Post-Hire Recruitment Metrics and Higher Productivity: Mediating Role of Employee Engagement

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This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8143474/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study explores the mediating function of higher employee engagement in linking post-hire recruitment metrics to employee productivity from the perspective of human resources (HR) processes. Drawing upon the Resource-Based View (RBV) and Job Demands-Resources (JD-R) theories, the study hypothesises that higher employee engagement serves as a crucial mechanism that converts the quality of HR processes into long-term performance outcomes. Data collected from HR personnel across various organizations, including consulting firms and companies of differing sizes, were analyzed using confirmatory factor analysis, simple and multiple linear regression, and mediation analysis. The linear regression analysis confirmed a significant relationship between higher employee engagement and productivity. The mediation analysis revealed that higher engagement, situated between post-hire recruitment predictors and productivity, accounts for 41–64% of the total effect. These findings substantiate that higher employee engagement is a vital outcome variable linking post-hire metrics to measurable organizational performance results. From a theoretical perspective, this study advances HR analytics and employee engagement literature by integrating engagement into models of post-hire processes. This study provides a solid foundation for future longitudinal research investigating the relationship between enhanced employee engagement and increased productivity within HR analytics frameworks. Management Post-Hire Recruitment Metrics Onboarding Process Compliance Hiring Manager Satisfaction First-year Retention Employee Engagement Employee Productivity Figures Figure 1 Figure 2 Introduction The increasing strategic significance of human resources (HR) within contemporary organizations has transformed the manner in which enterprises perceive their workforce. Industries that heavily depend on knowledge, particularly in information technology (IT), talent has become the pivotal factor shaping organizational adaptability, innovation, and efficiency. Consequently, HR analytics (HRA) has evolved from basic descriptive reporting to a more predictive and prescriptive applications that evaluate HR’s influence on business outcomes (Marler and Boudreau 2017 ; Hülter et al. 2024 ). Initial concepts regarding HR measurement (Boudreau 2002 ) proposed that the effectiveness of HR practices could be assessed based on their contribution to overall organizational performance. Recent advancements in analytics, digital technologies, and algorithmic intelligence have expanded this framework, integrating HR data into comprehensive decision-making models for strategic management and evidence-based practices (Chinenye et al. 2024). As a result, HRA now functions both as a diagnostic instrument and a strategic asset, linking individual behaviors with key organizational performance indicators such as productivity, retention, and profitability. In the recruitment function of HR, analytics is still primarily focused on pre-hire recruitment metrics such as cost-per-hire, time-to-fill, and applicant quality (Gupta 2022 ; Ali and Kallach 2024 ). While these metrics offer valuable operational insights, they do not always capture the long-term organisational benefits that emerge after employees are integrated into the firm. Post-hire recruitment metrics, including onboarding process compliance (OPC), hiring manager satisfaction (HMS), and first-year retention (FYR), needed more attention, as crucial indicators of workforce efficacy and organizational performance (Walker-Schmidt et al. 2022 ; Jankowski 2025 ). These factors indicate the extent to which new employees are assimilated, how well managerial expectations align with recruitment results, and the stability of the initial employment relationship. In fast-paced industries such as IT, where turnover rates soar and skill requirements change swiftly, these metrics may provide more predictive value for productivity than conventional recruitment indicators (Mohapatra and Sahu 2017 ; Prasad et al. 2019 ). A significant link between post-hire metrics and organizational outcomes is higher employee engagement (EE), described as the degree to which individuals invest cognitive, emotional, and behavioral energy into their work roles (Markos and Sridevi, 2010 ). Engagement not only boosts discretionary effort and innovation but also serves as a mediating factor that translates HR processes into performance outcomes (Hemanth et al. 2022; Quraishi and Sadath 2024 ). In IT firms, engagement is especially vital due to project-based work, rapid technological advancements, and the presence of distributed teams, all of which increase the necessity for psychological connection and clarity of roles (Singh and Satpathy 2015 ; Shree et al. 2023 ). However, despite the growing literature on employee engagement, there is a dearth of empirical research that combines post-hiring metrics with higher employee productivity (EP) within IT environments. From a theoretical perspective, this gap in research highlights the necessity to broaden the HR value-chain framework (Becker et al. 2001 ; Boudreau and Ramstad, 2004 ) to achieve a more detailed comprehension of how HR processes lead to business results. Conventional models of the HR, such as organizational performance and employee relations, represent HR practices as systems that affect organizational outcomes through employee attitudes and behaviors (Wright et al. 2001 ). However, HRA allows for a more refined explanation of these pathways, evaluating how compliance, satisfaction, and retention interact with engagement to enhance productivity. Practically, this focus is essential for IT organizations that encounter ongoing issues of turnover, talent shortages, and performance fluctuations (Prasad et al. 2019 ). By contextualizing post-hire recruitment metrics within an engagement-mediated framework of productivity, this study contributes to both theoretical insights and managerial practices, emphasizing HR’s overall role in driving business results in knowledge-centric environments. Need for the Study The importance of examining post-hire recruitment metrics in IT organizations arises from their direct impact on workforce stability and performance. Onboarding compliance ensures both legal adherence and the smooth transition of employees, fostering job satisfaction and initial productivity (Kirchner and Stull 2022 ; Chang 2023 ). Hiring manager satisfaction is vital for assessing the efficacy of the recruitment process, as it reflects the harmony between hiring outcomes and strategic workforce needs (Bernard and Ebenezer 2025). Furthermore, first-year turnover remains an ongoing challenge, signaling inconsistencies in recruitment and onboarding that can result in financial and performance issues (Das and Anjana 2020 ; Walker-Schmidt et al. 2022 ). Studies have also indicated that employee engagement plays a crucial mediating role, enhancing retention, minimizing turnover, and boosting discretionary effort (Markos and Sridevi 2010 ; Hemanth et al. 2022). In the context of IT organizations, where project success heavily relies on the engagement and productivity of employees, it becomes not only necessary but also strategic to evaluate these post-hire attributes to sustain competitive advantage (Quraishi and Sadath 2024 ). Research Gap Although there is extensive literature addressing recruitment analytics (RA) and pre-hire metrics, there is a notable lack of focus on post-hire recruitment metrics as indicators of organizational success, especially within the IT sector. Existing studies have primarily investigated individual components such as onboarding processes and employee engagement (Walker-Schmidt et al. 2022 ; Murgoski 2023 ), the role of analytics in reducing turnover (Das and Anjana 2020 ), and the satisfaction levels of hiring managers concerning recruitment efforts (Ali and Kallach 2024 ). Additionally, much of the existing research focuses on general sectors, overlooking the specific challenges faced in the IT industry, such as skill shortages, elevated turnover rates, and productivity influenced by particular projects (Mohapatra and Sahu 2017 ; Prasad et al. 2019 ). This gap highlights the necessity for an in-depth, IT-specific study that examines how post-hire recruitment metrics function collectively to affect productivity, mediated by employee engagement. Based on the identified gap, the following research questions are framed: RQ1. How does onboarding process compliance (OPC) influence employee engagement (EE) and improve employee productivity (EP)? RQ2. What is the significance of hiring manager satisfaction (HMS) on employee engagement (EE) and overall employee productivity (EP)? RQ3. What relationship exists between first-year retention (FYR) rates and the higher employee engagement (EE) in IT organizations? RQ4. Does employee engagement (EE) mediate the relationship between post-hire recruitment metrics (OPC, HMS, FYR) and employee productivity (EP)? Research Objectives Based on the above research questions, the following objectives are developed for the study: To evaluate the impact of onboarding process compliance on levels of employee engagement and employee productivity. To investigate how hiring manager satisfaction relates to employee engagement and employee productivity. To analyze the influence of first-year retention on employee engagement and productivity outcomes. To examine how employee engagement mediates between post-hire recruitment metrics and employee productivity. Literature Review Recruitment Function and Use of RA Recruitment analytics (RA) has transformed the recruitment process by prioritizing alignment with organizational objectives rather than just administrative efficiency. Previously, the recruitment focus was on speed and cost-effectiveness; however, contemporary techniques now stress long-term retention and employer branding (Gupta et al. 2018). The incorporation of artificial intelligence (AI) and analytics enables HR professionals to utilize predictive insights for workforce forecasting and strategic talent planning (Madanchian 2024 ). AI-enabled solutions improve recruitment accuracy by minimizing bias, enhancing candidate matching, and streamlining administrative functions (Allal-Chérif et al. 2021 ; Tasheva and Karpovich 2024 ). However, HR departments must tackle the ethical dilemmas related to algorithmic transparency and responsibility (Bernard and Ebenezer 2025). Within the IT sector, optimizing the recruitment pipeline through analytics speeds up hiring processes and elevates candidate quality (Mohapatra and Sahu 2017 ). Additionally, AI-driven recruitment methods can analyze resumes, forecast attrition, and aid in more equitable decision-making (Leicht-Deobald et al. 2019 ; Ali and Kallach 2024 ). New technologies such as blockchain and advancements from Industry 4.0 are further changing recruitment into a decentralized and verifiable process (Mehedi et al. 2018 ). Consequently, RA is increasingly regarded not merely as an operational tool but as a strategic facilitator that aligns talent acquisition with organizational efficacy and competitive sustainability. Post-Recruitment Metrics and Their Impact on Organizational Effectiveness Post-hire recruitment metrics including onboarding process compliance, hiring manager satisfaction, and early turnover rates, extend the assessment of recruitment effectiveness beyond mere candidate acquisition. These metrics provide insights into the long-term implications of recruitment choices, shedding light on integration, employee engagement, and employee retention (Walker-Schmidt et al. 2022 ; Atillo et al. 2025 ). Incorporating such metrics into HRA frameworks allows for proactive management of the workforce and early identification of potential retention challenges. Onboarding process compliance (OPC) has transitioned from being just an administrative requirement to a strategic process that guarantees adherence to legal standards while fostering engagement and alignment with organizational culture. Research shows that structured onboarding improves job satisfaction, decreases uncertainty, and boosts early productivity (Kirchner and Stull 2022 ). Atillo et al. ( 2025 ) likewise note favorable results in the banking sector, where adherence to onboarding practices has led to a reduction in turnover intentions. Effective onboarding also promotes cultural integration and alignment with organizational values (Chang 2023 ). In the IT industry, where turnover rates are high, organized onboarding programs are essential for retaining employees (Walker-Schmidt et al. 2022 ). Additionally, onboarding that focuses on sustainability, as explored by Carson and Westerman ( 2023 ), enhances the organization's credibility and ethical commitment. Together, these insights highlight the importance of onboarding compliance as both a regulatory safeguard and a basis for long-term employee engagement. Hiring manager satisfaction (HMS) serves as a crucial measure of recruitment effectiveness and alignment with organizational goals. Beyond simply looking at efficiency indicators, satisfaction relies on the perceived quality and suitability of new hires (Bernard and Ebenezer 2025). AI-enhanced recruitment tools have improved satisfaction by offering better candidate recommendations and reducing administrative burdens (Gupta 2022 ; Ali and Kallach 2024 ). However, concerns about transparency remain when hiring managers view AI systems as unclear or biased, both trust and satisfaction may decline (Leicht-Deobald et al. 2019 ). In IT settings, manager satisfaction is closely linked to candidate quality and the reduced risk of mismatches through data-driven recruitment (Prasad et al. 2019 ). Employer branding also plays an indirect role in shaping satisfaction by influencing applicant quality and alignment with organizational values (Baratelli and Colleoni 2022 ). Therefore, hiring manager satisfaction is both an outcome of effective recruitment and a mediator for overall talent management success. First-year retention (FYR) represents a significant issue for organizations, indicating potential mismatches in recruitment or onboarding methods. Predictive analytics is vital in recognizing prospective attrition by examining patterns in engagement and performance (Das and Anjana 2020 ; Shrivastava and Dhaigude 2022 ). Organizations that utilize such models have managed to enhance retention rates through targeted interventions (Ekka et al. 2022 ). In the IT sector, first-year turnover can markedly disrupt operations and raise recruitment expenses (Prasad et al. 2019 ). Jankowski ( 2025 ) highlights that engagement during onboarding is vital in limiting early departures. Consequently, reducing early turnover necessitates the incorporation of predictive analytics, effective onboarding, and ongoing engagement strategies. Employee Engagement as a Mediator Defined as cognitive, emotional, and behavioral elements, EE transforms HR interventions into enhanced performance (Aziz et al. 2018 ; Garg et al. 2018 ). Markos and Sridevi ( 2010 ) identified EE as a vital factor for competitive advantage, while Hemanth et al. (2022) stressed analytical significance in customizing engagement tactics. Cultural alignment and managerial coaching further reinforce engagement (Aziz et al. 2018 ; Chatterjee 2021). AI and analytics now empower HR teams to spot engagement deficiencies and personalize their approaches (Kayusi et al. 2025 ). During the onboarding process, engagement aids in early adaptation, commitment, and retention (Murgoski 2023 ; Jankowski 2025 ). Overall, engagement acts as both a preventive measure against turnover and a driver for performance, establishing the behavioral foundation for the strategic impact of HR. Aggregate HR Contribution to Business Outcomes The combination of analytics and employee engagement frameworks allows for measurable evaluation of HR’s influence on business results. HRA has improved this understanding by highlighting the specific ways in which HR processes affect productivity and profitability (Marler and Boudreau 2017 ; Anger and Tessema 2021 ). In technology-driven sectors, HR practices that utilize analytics, boost innovation while reducing inefficiencies related to employee turnover (Yang et al. 2023 ; Quraishi and Sadath 2024 ). Higher Productivity in the IT Sector Employee productivity in the IT industry relies on the combination of RA, the effectiveness of onboarding, and employee engagement. Recruitment supported by analytics reduces skill mismatches and assures cultural fit, leading to improved productivity (Mohapatra and Sahu 2017 ; Prasad et al. 2019 ). In environments driven by projects, employee engagement is crucial for maintaining high performance amidst changing and demanding situations (Quraishi and Sadath 2024 ). A well-structured onboarding process enables quick adaptation and shortens learning periods, which directly impacts productivity improvements (Walker-Schmidt et al. 2022 ). Strong employer branding further enhances this connection by attracting individuals who resonate with organizational values and exhibit quicker integration (Baratelli and Colleoni 2022 ). Innovative technologies like AI and ML refine workforce distribution, foresee performance patterns, and alleviate productivity drops arising from disengagement (Yang et al. 2023 ). Therefore, HRA acts as the cohesive element that connects recruitment, engagement, and performance to ongoing productivity in the IT sector. Resource-Based View (RBV) and Job Demands-Resources (JD-R) This research is significantly associated with two foundational theories: ‘the Resource-Based View (RBV) and the Job Demands-Resources (JD-R).’ The RBV (Barney, 1991 ) considers human capital and employee engagement as strategic assets of the organization ‘that are valuable, rare, difficult to imitate, and non-substitutable (VRIN).’ The JD-R theory (Bakker and Demerouti 2017 ) proposes that employee well-being and engagement occur when job resources such as organizational support, culture, and HR strategy, counterbalance job demands, thereby improving employee performance. Within this framework, organizational culture, HR responsiveness, and the alignment of HR strategies serve as critical resources that energize employees, leading to increased engagement and productivity. Collectively, these theories interpret the strategic processes demonstrating how post-hire HR practices enhance performance outcomes. By integrating JD-R’s emphasis on motivation with RBV’s strategic perspective, this research underscores employee engagement as the central process variable that converts HR investments into sustainable productivity gains and organizational success. The following framework (Fig. 1 ) is developed, with a focus on the above literature. (Source: Authors) Hypothesis Development Onboarding Process Compliance ensures that all new employees undergo standardized training, policy orientation, and cultural integration, which diminishes role ambiguity and promotes engagement (Kirchner and Stull 2022 ; Chang 2023 ). This compliance is particularly crucial in IT firms, where the complexity of client needs and project frameworks demands early functional alignment (Walker-Schmidt et al. 2022 ). Therefore, structured onboarding fulfils both compliance and strategic roles by embedding organizational values and expectations within the employee experience. H1: ‘Onboarding Process Compliance’ has a significant positive impact on ‘Employee Engagement’ Hiring Manager Satisfaction serves as an internal performance measure reflecting recruitment alignment. Gratified hiring managers are more inclined to provide successful mentoring, support integration, and enhance early performance, thereby affecting engagement and retention (Bernard and Ebenezer 2025). On the other hand, dissatisfaction may indicate a systemic disconnect between the recruitment process and workforce requirements, which can lead to disengagement and performance issues (Leicht-Deobald et al. 2019 ). H2: ‘Hiring Manager Satisfaction’ has a significant positive impact on ‘Employee Engagement’ First-Year Retention acts as a significant post-hire result, encapsulating the cumulative impacts of recruitment accuracy, onboarding quality, and engagement levels. Early turnover disrupts team cohesion, increases replacement expenditures, and emphasizes flaws in the employment relationship (Das and Anjana 2020 ; Shrivastava and Dhaigude 2022 ). Research within IT contexts confirm that elevated first-year turnover can undermine project continuity and the ability to innovate (Prasad et al. 2019 ), highlighting the importance of proactive and predictive RA. H3: ‘First-Year Retention’ has a significant positive impact on ‘Employee Engagement’ Research indicates that employees with high engagement levels demonstrate enhanced productivity, greater commitment to the organization, and lower intentions to leave (Quraishi and Sadath 2024 ). In the IT sector, the significance of employee engagement increases due to the nature of industry: project-based work, its focus on innovation, and its vulnerability to burnout and employee turnover (Singh and Satpathy 2015 ; Shree et al. 2023 ). As such, engagement links the quality of HR processes with employee performance, reinforcing the idea that HR practices produce organizational value only when they are integrated into behavior (Becker et al. 2001 ; Boudreau and Ramstad 2004 ). H4: Positive ‘Employee Engagement’ has a significant positive impact on ‘Employee Productivity’ Employee Engagement (EE) serves as the channel through which post-hire recruitment measures influence performance outcomes (Fig. 2 ). A structured and compliant onboarding process fosters engagement by improving clarity of roles and identification with the organization (Kirchner and Stull 2022 ; Murgoski 2023 ), while the satisfaction of hiring managers indirectly impacts engagement by influencing initial socialization and available support structures (Bernard and Ebenezer 2025). On the other hand, high turnover rates within the first year are indicative of disengagement in its most pronounced form symbolizing failures in integration and alignment (Das and Anjana 2020 ). H5: ‘Employee Engagement’ mediates the relationship between ‘Post-Hire Recruitment Metrics’ and ‘Employee Productivity’. Methodology A quantitative, cross-sectional methodology was employed to empirically examine the post-hire recruitment metrics pertinent to the IT sector. The IT industry was specifically because (i) there is a streamlined and continuous recruitment process, (ii) availability of knowledge worker composition, and (iii) the industry contributes 7.3% to the country’s GDP (MeitY Report, 2022) In the initial phase, a comprehensive review of the existing literature, including practitioner-oriented sources on RA, was conducted to identify relevant post-hire recruitment indicators. As a result, a set of potential metrics was established, covering time to productivity, probationary review outcomes, onboarding process compliance, job suitability, preliminary performance evaluations, hiring manager satisfaction, team performance, cultural fit, new hire turnover, and first-year retention. Further, a series of interviews was carried out with HR practitioners to identify the most significant post-hire metrics from an HR process perspective. The finalized framework included three operational metrics such as Onboarding Process Compliance (OPC), Hiring Manager Satisfaction (HMS), First-Year Retention (FYR) as independent variables (IVs), one output metric such as Employee Engagement (EE) as the mediating variable (MV), and one impact metric such as Employee Productivity (EP) serving as the dependent variable (DV). Pilot Testing and Reliability Assessment A questionnaire using Likert scale (five-point) was administered to a small group of HR personnel. Minor adjustments were made to ensure the clarity, reliability, and content validity of the instrument, based on the results of the pilot study. The final scale exhibited internal consistency (α = 0.939), with correlations (item-rest) exceeding 0.80, indicating good construct-level reliability, as shown in Table 1 . Sampling and Data Collection The finalized questionnaire was administered using a simple random sampling method to HR professionals who were actively involved in recruitment processes across large, medium, and small IT organisations and consulting firms, located in South India. After eliminating incomplete responses, a final set of 220 responses, was evaluated using Jamovi (v2.3.28). Validity of the responses was established as shown in Table 2 . The average variance extracted (AVE) values were below the 0.50 threshold. AVE < 0.50 is acceptable when CR exceeds 0.60, especially in exploratory research (Fornell and Larcker 1981 ; Hair et al. 2019 ). The confirmatory factor analysis (CFA) model demonstrated excellent fit (SRMR = 0.0448, CFI = 0.927, TLI = 0.918), supporting the overall construct validity, as shown in Table 3 . Table 1 Reliability Statistics Scale Reliability Indices Mean sd Cronbach’s alpha Scale 3.99 0.854 0.939 Item Reliability Indices Mean sd item rest correlation OPC 3.98 0.981 0.814 HMS 3.99 0.938 0.858 FYR 3.99 0.941 0.842 EE 3.95 0.971 0.856 EP 4.02 0.926 0.811 Table 2 CR and AVE Item Composite Reliability (CR) Average Variance Extracted (AVE) OPC 0.736 0.412 HMS 0.748 0.426 FYR 0.738 0.414 EE 0.747 0.425 EP 0.732 0.409 Table 3 CFA (Model Fit) Measures Type of Fit Fit Measure Estimate Threshold Values (Reference) Absolute Fit SRMR (Standardized Root Means Square Residual) 0.0448 0.90 : Acceptable Fit (Bentler 1990) TLI (Trucker Lewis Index) 0.918 > 0.90 : Acceptable Fit (Hu L. T. et al. 1999) Parsimonious Fit CMIN/DF 1.829 < 2 : Good Fit (Kline R. B. 2013) Results The analyses involved regression models, to assess the direct influence of EE on EP, and to evaluate the impact of OPC, HMS, and FYR on EE; and further, a mediation model to determine the effect of EE as a mediator in the relationships between these predictors and EP. Measuring the impact of OPC, HMS, FYR on EE ‘A multiple linear regression analysis was carried out to assess the efficiency of crucial post-hire recruitment metrics in forecasting overall EE (Tables 4 and 5 ). This statistical approach allows for the simultaneous evaluation of three independent variables (OPC, HMS, FYR) to analyze their individual and collective impacts on a single outcome variable.’ Table 4 Model Fit Indices Overall Model Test Model R R 2 Adjusted R 2 AIC BIC RMSE F df1 df2 P Multiple Linear Regression 0.849 0.721 0.717 340 357 0.512 186 3 216 < .001 Table 5 ‘Model Coefficients, and Omnibus ANOVA Test’ Model Coefficients Omnibus ANOVA Test Predictor Estimate SE t P Stand. Estimate Sum of Squares df Mean Square F Intercept 0.186 0.1636 1.14 0.257 OPC 0.303 0.0603 5.03 < .001 0.307 6.76 1 6.760 25.3 HMS 0.252 0.0678 3.71 < .001 0.243 3.68 1 3.685 13.8 FYR 0.389 0.0609 6.38 < .001 0.377 10.88 1 10.879 40.7 Residuals 57.70 216 0.267 Note. Type 3 sum of squares The model achieved a strong overall fit, F(3, 216) = 186, p < .001, explaining 72.1% of the variance in engagement (R² = 0.721, Adjusted R² = 0.717). Each predictor contributed significantly: OC (β = 0.307, t = 5.03, p < .001), HS (β = 0.243, t = 3.71, p < .001), and FR (β = 0.377, t = 6.38, p < .001). The Durbin-Watson (DW) statistic (2.11, p = 0.478) confirmed the absence of autocorrelation (ranges from zero to 4). Collinearity diagnostics (Table 6 ) showed acceptable tolerance values (0.30–0.37) and VIFs below 3.5, confirming model stability (Hair et al. 2019 ). Table 6 Collinearity Assessment Metric VIF Tolerance OPC 2.87 0.348 HMS 3.32 0.301 FYR 2.69 0.371 Measuring the impact of EE on EP The simple linear regression model examined the predictive effect of employee engagement (EE) on employee productivity (EP), as shown in Tables 7 and 8 . Table 7 Model Fit Indices Overall Model Test Model R R 2 Adjusted R 2 AIC BIC RMSE f df1 df2 P Simple Linear Regression 0.752 0.565 0.53 413 423 0.610 283 1 218 < .001 Table 8 ‘Model Coefficients, and Omnibus ANOVA Test’ Model Coefficients Omnibus ANOVA Test Predictor Estimate SE T P Stand. Estimate Sum of Squares df Mean Square F Intercept 1.186 0.1733 6.84 < .001 EE 0.717 0.0426 16.83 < .001 0.752 106.2 1 106.22 283 Residuals 81.8 218 0.375 Note. Type 3 sum of squares The model demonstrated a strong and statistically significant fit, F(1, 218) = 283, p < .001, with an R² value of 0.565, indicating that EE accounted for approximately 56.5% of the variance in EP. The standardized regression coefficient was β = 0.752 (SE = 0.0426, t = 16.83, p < .001), suggesting a positive relationship between engagement and productivity. The intercept was 1.186, implying that when engagement is minimal, baseline performance levels remain low. Model diagnostics indicated adequate assumptions of linearity and independence (DW = 1.71, p = 0.032), suggesting the absence of autocorrelation. Mediation Analysis Mediation analyses were conducted to determine whether employee engagement (EE) mediates the relationship between post-hire recruitment predictors (OPC, HMS, and FYR) and employee productivity (EP). The model yielded significant indirect effects across all paths. ‘Summary of the mediation result is stated in Table 9 .’ Table 9 Mediation Analysis Result (Standard Method) Predictor Indirect Effect SE Z P % Mediation OPC 0.411 0.0544 7.55 < .001 64.2 HMS 0.310 0.0512 6.05 < .001 41.2 FYR 0.310 0.0531 5.84 < .001 41.4 The findings indicated that HE functions as a crucial mediator. In every model examined, the indirect effect was found to be statistically significant. For OPC → EE → EP, the indirect effect (a×b) was 0.411 (SE = 0.0544, Z = 7.55, p < .001), representing 64.2% of the total effect. Similarly, for HMS → EE → EP, the indirect effect was 0.310 (SE = 0.0512, Z = 6.05, p < .001), accounting for 41.2% mediation, while FYR → EE → EP exhibited an indirect effect of 0.310 (SE = 0.0531, Z = 5.84, p < .001), which explained 41.4% mediation. All direct effects (c paths) were still significant, indicating partial mediation. Discussion This research highlights the crucial role of employee engagement as a mechanism linking post-hire recruitment metrics to employee productivity within an HR process framework. Based on the study findings, the significant and positive impact of post-hire metrics on employee engagement, and subsequently on overall employee productivity, is consistent with existing research that emphasizes the role of engagement as a mediator connecting HR practices to performance outcomes ( Bakker and Demerouti 2017 ). The measures identified and used in this study are indicative of the post-hire metrics and can be considered as yardsticks of recruitment efficiency. The substantial mediating effect of onboarding process compliance (64%) indicates that organized and effectively implemented onboarding practices are essential for early employee engagement and long-term employee productivity ( Jankowski 2025 ). Research across various organizational settings highlights that successful onboarding initiatives not only ease the transition into roles but also create a psychological bond with the organization, enhancing emotional commitment and encouraging proactive work behaviors ( Atillo et al. 2025 ). The study also aligns with the job demands-resources (JD-R) framework, which states that organizational resources, such as thorough onboarding compliance and managerial support, enhance employee engagement by addressing psychological needs and clarifying roles ( Bakker and Demerouti 2017 ). This study underlined that well-designed onboarding process, enhances employee’s socialization in organizations that positively impacts their engagement (Junça et al. 2025), which in turn is essential for retaining new hires ( Mosquera and Soares 2025 ). From the perspective of strategic HRA, tracking onboarding compliance as a crucial post-hire metric reinforces the HR Scorecard model's claim that quantifying and refining people-related processes can lead to measurable improvements in business performance ( Becker et al. 2001 ). The observation that hiring manager satisfaction and first-year retention also have positive impacts, indicates the interconnectedness of the post-hire experience factors with engagement and productivity ( Singh and Satpathy 2015 ; Walker-Schmidt et al. 2022 ). Nonetheless, onboarding process compliance appears to serve as an impactful driver of employee engagement by shaping initial impressions of organizational support and alignment with competencies ( Chang 2023 ; Carson and Westerman 2023 ). Likewise, AI-driven recruitment systems increasingly allow for the dynamic monitoring of post-hire metrics, enabling HR leaders to recognize engagement predictors and enhance interventions in real time ( Ali and Kallach 2024 ; Kayusi et al. 2025 ). This data-centric strategy supports earlier claims that HRA can connect operational metrics with strategic outcomes by quantifying the mediating effect of engagement ( Marler and Boudreau 2017 ; Hülter et al. 2024 ). Moreover, the findings reinforce both theoretical and empirical perspectives that employee engagement acts as a vital mediator, converting effective post-hire practices, especially systematic onboarding process, into long-lasting productivity enhancements, confirming the broader notion that strategic HR processes can provide a sustainable competitive edge ( Barney 1991 ; Quraishi and Sadath 2024 ). Theoretical Implications From a theoretical perspective, this study enhances the understanding of employee engagement as a mediating construct linking HR processes and performance outcomes. It provides empirical support for the Resource-Based View (RBV) of the firm, suggesting that engagement is an intangible strategic resource that can convert HR process quality into a competitive advantage ( Barney 1991 ). By incorporating engagement within a post-hire recruitment framework, the research addresses a significant gap in HR theory, typically centered on pre-hire selection and performance prediction, by illustrating how post-hire experiences contribute to sustained value creation. Furthermore, the research refines process-oriented models of HR effectiveness by defining engagement as both a measurable outcome of HR process alignment and a predictor of productivity. This duality strengthens the role of engagement as both a dependent and mediating construct within organizational behavior frameworks. Practical Implications From a managerial perspective, these findings highlight the importance of incorporating engagement-focused practices within post-hire recruitment processes. HR professionals should broaden the assessment of recruitment success beyond initial placement metrics to more engagement measures, including role fit, psychological safety, and feedback responsiveness ( Kahn 1990 ). HR initiatives should prioritize the personalization of onboarding, and related mechanisms, to enhance engagement as an ongoing endeavor rather than a one-time result. While FYR is one of the post-hire measures adopted in this study, longer retention mechanisms should be designed to engage and utilize the talent with the organization. By redefining HR processes, organizations can improve both employee experience and productivity outcomes, fostering sustainable performance through data-informed HR strategies. Limitations Although this research offers a robust analytical framework, it does have some limitations. The cross-sectional design restricts the capacity to draw causal inferences since engagement and performance could impact one another over time. Additional metrics, such as time-to-productivity, training completion rates, and probationary performance, might be incorporated into future models to enhance theoretical depth. The sample includes a variety of organizational types, and the findings may not apply to industries with varying levels of HR process maturity. Moreover, although engagement proved to be a significant mediator, other factors such as job satisfaction, commitment, or well-being may also serve as mediators or moderators. Future Research Future research should utilize longitudinal data to investigate these evolving relationships and consider cross-cultural comparisons to assess the generalizability of the proposed model. The effects of onboarding compliance and managerial satisfaction either persist or fade over time. The inclusion of HRA and ML may enable the real-time prediction of employee engagement and employee productivity outcomes based on post-hire metrics. Such predictive frameworks could transform talent management by identifying the factors that may lead to disengagement or turnover before they emerge. Conclusion This research offers strong empirical evidence supporting the intermediary role of employee engagement in the connection between post-hire recruitment metrics and employee productivity, from a HR process perspective. The findings clarify how organizational elements ingrained in post-hire practices, significantly enhance employee engagement, which consequently leads to tangible improvements in both individual and organizational productivity. The robustness of the observed mediation pathways highlights that the success of post-hire recruitment processes goes beyond simply acquiring talent; it lies in how well new employees are socially integrated, psychologically invested, and structurally supported through the organization’s engagement strategies. From a procedural viewpoint, this indicates that post-hire metrics should encompass more than just performance or retention figures; they should also include engagement-related indicators to evaluate the long-term effects of recruitment quality. On a practical level, these findings encourage HR leaders to integrate engagement-focused initiatives, such as tailored onboarding process, feedback incorporation, and role clarification, into their post-hire approaches, thus enhancing the recruitment-performance relationship. From a theoretical perspective, this research enriches the RA literature by illustrating that engagement serves as a dynamic linkage mechanism that translates recruitment results into sustained productivity improvements. Overall, this research promotes a process-oriented understanding of HR effectiveness, identifying employee engagement as the crucial factor through which post-hire recruitment metrics translate into lasting organizational performance. Declarations Participants consented to participate in the study voluntarily. Funding No funding was received to assist with the preparation of this manuscript. References Ali O, Kallach L (2024) Artificial Intelligence Enabled Human Resources Recruitment Functionalities: A Scoping Review. Procedia Comput Sci 232:3268–3277. https://doi.org/10.1016/j.procs.2024.02.142 Allal-Chérif O, Aránega Y, A., Castaño Sánchez R (2021) Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technol Forecast Soc Chang 169:120822. https://doi.org/10.1016/J.TECHFORE.2021.120822 Anger O, Tessema MT (2021) A framework for assessing the effectiveness of HR metrics and analytics: the case of an american healthcare institution. In Article in Journal of Human Resource Management . https://www.researchgate.net/publication/349412539 Atillo GNA, Cardeno RA, Olasiman DMAM (2025) Onboarding experience and job satisfaction in Philippine government banking sector. HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION , 15 (6), 3–18. https://doi.org/10.46223/HCMCOUJS.econ.en.15.6.3799.2025 Aziz S, Soc Sci C, Ali PJ, Aziz Lodhi M, Raza S, B., Ali W (2018) Standard-Nutzungsbedingungen: Examining the Impact of Managerial Coaching on Employee Job Performance: Mediating Role of Work Engagement, Leader-Member-Exchange Quality, Job Satisfaction, and Turnover Intentions. In Pakistan Journal of Commerce and Social Sciences (Vol. 12, Issue 1). PJCSS. https://hdl.handle.net/10419/188345 Bakker AB, Demerouti E (2017) Job demands-resources theory: Taking stock and looking forward. J Occup Health Psychol 22(3):273–285. https://doi.org/10.1037/ocp0000056 Baratelli G, Colleoni E (2022) Does Artificial Intelligence (AI) Enabled Recruitment Improve Employer Branding? Int J Bus Manage 17(2):45. https://doi.org/10.5539/ijbm.v17n2p45 Barney J (1991) Firm Resources and Sustained Competitive Advantage. J Manag 171:99–120. https://doi.org/10.1177/014920639101700108 Becker BE, Huselid MA, Ulrich D (2001) The HR scorecard: linking people, strategy, and performance. Harvard Business School Press Bernard Anthony BU, Ebenezer E (2025) Assessment of the Recruitment Stages and the Responsibility of Human Resources Management in the Task. In GASPRO INTERNATIONAL JOURNAL OF EMINENT SCHOLARS (Vol. 12). Online Boudreau JW, Ramstad PM (2002) Strategic I/O Psychology and the Role of Utility Analysis Models . http://digitalcommons.ilr.cornell.edu/cahrswp/57/ Boudreau JW, Ramstad PM (2004) Center for Effective Organizations TALENTSHIP AND HUMAN RESOURCE MEASUREMENT AND ANALYSIS: FROM ROI TO STRATEGIC ORGANIZATIONAL CHANGE Talentship and Human Resource Measurement and Analysis: From ROI to Strategic Organizational Change . http://www.marshall.usc.edu/ceo Carson JE, Westerman JW (2023) The Effectiveness of Organizational Sustainability Messaging to New Hires: An Exploratory Analysis of Signal Cost, Perceived Credibility, and Involvement Intention. Sustain (Switzerland) 15(2). https://doi.org/10.3390/SU15021167 Chang E (2023) Building An Effective Hiring and Smooth Employee Boarding Process-Company X pages in appendices BUILDING AN EFFECTIVE HIRING AND SMOOTH EMPLOYEE BOARDING PROCESS-COMPANY X Chatterjee Biswas P (2021) THE EFFECT OF ORGANIZATIONAL CULTURE ON EMPLOYEE ENGAGEMENT-A STUDY ACROSS THE RECRUITMENT SECTOR IN GUJARAT Okatta CG, FOR STRATEGIC DECISION MAKING (2024) OPPORTUNITIES AND CHALLENGES. Int J Manage Entrepreneurship Res 6(4):1304–1325. https://doi.org/10.51594/ijmer.v6i4.1060 . LEVERAGING HR ANALYTICS Das RC, Anjana DSC (2020) Conceptualizing the Importance of HR Analytics in Attrition Reduction. In International Research Journal on Advanced Science Hub (IRJASH) Special Issue of First International Conference on Advancements in Management, Engineering and Technology (Vol. 02). Prasad DV, Rao K, M., Vaidya R (2019) Recruitment metrics and accomplishments: A study with reference to information technology sector. J Manage Res Anal 6(2):106–111. https://doi.org/10.18231/j.jmra.2019.020 Ekka S, Singh P, Ranjan P (2022) The Impact of HR Analytics Adoption on Firm Return on Investment: A PSM Model Approach. In Int J Bus Econ (21) Fornell C, Larcker DF (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J Mark Res 18(1):39. https://doi.org/10.2307/3151312 Garg K, Dar IA, Mishra M (2018) Job Satisfaction and Work Engagement: A Study Using Private Sector Bank Managers. Adv Developing Hum Resour 20(1):58–71. https://doi.org/10.1177/1523422317742987 Gupta A (2022) Diffusion of Artificial Intelligence in Human Resource Domain with Specific Reference to Recruitment Function: A Literature Review Study. In Int J Innovative Sci Res Technol (7). Gupta B, Shaikh MS, P (2018) PEOPLE ANALYTICS: NOVEL APPROACH TO MODERN HUMAN RESOURCE MANAGEMENT PRACTICE. Int J Eng Technol Manage Res 5(7):78–83. https://doi.org/10.5281/zenodo.1341965 Hair JF, Black WC, Babin BJ, Anderson RE (2019) MULTIVARIATE DATA ANALYSIS EIGHTH EDITION . Hemanth Kumar T, Naga Kumari Y, v, Rao AN, Leela C, Kumari M, Janaki G, Lakshmi PA, Siva J, Krishna S (2022) HR Analytics as a ModeratingRole in Effective Employee Engagement and its Outcomes. 208:4913–4920. https://doi.org/10.14704/nq.2022.20.8.NQ44516 Hülter SM, Ertel C, Heidemann A (2024) Exploring the individual adoption of human resource analytics: Behavioural beliefs and the role of machine learning characteristics. Technol Forecast Soc Chang 208:123709. https://doi.org/10.1016/J.TECHFORE.2024.123709 JANKOWSKI Ł, HR ONBOARDING: THE IMPACT OF EMPLOYEE ENGAGEMENT ON THE PERFORMANCE OF NEW HIRES IN THE FIRST PERIOD OF WORK (2025) Humanit Social Sci Q 32(1):61–80. https://doi.org/10.7862/rz.2025.hss.05 Junça Silva A, Faria C, Lopes RR (2025) Onboarding for success: exploring the effects of socialization tactics on newcomer well-being. Int J Organizational Anal. https://doi.org/10.1108/IJOA-01-2025-5165 Kahn WA (1990) Psychological conditions of personal engagement and disengagement at work. Acad Manag J 33:692–724 Kayusi F, Chavula P, Keari Omwenga M, Juma L, Agura Kayus B, Vallejo G, R., Mishra R (2025) AI-Driven HR Analytics: Transforming Talent Management and Employee Engagement. Revista Multidisciplinaria Voces de América y El Caribe , 2 (1), 558–582. https://doi.org/10.69821/remuvac.v2i1.214 Kirchner M, Stull F (2022) Employee onboarding and satisfaction in US manufacturing companies. Industrial Commercial Train 54(2):267–278. https://doi.org/10.1108/ICT-06-2021-0044 Kline RB (2013) Beyond significance testing: Statistics reform in the behavioral sciences, 2nd edn. American Psychological Association. https://doi.org/10.1037/14136-000 Leicht-Deobald U, Busch T, Schank C, Weibel A, Schafheitle S, Wildhaber I, Kasper G (2019) The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity. J Bus Ethics 160(2):377–392. https://doi.org/10.1007/s10551-019-04204-w Hu LT, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model 6(1):1–55. https://doi.org/10.1080/10705519909540118 Madanchian M (2024) From Recruitment to Retention: AI Tools for Human Resource Decision-Making. In Applied Sciences (Switzerland) (Vol. 14, Issue 24). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/app142411750 Markos S, Sridevi MS (2010) Employee Engagement: The Key to Improving Performance. In Int J Bus Manage (5, Issue 12). Marler JH, Boudreau JW (2017) An evidence-based review of HR Analytics. Int J Hum Resource Manage 28(1):3–26. https://doi.org/10.1080/09585192.2016.1244699 Mehedi M, Onik H, Miraz MH, Kim C-S (2018) Recruitment and Human Resource Management Technique Using Blockchain Technology for Industry 4.0 MeitY. Ministry of Electronics and Information Technology (2022) Available at: https://www.meity.gov.in/ministry/our-groups/details/software-industry-promotion-gN1EDOtQWa . Accessed 13 September 2025 Mohapatra M, Sahu P (2017) Optimizing the Recruitment Funnel in an ITES Company: An Analytics Approach. Procedia Comput Sci 122:706–714. https://doi.org/10.1016/j.procs.2017.11.427 Mosquera P, Soares ME (2025) Onboarding: a key to employee retention and workplace well-being. RMS. https://doi.org/10.1007/s11846-025-00864-3 Murgoski T (2023) Relationship between Onboarding Process and Employee Engagement in the Pharmaceutical Industry in North Macedonia. Univers J Manage 11(3):76–85. https://doi.org/10.13189/ujm.2023.110303 Quraishi DAH, Sadath S (2024) A Study on Impact of Employee Engagement on Employee Productivity. Int J Res Publication Reviews 5(3):1000–1010. https://doi.org/10.55248/gengpi.5.0324.0641 Shree Vv, Krishnan L, Krishnan L (2023) Training View project Talent Management View project Employee Engagement and New Age Work Practices Driven by AI and Mediated by HR Analytics. Monthly, Peer-Reviewed, Refereed . Index J IC Value: 86 87(9):2455–0620. https://doi.org/10.2015/IJIRMF/202302027 Shrivastava S, Dhaigude AS (2022) Prime Data Tech Pvt. Ltd: Resolving Employee Recruitment Dilemma using HR Analytics. Communications of the Association for Information Systems , 50 1. https://doi.org/10.17705/1CAIS.05027 Singh S, Satpathy B (2015) A Study on HR Practices for Employee Retention in Select Indian IT Organizations. Int J Eng Manage Res Page Number 1:159–166 www.ijemr.net Tasheva Z, Karpovich V (2024) TRANSFORMATION OF RECRUITMENT PROCESS THROUGH IMPLEMENTATION OF AI SOLUTIONS. J Manage Econ 04(02):12–17. https://doi.org/10.55640/jme-04-02-03 Walker-Schmidt W, Kaul C, Crocker Papadakis L (2022) Onboarding Effects on Engagement and Retention in the IT Sector. Impacting Education: J Transforming Prof Pract 7(4):8–15. https://doi.org/10.5195/ie.2022.220 Wright PM, MacMahan GC, Snell S, Gerhart BA (2001) Comparing line and HR executives’ perceptions of HR effectiveness: Services, roles and contributions. Hum Resour Manag 40(2):111–123 Yang T, Liu Y, Chen Z, Deng J (2023) Change of productivity loss due to presenteeism among the ageing workforce: Role of work support, workplace discrimination, and the work-nonwork interface. Hum Resource Manage J 33(2):491–510. https://doi.org/10.1111/1748-8583.12475 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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08:02:07","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147999,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8143474/v1/99e7755803bc89682b70e2b3.html"},{"id":96605965,"identity":"d7d92081-1589-4d16-8be6-516a8387e061","added_by":"auto","created_at":"2025-11-24 09:24:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18430,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Model connecting Post-Hire Measures to Employee Engagement and Employee Productivity\u003c/p\u003e\n\u003cp\u003e(Source: Authors)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8143474/v1/3791ae304fce2c80819fc774.png"},{"id":96598095,"identity":"409e7dcc-bd70-4d54-9d0c-a22df26bca65","added_by":"auto","created_at":"2025-11-24 08:02:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16198,"visible":true,"origin":"","legend":"\u003cp\u003eMediation Analysis\u003c/p\u003e\n\u003cp\u003e(Source: Authors)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8143474/v1/484062589f9618a47b3b99f0.png"},{"id":96912965,"identity":"ab279ec0-369e-4c65-841c-903794316043","added_by":"auto","created_at":"2025-11-27 13:45:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3591054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8143474/v1/c5c7011d-ebe5-431b-b61f-050fdb87c42c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePost-Hire Recruitment Metrics and Higher Productivity: Mediating Role of Employee Engagement\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe increasing strategic significance of human resources (HR) within contemporary organizations has transformed the manner in which enterprises perceive their workforce. Industries that heavily depend on knowledge, particularly in information technology (IT), talent has become the pivotal factor shaping organizational adaptability, innovation, and efficiency. Consequently, HR analytics (HRA) has evolved from basic descriptive reporting to a more predictive and prescriptive applications that evaluate HR\u0026rsquo;s influence on business outcomes (Marler and Boudreau \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; H\u0026uuml;lter et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Initial concepts regarding HR measurement (Boudreau \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) proposed that the effectiveness of HR practices could be assessed based on their contribution to overall organizational performance. Recent advancements in analytics, digital technologies, and algorithmic intelligence have expanded this framework, integrating HR data into comprehensive decision-making models for strategic management and evidence-based practices (Chinenye et al. 2024). As a result, HRA now functions both as a diagnostic instrument and a strategic asset, linking individual behaviors with key organizational performance indicators such as productivity, retention, and profitability.\u003c/p\u003e\u003cp\u003eIn the recruitment function of HR, analytics is still primarily focused on pre-hire recruitment metrics such as cost-per-hire, time-to-fill, and applicant quality (Gupta \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ali and Kallach \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While these metrics offer valuable operational insights, they do not always capture the long-term organisational benefits that emerge after employees are integrated into the firm. Post-hire recruitment metrics, including onboarding process compliance (OPC), hiring manager satisfaction (HMS), and first-year retention (FYR), needed more attention, as crucial indicators of workforce efficacy and organizational performance (Walker-Schmidt et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jankowski \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These factors indicate the extent to which new employees are assimilated, how well managerial expectations align with recruitment results, and the stability of the initial employment relationship. In fast-paced industries such as IT, where turnover rates soar and skill requirements change swiftly, these metrics may provide more predictive value for productivity than conventional recruitment indicators (Mohapatra and Sahu \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Prasad et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA significant link between post-hire metrics and organizational outcomes is higher employee engagement (EE), described as the degree to which individuals invest cognitive, emotional, and behavioral energy into their work roles (Markos and Sridevi, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Engagement not only boosts discretionary effort and innovation but also serves as a mediating factor that translates HR processes into performance outcomes (Hemanth et al. 2022; Quraishi and Sadath \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In IT firms, engagement is especially vital due to project-based work, rapid technological advancements, and the presence of distributed teams, all of which increase the necessity for psychological connection and clarity of roles (Singh and Satpathy \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shree et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, despite the growing literature on employee engagement, there is a dearth of empirical research that combines post-hiring metrics with higher employee productivity (EP) within IT environments.\u003c/p\u003e\u003cp\u003eFrom a theoretical perspective, this gap in research highlights the necessity to broaden the HR value-chain framework (Becker et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Boudreau and Ramstad, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) to achieve a more detailed comprehension of how HR processes lead to business results. Conventional models of the HR, such as organizational performance and employee relations, represent HR practices as systems that affect organizational outcomes through employee attitudes and behaviors (Wright et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). However, HRA allows for a more refined explanation of these pathways, evaluating how compliance, satisfaction, and retention interact with engagement to enhance productivity. Practically, this focus is essential for IT organizations that encounter ongoing issues of turnover, talent shortages, and performance fluctuations (Prasad et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By contextualizing post-hire recruitment metrics within an engagement-mediated framework of productivity, this study contributes to both theoretical insights and managerial practices, emphasizing HR\u0026rsquo;s overall role in driving business results in knowledge-centric environments.\u003c/p\u003e\n\u003ch3\u003eNeed for the Study\u003c/h3\u003e\n\u003cp\u003eThe importance of examining post-hire recruitment metrics in IT organizations arises from their direct impact on workforce stability and performance. Onboarding compliance ensures both legal adherence and the smooth transition of employees, fostering job satisfaction and initial productivity (Kirchner and Stull \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chang \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hiring manager satisfaction is vital for assessing the efficacy of the recruitment process, as it reflects the harmony between hiring outcomes and strategic workforce needs (Bernard and Ebenezer 2025). Furthermore, first-year turnover remains an ongoing challenge, signaling inconsistencies in recruitment and onboarding that can result in financial and performance issues (Das and Anjana \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Walker-Schmidt et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies have also indicated that employee engagement plays a crucial mediating role, enhancing retention, minimizing turnover, and boosting discretionary effort (Markos and Sridevi \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hemanth et al. 2022). In the context of IT organizations, where project success heavily relies on the engagement and productivity of employees, it becomes not only necessary but also strategic to evaluate these post-hire attributes to sustain competitive advantage (Quraishi and Sadath \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch Gap\u003c/h2\u003e\u003cp\u003eAlthough there is extensive literature addressing recruitment analytics (RA) and pre-hire metrics, there is a notable lack of focus on post-hire recruitment metrics as indicators of organizational success, especially within the IT sector. Existing studies have primarily investigated individual components such as onboarding processes and employee engagement (Walker-Schmidt et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Murgoski \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the role of analytics in reducing turnover (Das and Anjana \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and the satisfaction levels of hiring managers concerning recruitment efforts (Ali and Kallach \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, much of the existing research focuses on general sectors, overlooking the specific challenges faced in the IT industry, such as skill shortages, elevated turnover rates, and productivity influenced by particular projects (Mohapatra and Sahu \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Prasad et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This gap highlights the necessity for an in-depth, IT-specific study that examines how post-hire recruitment metrics function collectively to affect productivity, mediated by employee engagement. Based on the identified gap, the following research questions are framed:\u003c/p\u003e\u003cp\u003eRQ1. How does onboarding process compliance (OPC) influence employee engagement (EE) and improve employee productivity (EP)?\u003c/p\u003e\u003cp\u003eRQ2. What is the significance of hiring manager satisfaction (HMS) on employee engagement (EE) and overall employee productivity (EP)?\u003c/p\u003e\u003cp\u003eRQ3. What relationship exists between first-year retention (FYR) rates and the higher employee engagement (EE) in IT organizations?\u003c/p\u003e\u003cp\u003eRQ4. Does employee engagement (EE) mediate the relationship between post-hire recruitment metrics (OPC, HMS, FYR) and employee productivity (EP)?\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResearch Objectives\u003c/h3\u003e\n\u003cp\u003eBased on the above research questions, the following objectives are developed for the study:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo evaluate the impact of onboarding process compliance on levels of employee engagement and employee productivity.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo investigate how hiring manager satisfaction relates to employee engagement and employee productivity.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo analyze the influence of first-year retention on employee engagement and productivity outcomes.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo examine how employee engagement mediates between post-hire recruitment metrics and employee productivity.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eRecruitment Function and Use of RA\u003c/h2\u003e\u003cp\u003eRecruitment analytics (RA) has transformed the recruitment process by prioritizing alignment with organizational objectives rather than just administrative efficiency. Previously, the recruitment focus was on speed and cost-effectiveness; however, contemporary techniques now stress long-term retention and employer branding (Gupta et al. 2018). The incorporation of artificial intelligence (AI) and analytics enables HR professionals to utilize predictive insights for workforce forecasting and strategic talent planning (Madanchian \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAI-enabled solutions improve recruitment accuracy by minimizing bias, enhancing candidate matching, and streamlining administrative functions (Allal-Ch\u0026eacute;rif et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tasheva and Karpovich \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, HR departments must tackle the ethical dilemmas related to algorithmic transparency and responsibility (Bernard and Ebenezer 2025). Within the IT sector, optimizing the recruitment pipeline through analytics speeds up hiring processes and elevates candidate quality (Mohapatra and Sahu \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, AI-driven recruitment methods can analyze resumes, forecast attrition, and aid in more equitable decision-making (Leicht-Deobald et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ali and Kallach \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). New technologies such as blockchain and advancements from Industry 4.0 are further changing recruitment into a decentralized and verifiable process (Mehedi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, RA is increasingly regarded not merely as an operational tool but as a strategic facilitator that aligns talent acquisition with organizational efficacy and competitive sustainability.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePost-Recruitment Metrics and Their Impact on Organizational Effectiveness\u003c/h3\u003e\n\u003cp\u003ePost-hire recruitment metrics including onboarding process compliance, hiring manager satisfaction, and early turnover rates, extend the assessment of recruitment effectiveness beyond mere candidate acquisition. These metrics provide insights into the long-term implications of recruitment choices, shedding light on integration, employee engagement, and employee retention (Walker-Schmidt et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Atillo et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Incorporating such metrics into HRA frameworks allows for proactive management of the workforce and early identification of potential retention challenges.\u003c/p\u003e\u003cp\u003e\u003cem\u003eOnboarding process compliance (OPC)\u003c/em\u003e has transitioned from being just an administrative requirement to a strategic process that guarantees adherence to legal standards while fostering engagement and alignment with organizational culture. Research shows that structured onboarding improves job satisfaction, decreases uncertainty, and boosts early productivity (Kirchner and Stull \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Atillo et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) likewise note favorable results in the banking sector, where adherence to onboarding practices has led to a reduction in turnover intentions. Effective onboarding also promotes cultural integration and alignment with organizational values (Chang \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the IT industry, where turnover rates are high, organized onboarding programs are essential for retaining employees (Walker-Schmidt et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, onboarding that focuses on sustainability, as explored by Carson and Westerman (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), enhances the organization's credibility and ethical commitment. Together, these insights highlight the importance of onboarding compliance as both a regulatory safeguard and a basis for long-term employee engagement.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHiring manager satisfaction (HMS)\u003c/em\u003e serves as a crucial measure of recruitment effectiveness and alignment with organizational goals. Beyond simply looking at efficiency indicators, satisfaction relies on the perceived quality and suitability of new hires (Bernard and Ebenezer 2025). AI-enhanced recruitment tools have improved satisfaction by offering better candidate recommendations and reducing administrative burdens (Gupta \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ali and Kallach \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, concerns about transparency remain when hiring managers view AI systems as unclear or biased, both trust and satisfaction may decline (Leicht-Deobald et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In IT settings, manager satisfaction is closely linked to candidate quality and the reduced risk of mismatches through data-driven recruitment (Prasad et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Employer branding also plays an indirect role in shaping satisfaction by influencing applicant quality and alignment with organizational values (Baratelli and Colleoni \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, hiring manager satisfaction is both an outcome of effective recruitment and a mediator for overall talent management success.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFirst-year retention (FYR)\u003c/em\u003e represents a significant issue for organizations, indicating potential mismatches in recruitment or onboarding methods. Predictive analytics is vital in recognizing prospective attrition by examining patterns in engagement and performance (Das and Anjana \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shrivastava and Dhaigude \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Organizations that utilize such models have managed to enhance retention rates through targeted interventions (Ekka et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the IT sector, first-year turnover can markedly disrupt operations and raise recruitment expenses (Prasad et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Jankowski (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) highlights that engagement during onboarding is vital in limiting early departures. Consequently, reducing early turnover necessitates the incorporation of predictive analytics, effective onboarding, and ongoing engagement strategies.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEmployee Engagement as a Mediator\u003c/h2\u003e\u003cp\u003eDefined as cognitive, emotional, and behavioral elements, EE transforms HR interventions into enhanced performance (Aziz et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Garg et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Markos and Sridevi (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) identified EE as a vital factor for competitive advantage, while Hemanth et al. (2022) stressed analytical significance in customizing engagement tactics. Cultural alignment and managerial coaching further reinforce engagement (Aziz et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chatterjee 2021). AI and analytics now empower HR teams to spot engagement deficiencies and personalize their approaches (Kayusi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). During the onboarding process, engagement aids in early adaptation, commitment, and retention (Murgoski \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jankowski \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Overall, engagement acts as both a preventive measure against turnover and a driver for performance, establishing the behavioral foundation for the strategic impact of HR.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAggregate HR Contribution to Business Outcomes\u003c/h3\u003e\n\u003cp\u003eThe combination of analytics and employee engagement frameworks allows for measurable evaluation of HR\u0026rsquo;s influence on business results. HRA has improved this understanding by highlighting the specific ways in which HR processes affect productivity and profitability (Marler and Boudreau \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Anger and Tessema \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In technology-driven sectors, HR practices that utilize analytics, boost innovation while reducing inefficiencies related to employee turnover (Yang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Quraishi and Sadath \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eHigher Productivity in the IT Sector\u003c/h3\u003e\n\u003cp\u003eEmployee productivity in the IT industry relies on the combination of RA, the effectiveness of onboarding, and employee engagement. Recruitment supported by analytics reduces skill mismatches and assures cultural fit, leading to improved productivity (Mohapatra and Sahu \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Prasad et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In environments driven by projects, employee engagement is crucial for maintaining high performance amidst changing and demanding situations (Quraishi and Sadath \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A well-structured onboarding process enables quick adaptation and shortens learning periods, which directly impacts productivity improvements (Walker-Schmidt et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Strong employer branding further enhances this connection by attracting individuals who resonate with organizational values and exhibit quicker integration (Baratelli and Colleoni \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Innovative technologies like AI and ML refine workforce distribution, foresee performance patterns, and alleviate productivity drops arising from disengagement (Yang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, HRA acts as the cohesive element that connects recruitment, engagement, and performance to ongoing productivity in the IT sector.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eResource-Based View (RBV) and Job Demands-Resources (JD-R)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis research is significantly associated with two foundational theories: \u0026lsquo;the Resource-Based View (RBV) and the Job Demands-Resources (JD-R).\u0026rsquo; The RBV (Barney, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) considers human capital and employee engagement as strategic assets of the organization \u0026lsquo;that are valuable, rare, difficult to imitate, and non-substitutable (VRIN).\u0026rsquo; The JD-R theory (Bakker and Demerouti \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) proposes that employee well-being and engagement occur when job resources such as organizational support, culture, and HR strategy, counterbalance job demands, thereby improving employee performance. Within this framework, organizational culture, HR responsiveness, and the alignment of HR strategies serve as critical resources that energize employees, leading to increased engagement and productivity. Collectively, these theories interpret the strategic processes demonstrating how post-hire HR practices enhance performance outcomes. By integrating JD-R\u0026rsquo;s emphasis on motivation with RBV\u0026rsquo;s strategic perspective, this research underscores employee engagement as the central process variable that converts HR investments into sustainable productivity gains and organizational success.\u003c/p\u003e\u003cp\u003eThe following framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is developed, with a focus on the above literature.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(Source: Authors)\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eHypothesis Development\u003c/h2\u003e\u003cp\u003eOnboarding Process Compliance ensures that all new employees undergo standardized training, policy orientation, and cultural integration, which diminishes role ambiguity and promotes engagement (Kirchner and Stull \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chang \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This compliance is particularly crucial in IT firms, where the complexity of client needs and project frameworks demands early functional alignment (Walker-Schmidt et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, structured onboarding fulfils both compliance and strategic roles by embedding organizational values and expectations within the employee experience.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH1: \u0026lsquo;Onboarding Process Compliance\u0026rsquo; has a significant positive impact on \u0026lsquo;Employee Engagement\u0026rsquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHiring Manager Satisfaction serves as an internal performance measure reflecting recruitment alignment. Gratified hiring managers are more inclined to provide successful mentoring, support integration, and enhance early performance, thereby affecting engagement and retention (Bernard and Ebenezer 2025). On the other hand, dissatisfaction may indicate a systemic disconnect between the recruitment process and workforce requirements, which can lead to disengagement and performance issues (Leicht-Deobald et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH2: \u0026lsquo;Hiring Manager Satisfaction\u0026rsquo; has a significant positive impact on \u0026lsquo;Employee Engagement\u0026rsquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFirst-Year Retention acts as a significant post-hire result, encapsulating the cumulative impacts of recruitment accuracy, onboarding quality, and engagement levels. Early turnover disrupts team cohesion, increases replacement expenditures, and emphasizes flaws in the employment relationship (Das and Anjana \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shrivastava and Dhaigude \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Research within IT contexts confirm that elevated first-year turnover can undermine project continuity and the ability to innovate (Prasad et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), highlighting the importance of proactive and predictive RA.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH3: \u0026lsquo;First-Year Retention\u0026rsquo; has a significant positive impact on \u0026lsquo;Employee Engagement\u0026rsquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eResearch indicates that employees with high engagement levels demonstrate enhanced productivity, greater commitment to the organization, and lower intentions to leave (Quraishi and Sadath \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the IT sector, the significance of employee engagement increases due to the nature of industry: project-based work, its focus on innovation, and its vulnerability to burnout and employee turnover (Singh and Satpathy \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shree et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As such, engagement links the quality of HR processes with employee performance, reinforcing the idea that HR practices produce organizational value only when they are integrated into behavior (Becker et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Boudreau and Ramstad \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH4: Positive \u0026lsquo;Employee Engagement\u0026rsquo; has a significant positive impact on \u0026lsquo;Employee Productivity\u0026rsquo;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEmployee Engagement (EE) serves as the channel through which post-hire recruitment measures influence performance outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A structured and compliant onboarding process fosters engagement by improving clarity of roles and identification with the organization (Kirchner and Stull \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Murgoski \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while the satisfaction of hiring managers indirectly impacts engagement by influencing initial socialization and available support structures (Bernard and Ebenezer 2025). On the other hand, high turnover rates within the first year are indicative of disengagement in its most pronounced form symbolizing failures in integration and alignment (Das and Anjana \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH5: \u0026lsquo;Employee Engagement\u0026rsquo; mediates the relationship between \u0026lsquo;Post-Hire Recruitment Metrics\u0026rsquo; and \u0026lsquo;Employee Productivity\u0026rsquo;.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003cp\u003eA quantitative, cross-sectional methodology was employed to empirically examine the post-hire recruitment metrics pertinent to the IT sector. The IT industry was specifically because (i) there is a streamlined and continuous recruitment process, (ii) availability of knowledge worker composition, and (iii) the industry contributes 7.3% to the country\u0026rsquo;s GDP (MeitY Report, 2022) In the initial phase, a comprehensive review of the existing literature, including practitioner-oriented sources on RA, was conducted to identify relevant post-hire recruitment indicators. As a result, a set of potential metrics was established, covering time to productivity, probationary review outcomes, onboarding process compliance, job suitability, preliminary performance evaluations, hiring manager satisfaction, team performance, cultural fit, new hire turnover, and first-year retention.\u003c/p\u003e\u003cp\u003eFurther, a series of interviews was carried out with HR practitioners to identify the most significant post-hire metrics from an HR process perspective. The finalized framework included three \u003cem\u003eoperational metrics\u003c/em\u003e such as Onboarding Process Compliance (OPC), Hiring Manager Satisfaction (HMS), First-Year Retention (FYR) as independent variables (IVs), one \u003cem\u003eoutput metric\u003c/em\u003e such as Employee Engagement (EE) as the mediating variable (MV), and one \u003cem\u003eimpact metric\u003c/em\u003e such as Employee Productivity (EP) serving as the dependent variable (DV).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003ePilot Testing and Reliability Assessment\u003c/h2\u003e\u003cp\u003eA questionnaire using Likert scale (five-point) was administered to a small group of HR personnel. Minor adjustments were made to ensure the clarity, reliability, and content validity of the instrument, based on the results of the pilot study. The final scale exhibited internal consistency (α\u0026thinsp;=\u0026thinsp;0.939), with correlations (item-rest) exceeding 0.80, indicating good construct-level reliability, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSampling and Data Collection\u003c/h2\u003e\u003cp\u003eThe finalized questionnaire was administered using a simple random sampling method to HR professionals who were actively involved in recruitment processes across large, medium, and small IT organisations and consulting firms, located in South India. After eliminating incomplete responses, a final set of 220 responses, was evaluated using Jamovi (v2.3.28).\u003c/p\u003e\u003cp\u003eValidity of the responses was established as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average variance extracted (AVE) values were below the 0.50 threshold. AVE\u0026thinsp;\u0026lt;\u0026thinsp;0.50 is acceptable when CR exceeds 0.60, especially in exploratory research (Fornell and Larcker \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Hair et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe confirmatory factor analysis (CFA) model demonstrated excellent fit (SRMR\u0026thinsp;=\u0026thinsp;0.0448, CFI\u0026thinsp;=\u0026thinsp;0.927, TLI\u0026thinsp;=\u0026thinsp;0.918), supporting the overall construct validity, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReliability Statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eScale Reliability Indices\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003esd\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eCronbach\u0026rsquo;s alpha\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.939\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eItem Reliability Indices\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003esd\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eitem rest correlation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFYR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCR and AVE\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComposite Reliability (CR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage Variance Extracted (AVE)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.426\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFYR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.414\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCFA (Model Fit) Measures\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of Fit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFit Measure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThreshold Values\u003c/p\u003e\u003cp\u003e(Reference)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsolute Fit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSRMR (Standardized Root Means Square Residual)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.08 : Good Fit\u003c/p\u003e \u003cp\u003e(Hu L. T. et al. 1999)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIncremental Fit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCFI (Comparative Fit Index)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90 : Acceptable Fit\u003c/p\u003e\u003cp\u003e(Bentler 1990)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTLI (Trucker Lewis Index)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.90 : Acceptable Fit\u003c/p\u003e\u003cp\u003e(Hu L. T. et al. 1999)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParsimonious Fit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCMIN/DF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2 : Good Fit\u003c/p\u003e\u003cp\u003e(Kline R. B. 2013)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe analyses involved regression models, to assess the direct influence of EE on EP, and to evaluate the impact of OPC, HMS, and FYR on EE; and further, a mediation model to determine the effect of EE as a mediator in the relationships between these predictors and EP.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eMeasuring the impact of OPC, HMS, FYR on EE\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u0026lsquo;A multiple linear regression analysis was carried out to assess the efficiency of crucial post-hire recruitment metrics in forecasting overall EE (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This statistical approach allows for the simultaneous evaluation of three independent variables (OPC, HMS, FYR) to analyze their individual and collective impacts on a single outcome variable.\u0026rsquo;\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Fit Indices\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u003cp\u003eOverall Model Test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003eAdjusted R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eRMSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003edf1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003edf2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple Linear Regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026lsquo;Model Coefficients, and Omnibus ANOVA Test\u0026rsquo;\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eModel Coefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e\u003cp\u003eOmnibus ANOVA Test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStand. Estimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMean Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e25.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFYR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e40.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResiduals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e57.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote. Type 3 sum of squares\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe model achieved a strong overall fit, F(3, 216)\u0026thinsp;=\u0026thinsp;186, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, explaining 72.1% of the variance in engagement (R\u0026sup2; = 0.721, Adjusted R\u0026sup2; = 0.717). Each predictor contributed significantly: OC (β\u0026thinsp;=\u0026thinsp;0.307, t\u0026thinsp;=\u0026thinsp;5.03, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), HS (β\u0026thinsp;=\u0026thinsp;0.243, t\u0026thinsp;=\u0026thinsp;3.71, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and FR (β\u0026thinsp;=\u0026thinsp;0.377, t\u0026thinsp;=\u0026thinsp;6.38, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The Durbin-Watson (DW) statistic (2.11, p\u0026thinsp;=\u0026thinsp;0.478) confirmed the absence of autocorrelation (ranges from zero to 4). Collinearity diagnostics (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed acceptable tolerance values (0.30\u0026ndash;0.37) and VIFs below 3.5, confirming model stability (Hair et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCollinearity Assessment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTolerance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFYR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eMeasuring the impact of EE on EP\u003c/h2\u003e\u003cp\u003eThe simple linear regression model examined the predictive effect of employee engagement (EE) on employee productivity (EP), as shown in Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Fit Indices\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u003cp\u003eOverall Model Test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003eAdjusted R\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eRMSE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003ef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003edf1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003edf2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimple Linear Regression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u0026lsquo;Model Coefficients, and Omnibus ANOVA Test\u0026rsquo;\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eModel Coefficients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c13\" namest=\"c7\"\u003e\u003cp\u003eOmnibus ANOVA Test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eStand. Estimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003edf\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eMean Square\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e106.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e106.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eResiduals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e81.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003e218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eNote. Type 3 sum of squares\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe model demonstrated a strong and statistically significant fit, F(1, 218)\u0026thinsp;=\u0026thinsp;283, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, with an R\u0026sup2; value of 0.565, indicating that EE accounted for approximately 56.5% of the variance in EP. The standardized regression coefficient was β\u0026thinsp;=\u0026thinsp;0.752 (SE\u0026thinsp;=\u0026thinsp;0.0426, t\u0026thinsp;=\u0026thinsp;16.83, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting a positive relationship between engagement and productivity. The intercept was 1.186, implying that when engagement is minimal, baseline performance levels remain low. Model diagnostics indicated adequate assumptions of linearity and independence (DW\u0026thinsp;=\u0026thinsp;1.71, p\u0026thinsp;=\u0026thinsp;0.032), suggesting the absence of autocorrelation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eMediation Analysis\u003c/h2\u003e\u003cp\u003eMediation analyses were conducted to determine whether employee engagement (EE) mediates the relationship between post-hire recruitment predictors (OPC, HMS, and FYR) and employee productivity (EP). The model yielded significant indirect effects across all paths. \u0026lsquo;Summary of the mediation result is stated in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u0026rsquo;\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation Analysis Result (Standard Method)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndirect Effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eZ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e% Mediation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOPC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHMS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFYR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe findings indicated that HE functions as a crucial mediator. In every model examined, the indirect effect was found to be statistically significant. For OPC \u0026rarr; EE \u0026rarr; EP, the indirect effect (a\u0026times;b) was 0.411 (SE\u0026thinsp;=\u0026thinsp;0.0544, Z\u0026thinsp;=\u0026thinsp;7.55, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), representing 64.2% of the total effect. Similarly, for HMS \u0026rarr; EE \u0026rarr; EP, the indirect effect was 0.310 (SE\u0026thinsp;=\u0026thinsp;0.0512, Z\u0026thinsp;=\u0026thinsp;6.05, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), accounting for 41.2% mediation, while FYR \u0026rarr; EE \u0026rarr; EP exhibited an indirect effect of 0.310 (SE\u0026thinsp;=\u0026thinsp;0.0531, Z\u0026thinsp;=\u0026thinsp;5.84, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), which explained 41.4% mediation. All direct effects (c paths) were still significant, indicating partial mediation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eThis research highlights the crucial role of employee engagement as a mechanism linking post-hire recruitment metrics to employee productivity within an HR process framework.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eBased on the study findings, the significant and positive impact of post-hire metrics on employee engagement, and subsequently on overall employee productivity, is consistent with existing research that emphasizes the role of engagement as a mediator connecting HR practices to performance outcomes (\u003c/b\u003eBakker and Demerouti \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003cb\u003e). The measures identified and used in this study are indicative of the post-hire metrics and can be considered as yardsticks of recruitment efficiency. The substantial mediating effect of onboarding process compliance (64%) indicates that organized and effectively implemented onboarding practices are essential for early employee engagement and long-term employee productivity (\u003c/b\u003eJankowski \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003cb\u003e). Research across various organizational settings highlights that successful onboarding initiatives not only ease the transition into roles but also create a psychological bond with the organization, enhancing emotional commitment and encouraging proactive work behaviors (\u003c/b\u003eAtillo et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). \u003cb\u003eThe study also aligns with the job demands-resources (JD-R) framework, which states that organizational resources, such as thorough onboarding compliance and managerial support, enhance employee engagement by addressing psychological needs and clarifying roles (\u003c/b\u003eBakker and Demerouti \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003cb\u003e). This study underlined that well-designed onboarding process, enhances employee\u0026rsquo;s socialization in organizations that positively impacts their engagement (Jun\u0026ccedil;a et al. 2025), which in turn is essential for retaining new hires (\u003c/b\u003eMosquera and Soares \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFrom the perspective of strategic HRA, tracking onboarding compliance as a crucial post-hire metric reinforces the HR Scorecard model's claim that quantifying and refining people-related processes can lead to measurable improvements in business performance (\u003c/b\u003eBecker et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). \u003cb\u003eThe observation that hiring manager satisfaction and first-year retention also have positive impacts, indicates the interconnectedness of the post-hire experience factors with engagement and productivity (\u003c/b\u003eSingh and Satpathy \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Walker-Schmidt et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cb\u003eNonetheless, onboarding process compliance appears to serve as an impactful driver of employee engagement by shaping initial impressions of organizational support and alignment with competencies (\u003c/b\u003eChang \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Carson and Westerman \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003cb\u003e). Likewise, AI-driven recruitment systems increasingly allow for the dynamic monitoring of post-hire metrics, enabling HR leaders to recognize engagement predictors and enhance interventions in real time (\u003c/b\u003eAli and Kallach \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kayusi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). \u003cb\u003eThis data-centric strategy supports earlier claims that HRA can connect operational metrics with strategic outcomes by quantifying the mediating effect of engagement (\u003c/b\u003eMarler and Boudreau \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; H\u0026uuml;lter et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cb\u003eMoreover, the findings reinforce both theoretical and empirical perspectives that employee engagement acts as a vital mediator, converting effective post-hire practices, especially systematic onboarding process, into long-lasting productivity enhancements, confirming the broader notion that strategic HR processes can provide a sustainable competitive edge (\u003c/b\u003eBarney \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Quraishi and Sadath \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eTheoretical Implications\u003c/h2\u003e\u003cp\u003e\u003cb\u003eFrom a theoretical perspective, this study enhances the understanding of employee engagement as a mediating construct linking HR processes and performance outcomes. It provides empirical support for the Resource-Based View (RBV) of the firm, suggesting that engagement is an intangible strategic resource that can convert HR process quality into a competitive advantage (\u003c/b\u003eBarney \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e\u003cb\u003e). By incorporating engagement within a post-hire recruitment framework, the research addresses a significant gap in HR theory, typically centered on pre-hire selection and performance prediction, by illustrating how post-hire experiences contribute to sustained value creation. Furthermore, the research refines process-oriented models of HR effectiveness by defining engagement as both a measurable outcome of HR process alignment and a predictor of productivity. This duality strengthens the role of engagement as both a dependent and mediating construct within organizational behavior frameworks.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003ePractical Implications\u003c/h2\u003e\u003cp\u003e\u003cb\u003eFrom a managerial perspective, these findings highlight the importance of incorporating engagement-focused practices within post-hire recruitment processes.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHR professionals should broaden the assessment of recruitment success beyond initial placement metrics to more engagement measures, including role fit, psychological safety, and feedback responsiveness (\u003c/b\u003eKahn \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1990\u003c/span\u003e\u003cb\u003e). HR initiatives should prioritize the personalization of onboarding, and related mechanisms, to enhance engagement as an ongoing endeavor rather than a one-time result. While FYR is one of the post-hire measures adopted in this study, longer retention mechanisms should be designed to engage and utilize the talent with the organization. By redefining HR processes, organizations can improve both employee experience and productivity outcomes, fostering sustainable performance through data-informed HR strategies.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003e\u003cb\u003eAlthough this research offers a robust analytical framework, it does have some limitations. The cross-sectional design restricts the capacity to draw causal inferences since engagement and performance could impact one another over time. Additional metrics, such as time-to-productivity, training completion rates, and probationary performance, might be incorporated into future models to enhance theoretical depth. The sample includes a variety of organizational types, and the findings may not apply to industries with varying levels of HR process maturity. Moreover, although engagement proved to be a significant mediator, other factors such as job satisfaction, commitment, or well-being may also serve as mediators or moderators.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eFuture Research\u003c/h2\u003e\u003cp\u003e\u003cb\u003eFuture research should utilize longitudinal data to investigate these evolving relationships and consider cross-cultural comparisons to assess the generalizability of the proposed model. The effects of onboarding compliance and managerial satisfaction either persist or fade over time.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe inclusion of HRA and ML may enable the real-time prediction of employee engagement and employee productivity outcomes based on post-hire metrics. Such predictive frameworks could transform talent management by identifying the factors that may lead to disengagement or turnover before they emerge.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research offers strong empirical evidence supporting the intermediary role of employee engagement in the connection between post-hire recruitment metrics and employee productivity, from a HR process perspective. The findings clarify how organizational elements ingrained in post-hire practices, significantly enhance employee engagement, which consequently leads to tangible improvements in both individual and organizational productivity. The robustness of the observed mediation pathways highlights that the success of post-hire recruitment processes goes beyond simply acquiring talent; it lies in how well new employees are socially integrated, psychologically invested, and structurally supported through the organization\u0026rsquo;s engagement strategies. From a procedural viewpoint, this indicates that post-hire metrics should encompass more than just performance or retention figures; they should also include engagement-related indicators to evaluate the long-term effects of recruitment quality. On a practical level, these findings encourage HR leaders to integrate engagement-focused initiatives, such as tailored onboarding process, feedback incorporation, and role clarification, into their post-hire approaches, thus enhancing the recruitment-performance relationship. From a theoretical perspective, this research enriches the RA literature by illustrating that engagement serves as a dynamic linkage mechanism that translates recruitment results into sustained productivity improvements. Overall, this research promotes a process-oriented understanding of HR effectiveness, identifying employee engagement as the crucial factor through which post-hire recruitment metrics translate into lasting organizational performance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eParticipants consented to participate in the study voluntarily.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli O, Kallach L (2024) Artificial Intelligence Enabled Human Resources Recruitment Functionalities: A Scoping Review. Procedia Comput Sci 232:3268\u0026ndash;3277. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.procs.2024.02.142\u003c/span\u003e\u003cspan address=\"10.1016/j.procs.2024.02.142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllal-Ch\u0026eacute;rif O, Ar\u0026aacute;nega Y, A., Casta\u0026ntilde;o S\u0026aacute;nchez R (2021) Intelligent recruitment: How to identify, select, and retain talents from around the world using artificial intelligence. Technol Forecast Soc Chang 169:120822. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.TECHFORE.2021.120822\u003c/span\u003e\u003cspan address=\"10.1016/J.TECHFORE.2021.120822\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnger O, Tessema MT (2021) A framework for assessing the effectiveness of HR metrics and analytics: the case of an american healthcare institution. In \u003cem\u003eArticle in Journal of Human Resource Management\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/349412539\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/349412539\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAtillo GNA, Cardeno RA, Olasiman DMAM (2025) Onboarding experience and job satisfaction in Philippine government banking sector. \u003cem\u003eHO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(6), 3\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.46223/HCMCOUJS.econ.en.15.6.3799.2025\u003c/span\u003e\u003cspan address=\"10.46223/HCMCOUJS.econ.en.15.6.3799.2025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAziz S, Soc Sci C, Ali PJ, Aziz Lodhi M, Raza S, B., Ali W (2018) Standard-Nutzungsbedingungen: Examining the Impact of Managerial Coaching on Employee Job Performance: Mediating Role of Work Engagement, Leader-Member-Exchange Quality, Job Satisfaction, and Turnover Intentions. In \u003cem\u003ePakistan Journal of Commerce and Social Sciences\u003c/em\u003e (Vol. 12, Issue 1). PJCSS. https://hdl.handle.net/10419/188345\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBakker AB, Demerouti E (2017) Job demands-resources theory: Taking stock and looking forward. J Occup Health Psychol 22(3):273\u0026ndash;285. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/ocp0000056\u003c/span\u003e\u003cspan address=\"10.1037/ocp0000056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaratelli G, Colleoni E (2022) Does Artificial Intelligence (AI) Enabled Recruitment Improve Employer Branding? Int J Bus Manage 17(2):45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5539/ijbm.v17n2p45\u003c/span\u003e\u003cspan address=\"10.5539/ijbm.v17n2p45\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarney J (1991) Firm Resources and Sustained Competitive Advantage. J Manag 171:99\u0026ndash;120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/014920639101700108\u003c/span\u003e\u003cspan address=\"10.1177/014920639101700108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBecker BE, Huselid MA, Ulrich D (2001) The HR scorecard: linking people, strategy, and performance. Harvard Business School Press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBernard Anthony BU, Ebenezer E (2025) Assessment of the Recruitment Stages and the Responsibility of Human Resources Management in the Task. In \u003cem\u003eGASPRO INTERNATIONAL JOURNAL OF EMINENT SCHOLARS\u003c/em\u003e (Vol. 12). Online\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoudreau JW, Ramstad PM (2002) \u003cem\u003eStrategic I/O Psychology and the Role of Utility Analysis Models\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://digitalcommons.ilr.cornell.edu/cahrswp/57/\u003c/span\u003e\u003cspan address=\"http://digitalcommons.ilr.cornell.edu/cahrswp/57/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoudreau JW, Ramstad PM (2004) \u003cem\u003eCenter for Effective Organizations TALENTSHIP AND HUMAN RESOURCE MEASUREMENT AND ANALYSIS: FROM ROI TO STRATEGIC ORGANIZATIONAL CHANGE Talentship and Human Resource Measurement and Analysis: From ROI to Strategic Organizational Change\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.marshall.usc.edu/ceo\u003c/span\u003e\u003cspan address=\"http://www.marshall.usc.edu/ceo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarson JE, Westerman JW (2023) The Effectiveness of Organizational Sustainability Messaging to New Hires: An Exploratory Analysis of Signal Cost, Perceived Credibility, and Involvement Intention. Sustain (Switzerland) 15(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/SU15021167\u003c/span\u003e\u003cspan address=\"10.3390/SU15021167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang E (2023) \u003cem\u003eBuilding An Effective Hiring and Smooth Employee Boarding Process-Company X pages in appendices BUILDING AN EFFECTIVE HIRING AND SMOOTH EMPLOYEE BOARDING PROCESS-COMPANY X\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChatterjee Biswas P (2021) \u003cem\u003eTHE EFFECT OF ORGANIZATIONAL CULTURE ON EMPLOYEE ENGAGEMENT-A STUDY ACROSS THE RECRUITMENT SECTOR IN GUJARAT\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOkatta CG, FOR STRATEGIC DECISION MAKING (2024) OPPORTUNITIES AND CHALLENGES. Int J Manage Entrepreneurship Res 6(4):1304\u0026ndash;1325. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.51594/ijmer.v6i4.1060\u003c/span\u003e\u003cspan address=\"10.51594/ijmer.v6i4.1060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. LEVERAGING HR ANALYTICS\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDas RC, Anjana DSC (2020) Conceptualizing the Importance of HR Analytics in Attrition Reduction. In \u003cem\u003eInternational Research Journal on Advanced Science Hub (IRJASH) Special Issue of First International Conference on Advancements in Management, Engineering and Technology\u003c/em\u003e (Vol. 02). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.rspsciencehub.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrasad DV, Rao K, M., Vaidya R (2019) Recruitment metrics and accomplishments: A study with reference to information technology sector. J Manage Res Anal 6(2):106\u0026ndash;111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18231/j.jmra.2019.020\u003c/span\u003e\u003cspan address=\"10.18231/j.jmra.2019.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEkka S, Singh P, Ranjan P (2022) The Impact of HR Analytics Adoption on Firm Return on Investment: A PSM Model Approach. In Int J Bus Econ (21)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFornell C, Larcker DF (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J Mark Res 18(1):39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/3151312\u003c/span\u003e\u003cspan address=\"10.2307/3151312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarg K, Dar IA, Mishra M (2018) Job Satisfaction and Work Engagement: A Study Using Private Sector Bank Managers. Adv Developing Hum Resour 20(1):58\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1523422317742987\u003c/span\u003e\u003cspan address=\"10.1177/1523422317742987\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta A (2022) Diffusion of Artificial Intelligence in Human Resource Domain with Specific Reference to Recruitment Function: A Literature Review Study. In Int J Innovative Sci Res Technol (7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.ijisrt.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta B, Shaikh MS, P (2018) PEOPLE ANALYTICS: NOVEL APPROACH TO MODERN HUMAN RESOURCE MANAGEMENT PRACTICE. Int J Eng Technol Manage Res 5(7):78\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.1341965\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.1341965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHair JF, Black WC, Babin BJ, Anderson RE (2019) \u003cem\u003eMULTIVARIATE DATA ANALYSIS EIGHTH EDITION\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.cengage.com/highered\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHemanth Kumar T, Naga Kumari Y, v, Rao AN, Leela C, Kumari M, Janaki G, Lakshmi PA, Siva J, Krishna S (2022) HR Analytics as a ModeratingRole in Effective Employee Engagement and its Outcomes. 208:4913\u0026ndash;4920. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14704/nq.2022.20.8.NQ44516\u003c/span\u003e\u003cspan address=\"10.14704/nq.2022.20.8.NQ44516\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eH\u0026uuml;lter SM, Ertel C, Heidemann A (2024) Exploring the individual adoption of human resource analytics: Behavioural beliefs and the role of machine learning characteristics. Technol Forecast Soc Chang 208:123709. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.TECHFORE.2024.123709\u003c/span\u003e\u003cspan address=\"10.1016/J.TECHFORE.2024.123709\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJANKOWSKI Ł, HR ONBOARDING: THE IMPACT OF EMPLOYEE ENGAGEMENT ON THE PERFORMANCE OF NEW HIRES IN THE FIRST PERIOD OF WORK (2025) Humanit Social Sci Q 32(1):61\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7862/rz.2025.hss.05\u003c/span\u003e\u003cspan address=\"10.7862/rz.2025.hss.05\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJun\u0026ccedil;a Silva A, Faria C, Lopes RR (2025) Onboarding for success: exploring the effects of socialization tactics on newcomer well-being. Int J Organizational Anal. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/IJOA-01-2025-5165\u003c/span\u003e\u003cspan address=\"10.1108/IJOA-01-2025-5165\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKahn WA (1990) Psychological conditions of personal engagement and disengagement at work. Acad Manag J 33:692\u0026ndash;724\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKayusi F, Chavula P, Keari Omwenga M, Juma L, Agura Kayus B, Vallejo G, R., Mishra R (2025) AI-Driven HR Analytics: Transforming Talent Management and Employee Engagement. \u003cem\u003eRevista Multidisciplinaria Voces de Am\u0026eacute;rica y El Caribe\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 558\u0026ndash;582. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.69821/remuvac.v2i1.214\u003c/span\u003e\u003cspan address=\"10.69821/remuvac.v2i1.214\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKirchner M, Stull F (2022) Employee onboarding and satisfaction in US manufacturing companies. Industrial Commercial Train 54(2):267\u0026ndash;278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/ICT-06-2021-0044\u003c/span\u003e\u003cspan address=\"10.1108/ICT-06-2021-0044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKline RB (2013) Beyond significance testing: Statistics reform in the behavioral sciences, 2nd edn. American Psychological Association. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/14136-000\u003c/span\u003e\u003cspan address=\"10.1037/14136-000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeicht-Deobald U, Busch T, Schank C, Weibel A, Schafheitle S, Wildhaber I, Kasper G (2019) The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity. J Bus Ethics 160(2):377\u0026ndash;392. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10551-019-04204-w\u003c/span\u003e\u003cspan address=\"10.1007/s10551-019-04204-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu LT, Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model 6(1):1\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10705519909540118\u003c/span\u003e\u003cspan address=\"10.1080/10705519909540118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMadanchian M (2024) From Recruitment to Retention: AI Tools for Human Resource Decision-Making. In \u003cem\u003eApplied Sciences (Switzerland)\u003c/em\u003e (Vol. 14, Issue 24). Multidisciplinary Digital Publishing Institute (MDPI). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app142411750\u003c/span\u003e\u003cspan address=\"10.3390/app142411750\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarkos S, Sridevi MS (2010) Employee Engagement: The Key to Improving Performance. In Int J Bus Manage (5, Issue 12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.ccsenet.org/ijbm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarler JH, Boudreau JW (2017) An evidence-based review of HR Analytics. Int J Hum Resource Manage 28(1):3\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09585192.2016.1244699\u003c/span\u003e\u003cspan address=\"10.1080/09585192.2016.1244699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMehedi M, Onik H, Miraz MH, Kim C-S (2018) \u003cem\u003eRecruitment and Human Resource Management Technique Using Blockchain Technology for Industry 4.0\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeitY. Ministry of Electronics and Information Technology (2022) Available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.meity.gov.in/ministry/our-groups/details/software-industry-promotion-gN1EDOtQWa\u003c/span\u003e\u003cspan address=\"https://www.meity.gov.in/ministry/our-groups/details/software-industry-promotion-gN1EDOtQWa\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 13 September 2025\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohapatra M, Sahu P (2017) Optimizing the Recruitment Funnel in an ITES Company: An Analytics Approach. Procedia Comput Sci 122:706\u0026ndash;714. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.procs.2017.11.427\u003c/span\u003e\u003cspan address=\"10.1016/j.procs.2017.11.427\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMosquera P, Soares ME (2025) Onboarding: a key to employee retention and workplace well-being. RMS. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11846-025-00864-3\u003c/span\u003e\u003cspan address=\"10.1007/s11846-025-00864-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurgoski T (2023) Relationship between Onboarding Process and Employee Engagement in the Pharmaceutical Industry in North Macedonia. Univers J Manage 11(3):76\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.13189/ujm.2023.110303\u003c/span\u003e\u003cspan address=\"10.13189/ujm.2023.110303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuraishi DAH, Sadath S (2024) A Study on Impact of Employee Engagement on Employee Productivity. Int J Res Publication Reviews 5(3):1000\u0026ndash;1010. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.55248/gengpi.5.0324.0641\u003c/span\u003e\u003cspan address=\"10.55248/gengpi.5.0324.0641\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShree Vv, Krishnan L, Krishnan L (2023) Training View project Talent Management View project Employee Engagement and New Age Work Practices Driven by AI and Mediated by HR Analytics. \u003cem\u003eMonthly, Peer-Reviewed, Refereed\u003c/em\u003e. Index J IC Value: 86 87(9):2455\u0026ndash;0620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2015/IJIRMF/202302027\u003c/span\u003e\u003cspan address=\"10.2015/IJIRMF/202302027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShrivastava S, Dhaigude AS (2022) Prime Data Tech Pvt. Ltd: Resolving Employee Recruitment Dilemma using HR Analytics. \u003cem\u003eCommunications of the Association for Information Systems\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17705/1CAIS.05027\u003c/span\u003e\u003cspan address=\"10.17705/1CAIS.05027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh S, Satpathy B (2015) A Study on HR Practices for Employee Retention in Select Indian IT Organizations. Int J Eng Manage Res Page Number 1:159\u0026ndash;166 www.ijemr.net\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTasheva Z, Karpovich V (2024) TRANSFORMATION OF RECRUITMENT PROCESS THROUGH IMPLEMENTATION OF AI SOLUTIONS. J Manage Econ 04(02):12\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.55640/jme-04-02-03\u003c/span\u003e\u003cspan address=\"10.55640/jme-04-02-03\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalker-Schmidt W, Kaul C, Crocker Papadakis L (2022) Onboarding Effects on Engagement and Retention in the IT Sector. Impacting Education: J Transforming Prof Pract 7(4):8\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5195/ie.2022.220\u003c/span\u003e\u003cspan address=\"10.5195/ie.2022.220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWright PM, MacMahan GC, Snell S, Gerhart BA (2001) Comparing line and HR executives\u0026rsquo; perceptions of HR effectiveness: Services, roles and contributions. Hum Resour Manag 40(2):111\u0026ndash;123\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang T, Liu Y, Chen Z, Deng J (2023) Change of productivity loss due to presenteeism among the ageing workforce: Role of work support, workplace discrimination, and the work-nonwork interface. Hum Resource Manage J 33(2):491\u0026ndash;510. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1748-8583.12475\u003c/span\u003e\u003cspan address=\"10.1111/1748-8583.12475\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Post-Hire Recruitment Metrics, Onboarding Process Compliance, Hiring Manager Satisfaction, First-year Retention, Employee Engagement, Employee Productivity","lastPublishedDoi":"10.21203/rs.3.rs-8143474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8143474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study explores the mediating function of higher employee engagement in linking post-hire recruitment metrics to employee productivity from the perspective of human resources (HR) processes. Drawing upon the Resource-Based View (RBV) and Job Demands-Resources (JD-R) theories, the study hypothesises that higher employee engagement serves as a crucial mechanism that converts the quality of HR processes into long-term performance outcomes. Data collected from HR personnel across various organizations, including consulting firms and companies of differing sizes, were analyzed using confirmatory factor analysis, simple and multiple linear regression, and mediation analysis. The linear regression analysis confirmed a significant relationship between higher employee engagement and productivity. The mediation analysis revealed that higher engagement, situated between post-hire recruitment predictors and productivity, accounts for 41\u0026ndash;64% of the total effect. These findings substantiate that higher employee engagement is a vital outcome variable linking post-hire metrics to measurable organizational performance results. From a theoretical perspective, this study advances HR analytics and employee engagement literature by integrating engagement into models of post-hire processes. This study provides a solid foundation for future longitudinal research investigating the relationship between enhanced employee engagement and increased productivity within HR analytics frameworks.\u003c/p\u003e","manuscriptTitle":"Post-Hire Recruitment Metrics and Higher Productivity: Mediating Role of Employee Engagement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 08:02:02","doi":"10.21203/rs.3.rs-8143474/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e13ec73e-ac06-4ff5-875f-6c93472fce33","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58171180,"name":"Management"}],"tags":[],"updatedAt":"2025-11-24T08:02:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-24 08:02:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8143474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8143474","identity":"rs-8143474","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}