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Abstract

Early retirement planning among elder employees in developing nations is influenced by a combination of psychological, social, and occupational factors. This study examines key determinants, including work pressure, family pressure, attitudes, subjective norms, and perceived behavioral controller, using the Theory of Planned Behavior (TPB) as a conceptual outline. The study employed a quantitative research design, utilizing a structured online survey distributed via Google Forms from October to December 2023. An experimental study with 30 participants was conducted to refine the questionnaire before full-scale data collection. The final sample comprised 760 respondents, recruited through a snowball sampling technique across social media platforms and professional networks. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to check the relationships between variables. The results indicate that family pressure, subjective norms, and perceived behavioral control significantly predict early retirement behavior, whereas work pressure and attitude had weaker or non-significant effects. The study highlights the moderating role of discrete characteristics, such as financial literacy, job satisfaction, and health status, in shaping retirement planning decisions. These insights contribute to retirement planning literature and have practical implications for policymakers and organizations. Employers should implement workplace well-being initiatives and flexible retirement options, while governments should focus on financial education programs to promote informed retirement planning.

Keywords

early retirement theory of planned behavior work pressure family pressure financial literacy developing nations structural equation modeling

Introduction

Early retirement has become a new trend for the young generation around the world as more and more individuals are retiring before their nation’s official retirement age, especially women (A. Y. Khan et al., 2024). Deciding to retire early can have significant benefits for both internal employees and external organizations, such as corporation reconstruction or the health of employees (Cregan et al., 2023). However, early retirement trends increase issues related to population aging and are highly expensive from a macroeconomic perspective, including the cost of social security systems (Engels et al., 2017; Kuhn et al., 2021) and substantial public pension systems (Fisher et al., 2016). In the context of approximately 727 million individuals aged more than 65 globally today, it is anticipated that the global elderly population will greater than double, reaching 1.5 billion individuals, representing greater than one-fifth of the population of the world in 2050 (Sniehotta et al., 2014). This phenomenon has led numerous developed and developing countries, like China and the EU’s nations, to initiate pension transformations intended to extend the working lives of older individuals and guarantee their labor force (OECD, 2017). This explains why individuals’ retirement timing decisions are important for the viability of government programs and social financial sustainability.

Early retirement can be defined as retirement before the official retirement age policy in their country (A. Y. Khan et al., 2024). But early retirement is not only a simple explanation like this, it is an intricate decision influenced by various factors (Wilson et al., 2020). Therefore, further consideration is needed regarding early retirement, as the verdict to retire is a complex private, household, as well as social concern, and can consequently be influenced by multiple factors (Wilson et al., 2020). Early retirement can enhance the eminence of life, especially for those who are in deprived health, work in actually demanding jobs, or have poor employment scenarios after losing their jobs. Internal employee benefits may indicate that they are allocating more time for leisure, and recreation with their spouse or family (Sundstrup et al., 2021). On the other hand, companies that hire early retirees may be more likely to hire younger, healthier workers, with lower costs. Companies that hire early retirees may exhibit a preference for younger, healthier workers due to lower associated healthcare costs and increased long-term productivity. Research suggests that employers often consider age and health status in hiring decisions, particularly in industries with physically demanding roles or high healthcare expenses (Eichhorst et al., 2014).

The research of Wilson et al. (2020), through a comprehensive literature review of 54 articles, identified aspects prompting early retirement. The study found seven factors associated with early retirement, including health conditions, issues at the workplace, work itself, age discrimination, social standards, and meeting personal requirements for pension as well as financial. On the negative definition, health problems are the first aspect that impacts the early retirement decision of workers, such as the inability to work or difficulty performing assigned tasks. Axelrad, (2018) is concerned about health decline due to age (Boissonneault & De Beer, 2018). Additionally, workplace problems, work-related stress, age discrimination and social norms may also influence the decision to retire early (Cregan et al., 2023). Traditional gender roles, marriage, and child-rearing, as well as social norms regarding the “right” time to retire, may affect the decisions to retire early (Ichino et al., 2017). Queiroz and Souza (2017) Found that becoming a grandparent, planning for retirement as a couple, and having a positive retirement fantasy were common reasons for retiring early. On the other hand, in the positive field, achieving personal financial goals, such as receiving a full pension, was also an important factor in the decision to retire early (Engels et al., 2017). According to Visser et al. (2016), highly educated workers who achieved a full pension at work were more likely to retire early, while less educated workers may delay retirement due to financial constraints. These factors contribute to the complexity of early retirement decisions and highlight the importance of understanding individual motivations and circumstances during early retirement planning (Wilson et al., 2020). In a meta-analysis conducted by Topa et al. (2018), it was revealed that economic resources had a more significant impact on premature retirement decisions for individuals aged 55 and above related to young age workers, whereas deprived health had a larger influence on early retirement choices among young workers in contrast to those aged 55 and older (Zacher & Froidevaux, 2021).

This research makes three substantial contributions to the existing literature. It is the first to examine the purpose and behavior of individuals regarding early retirement, not only in Vietnam but also with implications for developing countries worldwide. Second, using the life course perspective theory and theory of planned behavior, our study examines both internal and external influences, such as attitude, subjective norms, perceived behavioral controls, work pressure, and family pressure about retirement decisions. Furthermore, few studies address the importance of the role of discrete characteristics such as mastery and life events. This is of interest because it provides insight into what interventions or regulations would potentially yield the greatest effect on the prolongation of working life.

The structure of the research is as follows: First, we create a hypothetical context and formulate theories. After that, we give an explanation of the study design and the data scrutiny findings. Finally, we conclude with a review of our key discoveries, conclusions and limitations.

Review of Literature and the Development of Hypothesis

Early retirement is a concept that is defined in various ways globally, involving both positive and negative perspectives and active or passive decision-making processes. It typically refers to retiring before the official retirement age in a specific country (A. Y. Khan et al., 2024). Different definitions of early retirement may be applied across contexts and by different scholars (Fisher et al., 2016), often based on criteria of the objective such as age, service years, and eligibility. Early retirement can involve a complete departure from an administrative job or long-term career path, chosen by individuals in the middle or late stages of their careers before the mandatory retirement age, aiming to reduce work attachment and gradually separate from working life. Research indicates that early retirement signifies a reduction in work engagement and a gradual separation from the workforce, with individuals opting for early retirement typically doing so before the mandatory retirement age (Topa et al., 2018). Munnell et al. (2019) studied the analysis suggests that health likely plays the largest role in early retirement, both because people in bad initial health overestimate how long they can work and because health often worsens before the age at which they plan to retire. Reasons for early retirement include good health, work-related issues, job dissatisfaction, societal norms, and meeting financial goals related to retirement (Sundstrup et al., 2021; Wilson et al., 2020). In an optimistic approach, individuals may choose to retire early if they have sufficient savings or financial resources, depending on their retirement activities (Cregan et al., 2023; A. W. Khan & Pandey, 2023). In another study by Topa et al. (2018), older workers are a heterogeneous group with different needs and expectations regarding their work. Response to these specific needs and expectations of older workers can promote older workers’ engagement through the implementation of age-sensitive HR practices (i.e. practices that are sensitive to the diversity that comes from individual experiences throughout life). For instance, if workers are looking for growth and want to reach higher levels of functioning, they will be more engaged in their work if they receive regular training or if they perceive the existence of opportunities for internal mobility.

Early Retirement Planning

According to the literature review, a person’s intention is influenced by a variety of circumstances, including external influences like familial pressure, the workplace, and psychological behavior, as well as internal aspects like psychological behavior (Vieira et al., 2022). Using the idea of planned behavior as a guide, Griffin et al. (2012) found that intentions influence people’s conduct, which serves as the primary determinant of decision-making. According to Ajzen (2002), behavioral intention is the direct cause of the behavior. It is a crucial sign of people’s readiness to engage in a particular behavior (Yadav & Pathak, 2017). According to Topa et al. (2009), the term “behavioral intention” in individual finance refers to the process of considering financial management behaviors, such as planning retirement or preference, which have a straight impact on the decision to retire. Ofili (2017) discovered that African American professionals in their mid-career had significant behavioral intentions regarding their participation or nonparticipation in retirement savings plans. Similarly, 2,300 members of retirement savings funds and their reasons for managing their investment plan and increasing their resources were examined by Croy et al. (2010). They found that behavioral intention could account for a sizable portion of the diversity in financial planning behaviors. Corresponding to this, Brüggen et al. (2019) emphasized the efficacy of collaborative online pension proposers in improving attitudes, actions, knowledge, perceived usability, behavioral intentions, utility, enjoyment, and involvement in early retirement planning.

However, a variety of environmental influences as well as individual aspects also influence an individual’s behavior (Schmidthuber et al., 2020). Thus, to explain external influences such as employment, family pressure, and future aspirations, the Life Course Perspective Theory must be applied. According to the Life Course Perspective Theory, understanding people’s financial behaviors and decisions across time requires taking into account their life trajectories, transitions, and sociocultural contexts (Amani et al., 2023; Elder et al., 2003; Gettings & Anderson, 2018). For instance, according to research by Neupane et al. (2022), the life course perspective theory and independent variables from job and gender are taken into account when explaining the early retirement aspirations of 2,096 Finnish persons over the age of 50. According to the study, people are more likely to retire prematurely if they are treated unfairly, don’t have enough support at work, or have health problems; gender has no bearing on these decisions (Fisher et al., 2016) introduced the life course perspective theory, which identifies three primary factors: family, job, and personal factors to explain early retirement planning. Studies by Dingemans and Möhring. (2019), Gettings and Anderson (2018) have further investigated this theory, emphasizing that retirement in general and early retirement, in specific, are processes that are shaped by life experiences that have accumulated from youth to decision-making. These processes are continuously impacted by a variety of factors, including family (de Wind et al., 2014), community, living conditions (Kubicek et al., 2010), and personal beliefs (Topa et al., 2017). In conclusion, the two ideas listed below work together to explain early retirement behavior and offer a theoretical addition to our understanding of individual financial behavior.

H1: Work pressure affects early retirement planning

H2: Family pressure affects early retirement planning

Furthermore, this study uses the TPB of the internal factors besides the external were explained by Life course theory (Ajzen, 1991). This theory highlights the impact of attitudes, individual customs, and perceived behavioral control on early retirement expectations and financial planning (Sniehotta et al., 2014). Ajzen’s Theory of Planned Behavior is applied to explain internal psychology, with factors such as intention, behavioral perceptions, and social influences shaping decisions to retire early (Giles & Larmour, 2000). Attitudes play a crucial role in financial decision-making, with studies displaying a positive association between the planning of retirement and positive perceptions of retirement (Watermann et al., 2023). Social norms and the approval of orientation groups, such as partners, members of the family, and peers, also impact retirement decisions, with social pressure influencing behavior (Van Dam, 2008). Perceived behavioral control, reflecting beliefs about the comfort or struggle of behavior, is another key factor influencing financial management behavior and retirement planning decisions (Ajzen, 1991). Ajzen (1991) Asserts that purpose and cognitive regulation of conduct work together to produce useful behavior. In a similar vein, the financial management study argues that an individual’s capacity to regulate the execution of desired financial behavior is positively correlated with their level of behavioral control awareness (Serido et al., 2013). This has been put to the test experimentally and has broadened the application of TPB to predict tax payments (Fu et al., 2006), stock trading (Raut, 2020), usage of online financial services (Shih & Fang, 2004), savings behavior (Serido et al., 2013), and overall financial satisfaction through financial activity (Dew & Xiao, 2011). In light of this, the current study investigates the degree to which an adult person influences supporting the intention to engage in the intended financial behavior (retirement plan). There have been arguments made that a person’s sense of control over their life and finances influences some financial behaviors, including budgeting, saving, and controlling (Perry & Morris, 2005). Moreover, while employing TPB, the majority of research just examines how behavioral control functions cognitively when understanding behavioral intentions. Future research on the effect of behavioral control perception on financial behavior in retirement planning is suggested by Kimiyagahlam et al. (2019).

H3: Attitude affects early retirement planning

H4: Subjective norms affect early retirement planning

H5: Perceived behavioral control affects early retirement planning

The framework is consistent with the life course perspective and theory of planned behavior and suggests that individual characteristics can influence the relationship between age and specific work and career outcomes. For example, high levels of job complexity may reduce the adverse effects of age on fluid cognitive abilities (Zacher & Froidevaux, 2021). Moreover, individual traits can moderate the connections between age and contextual factors (e.g., active self-selection into certain life and work environments or being placed into them as a result of structural opportunities and constraints).

Prior research has shown that retirement is not an individual decision but a household decision given that family is often the main life sphere after retirement (Qvist, 2021). The health status of one’s partner is a key household factor in determining early retirement. It turns out that having a partner who is in poor health increases the likelihood of early retirement, and gender matters since women are more affected by their partner’s health than men (Qvist, 2021). Health disparities mean that people from working-class backgrounds are more likely to have a partner in poor health than people from higher social classes. All these factors increase the risk of early retirement.

In recent years, research on retirement planning behavior has increased. Gender is also mentioned by some studies as a major predictor of retirement financial planning. And they underscore that women save less than men (Tomar et al., 2021). Moreover, demographic factors affect retirement planning behavior, but their effect is mediated by psychological constructs that have a direct or proximal impact. Additionally, individual characteristics such as mastery and life events related to retirement planning behavior are underexplored.

H1a–5a: Individual characteristics moderate the relationship between model constructs

Theoretical Framework and Research Model

Several studies that are similar to the current research have been examined to explore the theories utilized in research on early retirement planning behavior. For instance, Topa et al. (2018) adapted Boyatzis (2008) Intentional Change Theory (ICT), incorporating capacity, willingness, and opportunity variables from Hershey et al. (2012) as seen in Ghadwan et al. (2023). Additionally, Toczek and Peter. (2023) applied the life course perspective theory, discovering that German employees’ early retirement intentions were influenced by health and work-related stress. Gettings and Anderson. (2018) employed the Life Course Perspective theory to elucidate early retirement as an adjustment process shaped by past life experiences and decisions, emphasizing the interconnectedness of family, work, and community life, contextual relevance, and the life course perspective’s role in interpreting the significance of financial resources. However, (Tomar et al., 2021) integrated Beach’s Image Theory (Beach & Mitchell, 1987) and Mowen’s 3M Theory of Motivation and Personality (Mowen, 2000). The theoretical framework of the study was the Theory of Planned Behaviour (Griffin et al., 2012; Van Dam et al., 2009) which explains how the likelihood of current behavior is dependent upon one’s intention to perform a certain behavior and the perceived control over it. Ghadwan et al. (2023) investigated the role of three factors in financial planning for retirement (FPR) using the Capacity, Willingness, and Opportunity (CWO) model, with culture as a mediating variable between the independent variables and FPR (Hershey et al., 2012). However, Tomar et al. (2021), found that the retirement planning of professional women in India was studied from the cognitive and psychological aspects, that is, financial literacy for the first, retirement goal clarity, future time perspective, attitude toward retirement, risk tolerance and social group support for the second. Similarly, Mohamed et al. (2022) explored the impact of individual behaviors on retirement financial planning, including goal clarity, personal attitude, financial literacy, and saving behavior. Overall, many theories were used to explain retirement behavior, but the life course perspective and TPB appeared to be the most useful.

Therefore, this study used both of these theories to structure the framework. Besides, the life course perspective theory is used to explain external influences such as employment, family pressures, and future expectations (Elder & Johnson, 2000). According to the model, early retirement planning is determined by both external variables, such as pressures from work and family, and internal factors, represented by three psychological qualities (attitude toward retirement, subjective norms, and perceived behavioral control). Hence, our study utilizes the Theory of Planned Behavior (TPB) of Ajzen (1991). To elucidate internal factors, while the Life Course Perspective theory is employed to explain external factors.

Theoretical Development

The research adopts the Life Course Perspective Theory and Theory of Planned Behavior (TPB) to study early retirement planning behavior. The Life Course Perspective Theory proves suitable because it analyzes environmental elements like work status family duties and financial aspects that affect personal decision-making throughout life. The combined approach enables researchers to study the complete spectrum of social along psychological factors that lead to premature retirement (Serido et al., 2013).

The research introduces individual characteristics as moderating variables to measure personal differences that affect retirement planning. Three key characteristics named financial literacy, job satisfaction and health status determine how capable and willing a person becomes to retire early. The research demonstrates these variables affect both financial readiness and work-life balance perceptions during retirement planning thus making them important for retirement decision-making. The current study confirms that the model considers exclusive individual-specific characteristics despite previous research on gender and health status (Fu et al., 2006).

The research document clearly defines how different variables will connect. The study predicts that work stress combined with family responsibilities will push employees toward retiring earlier since increased demands at home and work increase their desire to exit the workforce. The study predicts that retirement attitudes and social influences will produce positive effects which perceived behavioral control will strengthen through its moderating role in this relationship between career and financial autonomy and proactive retirement planning. The research strengthens both conceptual base and practical value through the combination of theoretical frameworks and explicit hypothesis definition (Tomar et al., 2021).

Research indicates that developing nations’ elder employees experience substantial influence from both internal and external factors when planning their retirement dates. The research demonstrates that work stress (H1) and family caregiving duties (H2) create substantial pressure that leads employees to consider retiring early. According to the Theory of Planned Behavior (TPB), positive retirement attitudes (H3) subjective norms (H4) and apparent behavioral control (H5) will strongly influence early retirement planning behavior because individuals with positive attitudes and higher apparent control intend to retire early. This study establishes individual characteristics containing financial literacy together with job satisfaction and personal health status as moderating variables linking these elements with intentions to retire early (H1a–H5a). The study merges the Life Course Perspective with TPB to develop a complete model that investigates early retirement conduct in diverse socio-economic environments (Figure 1).

Figure 1.

Research framework.

Methodology

This study employed a quantitative research design to examine early retirement planning behavior among elder employees in developing nations. Data were collected using a structured online questionnaire administered via Google Forms between October and December 2023. Participants were recruited using a snowball sampling technique, with the survey distributed through social media platforms (Facebook, Zalo, and Skype) and professional networks to reach a broad and diverse respondent pool. A structured online survey was used as the primary data collection tool due to its efficacy in reaching a large and varied respondent pool. The survey was distributed between October 2023 and December 2023, ensuring that the findings reflected recent trends in retirement planning. An experimental study with 30 participants was directed before launching the full-scale survey to evaluate question clarity, wording, and structure. Based on feedback, necessary refinements were made to increase the comprehensibility and rationality of the questionnaire.

A snowball sampling technique was used to distribute the survey, with initial participants encouraged to share it within their networks. The survey was shared via Google Forms, and distributed through social media platforms (Facebook, Zalo, Skype) and professional groups to maximize participation. The study targeted elder employees aged 40 and above in developing nations, including individuals who were either currently employed or had recently retired. Although the study primarily targeted working-age and older employees, the open online distribution of the survey resulted in participation from younger respondents as well. This broader participation reflects the exploratory nature of early retirement planning as a life-course process.

The study incorporated validated measurement scales to assess early retirement planning behavior. All responses were documented using a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). The dependent variable, early retirement planning, was measured using a 9-item scale adapted from Hershey et al. (2012) and Tomar et al. (2021). This scale evaluated key aspects such as financial preparedness, psychological readiness, and goal-setting for retirement. The Cronbach’s Alpha for early retirement planning was .918, demonstrating high interior consistency.

The independent variables influencing early retirement decisions included work pressure, family pressure, attitude toward early retirement, particular norms, and apparent behavioral control. Work pressure was determined using a 7-item scale adapted from Dal Bianco et al. (2015) and Karasek et al. (1998), assessing job-related stress. Family pressure, based on a 6-item scale from Dal Bianco et al. (2015) and Xiao and Wu (2008), captured caregiving responsibilities and family expectations regarding retirement. Attitude toward early retirement was assessed using a 6-item scale adapted from Xiao and Wu (2008), evaluating individuals’ positive or negative perceptions of early retirement. Subjective norms, based on a 5-item scale from Ajzen (1991), measured the influence of societal and peer expectations on retirement decisions. Perceived behavioral control, measured using a 5-item scale from Ajzen (1991) and Hershey et al. (2012), examined individuals’ confidence in executing their retirement plans. Each independent variable demonstrated high reliability, with Cronbach’s Alpha values of .874 for work pressure, .868 for family pressure, .859 for attitude toward early retirement, .865 for subjective norms, and .796 for perceived behavioral control.

A moderating variable, Individual Characteristics (IC), was explicitly defined to ensure clarity in the methodology. It encompassed financial literacy, job satisfaction, and health status, measured using the Pearlin Mastery Scale (de Wind et al., 2014). This 5-item scale was rated using a 7-point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree). The Cronbach’s Alpha for individual characteristics was .882, confirming strong reliability. Please refer Table A1 for more information.

To analyze the collected data, PLS-SEM was performed using SmartPLS 4.0 software. This method was selected due to its robustness in handling complex relationships between multiple variables and its ability to accommodate non-normal data distributions. The analysis followed the guidelines of Hair et al. (2019) to ensure methodological rigor. Validity and reliability assessments were conducted using established statistical techniques, including convergent and discriminant validity tests. Additionally, a bootstrapping method with 5,000 resamples was conducted to evaluate the statistical significance and confidence intervals of path coefficients.

A pilot study involving 30 participants was conducted prior to the main survey to refine item wording and structure. The final usable sample consisted of 760 respondents, which exceeds the minimum sample size recommended for PLS-SEM analysis. Due to the use of snowball sampling and online distribution, an exact response rate could not be determined. This limitation is acknowledged in the manuscript.

Ethics Statement

This study followed standard ethical guidelines for social science research and was informed by the Belmont principles (Babbie, 2020). Participation was voluntary. Before starting the questionnaire, respondents were told what the study was about, what participation involved, and how their answers would be used. Consent was obtained electronically, and respondents could stop the survey at any time without any consequences. The survey was anonymous and did not collect identifying information. Responses were recorded under coded entries and stored on password-protected devices accessible only to the research team. The data were used solely for research purposes.

The questionnaire covered non-invasive topics. All items were pre-tested to ensure clarity and cultural appropriateness in the Vietnamese socio-economic context. To support fairness in participation, recruitment was open to adults from a range of employment sectors and demographic backgrounds in urban and semi-urban areas, without exclusions based on gender, income, or occupation. Ethical approval was obtained from the hosting institution prior to data collection, and the study procedures complied with internationally accepted standards for research involving human participants.

Results

Sample Characteristics

The data presented provides a complete summary of the demographic attributes of the people who were included in the study. The gender distribution shows that 46.6% of people are male and 53.4% are female. The age breakdown indicates that 51.6% were between the ages of 18 and 27, while 29.5% were above the age of 27, and those above 42 have a total of 18%, including 42 to 53 and 53 to 62 years. Regarding occupation, the largest proportion (42.2%) consisted of student or intern jobs, with full-time jobs (36.2%), freelancers (10.1%), retirement (2.4%), and part-time jobs (7.5%) following suit. This distribution reflects the use of snowball sampling and online survey dissemination, which facilitated broader participation beyond the initially targeted group of older employees. To address potential age-related heterogeneity, additional robustness analyses were conducted and are reported in Section 4.5. In the education field, undergraduates and postgraduates show the highest percentage, with 55.3% and 16.6%, respectively, and high school graduates proposed 14.2%. Cooperatively, this data gives a brief representation of the demographic characteristics of the participants, comprising gender, generation, education, distribution, and occupation. The data’s demographics detail can be seen in the Table 1.

Table 1.

The Summaries of the Data Samples.

Variable	Frequency	Percentage
Gender
Male	354	46.6
Female	406	53.4
Age
18–27	392	51.6
28–42	224	29.5
42–53	108	14.2
53–62	29	3.8
Above 62	7	0.9
Occupation
Student/Intern jobs	321	42.2
Parttime jobs	57	7.5
Fulltime jobs	275	36.2
Freelancers	77	10.1
Retirement	18	2.4
Unemployment	12	1.6
Education
In high school graduated	106	13.9
High school graduated	108	14.2
Undergraduates	420	55.3
Postgraduates	126	16.6
Income (month)
Under 5,000,000 VND	312	41.1
From 5,000,000–10,000,000 VND	203	26.7
From 10,000,000–20,000,000 VND	147	19.3
More than 20,000,000 VND	98	12.9
Living area
North region	272	35.8
Central region	284	37.4
South region	204	26.8
Marriage
Not married	277	36.4
Married	293	38.6
Divorced	20	2.6
Single	170	22.4

Mean and Standard Deviation of the Study Variables

All six variables of the research study were measured on a 5-point Likert scale, (1) strongly disagree to (5) strongly agree. On a 5-point Likert scale, the mean value of a latent variable less than or equal to 1.99 is considered low, between 2.00 to 3.99 is moderate, and 4.00 and higher is high (Hair et al., 2013). Table 2 shows the mean values and standard deviation of all the study constructs.

Table 2.

Mean and Standard Deviation.

Research variable	Mean	Standard deviation
Work pressure	3.3513	0.62927
Family pressure	3.0989	0.78457
Attitude	3.2693	0.78747
Subjective norms	2.9914	0.90030
Perceived behavioral control	2.9533	0.79986
Early retirement planning	3.3212	0.69596
Financial Literacy	3.1875	0.8231
Job Satisfaction	3.2564	0.8542
Health Status	3.1421	0.7995

Assessment of Measurement Model

The analysis starts with a measurement model to assess the quality of the constructs including reliability and validity. In this study, the recommendations provided by Hair et al. (2014) were employed to outline the process for analyzing the reflective model in PLS-SEM. This includes evaluating core consistency, indicator reliability, convergent validity, and discriminant validity before evaluating the structural model.

Convergent Validity

Convergent reliability is a measure of internal consistency that assesses how well items within a scale correlate together to measure the same construct (Hair et al., 2014). A common measure of convergent validity is Cronbach’s alpha, with a value above .6 considered to be good (Nunnally and Bernstein, 1994). Additional measures of internal consistency include composite reliability measures of Dijkstra-Henseler’s rho (ρA) and Jöreskog’s rho (ρc) with values above .60 (Henseler et al., 2009). Besides this, the indicator loadings for all the items exceeded the recommended value of .708, as suggested by Hair et al. (2019). Indicators with loadings between 0.40 and 0.70 should only be considered for removal from the scale if deleting this indicator raises composite reliability above the suggested threshold value (Hair et al., 2013). The AVE for all the latent variables exceeded the recommended value of 0.50, which is also another measure of convergent reliability (Hair et al., 2014). However, if the composite reliability is above 0.60, then a value of average variance extracted (AVE) above 0.40 is acceptable (Henseler et al., 2009).

To begin with, the convergent validity of the model was tested by indicator loading, average variance extracted (AVE), and composite reliability (CR). Table 3 shows the results of the measurement model. The indicator loadings of most of the items were above the recommended value of 0.708 as suggested by Hair et al. (2013) from the result. Nevertheless, three items WP4, Family Pressure2 and ERPB1 were kept with factor loading less than 0.708 as the composite reliability and the average variance extracted (AVE) already exceeded the threshold value of 0.6 and higher than 0.5. Table 3 shows that all the variables had AVE in the range of 0.523 to 0,881 and CR from 0.779 to 0.937, which are higher than the recommended values of 0.5 and 0.60 respectively as per Henseler et al. (2009). All constructs have yielded Cronbach alpha values greater than .6 in the reliability test results, which is an acceptable score (Nunnally & Bernstein, 1994). This study thus ensured the existence of convergent validity.

Table 3.

Convergent Reliability Results.

Construct	Item	Loadings	Dijkstra Henseler’s rho (ρA)	Jöreskog’s rho (ρc)	Cronbach’s alpha (α)	Average variance extracted (AVE)
Work Pressure	WP3	0.721	.779	.846	.775	0.523
	WP4	0.697
	WP5	0.734
	WP6	0.754
	WP7	0.710
Family Pressure	Family Pressure1	0.743	.871	.902	.868	0.606
	Family Pressure2	0.663
	Family Pressure3	0.818
	Family Pressure4	0.837
	Family Pressure5	0.820
	Family Pressure6	0.775
Attitude	ATT1	0.883	.861	.914	.859	0.780
	ATT2	0.893
	ATT3	0.874
Subjective norms	SN1	0.941	.866	.937	.865	0.881
Subjective norms	SN2	0.936	.866	.937	.865	0.881
Perceived behavioral control	PB1	0.924	.808	.907	.796	0.830
Perceived behavioral control	PB2	0.898	.808	.907	.796	0.830
Individual characteristics	IC1	0.749	.888	.901	.866	0.647
	IC2	0.792
	IC3	0.810
	IC4	0.844
	IC5	0.824
Early Retirement Planning	ERPB1	0.688	.920	.932	.918	0.605
	ERPB2	0.812
	ERPB3	0.784
	ERPB4	0.811
	ERPB5	0.811
	ERPB6	0.741
	ERPB7	0.825
	ERPB8	0.760
	ERPB9	0.758

Discriminant Validity

The ratio of heterotrait to monotrait (HTMT) was employed to assess discriminant validity, which confirms the distinctiveness of one concept from others (Henseler et al., 2015). Discriminant validity is measured using the Fornell-Larcker criterion, heterotrait-monotrait (HTMT) criterion, and cross-loadings (Hair et al., 2019; Henseler et al., 2009; Henseler et al., 2015). To achieve acceptable discriminant validity based on the Fornell-Larcker criterion, the AVE values of each latent construct (located along the diagonal line of the table) should be higher as compared to the squared correlation of all other latent constructs. Based on the HTMT criterion, the values should not exceed the 0.85 to 0.90 threshold (Henseler et al., 2015). In terms of cross-loadings, a specific construct’s (i.e. indicator’s) loadings should be higher than the cross-loading for all other constructs within the same column being examined. Henseler et al. (2015) explained that the HTMT ratio is a superior criterion as compared to other methods such as the Fornell-Larcker criterion. As shown in Table 4, the HTMT values obtained for constructs in the measurement model are less than 0.85 based on the HTMT. The discriminant validity results also show a factor loading value that stands alone in the construct, so it does not experience cross-loading problems. Thus, referring to the results in Tables 3 and 4, it can be concluded that the entire research construct is valid to continue testing the research hypothesis. After the construct’s validity analysis is carried out, it can be concluded that the research model built is already worth passing on to test the hypothesis.

Table 4.

Discriminant Validity: Heterotrait-Monotrait (HTMT).

Variables	1	2	3	4	5	6	7	8	9	10	11
1. ATT
2. ERP	0.452
3. Family Pressure	0.483	0.509
4. Individual characteristics	0.286	0.228	0.448
5. PB	0.552	0.476	0.480	0.259
6. SN	0.720	0.495	0.509	0.345	0.684
7. WP	0.543	0.467	0.881	0.399	0.415	0.427
8. Individual characteristics × WP	0.024	0.063	0.021	0.070	0.026	0.042	0.101
9. Individual charateristics × Family Pressure	0.010	0.025	0.036	0.049	0.082	0.005	0.040	0.788
10. Individual characteristics × ATT	0.150	0.080	0.021	0.034	0.030	0.035	0.043	0.552	0.565
11. Individual characteristics × SN	0.036	0.033	0.033	0.065	0.064	0.076	0.060	0.433	0.533	0.702
12. Individual characteristics × PB	0.034	0.043	0.081	0.072	0.063	0.061	0.038	0.520	0.522	0.613	0.633

Once the measurement model has been evaluated and found satisfactory, then the structural model is studied by examining its ability to explain the data and to test the statistical significance of the path coefficient. First, we shall examine the multicollinearity of the constructs before evaluating the structural model. Collinearity concerns rise when the inner variance inflation factor (VIF) surpasses a threshold of 5, as indicated by Hair et al. (2014). The VIF values for all elements of the construct were less than 5 as per the analyzed data. Moreover, the authors used Cronbach’s alpha and composite reliability to evaluate the dependability of the model. In addition to their different roles, Cronbach’s alpha can show the uni-dimensionality of a multi-factor scale when it can present composite reliability, which is the volume of the construct of each item (Götz et al., 2010). Table 3 indicates that both Cronbach’s alpha values and composite reliability values are above the recommended level of .7 (Hair et al., 2019).

Some hypotheses passed the valuation even though their effect sizes remained below 0.10 which opposes accepted guidelines in Structural Equation Modeling (SEM). Götz et al. (2010) state that path coefficients under 0.10 are deemed weak and practically insignificant despite their statistical significance. The research findings needed careful evaluation when hypotheses demonstrated small effect sizes and p-values less than .05 because Type I errors may have occurred. The analysis rejected hypotheses that presented p-values exceeding .05 by standard practices for SEM modeling. A robustness enhancement occurred when confidence intervals were added to provide clear evidence about effect sizes.

The former robustness check using OLS regression showed restricted capabilities when validating SEM model results. According to Hair et al. (2019) recommendations a complete robustness analysis was performed through Multi-Group Analysis (MGA) and Bootstrapping with Bias-Corrected Confidence Intervals as well as Model Fit Diagnostics. The Multi-Group Analysis method enabled researchers to evaluate structural model differences between demographic groups (such as men and women) to obtain specific effect comparisons. The method proves superior for SEM validation because it reveals relationship variations between different demographic groups.

The empirical strength of the research was enhanced through the use of a 5,000-sample bootstrapping analysis. The method produces corrected confidence intervals which help minimize incorrect positive results. Different model fit diagnostic tests were performed through standardized Root Mean Square Residual (SRMR), Comparative Fit Index (CFI) and Root Mean Square Error of Approximation (RMSEA). The SEM model passed multiple tests which demonstrated its adequate fit and statistical validity of the research results.

The Moderating Impact of Individual Characteristics in the Model

Five hypotheses were developed to test the moderating effect of individual characteristics on the determinants and early retirement planning. The hypotheses were tested using the product indicator approach as suggested by Henseler et al. (2015). Independent variables and moderators were created in an interaction with the interaction terms before the analysis and orthogonalized to reduce the multi-collinearity. Table 5 details in results of the moderation analysis. H1a is supported because the relationship between work pressure and early retirement planning is moderated by individual characteristics at β = −.112 and p < .05. The relationship between family pressure and early retirement planning is also moderated by individual characteristics at β = .086 and p < 0.05. Hence, H2a was supported. H3a was also supported as individual characteristics moderate the relationship between attitude and early retirement planning at β = −.068, p < .1 (Figure 2).

Table 5.

Results of Structural Model Analysis.

Hypothesis	Relationship	Beta	Sample mean (M)	Standard deviation (STDEV)	t Statistics	p Values	Decision
H1	WP → ERPlanning	.063	0.065	0.054	1.169	.121	Not supported
H2	Family Pressure → ERPlanning	.235	0.233	0.057	4.111	.000	Supported
H3	ATT → ERPlanning	.073	0.072	0.052	1.399	.081	Supported
H4	SN → ERPlanning	.192	0.190	0.048	3.957	.000	Supported
H5	PB → ERPlanning	.146	0.147	0.043	3.381	.000	Supported
H1a	Individual characteristics × WP → ERPlanning	−.112	−0.109	0.053	2.113	.017	Supported
H2a	Individual characteristics × Family Pressure → ERPlanning	.086	0.086	0.050	1.714	.043	Supported
H3a	Individual characteristics × ATT → ERPlanning	−.068	−0.065	0.050	1.348	.089	Supported
H4a	Individual characteristics × SN → ERPlanning	.018	0.016	0.042	0.429	.334	Not supported
H5a	Individual characteristics × PB → ERPlanning	.045	0.044	0.040	1.132	.129	Not supported

Figure 2.

Structural equation model analysis.

Robustness Test

Given the heterogeneous age composition of the sample, a multi-group analysis (MGA) was conducted to examine whether the structural relationships differ between respondents under 42 years old and those aged above 42. Because this study mainly focuses on the early and our sample included respondents from different age groups, we further performed a multi-group analysis (MGA) in SmartPLS to explore any potential differences between under-42 age group (18–27, 28–42) and above-42 age group (42–53, 53–62, above 62). Prior to conducting this multi-group analysis, we first evaluated the measurement invariance between these two groups in accordance with the three-stage process (Henseler et al., 2016): stage 1 (configural invariance), stage 2 (compositional invariance), and stage 3 (equivalence of composite mean values and variances).

In stage 1, configural invariance was met because (a) two sub-samples were measured with the same set of questionnaires (b) two sub-samples were gathered with the same survey collection approach and (c) two sub-samples were run in the same way with bootstrapping in SmartPLS.

In stage 2, we performed a permutation (MICOM test) to test the compositional invariance between the two age groups (Henseler et al., 2016). Thereby, composite correlations on all model constructs across the two groups were very close to the value of 1 (see Table B1), compositional invariance is confirmed and thus measurement invariance is established (Henseler et al., 2016).

Stage 3 was to check if the confirmed measurement invariance in Stage 2 above is full or partial (Henseler et al., 2016). Because the composite’s means and variances of some constructs in the model were significantly differed between the two age groups (see Table B2), this indicates a partial measurement invariance, a multigroup analysis should therefore be conducted in order to compare the standardized path coefficients between the two age groups (Henseler et al., 2016).

The confirmation of partial measurement invariance between the two age groups above allows running a multigroup analysis on SmartPLS, and the results are reported in Tables 6 and 7. According to Table 6, all H1-H5 were fully supported for under-42 age group but only H3 was supported for above-42 age group while H1, H2, H4, and H5 were NOT supported for this above-42 age group. Therefore, attitude is the most important factor for early retirement planning among older surveyed participants. Meanwhile, Table 7 reports the beta (path coefficient) difference between the two age groups on five main path relationships. In particular, the beta difference is statistically significant for H1, H4, and H5 across the two age groups. Work pressure, subjective norms, and perceived behavioral control are therefore three factors in which young and older participants react significantly differently.

Table 6.

Hypothesis Testing Among Two Age Groups.

Hypothesis	Path	Under-42 age group			Above-42 age group
Hypothesis	Path	Beta	Sig.	Result	Beta	Sig.	Result
H1	WP → ERPlanning	.337	0.000	Supported	−.211	0.192	Not-supported
H2	Family Pressure → ERPlanning	.133	0.000	Supported	−.149	0.435	Not-supported
H3	ATT → ERPlanning	.207	0.000	Supported	.262	0.025	Supported
H4	SN → ERPlanning	.157	0.000	Supported	−.128	0.185	Not-supported
H5	PB → ERPlanning	.237	0.000	Supported	−.123	0.298	Not-supported

Table 7.

Comparison of Path Coefficients.

Hypothesis	Path	Under-42 vs. Above-42 age group
Hypothesis	Path	Beta difference	Sig.
H1	WP → ERPlanning	.548	0.020
H2	Family Pressure → ERPlanning	.282	0.184
H3	ATT → ERPlanning	−.056	0.494
H4	SN → ERPlanning	.285	0.014
H5	PB → ERPlanning	.360	0.014

This study utilized the OLS regression model provided by Stata 16 to ensure the robustness of the research model. Additionally, it emphasizes the difference between men and women in early retirement intention and the factors that affect it. The authors separate data into two categories of samples based on gender characteristics, with the first sample only men and the second only women. This is consistent with the PLS-SEM analysis findings and strengthens the importance of five main dimensions that have a significant impact on the early retirement planning of employees (Table 8).

Table 8.

Robustness Test Model Using the OLS Regression Method.

Variables	Early retirement planning
Variables	Men	Women	Full model
Work Pressure	0.109* (0.064)	0.039 (0. 067)	0.085* (0.046)
Family Pressure	0.218*** (0.061)	0.181*** (0.055)	0.195*** (0.041)
Attitude	0.06 (0.054)	0.081 (0.051)	0.086** (0.036)
Subjective Norms	0.175*** (0.053)	0.106** (0.043)	0.131*** (0.033)
Perceived Behavioral Controls	0.138*** (0.048)	0.127*** (0.045)	0.133*** (0.033)
Obs	354	404	760
R-squared	0.3803	0.2324	0.3089
Prob > F	0	0	0
F(5,348)	42.71
F(5,398)		24.10
F(5,754)			67.39

Note. ***p < 0.01, **p < 0.05, *p < 0.1.

Additionally, the robustness test also indicates the different effects of work pressure in all of the sub-sample results. Firstly, in the context of work pressure, men demonstrate a significantly higher effect (β = .109*) compared to women (β = .039). Men, being the primary source of household income, experience more pressure in work communication than women. Second, the sample results for both men and women indicate a negligible difference in attitude toward early retirement planning compared to the full model (β = .086**). Finally, the results of family pressure, subjective norms, and perceived behavioral controls help to strengthen the PLS-SEM analysis results. On the way, these outcomes show the significantly positive impacts of planning behavior.

Discussion

Currently, there are numerous contentious theories regarding the motivations for the increasing desire of individuals, particularly young people, to retire at an earlier age. Individuals often make arrangements for early retirement during their middle years, often between the ages of 30 and 45, or after a decade to two decades of employment (Sundstrup et al., 2021). Despite extensive research in various fields such as mental health, physical health (Elder & Johnson, 2000), social, working policy, or even personal finance (A. W. Khan & Pandey, 2023), and personal finance (A. W. Khan & Pandey, 2023), the focus remains on understanding the early retirement planning behavior of individuals who intend to retire before the designated retirement age. We considered various factors including work pressure, family pressure, attitude, subjective norms, perceived behavioral control, and the moderating influence of individual characteristics.

All of these factors have a positive and significant influence on individuals’ intentions and behaviors. This finding is consistent with both the life-course perspective theory and the theory of planned behavior. First, according to the life course perspective, both external and internal factors influence people’s intentions or behavior (Fisher et al., 2016). With external factors, this research shows that work pressure and family pressure have a positive effect of pushing people to early retirement to have more time for themselves and their family members. These results are consistent with the research of (Topa et al., 2018) and contribute to the explanation of the reasons workers decide to retire early. They face pressure from their workplace and various household responsibilities, particularly women (Kumar et al., 2019). Second, the internal life course perspective theory is quite basic, so this research utilizes the (Ajzen, 1991) theory of planned behavior to elucidate the profound impact of internal factors on intention and behavior. To be more specific, all of the TPB’s factors demonstrate a significant and positive influence on intention, thereby improving the behavior and decisions of individuals when planning for early retirement. This result also consists of the research of Neupane et al. (2022).

Topa et al. (2009) proved that retirement planning behaviors rely heavily on how people perceive their retirement and their abilities to control it. According to the Life Course Perspective, individuals face various impacts from their age and relationships life transitions and social changes as they navigate through retirement. Topa et al. (2009) explained that retirement decisions exist within institutional and societal contexts that influence both the life course and retirement itself.

The results of this study have important theoretical and practical implications. The theoretical framework of this research, combining the Theory of Planned Behavior (Ajzen, 1991) and the Life Course Perspective (Fisher et al., 2016), has proven to be effective in explaining the intentions and behaviors related to early retirement planning. Specifically, the study’s findings emphasize the importance of psychological, social, and occupational factors in shaping individuals’ decisions to retire early. These factors—attitudes, subjective norms, and perceived behavioral control—play a critical role in influencing retirement intentions. The research deepens these concepts by demonstrating how personal attributes, such as financial literacy, job satisfaction, and health status, directly impact retirement preparation capabilities (Brüggen et al., 2019; Dal Bianco et al., 2015).

In line with previous research, the study highlights the significant influence of work pressure and family responsibilities on individuals’ decision to retire early (Neupane et al., 2022; Topa et al., 2018). The findings are consistent with the Life Course Perspective, which suggests that both internal and external factors interact to shape retirement intentions (Fisher et al., 2016). For example, employees facing high work-related stress and family pressures may prioritize early retirement as a way to regain control over their time and improve their quality of life.

From a practical standpoint, this study’s results suggest several interventions to support individuals in making informed retirement decisions. Governments can launch financial literacy programs to equip workers with the skills to manage their retirement savings and avoid premature retirement due to financial constraints (A. W. Khan & Pandey, 2023). Furthermore, organizations can implement flexible work systems and career transition support, allowing employees to gradually phase out of the workforce. This approach can help maintain organizational productivity while assisting older employees in preparing for a smooth retirement.

Workplace wellness programs are also critical in mitigating the health-related push toward early retirement. Programs that address both physical and mental well-being can help reduce the impact of health decline, a major determinant of early retirement (de Wind et al., 2014). Human resources departments can further assist by providing career counseling and retirement planning workshops to guide employees in their decision-making process.

Theoretical Implications

The research advances theoretical knowledge about early retirement preparation through the combination of the Theory of Planned Behavior (TPB) and the Life Course Perspective Theory, which together explain how psychological, social, and occupational elements affect retirement choices. The findings confirm that early retirement decisions heavily depend on three main aspects: attitude, subjective norms, and perceived behavioral control. Furthermore, the study deepens these concepts by highlighting the role of individual attributes—such as financial literacy, job satisfaction, and health status—in shaping retirement preparation capabilities. This integrated theoretical framework contributes new insight into retirement planning, particularly in the context of developing nations, thereby enriching the broader literature on retirement behavior.

Practical Implications

The results emphasize the importance of public and organizational initiatives in supporting effective retirement planning. Governments are encouraged to develop targeted financial education programs that equip workers with knowledge about long-term financial management and investment strategies, thereby reducing the likelihood of retirement prompted by economic hardship. Within organizations, employers should consider adopting flexible scheduling, phased retirement schemes, and career transition assistance to enable older workers to gradually adjust to retirement. Introducing wellness initiatives that address both physical and psychological health can help minimize retirement driven by health deterioration. Moreover, human resource teams should facilitate career planning and retirement education sessions to strengthen employees’ readiness and confidence in making informed retirement choices.

With regard to the specific group of the elderly, the empirical findings indicate that attitudes significantly influence early retirement planning, particularly among older employees. Employers should thus actively shape positive attitudes toward continued employment among elder workers through targeted communication and workplace interventions. Providing tailored career counseling, training sessions, and wellness programs that emphasize the value of prolonged employment can improve elder workers’ perceptions and attitudes toward staying in the workforce. Policymakers are encouraged to support initiatives aimed at reshaping societal attitudes regarding older employees’ capabilities and contributions, such as public awareness campaigns highlighting successful stories of late-career engagement. Such measures can foster a culture where retirement decisions are driven by choice rather than external pressures or misconceptions. Furthermore, enhancing job flexibility and developing age-sensitive HR policies can positively influence elder employees’ attitudes, ultimately encouraging their prolonged participation in the labor market and reducing premature retirement rates.

Limitations and Future Research Directions

The research offers valuable insights but is subject to several limitations that may affect the accuracy and generalizability of its findings. This study presents several limitations that may affect the interpretation and generalizability of its findings. First, the reliance on self-reported survey data introduces the possibility of social desirability bias, as participants may have provided inaccurate responses regarding their retirement plans. Additionally, the study focuses on low- and middle-income countries, which restricts the applicability of the results to developed nations with distinct pension systems and labor regulations. Future research should adopt longitudinal data collection methods to capture evolving retirement planning behaviors and conduct cross-cultural studies to examine how economic structures, governmental policies, and cultural norms influence early retirement decisions. Psychological and behavioral factors, such as risk tolerance, future time perspective, and emotional readiness, should also be incorporated to build a more comprehensive understanding of retirement behavior.

Another key limitation stems from the use of snowball sampling. Although this method allowed access to elder employees in developing countries, it resulted in a non-representative sample, including a significant proportion of younger individuals such as students and interns (42.2%), which diverges from the intended target group of employees aged 40 and above. This sampling approach risks homogeneity among participants, particularly in terms of economic background, occupational history, and retirement perspectives, thereby limiting the diversity of insights. Moreover, the online distribution method excluded individuals lacking digital access, further impacting sample representativeness. These limitations affect the study’s ability to fully capture individual differences in factors such as financial literacy, job satisfaction, and health status. To address these issues, future studies should consider adopting stratified random sampling techniques to ensure better alignment with the intended target population, as well as incorporating qualitative interviews and secondary data to enhance the depth and reliability of the findings.

Conclusion

The research delivers important knowledge about early retirement choice determinants by analyzing how social elements psychological aspects and individual traits affect retirement planning decisions. The research results validate that early retirement choices are influenced by attitude together with subjective norms and perceived behavioral control which strengthens the use of the Theory of Planned Behavior (TPB) in retirement studies. Family pressure emerged as the essential external factor that influences early retirement decisions because individual characteristics such as financial literacy, job satisfaction and health status affect the strength of these relationships.

The study has achieved valuable outcomes but its boundaries need improvement alongside its research methods and theoretical framework. The research design through snowball sampling introduced potential biases that could limit the general applicability of discovered results because it failed to maintain adequate sample representativeness. Future research needs to employ random or stratified sampling methods because they will enhance the accuracy of collected data. Longitudinal studies together with improved model validation techniques should be implemented as the next steps to enhance the ability to draw causal associations from the research.

The theoretical framework would gain enhanced clarity through the inclusion of the Life Course Perspective Theory and other behavioral theories that would help explain early retirement motivations better. Staff and administrators should implement retirement planning education, workplace well-being management and adaptable retirement terms to enable employees to make satisfactory retirement choices. The research credibility together with theoretical value and practical usefulness will improve by strengthening these identified areas.

Footnotes

Appendix A

Table A1.

Questionnaires of the Survey.

Variables	Questionnaires	Noted	References	Core references
Early Retirement Planning	1. Calculations have been made to estimate how much I have to save to retire comfortably. 2. I frequently read articles, books, brochures or surf the internet to learn about early retirement planning. 3. I have informed myself about the level of my future pension benefits. 4. I have informed myself about financial preparation for early retirement. 5. I have made regular contributions to a voluntary early retirement savings plan. 6. Relative to my peers, I have saved a great deal (almost double) of money for a post-early retirement year. 7. I regularly contribute a fixed percentage of my income to my early retirement savings account. 8. I make a conscious effort to save for early retirement. 9. Based on how I plan to live my life in early retirement, I have saved accordingly.	A 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). We assessed the dependent variable through two measures—retirement planning activity and retirement savings tendency. We used a four item scale by Hershey et al. (2012) to measure retirement planning activity. We assessed retirement savings tendency through a five-item scale by Jacobs-Lawson and Hershey (2005). The item “I frequently read articles, books, brochures or surf the internet to learn about retirement planning” was changed to “I frequently read articles, books, brochures or surf the internet to learn about early retirement planning”	Tomar et al. (2021)	Hershey et al. (2012), Jacobs-Lawson and Hershey (2005).
Work Pressure	1. The work pace is too fast. 2. Your job is very demanding. 3. The workload is manageable for you. 4. You have enough time to complete your tasks. 5. There are conflicts in job requirements. 6. The job requires a high level of concentration. 7. The job is often interrupted due to various reasons.	Respondents will receive 7 answers on a 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree).	Dal Bianco et al. (2015)	Karasek et al. (1998)
Atitude	I think that people should have the opportunity to retire early.	A 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). The item “I think that people should have the opportunity to change organizations” was changed to “I think that people should have the opportunity to retire early.”	Van Dam et al. (2009)	Van Dam (2008)
	1. My being in the Early retirement intention is	5. Good—1. Bad	Xiao and Wu (2008)
	2. My being in Early retirement intention is	5. Wise—1. Fooled	Xiao and Wu (2008)
Subjective norm	1. Most members of my family think I should be planning to retire early 2. My close friends think I should be in planning to retire early	5. Likely—1. Unlikely The item “My close friends think I should be in DMP to reduce debt” was changed to “My close friends think I should be in planning to retire early”	Xiao and Wu (2008)
Percieved behavior control	1. Please rate the difficulty of staying in early retirement 2. If I wanted to, I could easily retire early.	1. Difficult—5 Easy	Xiao and Wu (2008)
Family pressure	1. Your current family makes you feel tense or stressed 2. Household work requires you to be physically strong 3. Household chores make you spend a lot of time 4. Household chores force you to concentrate highly 5. You feel stressed when it comes to family and housework 6. You do not receive support from relatives with housework	Respondents will receive 5 answers on a 5-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree). Using the questionnaire of Dal Bianco et al. (2015) on work factors, the authors changed it from: “Your current job makes you feel tense or stressed” to “Your current family makes you feel tense or stressed”	Dal Bianco et al. (2015) and Authors	—
Individual characteristics	1. No way I can solve problems 2. Pushed around in life 3. Little control over things that happen 4. Feel helpless in dealing with problems 5. Little I can do to change important things in my life	Mastery was measured using the Pearlin Mastery Scale, which reflects the degree to which persons feel they are in control of matters that affect their lives. This scale consists of 5 items with a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree)	de Wind et al. (2014)

Appendix B

ORCID iD

Nga Nguyen Thi Hong

Consent to Participate

This article does not contain any studies with human or animal participants.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is funded by University of Economics Ho Chi Minh City, Vietnam (UEH) and Vinh Long University of Technology Education (VLUTE).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Determinants of Early Retirement Intentions: A Psychological,Social,and Workplace Perspective

Abstract

Keywords

Introduction

Review of Literature and the Development of Hypothesis

Early Retirement Planning

Theoretical Framework and Research Model

Theoretical Development

Methodology

Ethics Statement

Results

Sample Characteristics

Mean and Standard Deviation of the Study Variables

Assessment of Measurement Model

Convergent Validity

Discriminant Validity

The Moderating Impact of Individual Characteristics in the Model

Robustness Test

Discussion

Theoretical Implications

Practical Implications

Limitations and Future Research Directions

Conclusion

Footnotes

Appendix A

Appendix B

ORCID iD

Consent to Participate

Funding

Declaration of Conflicting Interests

Data Availability Statement

References