HR analytics maturity indicators: developing a measurement scale

HR analytics, often referred to as people analytics, encompasses varied definitions but shares a core purpose. Van den Heuvel and Bondarouk (2017) define it as “The systematic identification and quantification of the people-drivers of business outcomes, with the purpose of making better decisions.” Tursunbayeva et al. (2018) describe people analytics as leveraging technologies and analytics tools to generate actionable workforce insights that enhance organizational effectiveness, performance and employee experience. Despite terminological variations, these perspectives emphasize deriving meaningful insights from employee data to drive business outcomes. Debates persist regarding HR analytics' organizational positioning. Some advocate integration with broader business analytics (BA) functions (Rasmussen and Ulrich, 2015), while others argue for its retention within HR to prioritize ethical considerations and employee well-being (Falletta and Combs, 2021).

However, a critical question remains: what capabilities enable organizations to create strategic value through HR analytics? HR analytics maturity reflects an organization's ability to collect and analyze employee data for strategic decisions, offering a systematic framework to address this challenge. Despite growing interest, the field lacks psychometrically validated instruments to assess this maturity, creating both academic and practical gaps. Academically, this limits empirical research on HR analytics maturity and its relationships with organizational outcomes, hindering theory development. Practically, while consulting firms provide assessment services, these rely on proprietary methodologies lacking standardization and validation necessary for reliable organizational self-assessment, peer comparison and capability development. Addressing this dual gap is essential for advancing both theoretical knowledge and practical application of HR analytics.

This study develops and validates the HR Analytics Maturity Indicators Scale (HRAMIS) to reveal HR analytics maturity indicators of organizations operating in Türkiye, where HR analytics adoption is emerging but understudied. Drawing on the DELTA Plus Model (Davenport and Harris, 2017), the study employs rigorous psychometric testing, including exploratory and confirmatory factor analyses, reliability assessment, convergent and discriminant validity testing and criterion-related validity examination through relationships with organizational effectiveness and performance.

HR analytics maturity encompasses four analytics types: descriptive, predictive, prescriptive and autonomous (Giermindl et al., 2021; Margherita, 2021). Descriptive HR analytics utilizes operational data to generate HR ratios, metrics, dashboards and reports for historical analysis. Predictive HR analytics analyzes process and workflow data to provide forecasts by applying statistical techniques and advanced algorithms. Prescriptive HR analytics leverages diverse and large HR datasets to optimize performance and guide strategic human capital decision-making processes (Margherita, 2021). Autonomous analytics, driven by artificial intelligence (AI), enables systems to independently make, execute and communicate decisions, transitioning from AI as a decision-support tool to a decision-maker that replaces human involvement in entire work processes (Giermindl et al., 2021). Leveraging these analytics requires diverse technical, technological and organizational capabilities. Various maturity models provide frameworks to assess and develop these capabilities, guiding organizations toward higher levels of analytical sophistication.

Maturity models are systematic frameworks that define and assess analytical capabilities through developmental stages from less to more mature states. These frameworks enable organizations to benchmark their current capabilities, identify improvement pathways and facilitate industry comparisons (Caralli et al., 2012). Despite criticisms regarding sequential progression and oversimplification of reality, maturity models offer valuable insights into organizational capabilities (Ferrar and Green, 2021; Wendler, 2012).

HR analytics maturity models originated from practitioner and consulting efforts, starting with the talent analytics maturity model (Bersin, 2013). Criticized for focusing solely on analytical complexity, it evolved into the people analytics maturity model (Bersin, 2021). Ferrar and Green (2021) introduced the nine dimensions for excellence in people analytics, addressing limitations of linear progression. More recently, the human resources analytics maturity model was developed from an academic perspective using a qualitative research approach (Rigamonti et al., 2024).

Conversely, business analytics maturity models have extensive academic and practitioner foundations with substantial diversity. Chen and Nath (2018) identified four distinct model categories: technology-focused models prioritizing technical architecture, organization-focused models integrating business and technical considerations, capability-focused models addressing resource structuring and impact-focused models examining decision-making outcomes. Their analysis reveals that effective maturity models must avoid fragmented approaches that overemphasize either technical or organizational dimensions while overlooking their fundamental interconnectedness. This approach emphasizes that effective models should integrate technical, technological and organizational factors.

The DELTA Plus Model (Davenport and Harris, 2017), a BA framework, achieves this integration through seven dimensions working synergistically to create analytical capability. The data dimension encompasses data management and governance, establishing the foundation for analytical capabilities. The enterprise dimension addresses enterprise-wide analytics strategy, organizational culture and coordinated governance. The leadership dimension addresses leadership commitment at all levels to promote data-driven decision-making. The targets dimension ensures strategic focus and alignment of analytics efforts with business objectives. The analysts dimension addresses the knowledge, skills and abilities (KSAs) required for analytical work. The technology dimension addresses infrastructure and platforms supporting analytics deployment. The analytic techniques dimension encompasses the range and sophistication of analytical methods available to organizations.

The model's strength lies in recognizing that analytical maturity emerges from synergistic interaction of these dimensions rather than isolated components. Strong leadership ensures enterprise-wide adoption and strategic alignment through targets. The integration of high-quality data with sophisticated analytic techniques generates actionable insights. Technology infrastructure supports both data management and analytical implementation, while skilled analysts leverage these elements to create value.

The model's empirical validation and organizational adoption in both practitioner and academic contexts (Davenport, 2018; Lismont et al., 2017) demonstrates its validity and practical applicability. The framework's flexibility allows adaptation to specific functional areas while maintaining its theoretical foundation, making it appropriate for developing an HR analytics maturity measurement instrument.

Based on the DELTA Plus Model framework and existing HR analytics literature, this research is guided by these expectations:

The methodology consists of three distinct phases: (1) generating a pool of potential items for each construct; (2) structuring the scale through systematic organization of items; and (3) evaluating the scale’s psychometric properties through reliability and validity testing (Schwab, 1980).

Following a comprehensive review of academic and applied HR analytics literature, the seven dimensions of the DELTA Plus Model (Davenport and Harris, 2017) served as the theoretical framework for scale construction. Based on this framework, an initial pool of 41 items was developed to operationalize these seven dimensions. To ensure content validity, two subject-matter experts who are experienced HR analytics practitioners reviewed the initial item pool and provided feedback on item clarity, relevance and theoretical alignment with each dimension. This preliminary questionnaire was administered to 204 professionals in HR analytics and HR management roles in Türkiye, yielding a three-factor structure comprising 19 items. Subsequently, this 19-item questionnaire was enhanced and expanded with additional elements informed by contemporary literature discourse, feedback from the preliminary study and additional content validity recommendations from the same expert panel, resulting in a 26-item measurement tool guided by the DELTA Plus Model's seven dimensions. This process involved iterative consultations with the expert panel to ensure consistency and comprehensive evaluation throughout scale development. The following section examines the content domain coverage of the scale items.

Data encompasses the organizational data environment and characteristics (Davenport and Harris, 2017). HR analytics processes structured data (personal information, performance scores and salary details) and unstructured data (feedback, reviews) (Van Vulpen, 2017). Effective HR analytics requires integrated, accessible, high-quality data from multiple sources (Dahlbom et al., 2020; Fernandez and Gallardo-Gallardo, 2021), supported by robust security, privacy and governance policies (Peeters et al., 2020; Sharma et al., 2025).

Enterprise requires an organization-wide approach integrating systems, data and people (Davenport and Harris, 2017). Successful HR analytics demands effective governance programs (Peeters et al., 2020), a data-driven culture valuing analytical insights (Fernandez and Gallardo-Gallardo, 2021), appropriate resource allocation for organizational and technical capabilities (Minbaeva, 2018) and consideration of all stakeholders affected by analytics initiatives (Peeters et al., 2020).

Leadership addresses the importance of top management support and leadership for the successful management and development of HR analytics capabilities (Davenport and Harris, 2017; Green, 2017).

Targets aligns HR analytics with strategic targets, ensuring analytical efforts support corporate priorities (Falletta and Combs, 2021; Green, 2017). Analytics initiatives should enhance organizational performance and competitive advantage (Davenport and Harris, 2017) while improving employee experience (Tursunbayeva et al., 2018). Key targets include developing organization-wide ethical guidelines and maintaining transparency (Tursunbayeva et al., 2022).

Analysts emphasizes the KSAs that HR analytics teams should possess (Davenport and Harris, 2017; Falletta and Combs, 2021).

Technology encompasses information technology (IT) infrastructure, tools and systems facilitating HR analytics (Davenport and Harris, 2017; Margherita, 2021).

Analytical techniques pertains to different analytical methods and techniques, ranging from descriptive through predictive and prescriptive to autonomous analytics (Davenport and Harris, 2017; Giermindl et al., 2021; Margherita, 2021).

Sample size determination for factor analysis studies varies in literature. Exploratory factor analysis (EFA), used to identify underlying factor structures, requires 2:1 to 10:1 sample-to-item ratios, with 100–200 participants generally adequate; confirmatory factor analysis (CFA), used to test pre-specified factor models, needs 100–200 participants or 5–10 times the number of items (Hair et al., 2010). This study used two samples: sample 1 (n = 130) for EFA with a 5:1 ratio (130:26) and sample 2 (n = 189) for CFA with a 14:1 ratio (189:13), both meeting accepted requirements.

Sample 1 (n = 130): 52% female and 48% male; ages 22–47 (M = 33.5, SD = 6.02); 52% bachelor's, 38% master's degree. Primary roles: HR Managers (21.5%), Senior HR Analytics Specialists (10.8%), HR Analytics Managers (10%), HR Analytics Specialists (9.2%), HR Data Analysts (6.2%). Average experience: 9 years; 56.9% from organizations with >1,000 employees.

Sample 2 (n = 189): 43% female and 57% male; ages 24–56 (M = 37.5, SD = 8.16); 55% bachelor's, 31% master's, 9% PhD. Primary roles: HR Managers (16.9%), HR Analytics Managers (13.8%), Senior HR Analytics Specialists (9.5%), HR Analytics Specialists (7.9%). Average experience: 9 years 7 months; 53.4% from organizations with >1,000 employees.

Both samples represented organizations across diverse sectors, including IT, chemistry and petroleum, consumer durables, finance and banking, automotive, food, energy, defense, healthcare and textiles.

HRAMIS: HRAMIS consists of 26 items developed in this study. The response options for the scale items were designed according to a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Organizational effectiveness scale: Mott's (1972) 8-item scale was used to assess organizational effectiveness – an organization's ability to achieve goals by utilizing resources, structuring processes, engaging employees and adapting to its environment (Zentis, 2024). HR analytics provides a data-driven framework that identifies human capital decisions linked to organizational effectiveness, transforming workplace behaviors through data-driven insights (Levenson and Pillans, 2017; Marler and Boudreau, 2017). Responses ranged from “not true” (1) to “always true” (5).

Organizational performance scale: Khandwalla's (1977) 5-item scale was adopted to evaluate overall organizational performance – defined as “evaluating how well organizations are managed and the value they deliver for customers and other stakeholders” (Moullin, 2002, p. 188). HR analytics enhances organizational performance through data-driven human capital decisions, systematic data analysis, optimized human capital management and alignment of HR strategies with business objectives (Guenole et al., 2017). Responses used a 5-point scale from “strongly disagree” (1) to “strongly agree” (5).

This study was conducted with 319 HR professionals working in organizations operating in Türkiye. Data were collected from two distinct samples at different time periods. For the first sample, data were collected between June 2023 and December 2023 and for the second sample between December 2023 and May 2024. Surveys from the first sample were used for EFA, whereas those from the second sample were used for CFA. Participants were recruited via purposive sampling through LinkedIn and HR professional networks, targeting individuals with HR analytics experience. An online questionnaire was distributed across various sectors via Google Forms, achieving a 28% response rate. Anonymity was ensured and participation was voluntary. Ethical approval was obtained from Marmara University Ethics Committee (Decision No: 2021–73, 13.07.2021).

EFA is a statistical technique specifically designed to uncover the hidden patterns and fundamental components of numerous interconnected variables (Tabachnick and Fidell, 2013). Following scale development methodologies, HRAMIS's factorial structure was initially examined through EFA using the first sample (n = 130).

Dataset adequacy for factor analysis was established via Kaiser-Meyer-Olkin sampling adequacy (0.95) and Bartlett's test of sphericity (χ² (n = 130, 325) = 2825.975, p < 0.001). Principal component analysis with varimax rotation was then conducted using standard criteria: eigenvalues ≥1.00, scree plot analysis, factor loadings ≥0.35 and minimum 0.10 differential between cross-loadings (Tabachnick and Fidell, 2013).

Following these analytical procedures, a final structure emerged comprising 23 items across three factors. Three items (I1, I6 and I19) [¹] were excluded due to low factor loadings. This three-factor structure accounts for 68.84% of the total variance, exceeding the recommended 50% threshold (Streiner, 1994).

The emerged three factors were named based on their item content: strategic alignment (SA) with items 13, 16, 17, 18, 11, 10, 8, 12, 9, 15 and 14 (28.03% variance); technology and analytics techniques (TAT) with items 22, 21, 26, 20, 25, 23 and 24 (22.53% variance); and data capabilities (DC) with items 4, 5, 2, 3 and 7 (18.28% variance). Factor loadings across all items ranged from 0.58 to 0.87, indicating a robust factorial structure (Hair et al., 2010). Table 1 shows the factors, their respective items and factor loadings.

CFA is used to examine whether a pre-specified factor model explains the relationships between observed variables (Gallagher and Brown, 2013). Using the second sample (N = 189), multiple CFAs were conducted to test and refine the EFA-derived factor structure through comparative model analysis. Thus, three competing models tested were: (1) a first-order model with three correlated factors, SA, TAT and DC (Figure 1, Model A); (2) a second-order model representing SA, TAT and DC as primary factors and HRAMIS as a higher-order factor (Figure 1, Model B); and (3) a bifactor model (Figure 1, Model C).

Maximum likelihood estimation was employed to analyze the covariance matrix. Model adequacy was assessed using multiple fit indices: chi-square statistics (χ²/df) for overall model fit, root mean square error of approximation (RMSEA) for approximation quality, standardized root mean square residual (SRMR) for residual analysis and incremental fit indices (normed fit index [NFI], comparative fit index [CFI], goodness-of-fit index [GFI], adjusted goodness-of-fit index [AGFI] and Tucker–Lewis index [TLI]) comparing the proposed model to a baseline model. Lower values indicate better fit for χ²/df, RMSEA and SRMR, while values closer to 1.0 indicate superior fit for incremental indices. Standard cut-off criteria were applied: χ²/df ≤ 2, RMSEA ≤0.05, SRMR ≤0.05, NFI ≥0.95, CFI ≥0.97, GFI ≥0.95, AGFI ≥0.90 and TLI ≥0.95 for good fit; and χ²/df ≤ 5, RMSEA ≤0.08, SRMR ≤0.10, NFI ≥0.90, CFI ≥0.95, GFI ≥0.90, AGFI ≥0.85 and TLI ≥0.90 for acceptable fit (Hooper et al., 2008; Hu and Bentler, 1999; Kline, 2005).

The initial three-factor, 23-item measurement model derived from EFA results showed mixed performance. While the χ²/df ratio (2.25) demonstrated good fit and RMSEA (0.08) and SRMR (0.07) were at acceptable levels, other fit indices were suboptimal (NFI = 0.81, CFI = 0.88, GFI = 0.81, AGFI = 0.77, TLI = 0.87) with several items exhibiting weak factor loadings (below 0.70). This indicated the need for model refinement and the overall pattern suggested systematic item elimination to achieve a more parsimonious solution.

Item removal was conducted systematically using two criteria: statistical performance and theoretical consistency with the emerging factor structure. The 10 removed items represented different aspects of the original DELTA Plus Model dimensions (see discussion section for detailed rationale). This refinement process yielded a 13-item model (SA factor: I13, I11, I8, I9, I15; TAT factor: I21, I20, I25, I24; DC factor: I4, I2, I3, I7) with substantially improved fit indices, representing the most parsimonious solution while maintaining adequate theoretical coverage. The resulting three-factor scale encompasses distinct but correlated dimensions (SA, TAT and DC) of the HRAMIS. Consequently, these constructs were examined through comparison of first-order, second-order and bifactor models.

All three refined models demonstrated acceptable to good fit indices (Table 2). The first-order model exhibited significant factor loadings (0.71 to .83) with moderate inter-factor correlations: SA with TAT (r = 0.62), SA with DC (r = 0.63) and TAT with DC (r = 0.59). The second-order model showed stronger factor loadings 0.73 to 0.86 (Table 1) for first-order factors and 0.77 to 0.85 for the higher-order factor. The bifactor model also achieved an acceptable fit across all indices. Despite comparable overall fit performance, the second-order model emerged as superior due to its stronger factor loadings and optimal theoretical alignment with HR analytics maturity conceptualization, providing the most accurate reflection of the underlying hierarchical structure where SA, TAT and DC represent distinct but interrelated dimensions of overall HR analytics maturity.

Convergent validity confirms that measures of the same construct correlate appropriately, while discriminant validity demonstrates that different constructs remain distinct (Lim, 2024). Following Fornell and Larcker's (1981) methodology, convergent validity was assessed using average variance extracted (AVE) and composite reliability (CR), with criteria requiring CR > AVE >0.5 and CR > 0.70. For discriminant validity, AVE values should exceed maximum shared variance (MSV) and square roots of AVE should exceed inter-construct correlations.

HRAMIS results demonstrated adequate convergent validity, with all factors showing AVE values above 0.50 and CR values above 0.70, thus meeting the CR > AVE >0.50 criteria (Table 3). Results indicate that items within each factor adequately represent their intended constructs. Discriminant validity was also confirmed as all factors’ MSV values were lower than AVE values and square roots of AVE exceeded inter-factor correlations. These findings demonstrate that each factor captures unique variance, while moderate correlations (0.59 to .63) support the theoretical expectation of related yet distinct dimensions.

Criterion-related validity requires significant relationships with theoretically associated criteria (Nunnally and Bernstein, 1994). To assess HRAMIS's criterion-related validity, correlations were examined between HRAMIS and organizational effectiveness (OE) and organizational performance (OP). The analysis revealed significant positive relationships between HRAMIS and OE (r = 0.61, p < 0.01), HRAMIS and OP (r = 0.60, p < 0.01) and OE and OP (r = 0.63, p < 0.01) (Table 4). These moderate correlations provide empirical support for criterion-related validity, confirming that higher HR analytics maturity is associated with better organizational outcomes.

To assess HRAMIS reliability, internal consistency coefficients (α) were calculated for both samples. Reliability coefficients above 0.70 indicate adequate internal consistency (Nunnally and Bernstein, 1994). The total HRAMIS demonstrated excellent reliability in both exploratory (α = 0.96) and confirmatory (α = 0.91) samples, with all subscales also exceeding this threshold (Tables 1 and 4). OE (α = 0.92) and OP (α = 0.87) also demonstrated strong reliability.

This study addresses the growing interest in HR analytics within Turkish organizations by developing and validating HRAMIS based on the DELTA Plus Model (Davenport and Harris, 2017). Through psychometric validation, the original seven theoretical dimensions consolidated into three empirically derived factors: SA, TAT and DC, accounting for 68.84% of the variance.

The measurement model evaluation began with 26 items, which through EFA initially yielded 23 items across three factors. Multiple CFAs resulted in the final 13-item solution demonstrating good model fit indices. Among the tested models (first-order, second-order and bifactor), the second-order model emerged as optimal, providing statistically parsimonious explanation while aligning with theoretical expectations. This structure encompasses distinct but correlated dimensions of HRAMIS. While SA, TAT and DC represent separate dimensions, they function as integrated manifestations of a unified HR analytics maturity construct. This supports the validity of using both individual dimension scores and total HRAMIS scores.

The SA factor emerged by combining four original DELTA Plus dimensions: enterprise, leadership, targets and analysts. This consolidation challenges the assumption that organizational, leadership, strategic and human capital aspects develop independently. Instead, it supports a unified capability perspective where these elements co-evolve as interdependent components. This integration aligns with governance frameworks (Peeters et al., 2020) that emphasize how multiple elements interconnect to achieve analytical maturity. Consequently, successful HR analytics implementation depends on strategic capability convergence – the simultaneous development of these interrelated dimensions.

The SA factor retained five items across the four original DELTA dimensions: HR analytics governance program and data-driven culture (enterprise), responsible leadership enabling analytics development (leadership), strategic goal alignment and competitive advantage (targets) and employee competencies for analytics tasks (analysts). This consolidation reflects the integrated nature of strategic readiness in HR analytics implementation.

This clustering reveals that HR analytics requires simultaneous development of governance structures, leadership support, strategic alignment and analytical expertise rather than developing these competencies separately. The convergence pattern aligns with resource-based view theory (Barney, 1991), where sustainable competitive advantage emerges from the complex interplay of organizational resources rather than individual elements.

Items with weak factor loadings were excluded. Resource allocation (enterprise dimension) and senior management support (leadership dimension) were not significant, suggesting that because HR analytics represents a resource-intensive investment (Green, 2017), these elements are viewed as foundational prerequisites rather than differentiators. The exclusion of role definitions (analysts dimension) may indicate that organizations adopt strategic approaches to HR analytics through governance programs rather than formal role specifications (Peeters et al., 2020).

The exclusion of ethics and transparency, employee experience improvement (both from targets dimension) and stakeholder values (enterprise dimension) may suggest that Turkish organizations currently focus on technical and operational competencies before developing comprehensive ethical frameworks, reflecting a developmental progression. This pattern indicates that organizations prioritize fundamental foundational skills before progressing to advanced ethical considerations, suggesting a sequential rather than integrated approach to maturity development.

The TAT factor contains four items: predictive analytics tools, prescriptive analytics tools, predictive techniques and prescriptive techniques. However, four items referring to descriptive and autonomous analytics techniques and tools were excluded due to weak factor loadings.

The exclusion pattern reveals important insights about analytics adoption in Turkish organizations. The exclusion of descriptive analytics items may be related to the business intelligence (BI) vs. BA debate, where literature shows varying perspectives on whether descriptive analytics belongs to BI or BA (Sisense Team, 2023; Van Rijmenam, 2014). This suggests descriptive analytics has become widely adopted across Turkish organizations rather than serving as a competitive differentiator. Conversely, autonomous analytics items were excluded because this type may be too advanced for most organizations. Autonomous analytics might still be more theoretical than practical for most organizations (Meijerink et al., 2021). The retention of predictive and prescriptive capabilities while excluding both descriptive and autonomous suggests that these analytics represent the current “sweet spot” in advanced analytics adoption – beyond basic reporting but short of full automation.

The DC factor retained four items emphasizing sophisticated data handling: unstructured data usage, integrated data from different sources, high data quality and data governance programs. This factor remained most consistent with the original DELTA Plus framework, confirming that data management represents a foundational capability area. The emphasis on data quality, integration, governance and unstructured data usage indicates that competitive advantage in data capabilities derives from organizations' ability to manage complex, high-quality data ecosystems rather than basic data operations. This pattern supports information systems literature emphasizing data quality, integration and governance as foundational elements that enable analytical success (Davenport and Harris, 2017).

The exclusion of three data items provides evidence of capability standardization across Turkish organizations. Structured data usage and data access were excluded, indicating these capabilities have become standardized for discriminating maturity levels. Most organizations likely possess foundational structured data handling and access capabilities as operational requirements. Türkiye's Personal Data Protection Law (KVKK) compliance and privacy policies were also excluded, indicating that legal compliance has evolved into a hygiene factor rather than a differentiator. This suggests that Turkish organizations have achieved foundational regulatory competency and now differentiate based on advanced data management capabilities rather than compliance sophistication.

These findings demonstrate that HRAMIS represents a theoretically grounded, empirically validated instrument for assessing HR analytics maturity while revealing culture-specific maturity patterns that contribute to global HR analytics literature. The Turkish context findings particularly highlight how institutional factors shape the relative importance of different dimensions.

This study has several important limitations. The Turkish organizational context may limit universal applicability of the results. Türkiye's position as an emerging economy undergoing rapid digital transformation creates a specific institutional context. Its unique blend of European and Middle Eastern business cultures may not represent other geographic regions.

KVKK, while aligned with GDPR principles, operates with different enforcement mechanisms. The GDPR's more stringent penalty framework creates stronger deterrent effects, potentially leading Turkish organizations to prioritize operational efficiency over comprehensive ethical frameworks, unlike their EU counterparts (Kesikli and Bayar, 2025). These institutional differences may explain why ethical considerations did not emerge as significant indicators in this study.

To enhance broader applicability of the HRAMIS, future research should conduct cross-validation studies across diverse economic and institutional contexts. Studies in EU countries could explore how data protection regulations shape ethical considerations in HR analytics. Similarly, studies in ASEAN economies with varying technological infrastructure could test factor structure across different developmental stages, while North American, African and Latin American contexts could examine generalizability across diverse market contexts. Such studies would establish whether HRAMIS represents a universal framework or requires culture-specific adaptations.

The purposive sampling approach may introduce selection bias, as recruitment through professional networks likely favored organizations with advanced HR analytics capabilities, potentially excluding those in traditional sectors or with limited analytics exposure. The 28% response rate, while typical for HR professionals in online surveys (Holtom et al., 2022), may amplify this bias, as respondents may represent a more analytics-oriented segment. Self-reported data may lead to over- or underestimation of capabilities due to favorable reporting or limited benchmark exposure (Donaldson and Grant-Vallone, 2002). The cross-sectional design limits insights into the evolution of HR analytics or causal relationships. Longitudinal studies could explore capability development and factor structure stability over time.

The psychometric evaluation of HRAMIS offers significant implications for both practice and research. The validated second-order model allows use of either individual subscales (SA, TAT, DC) or total scores to assess HR analytics capabilities.

HRAMIS enables organizations to evaluate current capabilities, identify improvement areas and benchmark against peers while providing a developmental roadmap for systematic capability building. The diagnostic value of HRAMIS supports targeted interventions. Organizations scoring low on SA should focus on governance structures and leadership buy-in before investing in advanced technologies, while those with weak DC should prioritize data quality and integration initiatives as foundational elements. This staged approach reflects the interconnected nature of HR analytics maturity, where organizations can identify their current position across the three dimensions and develop appropriate improvement strategies. For example, a ‘beginner' organization might focus on SA development, while an ‘intermediate' organization might prioritize TAT capabilities.

For research, HRAMIS provides a psychometrically sound tool to assess capabilities and investigate its relationships with organizational constructs. Significant correlations between HRAMIS and organizational performance and effectiveness lay the groundwork for longitudinal studies exploring long-term impacts. Comparative analyses across sectors could uncover mechanisms through which HR analytics influences performance, with mediating and moderating variables meriting further study. Research on the integration of HR analytics with broader BA functions could also enhance understanding of analytical synergy.

Future applications would benefit from incorporating qualitative methods to capture contextual nuances missed by quantitative approaches. For example, open-ended questions such as “Describe one ethical challenge in your HR analytics practice?” or “What are the primary barriers to advancing HR analytics maturity in your organization?” could provide deeper insights into ethical and implementation challenges.

Research should prioritize validation studies across different cultural contexts to establish the scale's generalizability, followed by longitudinal studies to understand evolution patterns. Mixed methods approaches, combining quantitative metrics with qualitative data, would enhance cultural sensitivity and understanding of dynamic capabilities, transforming HRAMIS into a dynamic framework for assessing HR analytics maturity across diverse contexts.

This study confirms that the three-factor 13-item HRAMIS is a valid and reliable instrument for assessing HR analytics maturity. This represents the first scale development study, to the best of our knowledge, to operationalize HR analytics maturity characteristics using the DELTA Plus Model. HRAMIS offers researchers and organizations to evaluate maturity, identify development areas and benchmark capabilities. These findings contribute to understanding the development of HR analytics and provide a foundation for future research on its evolution across diverse organizational and cultural settings.

The authors declare that they received no financial support for the research.