What Factors Predict Employee Performance? A Regression Analysis of Age, Gender, and Work Experience

What Factors Predict Employee Performance? A Regression Analysis of Age, Gender, and Work Experience

Josephine E. Prudente, Esther R. Cuaresma, Annabel D. Quilon, Alvin M. Nieva

San Beda University, Manila, Philippines

DOI: https://dx.doi.org/10.47772/IJRISS.2025.914MG00163

Received: 01 October 2025; Accepted: 08 October 2025; Published: 14 October 2025

ABSTRACT

Given the organizational shift toward achieving competitiveness, managers are keen to understand how various employee characteristics, such as age, gender, and years of experience, influence performance ability. Consequently, this research determined the predictive influence of these demographic factors on the three dimensions of employee performance: task, adaptive, and contextual, among Filipino workers. Using convenience sampling, a total of 534 participants (56.18% female and 43.82% male), with ages ranging from 18 to 63, were recruited from public and private companies. To measure employee performance, the Employee Performance Scale for Filipino Workers (EPS-FW) was utilized because this instrument has established strong validity and reliability for the Filipino context. The results of the multiple regression analyses indicated that age, gender, and years of experience were not significant predictors of employee performance across any of the task, adaptive, or contextual domains. Significantly, this non-significant result is interpreted as a positive finding, suggesting that effectiveness is independent of these demographic factors. Therefore, managers have no statistical basis for hesitation or discrimination when selecting employees based solely on these variables, which supports the principle of non-discrimination. Finally, the findings indicate that future research should shift its focus to other organizational or individual characteristics as potentially stronger predictors of employee performance.

Keywords: Filipino Workers, Age, Gender, Years of Work Experience, Employee Performance

INTRODUCTION

The understanding of unique worker characteristics is vital for managers seeking to optimize performance. According to Adero and Odiyo (2020), employee traits have an impact on an organization’s productivity, which directly impacts employee satisfaction, service quality, and productivity (Hasinat et al., 2024). In addition to bringing out the much-needed qualities of leadership, flexibility, accomplishment, optimism, and emotional intelligence, a person’s character is the means by which they communicate their ideals. Employee conduct at work is influenced by various personal traits, including intelligence, ability, attitude, personality, and emotional intelligence. Therefore, organizational leaders must monitor how to leverage the unique and varied personnel traits to enhance organizational success (Adero & Odiyo, 2020).

Doing different tasks can help people learn and enhance their cognitive abilities (Liu & Fleisher, 2022). According to Robbins and Judge (2022), these intellectual and physical capacities reflect an individual’s current ability to successfully complete various job duties, especially mental and physical activities. Given that cognitive ability may erode over a life course, a worker’s lifespan becomes an intriguing characteristic for managers to consider, particularly because task performance differs significantly across various jobs, scopes, and industries.

Organizations can now easily deliver products, services, and information to stakeholders by using different approaches to maintain optimal functioning, a capability significantly enhanced by technological developments in Human Resource Management (HRM). As new technologies were introduced across manufacturing, communication, and HRM, the demand for specialized employees increased (DeNisi & Griffin, 2025). The massive growth of technology has resulted in a significant need for workers who can manage and apply these innovations (DeNisi & Griffin, 2025). Pradhan and Jena (2017) define this capability to suit changing workplace conditions as adaptive performance, which is an individual’s ability to acclimatize and conform to changes as they achieve proficiency and engagement in their job.

The Triarchy Model of Employee Performance, illustrated in Figure 1, shows that higher cognitive ability (task performance) is primarily supported by task knowledge (the necessary technical knowledge or principles for job performance and the capability to manage multiple assignments), task skill (the application of technical knowledge to successfully complete tasks with minimal supervision), and task habits (an inherent ability to respond effectively to assigned jobs, which can either enhance or hinder performance), all of which contribute to employee productivity. Pradhan and Jena (2017) further regarded the evolution of new occupations (adaptive performance) as an offshoot of technological innovations that will require the employees to engage in fresh learning. Lastly, the benefits of organizational citizenship behavior are those that encourage the betterment of the individual, group, or organization in the firm’s direction.

Figure 1: The Triarchy Model of Employee Performance (Pradhan and Jena, 2017)

Task Performance

Task performance measures how well workers accomplish particular jobs, which helps the organization achieve its operational goals (Hasinat et al., 2024). The evolving generational, cultural, and structural dynamics in the global workforce present managers with a myriad of ways to promote organizational engagement. Because people with special skills are often in short supply, HR staff require constant development, considering that knowledge and technology may erode over time and over the employees’ working lifespan. Businesses view such an event as unfavorable, as the loss of individuals with scarce abilities and excellent performance can significantly impact a firm’s overall performance. With effective human resources management, managers can increase productivity by optimizing the fit between the demands and requirements of the job. The combination of effectiveness and efficiency in performing core job tasks is a reflection of task performance (Robbins & Judge, 2022).

Adaptive Performance

In a workplace that is constantly changing, adaptive performance is especially important, highlighting an employee’s capacity to handle unforeseen changes and learn new skills (Hasinat et al., 2024). The integration of technology into office workflow has profound and often mixed impacts on work systems. While new technology positively contributes to job performance for some employees, it can simultaneously cause a decline in work engagement for others due to insecurity, complexity, and difficulty adjusting, particularly as workers age. Singh et al., (2023) highlight various adverse effects of technology integration on worker behavior, including reduced interpersonal relationships, resistance to change, and increased stress as firms attempt to streamline work processes. Consequently, employee development (or ongoing education) is crucial. The compelling reason for this development is to promptly improve workers’ knowledge and skills whenever a company integrates technology to ensure sustained productivity. However, the success of these development efforts depends on the organizational environment. Positive relationships with managers and trust in supervisors (Quilon & Perreras, 2020), alongside favorable working conditions, are also fundamentally associated with increased employee productivity, commitment, and overall well-being. Ultimately, high employee performance serves as a source of competitive advantage when individuals feel that their work aligns with their expectations and is supported by the organizational climate.

Contextual Performance

Among other actions that foster a healthy work environment, contextual performance includes teamwork and engagement (Hasinat et al., 2024). Higher levels of job satisfaction, commitment, and Organizational Citizenship Behavior (OCB) demonstrate employee engagement (Wilkinson et al., 2025). OCB is defined as behavior that exceeds formal job duties, providing performance beyond expectations (Robbins & Judge, 2022). Helping a co-worker beyond one’s prescribed duties fosters this citizenship, which is presumed to increase employee participation (Santander and Prudente, 2020) by assuring individuals of peer support and assistance. Literature relating to gender roles suggests women are often expected to perform more helping roles in an employment context due to perceived nurturing qualities (Asadullah et al., 2021). However, when tested empirically, the findings on cross-gender differences in helping behavior were not statistically supported in that same study. Considering this, the researchers believe that regardless of gender, extending help to a co-worker—especially that which goes beyond job requirements—is vital for developing cohesiveness among employees in a given workplace. The effort to develop organizational effectiveness hinges on enhancing positive behaviors and attitudes like citizenship while minimizing dysfunctional behaviors (Griffin & Phillips, 2023). Pradhan and Jena (2017) refer to this dimension as contextual performance, which reflects a worker’s perception and understanding of their role beyond technical tasks. Leaders strive to improve this type of behavior to meet strategic goals and secure a positive organizational climate.

Age and Employee Performance

Managing an aging workforce has serious consequences because crucial evidence indicates that various types of job performance differ by age and follow distinct trajectories over an individual’s career (Karanika-Murray et al., 2024). Some scholars argue that the typical decline in physical, mental, and cognitive abilities associated with aging can be offset by the knowledge gained from lifelong experience, which allows older workers to achieve maturity and mastery over time (Roberts, 2020; Bashir et al., 2021). However, not everyone receives this compensation. Other observations suggest that older employees may become less motivated and satisfied as they approach retirement (Roberts, 2020; Bashir et al., 2021). To address these dynamics, research emphasizes the value of intergenerational synergy: when young, innovative minds are combined with experienced and skilled individuals (Bashir et al., 2021), organizations can maintain their market share and continually boost their performance. Ultimately, different periods of life are characterized by unique time-role transitions, challenges, shifts in priorities, life stages, and changes in competencies (Karanika-Murray et al., 2024). Given these factors, the researchers propose that fostering balance and diversity across a worker’s lifespan, particularly regarding age and life course changes, may lessen the negative generational effect on overall workplace performance. While some recent empirical studies reported no significant effects between age and individual adaptivity (Karanika-Murray et al., 2024), there is still evidence demonstrating the value of adaptivity and how it changes with age. Hence, job performance has a complex, mixed relationship with age, where experience often offsets cognitive decline, but motivation may drop.

Gender and Employee Performance

Despite decades of progress in gender parity, global efforts to reduce inequality remain challenging for various economies, especially for those firms seeking to close the gap. The Gender Gap Report 2025 (World Economic Forum, 2025, June 11) has indicated the greatest progress toward gender parity in the areas of economic participation and opportunity as well as political empowerment. Seventeen of the 19 economies in the East Asia and Pacific region maintain a rate of over 40% for women’s participation in the labor force, which shows an improved and growing global presence of women in the total labor market. While the Philippines has scored the 20th spot among 148 countries (PCW, 2025, June 19), it is compelling to note the critical areas for improvement, particularly in education, health & survival, and political empowerment. Cabegin and Gaddi (2019) also underscore the multidimensional factors that have led to low labor force participation among Filipino women. These factors include stereotyped gender roles, lack of access to skills training for virtual jobs and e-commerce, and discriminatory practices by employers, among others. These studies collectively demonstrate the inequality that persists in the country. In their study seeking the relationship between gender diversity and employee work performance, Yasmeen et al. (2022) highlighted the positive connection between gender heterogeneity in the workplace and achieving a greater competitive advantage. The same study contends that men and women may have diverse psychological capacities, and these competencies can improve cohesion and innovation, ultimately leading to improved organizational performance. In sum, despite global progress in gender parity, significant inequality persists, especially in the Philippines, which must address critical socio-economic barriers to fully leverage the benefits of gender diversity for improved organizational performance.

Years of Experience and Employee Performance

Work experience is a powerful driver of employee performance, according to research by Sirait and Associates (2025). The authors argue that as employees accumulate experience, they develop essential skills and knowledge, enabling them to complete tasks more efficiently, effectively solve problems, and confidently face workplace challenges. This acquired expertise directly enhances overall performance by helping staff reduce errors and accelerate the completion of tasks. The study highlights that the positive influence of work experience on performance is more significant than that of compensation, primarily because it acts to boost employee confidence in managing their responsibilities. Recognizing that superior work experience translates to enhanced skills, Sirait and Associates (2025) linked shorter job tenure to a deficit in employee knowledge and skills, as staff are still in the early learning phase. Consequently, they view essential employee training as the primary means to ensure workers meet expected task efficiency. Thus, work experience is a powerful driver of employee performance because the knowledge and skills accumulated over time directly lead to increased efficiency, confidence, and problem-solving ability, making its influence more significant than that of compensation.

Men’s and women’s jobs differ greatly across occupations, disciplines, and industries where the firm operates. While previous studies can support the idea that such changes occur because of workplace developments brought about by the 4th Industrial Revolution, the resulting changes are not enough to eliminate the segregation of employment by age, gender, and years of work experience. This pertinent concern has convinced the researchers to find out its relationship to workers’ performance.

Figure 2: Conceptual Framework Depicting Demographic Predictors (Age, Gender, and Years of Experience) and Their Relationship with Employee Performance.

Age, gender, and years of experience are the presumed predictors of employee performance based on task, adaptive, and contextual performance, as shown in Figure 2. This means that demographic characteristics influence the intellectual and physical abilities of workers (Robbins & Judge, 2022). As employees perform work for the company throughout their working lifespan, their age and years of work experience allow them to develop mastery in doing their tasks and cultivate a sense of commitment and citizenship—especially among women because of their nurturing and caring qualities (Asadullah et al., 2021)—despite any technology changes being implemented in their company (Wilkinson et al., 2025).

Given the organizational shift toward achieving competitiveness, managers are keen to understand how various employee characteristics, such as age, gender, and years of experience, influence performance ability. Despite observing mixed results in the reviewed literature, we hypothesized in this study that H1: Age significantly predicts the dimensions of employee performance. H2: Gender significantly predicts the dimensions of employee performance. H3: Years of experience significantly predict the dimensions of employee performance.

The Present Research

This research sought to determine if the demographic factors of age, gender, and years of experience predict the three dimensions of job performance—task, adaptive, and contextual—among Filipino workers, using the Employee Performance Scale for Filipino Workers. Specifically, this study aimed to answer the question: To what extent do age, gender, and years of experience significantly predict the three dimensions of employee performance? The study is grounded in the Triarchy Model of Employee Performance (Pradhan & Jena, 2017). This model posits that overall employee performance is a cluster of behaviors stemming from three core components:(1) Task Performance: Specific expertise relevant to one’s field. (2) Adaptive Performance: The ability to understand and execute processes according to circumstances. (3) Contextual Performance: The capacity to build team spirit, allegiance, and interconnectedness. These combined factors are believed to drive positive organizational outcomes, including productivity enhancement, customer satisfaction, organizational development, and growth.

METHOD

Participants

Five hundred and thirty-four Filipino workers from public and private companies participated in this study. The sample, selected using convenience sampling, consisted of 300 females (56.18%) and 234 males (43.82%). Their ages ranged from 18 to 63 years. Convenience sampling, as described by Urdan (cited in Nieva, 2023; Nieva et al., 2024), involves selecting readily available and willing participants.

Measure

The Employee Performance Scale for Filipino Workers (Nieva et al., 2025) measures how well an employee performs his or her job. This instrument originated from the work of Pradhan and Jena (2017) but was modified and validated to suit Filipino culture. It comprises 20 items across three dimensions: task performance, adaptive performance, and contextual performance. The response format is a 7-point Likert scale where 1 = strongly disagree and 7 = strongly agree. The factor scores are computed by calculating the mean score for all items within each factor. These scores can then be interpreted categorically using a median split, which separates respondents into high- and low-scoring groups relative to the scale’s theoretical midpoint of 4.0. In this study, the computed reliability estimates for the scale’s dimensions, based on Cronbach’s alpha (α) and McDonald’s omega (ω), are as follows: task performance (α=0.713; ω=0.713), adaptive performance (α=0.788; ω=0.791), and contextual performance (α=0.847; ω=0.849), respectively. These values indicate that the scale is reliable.

Research Design and Procedure

This study used a quantitative approach, specifically the cross-sectional predictive study (Johnson, 2001). “Cross-sectional as a research dimension means that data were collected from participants only once, at a single point, while prediction as a research objective aimed to forecast future events or behaviors by analyzing patterns within the collected data” (Nieva, 2024, p. 156). The survey was administered online using Microsoft Forms. Before participants began the survey, they were presented with an informed consent form. Completion and submission of the survey constituted their voluntary agreement to participate in the research. Prior to the main analysis, preliminary data analyses were conducted, including descriptive statistics (assessing normality using skewness and kurtosis, calculating means and standard deviations) and zero-order correlations. Multiple regression served as the primary analysis. The reliability of the measures was estimated using Cronbach’s alpha and McDonald’s omega.

RESULTS AND DISCUSSION

The purpose of this study was to determine how demographic factors—specifically gender, age, and years of work experience—relate to the job performance of Filipino workers. In the preliminary data analysis, tests for normality based on skewness and kurtosis yielded ranges of 0.151 to -0.575 and -0.150 to -1.013, respectively. These values suggest that the score distributions are within acceptable limits according to Brown’s criteria (as cited in Nieva, 2023; Nieva et al., 2024; Rada & Nieva, 2024). Acceptable skewness values are generally considered to fall between -3 and +3, while kurtosis values are typically considered acceptable between -10 and +10.

For the descriptive data analyses, means and standard deviations of the three factors of the Employee Performance Scale for Filipino Workers, along with the participants’ demographic profiles (specifically age and years of work experience), are presented in Table 1. Using a median split (i.e., 4.0) as the basis for interpreting the scores on the Employee Performance Scale for Filipino Workers (EPS-FW), the computed mean scores for task performance, adaptive performance, and contextual performance are considered above the midpoint, suggesting high scores. Further, their individual scores are less dispersed, as indicated by their standard deviations for all three factors.

Regarding the mean scores for age and years of work experience, the average age of the participants is 38 years old, but the standard deviation indicates a wide spread in the age distribution. In terms of years of experience, the average is 13 years, but the standard deviation suggests that the distribution of numbers is also wide.

Table 1. Mean and Standard Deviation of the Factors of the Employee Performance Scale for Filipino Workers and Participant’s Demographic Profiles (Age and Years of Experience

Zero-order correlations were used to determine the relationships between age, gender, years of work experience, and task, adaptive, and contextual performance. The results are presented in Table 2. Age and years of work experience showed significant positive correlations with all three factors of the Employee Performance Scale for Filipino Workers. Meanwhile, no significant relationship was observed with gender. The correlation analysis utilized the point-biserial correlation—a measure required when correlating a dichotomous variable (gender) with a metric variable (performance score) (Kornbrot, 2005). Where negative coefficients were found, this signifies that the female group (coded 0) scored higher on the performance factor than the male group (coded 1).

A core requirement for robust regression analysis is the absence of multicollinearity (Nieva, 2022). However, a high correlation coefficient (0.848) was observed specifically between the predictor variables of age and years of experience. Consequently, the Variance Inflation Factor (VIF) was calculated, as it is the most common and standardized metric for quantifying the severity of multicollinearity—the correlation among predictors. This statistical procedure is done because the VIF is essential for researchers to determine if multicollinearity is severe enough to necessitate corrective action, such as removing a redundant variable or merging highly correlated variables. Although a VIF exceeding 5 or 10 generally signifies a problematic level of collinearity (James et al., 2013), the VIF results presented in Tables 3, 4, and 5 consistently fell within the acceptable limits, indicating that multicollinearity is not a significant concern for the overall regression model.

Table 2. Zero-Order Correlations of the Employee Performance Scale for Filipino Workers and Demographic Profiles (i.e., Age, Gender, and Years of Experience)

Standardized residuals were examined to detect potential outliers, as regression analysis is susceptible to their influence (Nieva, 2024). The standardized residuals for task performance ranged from -2.974 to 1.585, for adaptive performance from -2.979 to 1.812, and for contextual performance from -2.837 to 1.829. These values indicate no outliers because all absolute values were below the typical threshold of 3. Furthermore, the Durbin-Watson values suggest that the models exhibit acceptable independence of errors: task performance (1.897), adaptive performance (1.944), and contextual performance (1.838).

In the main analysis, we performed three separate multiple regression analyses using age, gender, and years of experience as the predictor variables, corresponding to the three outcome variables: task performance, adaptive performance, and contextual performance. These outcome variables correspond to the three dimensions of the Performance Scale for Filipino Workers. The results of the regression analyses are presented in Tables 3 to 5, respectively.

Table 3 presents the results of the regression analysis with task performance as the criterion variable and age, gender, and years of experience as predictor variables. The results indicated that age, gender, and years of experience did not predict task performance, explaining only 2.6% of the variance.

Table 3. Multiple Regression Result for Task Performance

R²= 0.032; F(3, 530)=5.786**

Note: *p < .05, **p < .01, ***p < .001.

Table 4 presents the results of the regression analysis with adaptive performance as the criterion variable and age, gender, and years of experience as predictor variables. The results indicated that age, gender, and years of experience did not predict adaptive performance, explaining only 2.6% of the variance.

Table 4. Multiple Regression Result for Adaptive Performance

R²= 0.026; F(3, 530)=4.743*

Note: *p < .05, **p < .01, ***p < .001.

Table 5 presents the results of the regression analysis with contextual performance as the criterion variable and age, gender, and years of experience as predictor variables. The results indicated that age, gender, and years of experience did not predict contextual performance, explaining only 2.4% of the variance.

Table 5. Multiple Regression Result for Contextual Performance

R²= 0.024; F(3, 530)=4.381*

Note: *p < .05, **p < .01, ***p < .001.

The results of the multiple regression analysis indicated that age, gender, and years of experience were not significant predictors of employee performance in all three domains: task, adaptive, and contextual performance. Despite the data not supporting the hypothesized predictive relationship, this outcome yields a critical and positive conclusion: employee effectiveness is independent of these fixed demographic characteristics.

Essentially, the results demonstrate that an employee can be effective regardless of their age, gender, or length of tenure. Consequently, managers have no statistical basis for hesitation or discrimination when selecting employees based solely on these variables. This finding reinforces the principle of non-discrimination and strategically directs future research toward investigating other organizational or individual characteristics that may be stronger predictors of performance.

This finding is consistent with the empirical study of Karanika-Murray et al., (2024), which reported no significant effects between age and individual adaptivity. Consequently, this may indicate that, despite the differences in the types of job performance, the varying age of workers does not directly affect the mental and cognitive abilities of an employee in doing their tasks. This argument is further supported by the idea that a diverse workforce may help sustain the firm’s position in the market, a strategy advocated by Bashir et al. (2021), who suggest combining young innovative minds with experienced and skilled workers.

In addition, the multidimensional factors that led to inequality in the Philippines, as identified by Cabegin and Gaddi (2019), can be overturned by having diverse competencies because diverse capacities improve cohesion and innovations (Yasmeen et al., 2022) and develop a more profound understanding of organizational citizenship.

While Sirait and Associates (2025) directly link superior work experience to enhanced skills, consequently, they highlight that employees with limited job tenure typically face knowledge and skill deficits because they are still navigating their early learning phase. As a result, the researchers emphasize that essential employee training is the primary mechanism required to ensure that these inexperienced workers meet expected task efficiency.

Specifically, this result statistically validates challenging bias in organizational practices, dismantling any basis for using these demographics as screening criteria. Managers are therefore empowered to shift their focus to genuine merit-based assessment and controllable factors, such as quality of experience, which focuses on the relevance and depth of acquired skills rather than the sheer quantity of years served, and organizational context, which means optimizing the work environment through effective leadership, supportive culture, and modern training and development programs. This shift allows managers to prioritize candidates based on their actual abilities and potential contributions rather than arbitrary metrics. Consequently, organizations can foster a more inclusive and effective workforce that thrives on diverse talents and perspectives.

CONCLUSION 

Employees’ performance is not significantly predicted by the demographic factors of age, gender, or years of work experience, a result that is viewed positively. This finding essentially demonstrates that employee effectiveness is independent of these fixed characteristics, suggesting success is instead driven by manageable, trainable organizational factors. Consequently, the data invalidates discrimination in selection processes based on these demographics, strongly supporting merit-based assessment. Furthermore, this outcome directs managerial focus and future research toward controllable elements like organizational context, quality of experience, and employee development, rather than fixed personal attributes.

This finding marks a clear boundary for subsequent inquiry. Future research should pivot away from these common demographic variables and seek endogenous, or internal, or organizational-level factors that possess stronger predictive power for performance. Potential areas for investigation include individual psychological traits, for instance, conscientiousness, self-efficacy, and adaptive capacity, and specific contextual variables, such as leadership style, work-life balance initiatives, and perceived organizational support.

REFERENCES

1. Adero, F., & Odiyo, W. (2020). Employee characteristics, contemporary human resource management practices and organization effectiveness. International Journal of Business Management, Entrepreneurship and Innovation, 2(2), 1–18. https://doi.org/10.35949/jbmed.v2i2.111
2. Asadullah, M. A., Ul Haq, M. Z., Wahba, K., Hashmi, S., Kim, H. (M.), & Hwang, J. (2021). Gender differences and employee performance: Evidence from the restaurant industry. Journal of Hospitality and Tourism Management, 48, 248–255. https://doi.org/10.1016/j.jhtm.2021.06.015
3. Bashir, M., Hameed, A., Bari, M. W., & Ullah, R. (2021). The Impact of Age-Diverse Workforce on Organization Performance: Mediating Role of Job Crafting.SAGE Open, 11(1). https://doi.org/10.1177/2158244021999058
4. Cabegin, E., & Gaddi, R. (2019). Determinants of female labor force participation in the Philippines. Department of Economic Planning and Development. https://depdev.gov.ph/wp-content/uploads/2021/09/Determinants-of-Female-Labor-Force-Participation-in-the-Philippines.pdf
5. DeNisi, A., & Griffin, R. (2025). HR 6: Human resources (6th ed.). Cengage.
6. Griffin, R. W., & Phillips, J. M. (2023). Organizational Behavior: Managing People and Organizations (14th ed.). Cengage.
7. Hasinat, I., Hjouji, Z., Loulidi, S., Hjouji, A. (2024). The multifaceted nature of individual job performance: a focus on individual behaviors. Pakistan Journal of Life and Social Sciences, 22(2):9554-9570. https://doi.org/10.57239/PJLSS-2024-22.2.00723
8. Hernaus, T. & Mikulic, J. (2014). Work characteristics and work performance of knowledge workers. EuroMed Journal of Business, vol 9, Issue 3. pp. 268-292. https://dx.doi.org/10.1108/EMJB-11-2013-0054.
9. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R. Springer. https://doi.org/10.1007/978-1-4614-7138-7
10. Johnson (2001). Towards a new classification of non-experimental quantitative research. Educational Researcher, 30(2). 3-13.
    https://doi.org/10.3102/0013189X030002003
11. Kornbrot, D. (2005). Point biserial correlation. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science. https://doi.org/10.1002/0470013192.bsa485
12. Karanika-Murray, M., Van Veldhoven, M., Michaelides, G., Baguley, T., Gkiontsi, D., & Harrison, N. (2024). Curvilinear relationships between age and job performance and the role of job complexity. Work, Aging and Retirement, 10(2), 156–173. https://doi.org/10.1093/workar/waac006
13. Liu, Q. & Fleisher, B. (2022). Job tasks and cognitive skill accumulation. Applied Economics. https://doi.org/10.1080/00036846.2022.2052009
14. Nieva, A. (2022). The Relationship between Career Interests and Academic Achievements in English, Mathematics, and Science of Grade 10 Students. International Journal of Arts, Sciences and Education, 3(2 June Issue). Retrieved from https://www.mail.ijase.org/index.php/ijase/article/view/122
15. Nieva, A. (2023). Construct validation of the teacher attitude to inclusion scale for Filipino pre-service teachers. Bedan Research Journal, 8(1), 305–329. https://doi.org/10.58870/berj.v8i1.56
16. Nieva, A. (2024). Personality Traits as Predictors of Pro-Environmental Behavior: Evidence from the Philippines. Bedan Research Journal, 9(1), 145–173. https://doi.org/10.58870/berj.v9i1.68
17. Nieva, A. M., Quilon, A. D., Butac, S. R., & Beltran, R. M. (2024). The Predictive Role of Hope on the Online Student Engagement of Filipino Pre-Service Teachers. International Journal of Research and Innovation in Social Science, 8(3s), 5465-5473. https://dx.doi.org/10.47772/IJRISS.2024.803411S
18. Nieva, A. M., Quilon, A. D., Prudente, J. E., & Cuaresma, E. R. (2025). The Structural Validity of the Employee Performance Scale for Filipino Workers (EPS-FW). International Journal of Research and Innovation in Social Science, 9(17), 247-256. https://dx.doi.org/10.47772/IJRISS.2025.917PSY0025
19. Pradhan, R. K., & Jena, L. K. (2017). Employee performance at workplace: Conceptual model and empirical validation. Business perspectives and research, 5(1), 69-85. https://doi.org/10.1177/2278533716671630
20. (2025, June 19). PH Reclaims Spot in GLobal Top 20, Remains Asia’s Leader in Gender Equality. https://pcw.gov.ph/ph-reclaims-spot-in-global-top-20-remains-asias-leader-in-gender-equality/
21. Quilon , A., & Perreras, R. (2020). Communication Climate as Predictor of Perceived Corporate Governance and Organizational Success. Bedan Research Journal, 5(1), 191–213. https://doi.org/10.58870/berj.v5i1.17
22. Rada, E., & Nieva, A. (2024). Translation and Validation of the Filipino Sustainability Consciousness Questionnaire. Bedan Research Journal, 9(1), 53–82. https://doi.org/10.58870/berj.v9i1.65
23. Robbins, S. P., & Judge, T. A. (2022). Organizational behavior (Updated 18th ed.). Pearson.
24. Roberts, R. (2020). Employee age and the impact on work engagement. Strategic HR Review. Retrieved from https://commons.erau.edu/publication/1429
25. Santander, N., & Prudente, J. (2020). Union citizenship: A furtive spirit of positive peace towards industrial peace. The Asian Journal of Technology Management, 13(3), 202–212. https://doi.org/10.12695/ajtm.2020.13.3.2
26. Singh, K., Kumar, P., & Kaushik, H. (2023). Impact of technology on employee behavior & performance. Lingaya’s Journal. https://www.lingayasvidyapeeth.edu.in/lingayas-journal/papers/vol-17-no-2/5.pdf
27. Sirait, F., Purwadi, A., & Rusdi, R. (2025). The Impact of Work Experience and Compernsation on Employee Performance in a Growing Company. https://journals.indexcopernicus.com/search/article?articleId=4407301
28. Wilkinson, A., Dundan, T., & Yates, E. (2025). Contemporary human resource management (Text and cases) (7th ed.). SAGE.
29. World Economic Forum. (2025, June 11). Global Gender Gap Report 2025. https://www.weforum.org/publications/global-gender-gap-report-2025/digest/
30. Yasmeen, A., Blouch, R., & Khan, M. (2022). The impact of gender diversity on employees’ work performance: The mediating role of interpersonal conflict. International Journal of Management Research and Emerging Sciences, 12. https://doi.org/10.56536/ijmres.v12i3.215