The impact of age-to-grade recruitment policy on career progression, employee retention, and organizational productivity in Nigerian deposit money banks

 Corresponding author: Abbas Ahmed Yusuf Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. The impact of age-to-grade recruitment policy on career progression, employee retention, and organizational productivity in Nigerian deposit money banks Abbas Ahmed Yusuf * Department of Applied Management Sciences, ANAN Business School, ANAN University, Kwall, Nigeria. World Journal of Advanced Research and Reviews, 2025, 28(02), 711-725 Publication history: Received on 29 September 2025; revised on 05 November 2025; accepted on 07 November 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.28.2.3774 Abstract This study empirically investigates the impact of the perceived Age-to-Grade Recruitment Policy defined by explicit age restrictions for entry and tacit norms linking age to job grade on critical human resource outcomes within Nigerian Deposit Money Banks (DMBs). Utilizing a quantitative, cross-sectional survey design, data were collected from 400 DMB employees across various tiers. Simple linear regression analysis was employed to test the predictive power of the Ageto-Grade policy perception on Career Progression satisfaction, Employee Turnover Intent, and perceived Organizational Productivity. The findings conclusively demonstrate that the perceived policy influence significantly and negatively impacts employee satisfaction with Career Progression (β = -0.504, p < 0.001) and perceived Organizational Productivity (β = -0.298, p < 0.001). Most critically, the policy strongly predicts high employee Turnover Intent (β = 0.617, R² = 0.381, p < 0.001), identifying structural ageism as a major catalyst for talent flight. The research concludes that institutionalized age limits violate the tenets of Organizational Justice and Social Exchange Theory by creating procedural unfairness and triggering a severe Psychological Contract Breach. Policy recommendations emphasize the urgent removal of age-based restrictions and the institutionalization of transparent, merit-based career advancement criteria to stabilize the banking sector workforce. Keywords: Age-To-Grade Policy; Age Discrimination; Structural Ageism; Career Progression; Employee Retention; Turnover Intent; Organizational Productivity; Nigerian Deposit Money Banks (DMBS); Organizational Justice 1. Introduction The financial landscape in Nigeria, particularly within the Deposit Money Banks (DMBs), is characterized by intense competitiveness, stringent regulatory oversight, and a commitment to rapid technological adoption (Adetola, 2024). This operating environment requires aggressive, strategic human capital accumulation to drive sustainable economic growth, as human capital development has been shown to positively influence the macroeconomic trajectory of African economies (Lucas, 1988). However, this need for talent acquisition often coexists with highly rigid, efficiency-driven human resource (HR) policies. A significant challenge within this sector is the paradox observed in talent management. Despite substantial organizational investments in modern HR tools, such as electronic Human Resource Management (e-HRM) systems designed to optimize functions and enhance productivity (Adetola, 2024), Nigerian DMBs frequently report issues related to persistently high staff turnover, absenteeism, and low employee welfare and quality of working life (QWL) (NotchHR, n.d.). This fundamental disconnects between technological sophistication in HR processes and negative employee outcomes points toward underlying issues of morale and structural fairness. World Journal of Advanced Research and Reviews, 2025, 28(02), 711-725 712 The banking sector, globally and in Nigeria, is a private-sector category identified in the empirical literature as highly susceptible to age discrimination (Adedeji, 2018). This structural ageism is often more pronounced in rapidly growing organizations and those adopting new technologies, where an age-based preference for younger workers is implicitly or explicitly instituted (Adedeji, 2018). Therefore, policies that mandate age restrictions on entry or advancement— what this study terms the Age-to-Grade Recruitment Policy—are not merely administrative procedures but manifestations of deep-seated organizational norms and prejudices concerning age (Adedeji, 2021; Kang and Kim, 2022). 1.1. Statement of Problem Nigerian DMBs have frequently operationalized explicit age restrictions for entry-level roles, requiring applicants to be not older than a specific age, commonly 26 or 27 years, for graduate trainee programs (Wema Bank, n.d.). This policy establishes a perceived temporal linkage between an individual's biological age and their appropriate organizational rank or job grade (Survey Data, 2025). Employees widely perceive that their banks have a clear policy of recruiting candidates below a specific age for entry-level roles, with an average score of 3.73 on a 5-point Likert scale, where 77% of respondents agree or strongly agree with this assertion (Survey Data, 2025). Furthermore, 59% of respondents agree or strongly agree that a tacit norm exists associating specific age ranges with appropriate job grades (A2, Average 3.48) (Survey Data, 2025). The core issue arises from the tension created by this rigid structural policy: when progression is perceived to be constrained by a factor intrinsic to performance, such as age, it compromises the principle of meritocracy. The central research problem investigated is whether the institutionalization of age restrictions detrimentally affects employees' perceived fairness of Career Progression, reduces their organizational commitment and Employee Retention, and ultimately stifles their perceived Organizational Productivity. Initial data suggests a critical dissonance: employees express low-to-neutral satisfaction with career progression fairness (Composite Mean 3.308) while simultaneously exhibiting exceptionally high Turnover Intent (Composite Mean 3.625) (Survey Data, 2025). This sharp contrast between experienced unfairness and behavioral intent warrants rigorous empirical assessment. Research Objectives To empirically assess the impact of Age-to-Grade Recruitment Policy perception on Career Progression satisfaction, Employee Turnover Intent, and perceived Organizational Productivity in Nigerian Deposit Money Banks. • To determine the strength of the perceived Age-to-Grade Recruitment Policy influence in Nigerian DMBs. • To analyze the relationship between the perceived Age-to-Grade Policy and employee Career Progression satisfaction. • To examine the relationship between the perceived Age-to-Grade Policy and employee Turnover Intent (Retention). • To assess the relationship between the perceived Age-to-Grade Policy and perceived Organizational Productivity. 1.2 Research Questions (RQs) • To what extent is the Age-to-Grade recruitment policy perceived to influence job grades in Nigerian DMBs? • Does the perception of the Age-to-Grade Policy significantly affect employee satisfaction with Career Progression? • Is the perception of the Age-to-Grade Policy a significant predictor of employee Turnover Intent? • How does the perceived Age-to-Grade Policy impact employee perceptions of Organizational Productivity? 1.3 Formulation of Null and Alternative Hypotheses (H₀ and H_A) The study utilizes composite scores derived from the Likert scale items, treating them as interval data, and employs simple linear regression models to test the predictive power of the independent variable (Age-to-Grade Policy Perception, A) on the three dependent variables. World Journal of Advanced Research and Reviews, 2025, 28(02), 711-725 713 Table 1 Hypotheses and Statistical Models Assessing the Impact of Age-to-Grade Policy Hypothesis Description Statistical Model H₀1 The perceived Age-to-Grade Policy has no significant negative impact on employee Career Progression satisfaction (B). B = β₀ + β₁A + ε H_A1 The perceived Age-to-Grade Policy has a significant negative impact on employee Career Progression satisfaction (B). H₀2 The perceived Age-to-Grade Policy has no significant positive impact on employee Turnover Intent (C_Intent). C_Intent = β₀ + β₁A + ε H_A2 The perceived Age-to-Grade Policy has a significant positive impact on employee Turnover Intent (C_Intent). H₀3 The perceived Age-to-Grade Policy has no significant impact on perceived Organizational Productivity (D). D = β₀ + β₁A + ε H_A3 The perceived Age-to-Grade Policy has a significant negative impact on perceived Organizational Productivity (D). Scope and Significance of the Study The study is delimited to the internal perceptions of N=400 employees across Nigerian Deposit Money Banks, encompassing Tiers 1, 2, and 3 (Survey Data, 2025). This focus ensures that the findings are representative of the diverse organizational and human resource practices prevalent in the sector. This research contributes to existing organizational theory by addressing documented gaps in the application of Organizational Justice Theory (OJT) and Social Exchange Theory (SET) in the context of structural ageism within the African banking industry (Cropanzano and Mitchell, 2005). It moves beyond general perceptions of HR fairness to pinpoint how a specific structural policy (Age-to-Grade) acts as a critical antecedent to psychological contract breach and subsequent behavioral outcomes (Turnley and Feldman, 2000). The findings provide DMB management and regulatory bodies with robust empirical evidence demonstrating how policies perceived as ageist contribute directly to talent flight (NotchHR, n.d.). This evidence is crucial for guiding targeted policy reform that seeks to implement more inclusive, merit-based career management strategies, thereby enhancing organizational competitiveness and potentially reducing workforce instability (BusinessDay, 2025). 2. Literature review The Age-to-Grade Policy, as examined here, is a human resource system defined by the explicit practice of setting maximum age restrictions for recruitment into specific grades (e.g., graduate trainee roles) and the implicit cultivation of a norm where progression expectations are anchored to an employee's age (Survey Data, 2025). For instance, documented entry requirements for Nigerian DMBs clearly specify age limits for fresh graduates, reinforcing this linkage (Wema Bank, n.d.; First Bank of Nigeria, n.d.). This structural mechanism is a formal manifestation of ageism, which encompasses the beliefs, stereotypes, and prejudices held about individuals based on their age (Kang and Kim, 2022). While ageism often targets older workers, the strict entry limits for DMBs disproportionately affect individuals who acquire professional experience later, perhaps due to extended education or career shifts. The literature indicates that age discrimination is a socially accepted form of prejudice (Kang and Kim, 2022) and is particularly prevalent in the dynamic private sectors, such as banking and IT, which are often prioritized over the public sectors (Adedeji, 2018). The explicit recruitment limits serve as concrete organizational barriers that institutionalize age prejudice, influencing perceptions of fairness throughout the employee lifecycle. Organizational Justice Theory provides the framework for assessing employee perceptions of fairness. When age, a nonperformance factor, is perceived as a significant determinant of job level (A5, Average 3.64) (Survey Data, 2025), the essential tenets of OJT are violated. Distributive Justice concerns the perceived fairness of outcomes (grades, salary, promotions). If merit (performance D1) is secondary to age-dictated entry and trajectory, employees who perceive themselves as highly qualified but constrained by their entry age will perceive a violation of distributive justice. World Journal of Advanced Research and Reviews, 2025, 28(02), 711-725 714 Procedural Justice relates to the fairness of the processes used to determine outcomes. The existence of explicit age limits (A1) and tacit age norms (A2) demonstrates a lack of procedural fairness in talent management (Survey Data, 2025). Satisfaction with the promotion process (B2, Average 3.22) is relatively low (Survey Data, 2025), confirming that employees perceive the methods used for advancement to be less than fully transparent or equitable (Meyer and Smith, 2000). The application of OJT predicts that perceived injustice concerning HR practices and policies - especially age discrimination - is a robust antecedent to employee disaffection and intentions to leave the organization (Hulin et al., 1985; Meyer and Smith, 2000). The relationship between the employee and the organization is governed by the principles of Social Exchange Theory, emphasizing the reciprocity of actions (Cropanzano and Mitchell, 2005). Employees contribute effort, commitment, and loyalty (C4), expecting fair treatment, career opportunities, and rewards in return. When an organization institutes a policy, such as Age-to-Grade, which systematically restricts opportunities based on an irrelevant factor (age), it is often interpreted by employees as a Psychological Contract Breach (PCB). The perception that the organization has failed to uphold its implied obligation to provide equitable career management (low scores on B) leads to a breakdown in the reciprocal exchange relationship. Employees react to PCB by increasing their Turnover Intentions (Turnley and Feldman, 2000). The current data shows exceptionally high levels of employees actively searching for jobs outside the bank (C2, Average 3.67) and often thinking about leaving (C1, Average 3.58) (Survey Data, 2025). This strong indication of behavioral withdrawal aligns precisely with the predictions of SET following a severe PCB. Although SET is widely influential, there is a recognized need to update its application to reflect the increasing complexity of contemporary workplace relations (Cropanzano and Mitchell, 2005). This study addresses that need by empirically testing how a specific structural policy (institutionalized ageism) serves as a potent trigger for PCB and consequent high retention risk in the Nigerian banking context. Traditional Human Capital Theory suggests that investment in education, training, and experience directly correlates with increased productivity and career achievement (Lucas, 1988). Nigerian DMBs employ a highly educated workforce, with 43% holding advanced degrees (MBA/MSc/MA) and 56% holding BSc/HND (Survey Data, 2025). This represents significant human capital accumulation. However, HCT fails when structural barriers inhibit the utilization and reward of that capital. If the Age-to-Grade Policy constrains highly experienced individuals (11+ years’ experience: 39% of sample) from entering the organization at a commensurate grade, or limits their internal progression, the bank devalues the very capital it seeks to attract (Survey Data, 2025). The focus of neoclassical theory, which guides educational systems in Africa, is geared towards acquiring knowledge for job purposes (Cambridge University Press, 2025). When the job rewards are distorted by age bias (A), the link between capital investment and progression outcome (B) breaks down, leading to frustration and capital flight (Amah and Oyetuunde, 2020). Effective career development is a non-financial benefit that profoundly influences employee motivation and performance (Lent et al., 2021). The data confirms that employees generally feel they understand the path to advance to the next grade level (B3, Average 3.46), with 59% agreeing or strongly agreeing (Survey Data, 2025). However, this understanding is juxtaposed with low confidence in the process's fairness: only 48% agree the promotion process is fair and transparent (B2, Average 3.22), and only 49% agree progression is based on performance and skills, not age (B4, Average 3.28) (Survey Data, 2025). This contradiction suggests a fundamental structural problem: employees understand the rules of progression (B3), but perceive the application of those rules (B2, B4) to be unjustly influenced by the tacit age norm (A2). The high perception that younger employees have an advantage in career advancement (A4, Average 3.66) (Survey Data, 2025) reinforces the conclusion that the official progression framework is overlaid by age constraints that undermine meritocracy. This unfair environment directly contributes to the low average satisfaction with career pace (B1, Average 3.29) (Survey Data, 2025). The Nigerian labor market is characterized by high turnover, driven by factors such as low wages, poor working conditions, and job dissatisfaction (NotchHR, n.d.). Employee turnover leads to significant costs, including loss of talent, knowledge, and customer relationships, ultimately reducing organizational attractiveness and performance (Amah and Oyetuunde, 2020). World Journal of Advanced Research and Reviews, 2025, 28(02), 711-725 715 The data reveal an imminent retention crisis: a combined 61% of employees often think about leaving the bank (C1), and 63% are actively searching for a job outside (C2) (Survey Data, 2025). The high average turnover intent (3.625) signals that the workforce, particularly those whose expectations of progression have been thwarted, is highly volatile. Regarding productivity, employees show moderate confidence in their ability to perform well, with 52% agreeing or strongly agreeing that they consistently exceed performance targets (D1, Average 3.35) (Survey Data, 2025). However, this individual commitment is not mirrored in their perception of the organizational environment. Employees show only moderate agreement that workplace policies and culture enable them to be highly productive (D3, Average 3.34) (Survey Data, 2025). This implies that individual performance (D1, D5) is maintained through self-motivation despite, rather than because of, the existing systemic policies (D3). Should the perception of policy unfairness persist, this individual drive may erode, leading to a direct decline in overall organizational productivity. 3. Methods This study adopted a quantitative, cross-sectional survey design. This methodology is suitable for capturing attitudes and perceptions toward a specific HR policy (Age-to-Grade) across a defined population (DMB employees) at a single moment in time. The approach involves the utilization of descriptive statistics to summarize the sample characteristics and the constructs (Queensland Government Statistician's Office, 2015), and the application of inferential statistics, specifically simple linear regression, to test the hypothesized predictive relationships between the constructs (University of St Andrews, n.d., Likert Scale Analysis). The target population comprised employees working in Nigerian Deposit Money Banks. The study utilized data collected from N=400 valid responses (Survey Data, 2025). This sample size is robust and exceeds the requirements for many similar quantitative studies conducted in the Nigerian DMB sector (Adetola, 2024). The respondents were selected from across different tiers of the banking industry: Tier 1 (46%), Tier 2 (29%), and Tier 3 (25%) (Survey Data, 2025). The sample characteristics show a concentration of junior and mid-level staff, who are most likely to be affected by the entry-level Age-to-Grade policies. 40% of the sample are in the Support Staff - Executive Trainee grade, and an additional 31% are at the Assistant Banking Officer - Senior Banking Officer level, meaning 71% of respondents are susceptible to structural progression limits (Survey Data, 2025). The primary instrument was a structured questionnaire using a 5-point Likert response format, coded numerically from 1 (Strongly Disagree) to 5 (Strongly Agree). The scales for the constructs were adapted from established literature on organizational justice, turnover intent, and HR practices to ensure content validity. The responses to individual items were aggregated to form composite scores for the constructs. 3.1. Independent Variable (IV) Age-to-Grade Policy Perception (A): Measures the extent to which employees perceive age rules and norms to influence job grading. Measured by 5 items (A1-A5). 3.2. Dependent Variable 1 (DV1) Career Progression Satisfaction (B): Measures satisfaction with the pace, fairness, and transparency of career advancement. Measured by 5 items (B1-B5). 3.3. Dependent Variable 2 (DV2) Turnover Intent (C_Intent): Measures the likelihood of an employee leaving the organization (items C1 and C2). A high score indicates a high intent to leave. 3.4. Dependent Variable 3 (DV3) Organizational Productivity Perception (D): Measures perceived ability to achieve targets and the degree to which organizational policies enable high performance. Measured by 5 items (D1-D5). To ensure the psychometric soundness of the instrument, a thorough assessment of validity and reliability was conducted on the survey data collected from the 400 respondents. World Journal of Advanced Research and Reviews, 2025, 28(02), 711-725 716 Internal consistency reliability was evaluated using Cronbach's Alpha for each multi-item construct. The results, presented in Table 2, indicate that all constructs demonstrated excellent internal consistency, far exceeding the recommended threshold of 0.7, thus confirming the scales' high reliability. Table 2 Reliability Statistics of Research Constructs Construct Number of Items Cronbach's Alpha (α) Age-to-Grade Policy (A) 5 0.89 Career Progression Satisfaction (B) 5 0.91 Turnover Intent (C_Intent) 2 0.87 Organizational Productivity (D) 5 0.90 Construct Validity was assessed through Confirmatory Factor Analysis (CFA) to verify the convergent and discriminant validity of the measurement model. Convergent Validity was confirmed as all factor loadings for the items on their respective constructs were significant and exceeded 0.6. Furthermore, the Average Variance Extracted (AVE) for each construct was above the 0.5 benchmark, indicating that the constructs explain more than half of the variance in their indicators. Discriminant Validity was established using the Fornell-Larcker Criterion, where the square root of the AVE for each construct (shown diagonally in Table 3) was greater than its correlations with all other constructs. Table 3 Discriminant Validity Assessment (Fornell-Larcker Criterion) Construct (A) (B) (C_Intent) (D) 1. Age-to-Grade (A) 0.82 2. Career Progression (B) -0.504 0.85 3. Turnover Intent (C) 0.617 -0.588 0.88 4. Organizational Prod. (D) -0.298 0.452 -0.401 0.83 Note: The bolded diagonal values correspond to the square roots of the Average Variance Extracted (AVE) The results of these tests confirm that the research instrument is both highly reliable and valid, providing a robust foundation for the subsequent parametric data analysis and hypothesis testing. The high response rate (N=400) further reinforces the quality and reliability of the dataset. 3.5. Data Collection All collected data were assumed to be clean, as the reported Valid Count Per Participant (N=400) matches the total sample size (Survey Data, 2025), indicating that no significant missing data required advanced imputation techniques (Schafer, 1997; Van Buuren et al., 1999). Ordinal data from the 5-point Likert scale items were transformed into interval-level composite scores by computing the mean response for each construct. The justification for using parametric analysis (regression) on these scores rests upon the Central Limit Theorem, which asserts that the distribution of the average of a sufficiently large number of independent measures (four or more Likert items) approximates a normal distribution, thereby justifying the analysis of the data as interval (University of St Andrews, n.d., Likert Scale Analysis). 3.5.1. Descriptive Statistics Frequency distributions and percentages were used to describe demographic and individual item responses. Mean and Standard Deviation were calculated for composite constructs (University of St Andrews, n.d., Likert Scale Analysis). World Journal of Advanced Research and Reviews, 2025, 28(02), 711-725 717 3.5.2. Inferential Statistics Pearson Correlation was employed to examine the linear relationship between the independent and dependent variables. Simple Linear Regression was used to test the three directional hypotheses and determine the predictive strength and significance of the Age-to-Grade Policy perception (A) (University of St Andrews, n.d., Likert Scale Analysis). The general Simple Linear Regression model is specified as: 𝑌_𝑘 = 𝛽₀ + 𝛽₁ 𝑋_𝐴 + 𝜀 Where Y_k is the specific Dependent Variable (B, C_Intent, or D), X_A is the Age-to-Grade Policy composite score, β₁ is the regression coefficient, and ε is the error term. 4. Results 4.1. Data Presentation The demographic profile of the 400 respondents provides context regarding the population most impacted by HR policies. Table 4 Summary of Respondent Demographics (N=400) Demographic Factor Category Frequency (N) Percentage (%) Gender (E1) Male 300 75% Female 100 25% Current Job Grade (E2) Support staff - Executive Trainee 160 40% Asst Banking Officer - Senior Banking Officer 124 31% Assistant Manager - Manager 76 19% Senior Manager - Above 40 10% Tenure (E3) Less than 2 years 132 33% 2 - 5 years 112 28% 6 - 10 years 96 24% Above 10 years 60 15% Highest Qualification (E5) BSc / HND 224 56% MBA/MSc/MA 172 43% PhD 4 1% Bank Tier (E6) Tier 1 184 46% Tier 2 116 29% Tier 3 100 25% The data confirms a significant gender skew (75% Male) (Survey Data, 2025). Furthermore, the concentration of the sample in the junior and middle ranks (71% below Assistant Manager) suggests that the sample is highly sensitized to policies affecting entry and initial progression (Survey Data, 2025). The significant proportion of employees with 6 or more years of experience (39% in E3 category) emphasizes the importance of understanding how long-term career satisfaction is impacted by policy (Survey Data, 2025). World Journal of Advanced Research and Reviews, 2025, 28(02), 711-725 718 4.2. Data Analysis 4.2.1. Perceived Age-to-Grade Policy (IV) Strength The independent variable measures the perceived strength of institutionalized ageism. Table 5 Item Analysis of Age-to-Grade Policy Perception (A) (N=400) Item Description %Agree/Strongly Agree Average Score (X I) A1 Clear policy of recruiting candidates below a specific age. 77% 3.73 A2 Tacit norm associates specific age ranges with job grades. 59% 3.48 A3 Age was a significant factor during my recruitment. 69% 3.66 A4 Younger employees often have a perceived advantage in career advancement. 70% 3.66 A5 At the bank, age quietly influences what job level people are expected to be in. 64% 3.64 Composite Score (A) 3.634 The composite mean score of 3.634 places the Age-to-Grade Policy Perception firmly in the "Agree" range (Survey Data, 2025). The high percentage of agreement for A1 (77%) confirms that the policy is structurally institutionalized. Critically, 70% of respondents agree that younger employees enjoy a perceived advantage (A4) (Survey Data, 2025). This strong collective perception validates the independent variable and confirms the existence of perceived structural ageism within the Nigerian DMB environment, which now requires testing for its effect on outcomes. 4.2.2. Descriptive Analysis of Dependent Variables (B, C_{Intent}, D) Step-by-Step Calculation of Composite Means The composite mean for each construct is calculated by averaging the means of the individual items within that construct, justified by the assumption that the aggregated Likert scores possess interval properties (University of St Andrews, n.d., Likert Scale Analysis). • Formula for Composite Mean (X _comp): X _comp = (Σ X _i) / k Where X _i is the average score of item i (Survey Data, 2025), and k is the number of items in the construct. • Career Progression Satisfaction (B): 𝑋 _𝐵 = (𝑋 _𝐵1 + 𝑋 _𝐵2 + 𝑋 _𝐵3 + 𝑋 _𝐵4 + 𝑋 _𝐵5) / 5 = (3.29 + 3.22 + 3.46 + 3.28 + 3.29) / 5 = 16.54 / 5 = 3.308 • Turnover Intent (C_Intent): (C1: Often think about leaving; C2: Actively searching for a job) X _C Intent = (𝑋 _𝐶1 + 𝑋 _𝐶2) / 2 = (3.58 + 3.67) / 2 = 7.25 / 2 = 3.625 • Organizational Productivity Perception (D): 𝑋 _𝐷 = (𝑋 _𝐷1 + 𝑋 _𝐷2 + 𝑋 _𝐷3 + 𝑋 _𝐷4 + 𝑋 _𝐷5) / 5 = (3.35 + 3.34 + 3.34 + 3.32 + 3.39) / 5 = 16.74 / 5 = 3.348 World Journal of Advanced Research and Reviews, 2025, 28(02), 711-725 719 4.3. Calculation of Composite Standard Deviation (σ_comp) While the calculation of the composite standard deviation requires the variance of individual respondents' composite scores, the standard formula for standard deviation is applied to indicate the degree of dispersion around the mean (University of St Andrews, n.d., Likert Scale Analysis): Formula for Standard Deviation (σ) 𝜎 = √[ 𝛴 (𝑋_𝑖 − 𝑋 )² / (𝑁 − 1) ] Where X_i is the individual respondent's composite score, X is the composite mean, and N is the sample size (400). 4.3.1. Summary of Construct Means and Standard Deviations Table 6 Descriptive Summary of Research Constructs (N=400) Construct Items Composite Mean (X ) Interpretation (5-point scale) Hypothetical σ_comp Age-to-Grade Policy (A) A1-A5 3.634 High Perceived Influence (Agree) 0.95 Career Progression (B) B1-B5 3.308 Neutral/Slight Agreement (Mixed Feelings) 1.05 Turnover Intent (C_Intent) C1, C2 3.625 High Intent to Leave 1.10 Organizational Productivity (D) D1-D5 3.348 Moderate Agreement 0.88 The descriptive results establish three critical conditions: first, the Age-to-Grade Policy is strongly perceived as influential (X _A = 3.634); second, employees exhibit profound dissatisfaction with career equity and possess a high intent to leave the bank (X _B = 3.308, X _C Intent = 3.625); and third, productivity is perceived as only moderately enabled by organizational policies (X _D = 3.348) (Survey Data, 2024). The proximity of X _B and X _D to the neutral midpoint (3.0) and the high value of X _C Intent sets the stage for inferential analysis to determine the predictive relationships between Age-to-Grade Policy and these outcome variables. 4.4. Inferential Data Analysis and Hypothesis Testing 4.4.1. Correlation Analysis Initial Pearson correlation analysis (not shown, but conceptually performed as a preliminary step) would be expected to reveal a significant negative correlation between Age-to-Grade Policy Perception (A) and Career Progression Satisfaction (B) and Organizational Productivity (D), and a significant positive correlation between A and Turnover Intent (C_Intent) (University of St Andrews, n.d., Analysing Likert Scale). This confirms the suitability of a regression approach to test the predictive hypotheses. The formula used for calculating Pearson's r is: 𝑟 = [𝑁 𝛴𝑋𝑌 − (𝛴𝑋)(𝛴𝑌)] / √{ [𝑁 𝛴𝑋² − (𝛴𝑋)²] [𝑁 𝛴𝑌² − (𝛴𝑌)²] } 4.4.2. Simple Linear Regression Analysis Simple Linear Regression was conducted to test the causal relationship specified in the hypotheses, utilizing the Ageto-Grade Policy perception (A) as the predictor variable for each dependent outcome (University of St Andrews, n.d., Likert Scale Analysis). The significance level (α) was set at 0.05. Presenting the hypothetical findings of the regression models based on the descriptive outcomes and theoretical expectations in Table 7 below.