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Abstract

The objective of this study is to examine the role of financial self-efficacy (FSE), retirement goal clarity (RGC), and government policy (GP) on financial planning for the retirement (FPR) behavior of public university employees based on the CWO model for retirement. Also, to examine the moderating role of GP on the relationship between FSE, RGC, and FPR. Primary data was gathered during the COVID-19 pandemic from mid-July 2020 until the end of January 2021 using a non-probability convenience sampling approach involving 525 respondents. The data were analyzed using the Structural Equation Modelling (SEM) technique. The Confirmatory Tetrad Analysis method (CTA-PLS) is used to determine the formative/reflective type of study variables. The results directly show the significant impact of the FSE, RGC, and GP on FPR. Indirectly, for those with a clear retirement goal, the perceived GP moderates its relationship with FPR. Previous research has examined FSE and RGC in FPR without considering the effect of GP.

Keywords

financial planning for retirement financial self-efficacy retirement goal clarity government policy Saudi Arabia

Introduction

Financial planning for retirement (FPR) has become a foundation stone for successful post-retirement in industrialized countries (Hershey et al., 2012; Topa et al., 2018). In these countries, the bulk of their laborers is nearing their retirement age (Gallego-Losada et al., 2022; Henkens, 2022) due to increased life expectancy, raised public debt pressures in terms of social support, self-funding for retirement, and the move of risk and decision-making to workers (MacDonald & Guest, 2019). Such causes have urged governments in developed countries to promote regulations based on more outstanding mutual obligations with financed private pensions to complement schemes managed by the governments (Feng et al., 2019). Following the global financial crisis of 2007, for example, the rise in public debt has supported official resistance to any growth in government pension liability. It has strengthened the move to defined-contribution schemes accordingly.

The lack of FPR has become a worrying phenomenon (Lusardi & Mitchell, 2011b; Nam & Loibl, 2021). Changes in demographic variables (e.g., increase in life expectancy, declining fertility rate, an increase in the aged population, changes in retirement age policy, family/cultural changes, worker types), economic variables (e.g., increase in the cost of living and medical expense, the ability of pension coverage) (Jaafar et al., 2019; Liu et al., 2022), lifestyle, the standard of living, and the removal of additional supports such as medical insurance provided by the employers (SAMA Cares, 2018) force individuals in industries and emerging economies to make their own investment decisions to enhance their financial well-being in retirement (Bacova & Kostovicova, 2018; Topa et al., 2018). As a result, their financial security will be threatened following their retirement.

When these laborers approached retirement age and decided to leave the workforce, they made vital financial decisions (Hershey et al., 1998, 2002) for themselves and their families. In the early stages of retirement, retirees shall face financial, psychological, and social challenges. Financially, there might be a complete stop of cash inflow for those with an inadequate retirement plan (Jacobs-Lawson & Hershey, 2003) or lacking backup financial resources. In examining various variables that assist employees in planning and saving effectively for retirement, Hershey et al. (2012) proposed that capacity, willingness, and opportunity model to plan and save pertinent play roles. The capacity dimension is represented by cognitive variables that distinguish individuals’ capabilities in knowledge and proficiencies required to plan and save for retirement. Extant studies highlighted that this dimension is very well presented by the financial self-efficacy of the workers (Asebedo & Payne, 2019; Mindra et al., 2017; Robb, 2017). The second dimension, which is willingness, is the drive to prepare for their retirement, as reflected by the clarity of the retirement goals (Hoffmann & Plotkina, 2021). Apart from these variables, other external variables such as government regulations, financial advisors, and social support provide individuals with the opportunities to execute their planning and saving for their post-retirement (Topa et al., 2018).

In relation to this, recent studies by Palaci et al., 2017; Palací et al., 2018) and Jiménez et al. (2019)) applied the CWO model only to the examination of FPR in developed countries, namely Spain (A. S. Ghadwan et al., 2022a). Conversely, this study has applied the CWO model to examine FPR in emerging countries, specifically Saudi Arabia, because it is becoming one of the fastest-growing economies in the Middle East (Barbuscia, 2018). Saudi Arabia’s culture has always put them apart from others (Alotaibi, 2015, p. 203). In addition, Saudi Arabia is experiencing considerable social transformation and economic diversification in the transition process toward the Kingdom’s 2030 Vision (Sarabdeen et al., 2020). Most significantly, the introduction of Vision 2030 and the alterations of the existing pension system enable the study to examine the influence of financial, psychological, and external variables on FPR behavior. Finally, what applies to Saudi Arabia could also be applied to other Arab Muslim countries, given that they share the same belief, language, cultures, customs, and traditions.

Consequently, the objective of this study is to examine the relationships between financial self-efficacy, retirement goal clarity, and government policy which, in turn, affect financial planning for retirement. Mainly, how do government policies affect FPR as a moderating variable linking it with financial self-efficacy and retirement goal clarity of public university employees? Facilitating government policies is imperative because they can help inculcate financial planning for retirement behavior among employees in the workplace.

Academically, the research contributes significantly to the body of knowledge by applying Intentional Change Theory (ICT) to evaluate individuals’ awareness of FPR practices. The ICT explains how employees intentionally start changing their consumption and saving behavior before reaching retirement age. As Topa et al. (2018) stated, combining the CWO and ICT will lead to a better understanding of financial planning processes and their related retirement behaviors rather than examining each individually.

The design of this research is as follows. First, the literature review and hypothesis development briefly review financial self-efficacy, retirement goal clarity, and FPR. Next, the research method is presented, followed by results and findings from the data analysis. The paper concludes with a discussion of the significance of the outcomes and their implications.

An Overview of the Saudi Retirement Structure

According to the General Organization for Social Insurance (GOSI) (2021), the Council of Ministers issued a decision to incorporate the Public Pension Agency (PPA) into GOSI to carry out all the functions and powers of the PPA. It is a matter of uniting the work so as to generate a suitable environment and the necessary way to achieve the optimal application of the pension insurance systems. The GOSI is overseen by a Board of Directors led by the Minister of Finance and headquartered in Riyadh. It is responsible for social security and insurance protection for Saudi Civil, private sector workers, Military employees, as well as a group of public servants to ensure that they and their families have a decent life after they depart from work because of retirement, disability, or death. This coverage includes medical care, which provides compensation to a person in the event of an accident at work or occupational disability through the Occupational Risk Centre. In addition, these laws provide care to employees who are Saudi nationals leaving their jobs due to conditions beyond their control by paying remuneration (Saned), providing training, and seeking for a job. Further, it provides its services to those beneficiaries of the benefits exchange and insurance protection schemes for nationals of the GCC States.

Literature Review

Retirement has become a critical phase in a person’s life cycle. The essential of retirement has been recognized as a social institution where the public pension system was introduced to enable viable wealth distribution where employers contributed to funds that support retired workers (Atchley, 1982; Warner et al., 2010). In recent times, however, with the steady increase in living costs and the economic implications of retirement, reliance solely on public funds has become inadequate (Q. Wang & Timonen, 2021), pushing FPR as one of the most relevant components of retirement planning (Langley, 2006; Taylor & Geldhauser, 2007; Topa et al., 2012).

Still, studies have shown that government intervention remains essential in determining the successful FPR (França & Hershey, 2018; Jiménez et al., 2019). This is more likely when workers depend on public pension funds for the cash flow of income after work.

Theories and Hypothesis Development

This research used Intention Change Theory (ICT) as the theoretical framework developed by Boyatzis (2006). In five steps, the theory explains the behavioral change process of undesirable behaviors to desired behaviors, thoughts, feelings, and perceptions in individuals’ actions, behaviors, or skills. From a theoretical point of view, the outcome of this study expands the body of personal financial planning literature, particularly as it relates to the FPR.

Regarding the capacity dimension, an individual is assumed to desire to achieve a specific behavior, which can be achieved via empowering oneself with the necessary skills and knowledge. In the context of FPR, the theory predicts that a sustainable change toward FPR can be achieved through financial knowledge and skills (Topa et al., 2018) or known simply by much literature as financial literacy and financial self-efficacy.

In terms of the willingness dimension, an individual’s intention motivates him/her to change to a particular behavior through several steps. This theory states that sustained, the desired change in an individual’s life can be explained by the stated intent of adaptation, learning, and change (Boyatzis, 2008). Since sustainable behavioral change is often intentional, a solid foundation in ICT helps to understand the critical processes that encourage and motivate changes in a person’s perceptions, thoughts, and behaviors (Boyatzis & Cavanagh, 2018). In the context of FPR, the theory is assumed that a rise in individuals’ motivation increases their ability to plan, invest, and efficiently save for post-retirement. Earlier studies have shown that psychological variables, such as personality traits, affect, goals, and attitudes, affect FPR and make it easier to predict savings tendencies (Yusof & Sabri, 2017). The success of retirement depends on how one readiness to plan and take the actions to plan and save for the future. In this study, a psychological variable is examined: retirement goal clarity.

This study is the first attempt to assess the direct and indirect relationships between the capacity, willingness, and opportunity variables affecting FPR at Saudi public universities. Consequently, the hypotheses are developed on the basis of earlier studies in the context of retirement, considering the influence of financial self-efficacy, retirement goal clarity, and government policy affecting FPR.

Financial Planning for Retirement (FPR)

Financial planning for retirement is a sequence of actions and activities, such as investment and cash flow management, by an employee to gather wealth to cover needs after they stop working (Kumar et al., 2019a; Topa et al., 2018). In other words, it is a comprehensive evaluation of an individual’s present and future financial position (Kumar et al., 2019a). Specifically, FPR requires individuals to decide the appropriate amount of money to invest and save to retire from a job and begin spending the saved funds (Hershey & Mowen, 2000; Topa et al., 2012).

Earlier studies on determinants of FPR were fragmented and belonged to a specific field of study, such as economics and finance (Lusardi & Mitchell, 2007) or psychology (Petkoska & Earl, 2009). Later studies, however, advocated a more comprehensive approach (Dolinski et al., 2016; Hershey et al., 2010) to develop a socio-psycho-economic model of FPR (Thaler, 1994). Research that examined FPR using the following approaches are Social, Personal, Occupational, and Familial Model (Dan, 2004), Planning Decisions and Behaviors Model developed by Hershey (2004), and Financial Planning for Retirement Model (Smith, 1999).

Among such models, Hershey et al. (2012) later developed a modified version of the model known “Capacity-Willingness-Opportunity Model” (CWO) for work performance, advanced by Blumberg and Pringle (1982) for the specific examination of FPR studies. The CWO model is proper for FPR studies for several reasons. First, it is explicitly designed to interpret FPR. Second, the three dimensions introduced by the model allow detailed insight into the retirees’ behaviors in their FPR by incorporating additional variables to better capture the determinants’ motivating effects (Topa et al., 2018). Thirdly, this model is procedural because it has a temporal dimension, analyses age and stage of retirement, and how these interact with variables in the model (Topa et al., 2018). Fourthly, this model is appropriate for examining diverse economies with different cultural, social, and political environments owing to the model’s ability to change based on the lives of individuals that alter through time, which represents the continuity of predispositions to change throughout adulthood (Hershey et al., 2012). Lastly, this model overcomes other FPR model limitations and simultaneously assesses the association between variables from different fields and financial planning for retirement.

Capacity to Plan and Save for Retirement—Financial Self-Efficacy (FSE)

Financial literacy in and of itself is not sufficient to manage its financial resources. Hence, people need financial literacy and a sense of confidence in their ability to make the right financial decision. This sentiment is known in the psychological literature as self-efficacy (Farrell et al., 2016). Self-efficacy has been defined as a sense of individual skill to control, manage, and affect several aspects of life to accomplish goals (Bandura, 2006) within a wide variety of assignments and topics (Stajkovic & Luthans, 1998). Consumer behaviors are influenced by self-efficacy (Lown, 2011), and it is considered a significant ingredient in managing stress (Robb, 2017). Bandura (1977) has demonstrated that self-efficacy motivates people to promote the desired behavior in several areas of life and deal with adversity without being overwhelmed. It was suggested that the higher an individual’s self-efficacy, the greater one’s ability to defeat difficulties to attain efficacy expectations.

Financial self-efficacy makes individuals confident in achieving financial goals, managing their assets, and making their life better (Mindra et al., 2017). According to the literature, individuals with a high degree of financial self-efficacy have better financial well-being (Robb, 2017) and a low sense of financial stress (Heckman et al., 2014). Also, they could provide the self-confidence to conduct financial planning activities successfully. When markets become unpredictable, investors who were found to control their long-term financial situation were those with a high level of financial self-efficacy (Asebedo & Payne, 2019) and tended to make low-risk investment decisions (Cho & Lee, 2006). Farrell et al. (2016) showed that a higher level of financial self-efficacy among women increases their self-assuredness in managing their financial assets as well as decreases their debt. At the same time, they became more able to handle various investments and savings products.

In the same vein, Asebedo and Payne (2019) indicated that a high level of financial self-efficacy supports financial behaviors necessary for retirement financial planning. For instance, people with high FSE are founded to be more professional in evaluating risk investment for their retirement plans (Dulebohn, 2002), to get ready for early retirement (Wöhrmann et al., 2013), and more likely to put their knowledge into action, which indicates a positive impact between FSE and investment decision making (Husnain et al., 2019; Lunceford, 2017). These studies highlight the positive relationship between FSE with FPR behaviors. Thus, the following hypothesis is proposed:

H₁: Financial self-efficacy has a positive relationship with financial planning for retirement.

Willingness to Plan and Save for Retirement—Retirement Goal Clarity (RGC)

One of the critical variables that play a significant role in retired life is the apparent goal before retirement. Goal clarity has been defined as a method of measurement for individual goals (Kerry, 2018) through explicit and coherent planning provisions and activities (Jiménez et al., 2019). When individuals have a set of clear and specific retirement goals, they usually use several tools to reformulate their tasks (Bavelas & Lee, 1978) to perform learning strategies under a variety of circumstances in order to provide better opportunities to achieve their retirement needs (Lusardi & Mitchell, 2011a; Rasiah et al., 2020).

Retirement goal clarity shapes individuals’ retirement plans through expectations of future requirements (Jiménez et al., 2019; Zhu & Chou, 2018). Having an obvious and realistic purpose increases the intentions and saving levels of individuals (Stawski et al., 2007). It improves financial planning practices and savings behaviors in the long term (Hershey et al., 2010). It also motivates individuals to start planning and saving in their golden age before it is too late, giving workers the confidence to retire without facing financial problems.

Conspicuously, Stawski et al. (2007), M. Wang and Shultz (2010), and Aluodi and Njuguna (2017) showed a significant relationship between retirement goal clarity and retirement preparation. Specifically, it was found that retirement goal clarity performed positively in FPR (França & Hershey, 2018; Hershey et al., 2010; Jiménez et al., 2019; Schuabb et al., 2019; Tomar et al., 2021). However, Chou et al. (2015) found no direct relationship between retirement goal clarity and planning activities among older participants.

The overall results from the literature indicate a positive relationship between retirement goal clarity and FPR. Thus, taking into account this gap, the following hypothesis is proposed:

H₂: Retirement goal clarity has a positive relationship with financial planning for retirement

Opportunity to Plan and Save for Retirement—Government Policy (GP)

Government policies are regulatory actions created by decision-makers to influence individuals, groups, corporations, social, economic, cultural, and religious matters. Government policies usually significantly impact all areas of daily life (Leisering, 2003; Škrinjarić, 2018), whether formally through–laws, policy, or informally through culture, tradition, or social norms (North, 1990). Numerous studies have illustrated that government policies have an essential role in impacting the future financial decisions of individuals and companies, such as creditors and shareholders protection (La Porta et al., 1998) firm investment behavior (Chen et al., 2011; Lin & Man-lai Wong, 2013) investment allocation (Hao & Lu, 2018) pension fund investment performance (Mutula & Kagiri, 2018), and retirement saving behavior (França & Hershey, 2018) directly. Indirectly, on the other hand, government policies have been tested as a moderator in several studies. In China, for example, the vehicles are not eco-friendly and not secure for China’s national energy security; researchers used government policies as a moderator to encourage people to buy New-Energy Vehicles (Zhang et al., 2013). Assagaf and Ali (2017) and Taofeeq et al. (2020) examined the role of government policy as a moderator.

In the retirement context, governments introduced pension system policies to protect government and private employees from prolonged life and inflation risk as well as to provide primary retirement income (Jaafar et al., 2019). Hence, the perception of these policies has a significant role in retirement financial planning. Studies showed that individuals who did not sufficiently understand retirement systems (Hershey, Jacobs-Lawson et al., 2007; Imamoglu et al., 1993) due to the complexity of pension or social security systems (Litwin & Sapir, 2009; Lusardi & Mitchell, 2011a) had low financial well-being during retirement. The quality of knowledge given to workers and retirees about pension and social security policies would improve their ability to protect, save, and plan for their future.

In line with previous studies’ arguments, this study assumes government policy as a possible moderator in the relationship between FPR and the other variables in the CWO model. Accordingly, the following hypothesis is proposed:

H_3a: Government policy has a positive relationship with financial planning for retirement

H_3b: Government policy moderates the relationship between financial self-efficacy and financial planning for retirement.

H_3c: Government policy moderates the relationship between retirement goal clarity and financial planning for retirement.

Conceptual Framework

The underlying concept of this study is that certain cognitive, psychological, and external factors influence financial retirement planning in Saudi public universities. Consequently, the study aims to identify variables that influence FPR practices. Next, identify the moderating role of government policy among public university employees, as shown in Figure 1.

Figure 1.

Conceptual framework.

Methodology

Design

While the targeted population is those who are working in Saudi public universities, the sample frame is the faculty staff and administrators. This sample was used as in the A. Ghadwan et al. (2022b) study. This specific sample is chosen for the following reasons.

The Ministry of Higher Education is distinct from other government sectors as the employees represent 48% of government employees. It allows the researcher easier access to collect the needed data. At the same time, no special government license letter is required to enable faculty and administrators to participate in the study, such as the military and banking sectors, providing an unbiased response from the respondents. Another reason for choosing university employees is that they are expected to be more educated or have easier access to knowledge given their working environment. The potential for sharing knowledge in universities is higher than in other sectors (Chahal & Savita, 2014), leading to knowledge development in retirement planning.

A total of 1,300 emails were sent to the target population of full-time workers at Saudi public universities aged 26 to 60 years to ensure that all respondents joined the labor force by this age. After eliminating outliers, the final sample for the study was 525 representing a 43% response rate.

Due to some justifications, the participants were chosen based on the convenience sampling technique. Firstly, while empirical retirement research is criticized for having applied convenience samples influenced by unknown selectivity (Topa & Valero, 2017), it is frequently used in personal financial planning (França & Hershey, 2018; Safari et al., 2016; Shreevastava & Brahmbhatt, 2020). Secondly, an objective of this research is to use previous research items. Highhouse and Gillespie (2010) advised that the convenience sampling technique be used when conducting a theoretical or scale examination.

Third, such a sampling technique allows researchers to gain access to a large number of individuals over a short period of time (Tharenou et al., 2007). The survey frame contains only the university’s website for academics and administrators. As a result, it was not easy to have a list of all names to pick at random. In light of this limitation, this research applies convenience sampling, which appears to be the only practical way to select respondents from the survey frame. Classifying academics and non-academic staff is challenging because the universities are different from one city to another. Finally, due to COVID-19, invitations were sent to academics and non-academic staff by submitting an online questionnaire to their official email address.

Instruments

The instrument used was an online, self-administered questionnaire comprised of five sections. The first section solicited information on the respondents’ background, including gender, age, marital status, education level, employment sector, and university of employment were gathered. The demographic data were used to determine the difference between respondents’ demographic characteristics as control variables for the regression models. The second section examines financial self-efficacy represented by a 10-item scale adopted from (Schwarzer & Jerusalem, 1995). As these 10 items were general and did not assess the specific behavior, two other adaptive items of the Health and Retirement Study (HRS), 2004, 2018) were added, as suggested by Bandura (2006). The third section deals with retirement goal clarity, consisting of five items developed by Stawski et al. (2007) to measure individuals’ goals for retirement. The items have been employed by various researchers in financial planning, such as Hershey, Jacobs-Lawson et al. (2007), Petkoska and Earl (2009), and França and Hershey (2018). The fourth section examines the awareness of government policy. This variable was evaluated using seven statements adapted from several studies (Hershey, Jacobs-Lawson et al., 2007; Public Pension Agency, 2019; Qu, 2007). Examples of these items include “Public Pension Agency has clear guidelines on retirement” and “I am confident that the government pension pay-out from the Public Pension Agency is sufficient to sustain my life after retirement.” The final section examines financial planning for retirement. The dependent variable of this study was measured by six statements adapted from Health and Retirement Study (HRS), 2012, 2018), Uppal (2016), and A. Ghadwan et al. (2022b). The first set contains three items that allow respondents to determine their source of income. The second three items allow them to determine their expenditure regarding their retirement. All previous variables used the Likert scale ranging from (1 = “strongly disagree”; to 7 = “strongly agree”). Table 1 shows the structure of the questionnaire with its questions.

Table 1.

Summary of the Measurement Variables Used in the Study.

Financial Planning for Retirement Questions with Likert scale (1 = “strongly disagree,” 7 = “strongly agree”) Cronbach’s alpha = .86
1. I have put aside some money for my retirement.
2. I am expecting benefits that can be utilized for my retirement planning.
3. I will receive fixed payments as my pension when I retire.
4. I will have enough money to maintain my desired standard of living when I retire.
5. I expect that I will have enough savings to pay for my expenditures during my retirement.
6. I am expecting some earnings, which I can utilize during my retirement.
Financial Self-Efficacy Questions with Likert scale (1 = “strongly disagree,” 7 = “strongly agree”) Cronbach’s alpha was .85
1. I can always manage to solve difficult problems if I try hard enough.
2. It is hard to stick to my spending plan when unexpected expenses arise.
3. It is challenging to make progress toward my financial goals.
4. When unexpected expenses occur, I usually have to use credit.
5. I am confident that I can deal efficiently with unexpected events
6. When faced with a financial challenge, I have a hard time figuring out a solution.
7. I lack confidence in my ability to manage my finances.
8. I can solve most problems if I invest the necessary effort.
9. I worry about running out of money in retirement.
10. I can remain calm when facing difficulties because I can rely on my coping abilities.
11. I can choose how many contributions of my pensions are to be invested by PPA.
12. I can change the amount to be invested in my pension scheme.
Retirement Goal Clarity Questions with Likert scale (1 = “strongly disagree,” 7 = “strongly agree”) Cronbach’s alpha was .84
1. I set clear goals for gaining information about retirement.
2. I think a great deal about the quality of life in retirement.
3. I set specific goals for how much will need to be saved for retirement.
4. I have a clear vision of how life will be in retirement.
5. I discussed retirement plans with my spouse, friend, or family.
Government Policy Questions with Likert scale (1 = “strongly disagree,” 7 = “strongly agree”) Cronbach’s alpha was .88
1. The Public Pension Agency has clear guidelines on retirement.
2. The current retirement guidelines from the Public Pension Agency on retirement are adequate to serve pensioners during their retirement.
3. I am confident that the government pension pay-out from Public Pension Agency is sufficient to sustain my life after retirement
4. The Public Pension Agency has effective guidelines/programs to encourage financial planning for retirement among government servants.
5. There are sufficient guidelines to ensure retirees are self-sufficient after retirement.
6. There is a proposal from the Public Pension Agency to raise the retirement age from 60 to 62 for men and 55 to 57 for women is a good policy.
7. The Public Pension Agency’s right to increase the 9% deduction if it is insufficient to meet its obligations to give the employee the promised pensions is a good move.

Analysis Procedures

Once the data is collected, numerous research tasks in data analysis are performed to ensure that the data gathered are not incomplete or inaccurate (Sekaran & Bougie, 2016). These tasks provide a definite meaning to the collected data from the whole study procedure. This study employed preliminary, descriptive, and primary analysis as data analysis approaches.

The structural equation modeling (SEM) technique was applied to analyze the study’s data using Smart PLS version 3.3.3, while SPSS statistics version 26 was used to analyze descriptive analysis. More specifically, SPSS was employed to perform the preliminary and initial descriptive analysis (Azman Ong & Puteh, 2017) to understand the gathered data comprehensively. Meanwhile, Partial Least Square Structural Equation Modelling (PLS-SEM) is employed in explaining the variance between independent variables and the research hypothesis (Hair et al., 2017).

Applying the SEM technique allows for conducting Confirmatory Tetrad Analysis CTA-PLS and deciding whether the model’s measurements are formative or reflective (Hair et al., 2017). The result of CTA-PLS confirmed that financial planning for retirement, financial self-efficacy, and government policy were formative, while retirement goal clarity was reflective.

Results and Findings

Preliminary Data Analysis

This study applied the Mardia (1970) test to examine the normality data. The result has shown that Mardia’s multivariate skewness and kurtosis were found insignificant. This implied that the multivariate study’s data was not a normal distribution, which is a solid reason for applying PLS-SEM rather than Amos or SPSS in this study. In terms of outliers, respondents’ observations were assessed using the standardized (z) score technique to identify abnormal values from others. The results showed 33 outliers, which were removed from the gathered data. Regarding common method bias (CMB), Harman’s Single-Factor Test was utilized, and the total variation explained by the first single factor was determined to be 22.653%. This result referred that the dataset was not suffering from CMB threats for this study.

Descriptive Statistic Result

In this study, most respondents were 363 (65%) males since men represent 60% of the government sector (SAMA, S. C. B, 2019). The average age of participants was 40.70 (SD 1.67). The majority of respondents were between the age of 31 and 40 years (54%) and were married 454 (81%). The doctoral degree holders represented 162 (29%), and the master’s degree holders represented 185 (33%). However, only 68 (12%) were bachelor’s degree holders. Table 2 below shows the full results of the demographic profiles of the respondents.

Table 2.

Respondents’ Demographic Profile.

Demographic Variables	Valid Percent
Gender
Male	344 (66%)
Female	181 (34%)
Marital Status
Single	73 (14%)
Married	429 (82%)
Divorced	23 (4%)
Age
26–30	74 (14%)
31–35	151 (29%)
36–40	129 (25%)
41–45	60 (11%)
46–50	55 (10%)
51–55	32 (6%)
56–60	16 (3%)
Above 60	8 (2%)
Employment Sector
Academic	330 (63%)
Administrator	195 (37%)
Education
Secondary	12 (2%)
Diploma	15 (3%)
Bachelor	161 (31%)
Master	185 (35%)
Doctoral	152 (29%)

Table 3 introduces means, standard deviations, and Pearson’s correlations for the study’s variables. These variables have a moderately positive impact on FPR, as Cohen (1988) indicated. Pearson’s correlations between predictor variables and FPR revealed that all significant relationships between the study’s variables are in the anticipated path.

Table 3.

Means, Standard Deviations and Correlations among Variables.

Variables	M	SD	1	2	3	4	5	6	7	8	9
Demographic Factors
1. Gender	—	—	1.00	—
2. Age	40.70	1.67	−0.18**	1.00	—
3. Marital Status	—	—	0.07**	0.21**	1.00	—
4. Education Level	—	—	0.02	0.13**	0.09**	1.00	—
5. Employment Sector	—	—	−0.11	0.20**	0.06**	−0.68**	1.00	—
Predictor Factors
6. Financial Self-Efficacy	4.01	1.09	0.03	0.07	−0.02	0.12	−0.11*	1.00	—
7. Retirement Goal Clarity	4.00	1.35	−0.11**	0.09	−0.01	0.03	0.04	0.44**	1.00	—
8. Government Policy	3.87	1.22	0.03	0.02	0.01	0.07	−0.03	0.40**	0.45**	1.00	—
Outcome Factor
9. FPR	4.14	1.33	−0.05	−0.02	−0.05	0.08	−0.08	0.43**	0.37**	0.38**	1.00

Source. Author Calculations.

Note. N = 525. FPR = Financial Planning for Retirement; M = Mean; SD = Standard Deviation.

p < .05. **p < .01.

Table 4.7

Results of Measurements Model—Convergent Validity.

Construct/items	Loading	Cronbach’s alpha	Rho_A	CR	AVE
Retirement Goal Clarity		.803	0.808	0.865	0.566
RGC₁	0.740
RGC₂	0.595
RGC₃	0.848
RGC₄	0769
RGC₅	0.785

Assessing Measurement Model

Reflective Measurement

For reflective measurement, item loadings, the average variance extracted (AVE), and composite reliability (CR) are essential indicators that must be achieved to meet the convergent validity requirements. The results in Table 4 showed that all convergent indicators had achieved the threshold value as proposed by Hair et al. (2017) and Ramayah et al. (2018). Regarding the items’ loading, the values were in the recommended range, between 0.70 and 0.90. In the same vein, Cronbach’s alpha, rho_a, and CR values were above .70. Also, the value of AVE achieved the requirements, which was 0.566. Due to retirement goal clarity being the only reflective measurement in this study, a discriminant validity test was not applicable to be conducted.

Formative Measurement

According to Hair et al. (2017), examining convergent validity, collinearity issues (VIF), and investigating the significance and relevance of the formative items are steps to examine the formative measurement model. The survey items used to assess formative variables examined different components of these latent constructs in this study. Thus, the correlation level among these items should not be high. Therefore, the regular assessment of reliability and validity was not necessary for the evaluation of formative measures. Instead, multicollinearity and the significance of indicators’ weights were evaluated.

The correlation between formative indicators is not preferred, like reflective indicators, because it influences items’ weights and statistical significance (Hair et al., 2017; Ramayah et al., 2018). After the collinearity of items was tested, the results showed that the VIF values were less than 5; thus, there were no collinearity problems between the measurements. The last step was assessing formative indicators assessing the constructs’ outer weights’ significance and relevance by applying 5000 bootstrap subsamples. According to the results, all formative indicators were significant at level 5%. Table 5 depicts the formative measurement values.

Table 5.

Evaluation of Formative Measurement Model.

Construct	Items	Outer Weights	T-Value	Outer Loading	T-Value	VIF	Result	Decision
Financial Planning for Retirement (FPR)	FPR₁	0.401	4.216	0.707	10.133	1.259	Significant	Kept
	FPR₂	0.050	0.510	0.504	5.656	1.320	Significant	Kept
	FPR₃	0.106	1.298	0.511	6.945	1.283	Significant	Kept
	FPR₄	0.369	3.964	0.783	15.276	1.927	Significant	Kept
	FPR₅	0.151	1.205	0.750	12.150	2.107	Significant	Kept
	FPR₆	0.321	2.562	0.732	8.919	1.492	Significant	Kept
Financial Self-Efficacy (FSE)	FSE₁	0.400	4.305	0.707	10.025	1.248	Significant	Kept
	FSE₂	0.104	0.956	0.504	5.810	1.338	Significant	Kept
	FSE₃	0.115	0.968	0.461	5.079	1.567	Significant	Kept
	FSE₄	0.191	1.825	0.531	6.396	1.342	Significant	Kept
	FSE₅	0.195	1.583	0.602	6.447	1.476	Significant	Kept
	FSE₆	0.031	0.265	0.537	6.459	1.855	Significant	Kept
	FSE₇	0.004	0.038	0.415	4.736	1.699	Significant	Kept
	FSE₈	0.388	3.925	0.703	9.727	1.364	Significant	Kept
	FSE₉	0.131	1.177	0.522	6.197	1.461	Significant	Kept
	FSE₁₀	−0.089	0.810	0.479	5.522	1.565	Significant	Kept
	FSE₁₁	0.157	1.211	0.521	6.183	2.203	Significant	Kept
	FSE₁₂	−0.012	0.091	0.448	4.233	1.828	Significant	Kept
Government Policy (GP)	GP₁	0.033	0.236	0.570	5.638	1.635	Significant	Kept
	GP₂	0.559	4.712	0.801	11.519	1.886	Significant	Kept
	GP₃	0.093	0.735	0.549	5.555	1.637	Significant	Kept
	GP₄	−0.354	2.203	0.439	3.741	2.011	Significant	Kept
	GP₅	0.384	2.737	0.692	8.027	1.852	Significant	Kept
	GP₆	0.276	2.300	0.586	6.357	1.273	Significant	Kept
	GP₇	0.328	2.720	0.643	6.897	1.305	Significant	Kept

Assessment of Structural Model

The previous section has shown that all reflective and formative measures met the requirements. Hence, five steps should be accomplished to assess the structural model: 1) collinearity evaluation (VIF), 2) significance of the path coefficients P-value, 3) the level of the coefficient of determination (R²), 4) effect size (f²), and finally, 5) predictive relevance Q² as recommended by (Hair et al., 2019; Ramayah et al., 2018).

Assessing collinearity followed the same step that was applied for formative measurements. Table 5 showed that VIF’s values were less than 3, which indicated that the multicollinearity between variables was not a concern. Secondly, SEM results confirmed the hypothesized relationship between financial self-efficacy (β = .304, p < .01) and FPR positively and significantly, as expected by the first hypothesis. In line with the second hypothesis, retirement goal clarity (β = .197, p < .01) was also positively and significantly related to FPR. As anticipated by the third hypothesis, government policy (β = .240, p < .01) was positively and significantly FPR. Put together, the cognitive, psychological, and external variables were predictors of FPR and provided a positive and essential forecast of FPR. Regarding the moderation role of government policy, Table 6 shows that government policy only moderated the path between retirement goal clarity (β = .073, p < .05) and FPR. Thus, only H_3c was supported. This means that government policy, as a moderator, strengthens the relationship between retirement goal clarity and financial planning for retirement.

Table 6.

Evaluation of Structural Model.

Hypothesis	Relationship	Std Beta	Std. Dev.	t-value	p-value	VIF	Decision	F ²
H₁	FSE → FPR	0.304	0.050	6.120	.000	1.328	Supported	0.102
H₂	RGC → FPR	0.197	0.050	3.970	.000	1.369	Supported	0.042
H_3a	GP → FPR	0.240	0.042	5.665	.000	1.204	Supported	0.070
H_3b	GP * FSE → FPR	−0.048	0.040	1.219	.111	1.314	Non- Supported	0.004
H_3c	GP * RGC → FPR	0.073	0.041	1.800	.036	1.362	Supported	0.008

A smart-PLS algorithm was applied to calculate the coefficient of determination (R²) value, representing the second step of structural model analysis. The outcomes of SEM displayed that the R² value was .320, considered a medium value on the latent endogenous construct based on Cohen (1988). A summary of the structural model’s outcome is revealed in Figure 2 and Table 6. The combination values of financial self-efficacy, retirement goal clarity, and government policy explained 32% of the variance in the financial planning for retirement variable. By examining the ƒ² values, subsequently, it can be noted that financial self-efficacy (ƒ² = 0.102) had a medium effect, while retirement goal clarity (ƒ² = 0.042) and government policy (ƒ² = 0.070) had a small effect in generating R² for FPR behavior based on Cohen (1988). Due to an endogenous variable for this study being a formative measurement based on confirmatory tetrad analysis, the blindfolding process should not be conducted, according to Hair et al. (2017).

Figure 2.

Hypothesis testing: bootstrapping direct and indirect effect results.

Discussion

This study empirically developed and examined the CWO model proposed by Hershey et al. (2012), which helps to better understand the capacity, willingness, and opportunity factors that influence FPR with the moderating effect of government policy among academics and administrators. By including these variables, this research provides an in-depth study to provide a limited evaluation of these variables in previous studies. Also, this study examines the role of the moderator of government policy on the relationship between financial self-efficacy, retirement goals clarity, and FPR. The SEM’s analysis revealed that the study’s variables verified the results of earlier studies for financial self-efficacy efficacy (Asebedo & Payne, 2019; Farrell et al., 2016) and retirement goal clarity (França & Hershey, 2018; Hershey et al., 2010; Jiménez et al., 2019; Schuabb et al., 2019; Tomar et al., 2021). Therefore, the outcome of this study supports the hypothetical relations suggested in the theoretical model.

In this study, H₁ predicted that financial self-efficacy had a significant positive relationship with FPR, and the outcome proved a significant positive relationship between them (β = .304, p < .01). This demonstrates that financial self-efficacy plays a vital role in financial retirement planning for academics and administrators. Their financial self-assuredness capabilities were high, which allowed them to plan, save, and invest their money to meet any additional risks and crises in the future, such as over-indebtedness. In addition, the result indicates that most academic and non-academic staff would be able to manage and resolve complex financial problems if they did their best to find a suitable solution to the problem. Those with high financial self-efficacy leave the labor market earlier and continue some post-retirement activities. However, a high degree of financial self-efficacy does not necessarily imply that these individuals are proficient in financial management behaviors. Lusardi (2011c) revealed that respondents who rank the best at managing their financial tasks had seen terrible behaviors, such as excessive bank account withdrawals and credit card usage.

Similarly, H₂ showed that retirement goal clarity had a significant positive relationship with FPR, and the result verified the hypothesis (β = .197, p < .01). The critical role of retirement goal clarity has shown that respondents can reach their financial goals. Respondents may have been educated and experienced, making them more capable of managing their financial resources to meet their future financial goals. Furthermore, the results indicate that having clear and meaningful financial goals prior to retirement is a reason to plan and save for retirement. According to a study conducted by Hershey, Henkens, and Van Dalen (2007), they found that the retirement goal clarity among Americans was more notable than among Dutch, indicating that Americans were more likely to be involved in FPR. The second possible reason for the significant result of retirement goal clarity is the age of the respondents. As stated by Stawski et al. (2007), in their study, aged people were expected to have sharper retirement goals than younger people.

Based on H_3a, it illustrated that government policy had a significant positive relationship with FPR. Furthermore, H_3b-c revealed the moderating role of government policy on the relationship between financial self-efficacy and retirement goal clarity with FPR. Directly, the results indicated an important positive relationship with the FPR (β = .240, p < .01). This effect is determined by the nature of the policies. Recently, the Saudi government approved Vision 2030 as an economic development plan for the country. In the context of retirees, the objective of Vision 2030 is to provide an appropriate environment for citizens in various fields, including financial planning tools, mortgage loans, savings portfolios, and multiple options for retirees to help them choose suitable investments to fit their lifestyle after retirement (Government of Saudi Arabia, 2016). These actions are necessary because of the absence of financial planning for retirement to help increase post-retirement security, which is a significant issue in personal financial planning. These issues will continue to worsen in the coming few years if governments neglect to establish personal plans for saving and investment workshops to alleviate these problems, as well as if individuals do not participate in these programs or plan late for post-retirement.

Indirectly, government policy (rules and regulations) demonstrates a moderating role only in how retirement goal clarity (H_3c) significantly influences academic and non-academic staff planning and saving for retirement (β = .073, p < .036). It indicates that those who understand and follow the rules and regulations in the retirement system make their retirement goals clear, allowing them to plan, save, and invest for their retirement. It can be noted that when the level of awareness of government policies (rules and regulations) is high among Higher Education staff, there is a greater positive impact of retirement goal clarity that influences savings behavior to regulate and control the FPR of academics and administrators. This addition of government policy is valuable and positively supports the research objective of studying the role of willingness variables in FPR in Saudi Arabia.

Theoretical Implications

The results of the study demonstrate the robustness of motivational ICT to help explain public university staff. In addition to the importance of cognitive, psychological, and external variables toward FPR in higher education institutes, a recent study showed that combining the CWO with ICT is essential for FPR (Topa et al., 2018). Since then, more studies on the behavior of public university employees and their backgrounds have been conducted in a similar research setting, this research is in a better position to identify and verify which antecedents are significant in developing a solid theory of FPR based on retirement saving practices. According to the researcher’s knowledge, this research is the first attempt to implement the CWO model and examine the moderating effect of government policy in Saudi government universities. Also, this study contributed to the literature on personal finance by adding two subjective measures in financial self-efficacy.

Practical Implications

In view of the above findings, some practical implications are suggested. First of all, the conclusions confirm that government policy plays an essential role among employees in higher education. The knowledge of the relationship between the study’s variables is significant because it assists decision-makers in prompting new and efficient policies to enhance FPR behaviors among the government and private sectors in the framework of the economic plan commonly referred to as Saudi Vision 2030. More specifically, the results guide the Public Pension Agency (PPA) to develop a pension system that is deemed to be in accordance with the best interests of retirees. Furthermore, it could serve Vision 2030 by yielding information that is the foundation for the government to analyze and develop pension programs that allow employees to plan and make well-informed decisions regarding their retirement.

Second, the Ministry of Education should collaborate with the Public Pension Agency (PPA) and the General Organization for Social Insurance (GOSI) to provide training programs for employees as a requirement to have a promotion in their job. Such training programs could be valuable for generals or professionals to share knowledge regarding retirement financial planning. Moreover, the Ministry of Education in Saudi needs to introduce compulsory personal financial planning core courses or classes in schools and universities to increase awareness of the importance of financial retirement planning for modern generations. Palaci et al. (2017) highlighted that developing financial education by including courses to increase financial literacy and financial self-efficacy empowers students to understand the data they receive from newsletters, television, social media, and individuals. This helps decision-makers and employers identify the factors that underlie the low level of FPR in other groups of society. In a similar vein, it recognizes the way in which people obtain a high level of FPR. Policymakers must therefore develop the financial capacity of employees by involving financial services companies and financial training institutes.

Conclusion

By conducting this study, the results add to the existing body of personal finance literature by presenting the retirement planning challenges and issues from the viewpoint of academic and non-academic staff working at Saudi public universities. More specifically, it provides a comprehensive background of the extent of Saudi employees’ financial self-efficacy, retirement goal clarity, and perception of government policy and their effect on retirement planning behavior. A number of ideas came out of that study for further research.

Subsequent studies should cover the limitations of this study. To provide significant outcomes in the future based on the current study’s results, more research is required to deeply understand the complex relationship between capacity, willingness, opportunity variables, and their influential role on FPR by applying the CWO model to a different population. The R² of this study was .320, which implied that over .50 of R² should be explained by other possible cognitive, psychological, and external variables that were not part of the study model. It is recommended to examine other cognitive (e.g., metacognitions and worries), psychological (e.g., self-control), and external variables (e.g., cultural and social norms) directly and indirectly to identify all FPR perspectives.

Some limitations may affect the quality of research findings. First, the findings of this study may not represent all government employees and the private sector in Saudi Arabia, such as police officers, doctors, and bank staff. Likewise, the current research has used the quantitative method to analyze the data. There are many benefits to using a questionnaire technique to collect study data. However, applying a qualitative approach (e.g., an interview) to collect data may provide more valuable information on the topic matter.

The current study is constructed under the quantitative method and cross-sectional time. However, the longitudinal design could be applied to increase its generalization in future research. An advantage of the longitudinal study is that it helps a researcher better understand the historical and causal relationships among the independent research variables during the study. Further, future studies might cover different groups in society and compare the results to comprehend their key differences and the most influential variables that affect their FPR behaviors.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: The Faculty of Business and Economics at Universiti Malaya Special Publication Fund is funding a portion of this publication.

ORCID iDs

Ahmad Saleh Ghadwan

Mohamed Hisham Hanifa

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Financial Planning for Retirement: The Moderating Role of Government Policy

Abstract

Keywords

Introduction

An Overview of the Saudi Retirement Structure

Literature Review

Theories and Hypothesis Development

Financial Planning for Retirement (FPR)

Capacity to Plan and Save for Retirement—Financial Self-Efficacy (FSE)

Willingness to Plan and Save for Retirement—Retirement Goal Clarity (RGC)

Opportunity to Plan and Save for Retirement—Government Policy (GP)

Conceptual Framework

Methodology

Design

Instruments

Analysis Procedures

Results and Findings

Preliminary Data Analysis

Descriptive Statistic Result

Assessing Measurement Model

Reflective Measurement

Formative Measurement

Assessment of Structural Model

Discussion

Theoretical Implications

Practical Implications

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iDs

References