Introduction
The project through the development of evidence-based strategies using predictive analytics techniques focuses on the optimisation of both employee and organisational outcomes. Building on the findings of assessment 2 that demonstrated variability in HR metrics across divisions, including, engagement, productivity, tenure, and performance at 90 days, the project explores the relationships between these variables to identify actionable recommendations to improve staffing practices, employee satisfaction, and organizational efficiency. The project will evaluate metrics such as employee engagement, speed to competency, and tenure to predict business outcomes including productivity and profitability. This serves not only to address the differences in current disparities across divisions but also as a basis from which data could guide improvement in HR policy. Ultimately, the project will guide the organisation in fostering a supportive work culture that drives the well-being of its employees as well as long-term success.
Conceptual Framework and Hypothesis Development
Framework
Sharma and Sharma (2017) state that conceptual frameworks provide theoretical visualisation that helps in understanding complex relationships between variables by organising and connecting ideas. The conceptual framework is designed to relate Engagement (dependent variable) with six independent variables: Speed to Competency, High Potential, Assessment Score at 90 Days, Time to Fill, Hiring Cost, and Sponsor Satisfaction at 90 Days.
Figure 1: Conceptual Framework (Self-made by the author using SmartArt)
Variables;
- Engagement: The variable determines performance outcomes representing higher performance through the engagement of employees.
- Speed to competency: Representing the amount of time it takes for a new employee to be completely productive. A negative relation is expected considering the higher time taken by employees to reach full productivity and engagement.
- High Potential: Indicates employees identified with exceptional skills or growth prospects. A positive relationship is expected as high-potential employees have more engagement compared to others.
- SponsorSatisfaction90: This captures how new hires experience effective support from mentors or sponsors during the onboarding process. A positive relationship is expected as strong support from mentors enhances engagement.
- AssessmentScore90: Reflects early performance and skill alignment with the expectations of the role. A positive relation is expected as increased engagement will develop self-confidence and competence to score.
- Time to Fill: It refers to the time taken to fill a vacant position. A longer hiring period may result in better-fit candidates. A weak or negative relation is expected as a longer time is taken to fill will reduce the probability of engagement in the workplace.
- Hiring Cost: This represents the financial investment in the recruitment process. An indirect relation is expected as more financial investment will increase employee satisfaction and thus, lead to better engagement.
Hypothesis Development
- H1: Speed to Competency has a negative relationship with Engagement.
- H2: There is a likelihood that High Potential employees have more Engagement compared to others.
- H3: Increased Sponsor Satisfaction by the end of 90 Days positively correlates with raised levels of Engagement.
- H4: Higher Assessment Scores by the end of 90 Days have a positive correlation with better Engagement levels.
- H5: Time to Fill has a neutral or minimal effect on Engagement.
- H6: Hiring Cost lacks a direct relationship with Engagement.
Regression Model
According to Lai et al. (2022), a multiple regression model provides statistics prediction determining the value of continuous outcome variables based on two or more independent or predictor variables. The dependent variable is Engagement. The six independent variables are Speed to Competency, High Potential, Sponsor Satisfaction at 90 Days, Assessment Score at 90 Days, Time to Fill, and Hiring Cost. The model specification will evaluate how collectively these factors predict and explain variations in employee engagement using the multiple linear regression technique.
Equation;
Engagementi=β0+β1Speed to Competencyi+β2High Potentiali+β3Sponsor Satisfaction 90i+β4Assessment Score 90i+β5Time to Filli+β6Hiring Costi+ϵi
Figure 2: Regression Model
Model Assumptions
- Independence: the observations are independent of each other.
- Homoscedasticity: Errors' variance is the same at all levels of independent variables.
- Normality: Residuals (errors) are normally distributed.
- No Multicollinearity: The independent variables are not highly related to each other.
Analysing relationships using predictive analysis methods
Correlation analysis
Correlation analysis helps determine the strengths of linear relationships between two variables (Fitz-Enz& John Mattox, 2014). The correlation matrix gives the strength and direction of the relationship between different HR metrics and employee engagement, the dependent variable in this analysis. Speed to Competency shows a high negative correlation with Engagement (−0.72) (Appendix 1). This indicates that whenever employees take more time to become competent in their roles, their engagement levels tend to lower. The negative correlation emphasizes the need for effective onboarding and training processes to improve employee engagement. High Potential indicates a strong positive correlation with Engagement of 0.49, which means that employees who are identified as high-potential employees tend to be at a higher level of engagement. This supports the argument that talent management, specifically managing high-potential employees, is vital for maintaining a highly engaged workforce. Sponsor Satisfaction at 90 Days also positively correlates well at (0.71) with Engagement. This means that an employee satisfied with his or her sponsor or mentor during the early period of work is likely to be engaged. It thus underlines the role of mentorship and sponsorship in developing engagement. The score of the assessment for 90 days has a positive correlation, as it relates to Engagement at (0.66). Employees who score well on testing also score high on engagement. This implies that early performance and assessment feedback increase employee confidence and job satisfaction and positively affect engagement. Time to Fill's strength is held positively by Engagement (0.71), meaning recruitment processes lasting less time have higher engagement. A streamlined recruitment process can lead to more effective integration of newcomers, which is crucial in engagement level. Hiring Cost has a weak positive correlation with Engagement (0.23). While it seems to have a small positive relationship, the actual correlation is weak, indicating that hiring costs do not impact engagement heavily.
Regression analysis
Regression analysis is a helpful technique to determine the impact or influence of independent variables or dependent variables (Fitz-Enz& John Mattox, 2014). The regression analysis was carried out to determine the relationships between the independent variables: Speed to Competency, High Potential, Sponsor Satisfaction at 90 Days, Assessment Score at 90 Days, Time to Fill, and Hiring Cost concerning the dependent variable Engagement. Multiple R is 0.80, which is a strong overall relationship between the predictors and Engagement (Appendix 2). The value of R Square is 0.65, which indicates a strong coefficient of determination: that is, 65% of the variation in Engagement can be explained by the selected independent variables. The ANOVA results show a highly significant F-statistic; the regression model is statistically significant because the independent variables collectively have a significant impact on Engagement, based on the value of F (F=40.18, p=0.00). The regression and correlation analyses, strengthen the concept that variables such as Speed to Competency, Sponsor Satisfaction at 90 days, and Assessment Scores at 90 days are great predictors of Engagement. On the other hand, Hiring Cost and Time to Fill add little in trying to influence employee engagement, and thus, recruitment efficiency and costs do not seem to matter as much as the other HR metrics. As such, the outcome should encourage human resource strategies to improve onboarding experiences, mentorship programs, and performance management systems to increase employee engagement.
Recommendations
Based on the results obtained from the correlation and regression analysis, several strategic recommendations could be made toward improving employee engagement at the organization. Such recommendations are on optimal adjustments of key HR practices to amend the change where there has been a proven impact on engagement and consequently the overall organizational performance.
1. Improve Speed to Competency Programs
A negative correlation between Speed to Competency and Engagement highlighted the need for improvement in this area. Scott et al. (2021) state that even though a quick onboarding process is necessary, it may result in employees feeling unprepared and less engaged. HR should focus on developing a well-structured onboarding program, again balancing efficiency with enough training given. Programs should include mentorship, hands-on training, and gradual integration into the job. This way, newly hired employees will feel confident and competent in their jobs while onboarding remains streamlined.
2. Develop High-Potential Employee Education
High Potential has a positive correlation with Engagement, indicating that employees categorised as having high potential tend to have higher engagement levels. Boehncke (2023), states that focused developmental programs for high-potential employees help in enhancing productivity and engagement of high-potential employees in the workforce. High-potential employees should receive appropriate training, leadership development opportunities, and career growth plans. Engaging the high potential at an early stage can prevent talent loss, leadership development in the organization, and overall employee engagement. Top talent can be retained, as well as ensuring success for organizations, by developing a clear career advancement route.
3. Sponsor/Supervisor Support Strengthen
The regression analysis shows a significant positive relationship between Sponsor Satisfaction at 90 Days and Engagement. Employees who feel supported by their mentors or supervisors are more likely to be engaged in their work (Ogueyungbo et al., 2020). HR should therefore implement structured mentorship and sponsorship programs where experienced employees or managers are assigned to new hires to provide guidance, support, and regular feedback. Check-ups should be conducted at intervals. Ideally, this is within the first 90 days of service to make the employee feel valued and supported. These relationships are integral in developing better engagement for the employees, making them able to tackle the problems thrown their way and grow skills more efficiently.
4. Pay attention to Performance and Assessment Feedback
The positive correlation between Assessment Scores at 90 Days and Engagement underscores the performance assessments' significance. Iqbal et al. (2023) state that there should be more frequent and constructive loops of feedback, and employees must know whether they are performing and, above all, where they should improve. All this has to be related to performance appraisals, and HR should try to make it prompt enough that managers and subordinates, in particular, should more often than annually give feedback. Since the feedback is given as an assessment process, this helps build confidence in employees' jobs, which then leads to higher engagement levels. HR should also see to it that feedback is action-focused and pertinent to the long-term career aspirations of the employees.
5. Analyse the recruitment and hiring practices
While the above factors of Time to Fill and Hiring Cost do not indicate strong correlations with engagement, faster hiring processes are correlated with higher engagement. Prolonged hiring processes cause frustration in both candidates and hiring managers (Albassam, 2023). HR can cut down recruitment and hiring process time without reducing the quality of candidate intakes by streamlining it. Engaging the candidate throughout the recruitment process would prevent disengagement during the hiring process itself. Moreover, Hiring Cost did not play a major role in increasing engagement, and cost-effective methods of attracting without diluting good quality will pay out in the long run (Fitz-enz, 2010). It is recommended that HR should make the maximum use of technology in recruitment through tools like AI-driven recruitment tools to expedite finding the best-fit candidates.
6. Employee Wellbeing and Engagement Initiatives
According to Mmutle (2022),engagement is a critical factor for organizational success, HR should continue to measure and monitor engagement levels through surveys, focus groups, and one-on-one interviews. Collecting feedback periodically on what motivates engagement and satisfaction will show where the organization needs to improve. Initiatives that involve employee wellness programs, team-building activities, and recognition schemes are likely to boost engagement levels. Such activities enhance a sense of belongingness and improve the morality among employees, thus fostering a positive work environment.
7. Utilize data analytics for continuous improvement
HR should frequently use data analytics to monitor the effectiveness of engagement-related initiatives. Kumar et al. (2023) provides that the analysis of employee engagement data will help HR understand the trends, and the impact of specific interventions, and refine HR policies based on that. Only through this data-driven approach will HR be able to implement continuous improvement and ensure that engagement strategies will remain relevant and effective over time.
Conclusion
This project highlights the growing importance of data-driven approaches in human resource policies to promote employee engagement and organisational outcomes. Founding ideas indicate that significant contributions to enhancing employee engagement include sources such as sponsor satisfaction, onboarding quality, and early performance assessments. Impersonal strategies, including better onboarding programs, development plans, and balanced training timelines, can promote a higher engagement workforce. Engagement gaps can be bridged and predictive analytics leveraged to optimise HR practices for enhancing employee satisfaction, retention, and productivity. These recommendations present a practical roadmap to creating a supportive, high-performing workplace that aligns with the organization's long-term objectives.