interact with each other and stimulate planning behavior. However, if financial institutions are eager to encourage individuals to plan for retirement by increasing risk tolerance, targeted measures should be provided to the male group, other than the female group. The impact will not be too significant, as men’s risk tolerance will only enhance their FPR by increasing financial literacy.

The impact of FTP, RT, and SFL on the outcome variables was examined through multigroup analysis to determine if there were any differences between the two gender groups. The results showed that only RT’s impact on SFL was higher in male groups in comparison to female groups. The Ministry of Human Resources and Social Security of the People’s Republic of China (2024) issued a personal pension system starting from December 2024. One of the policies is to provide different investment portfolios for residents with different risk tolerance levels toward retirement. Based on the findings, financial institutions can provide targeted consultations or lectures to male groups to enhance their risk tolerance. This proactive approach will foster their early awareness regarding retirement planning and savings, empowering them to make informed financial decisions in their future endeavors.

The multigroup analysis reveals that aside from the impact of RT on SFL, there is no gender gap. This contradicts many previous findings (Bucher-Koenen et al. 2017; Cupák et al. 2021; Nguyen et al. 2022). Perhaps China’s one-child policy (only one child is allowed per family), effective from 1979 to 2016, created an avenue for women to have equal access to education and learning opportunities as men. Women subsequently gained access and participated in economic and societal activities. Their sense of equality may also influence women’s psychological factors and behavioral decisions. Therefore, this may explain why gender does not play a role in retirement financial planning.

6. Conclusions

The process of making decisions about retirement financial planning is complex. This study has successfully extended the current line of research to FPR. Theoretically, this research helps fill the existing gap and provides a new perspective on FPR from a psychological perspective, examining the influence of different gender groups. The findings of a direct effect on FPR and a significant mediating relationship support the interaction among FTP, SFL, and RT and their effects on FPR. Consequently, the study’s findings will enrich the current literature on FPR and provide empirical evidence to support the CWO model. Due to greater female participation in society compared to the past, women’s retirement planning behavior is less vulnerable than men’s. It is consistent with the finding of no gender gap. To ensure a comprehensive understanding of individuals’ retirement savings needs, policymakers and financial institutions must make additional efforts to better serve them, as suggested by the findings. Measures such as retirement counseling, programs, or financial advice should be tailored to address specific psychological factors such as FTP and SFL. Digital technology can also assist governments and institutions in enhancing individuals’ psychological factors.

The potential limitations of the study are its age-group-specificity and the lack of representation of rural-income earners. Thus, caution must be exercised when using the results to explain the rural context or the entire population of China. In addition, the study offers empirical evidence on the influence of psychological variables on FPR behavior within China. To obtain insights across diverse global cultures, future research could apply the CWO models using cross-national data from developed and developing nations to yield richer comparative evidence. Future research may also go beyond cross-sectional research and apply a longitudinal approach to examine behavioral change over time. Longitudinal research would unravel the complexity of retirement financial planning over time, an area that remains much to be learned despite extensive research on the psychological determinants of FPR.