Abstract
Purpose
The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.
Design/methodology/approach
A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.
Findings
Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.
Research limitations/implications
Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.
Originality/value
Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.
Keywords
Citation
Patil, B.S. and Priya, M.R.S.R. (2024), "HR data analytics and evidence based practice as a strategic business partner", Vilakshan - XIMB Journal of Management, Vol. 21 No. 1, pp. 114-125. https://doi.org/10.1108/XJM-07-2023-0148
Publisher
:Emerald Publishing Limited
Copyright © 2023, B.S. Patil and M.R. Suji Raga Priya.
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Introduction
Today’s businesses use human resource (HR) data analytics and evidence-based practise to make data-driven choices to enhance performance. HR data analytics uses data and statistical tools to address HR issues, whereas evidence-based practise uses research to make decisions. HR professionals may leverage data analytics and evidence-based practise as strategic business partners to alter organisations, boost employee performance and boost profits. According to Azam (2023), HR administrators can maximise employee performance and productivity by using research-based technology training programs.
To have a strategic impact on their organisations, HR professionals must use evidence-based practices (EBPs) and HR data analytics. These strategies enable HRs professionals to acquire and evaluate data to enhance organisational performance and the firm’s bottom line. The concept of HR data analytics is not novel despite its increasing prevalence (Huselid, 2018). In addition, HR departments are now able to collect, manage and analyse massive amounts of employee data as a result of the vastly increased availability of HR technology, such as HR information systems (HRISs), cloud platforms and applications (Kim et al., 2021). This is due to the vastly increased availability of HR technology, including HRIS, cloud platforms and applications.
HRs departments have considerably increased their use of HR data analytics as a direct consequence of this change (Wirges et al., 2023). For instance, the HR data analytics team at Google has developed a method based on empirical evidence to enhance the company’s recruiting and selection process. This was accomplished by identifying a number of high-performance variables that can predict a candidate’s likelihood of success for a position. This was accomplished by using cutting-edge HR technology to collect and evaluate candidate and employee data. Figure 1 depicts the core areas influencing HR data analytics (Wirges and Neyer, 2023).
EBP, which integrates the best evidence with clinical expertise and patient values and preferences, must be used in contemporary healthcare (Sackett et al., 1996). This literature review examines EBP in health care and its adoption obstacles and prospects. EBP is widely recognised as beneficial; however, health care practitioners confront several challenges when using it. Lack of time, money and evidence-based resources are challenges (Melnyk and Fineout-Overholt, 2011). Health-care workers may also lack the ability to find and analyse evidence (Duffy et al., 2016). The culture and priorities of health-care organisations may not promote EBP implementation (Schaffer et al., 2018). Implementation of EBP despite these challenges, EBP promotion has several chances. Electronic health records and mobile health apps may make evidence-based materials easily accessible (Cresswell et al., 2013). EBP-focused training and education may also provide doctors with the ability to execute EBP (Stevens, 2013). Finally, organisational culture change initiatives that promote EBP and incorporate it in performance assessments (Melnyk and Fineout-Overholt, 2011) may encourage healthcare staff to use it.
Theoretical framework
Modern business is data-driven, and HR is changing. Penpokai et al. (2023) report that HR is shifting from transactional to strategic business partner. This transition is primarily attributable to technology and HR data analytics. Businesses are using EBP to make HR decisions. Technology, HR data analytics and EBP as strategic business allies are examined in this theoretical framework, focusing on their interdependence and cooperation.
Human resource as a strategic business partner
HR has traditionally handled administrative chores, regulatory compliance and payroll. HR professionals must meet Ulrich’s 2005 HR business partner model’s requirements to become strategic partners in enterprises. Due to the competitive corporate climate, HR must participate in strategic decision-making. Wright et al. (1992) said SHRM underpins HR’s strategic business partner role. It entails matching HR procedures with company goals.
The importance of human resource technology
Technology in HR procedures has transformed enterprises. HRIS and other tools improve recruiting, onboarding, performance management and learning and development. Technology and analytics may improve data-driven and evidence-based HR decision-making, according to Davenport et al. (2010). Technology and HR analytics provide data-driven insights for HR decision-making and organisational planning, according to Marler and Boudreau (2017).
Evidence-based human resource practice
Rasmussen et al. (2011) emphasised the necessity of EBP in HR practices to make evidence-based people management, employee engagement and performance choices. HR EBP integrates the most accurate and relevant research results with the business context and employee preferences. This strategy helps HR professionals make educated policy and practice choices.
Literature review
HR analytics, often known as people analytics, uses data analysis to solve business challenges affecting people (Marr, 2016). This literature study examines HR data analytics’ definition and development. Bersin (2017) states that early HR data analytics businesses focused on core HR metrics including employee attrition and job vacancy filling times (Boudreau and Cascio, 2017). HR data analytics offers several potential and difficulties for enhancing HR results. Lack of data collection and analysis standardisation is a big issue (Stanton, 2018). Organisations may also lack the technology infrastructure and analytical ability to apply HR data analytics (Boudreau and Cascio, 2017). To help workers use HR data analytics, companies might invest in technology and analytics training (Marr, 2016). Schleicher et al. (2019) examined HR data analytics and evidence-based approaches. The authors present a complete literature review and suggest further study. Rynes et al. (2016) described evidence-based HR management and its state. The authors believe that HR data analytics may support HR practices and policies with data and propose developing corporate data-supported HR. Bondarouk and Olivas-Lujan (2016) examined HR data analytics in evidence-based HRM.
Data analytics and evidence-based HR methods may help firms make better hiring, retention, training and performance management choices. Jiang et al. (2020) found that HR data analytics improved evidence-based HR processes. Evidence-based HR practices and HR data analytics were positively correlated by Kepes et al. (2014). In addition, Goldsmith et al. (2017) found that evidence-based HR approaches improved HR data analytics. In another research, Ma et al. (2018) found that HR data analytics improves evidence-based HR practices.
Studies show that strategic business alliances and organisational EBPs are linked. Coberley et al. (2016) discovered that evidence-based HR strategies use strategic business partners more often. A survey of 244 firms found that evidence-based procedures increase the idea that HR professionals are valuable business partners. Jiang et al. (2018) found that evidence-based HR practises improved strategic HR cooperation. In a survey of 260 HR professionals, evidence-based HR practices were positively correlated with strategic business partner status. Ulrich et al. (2015) found that strategic HR partnerships promote evidence-based HR practices. A study of 168 companies found that strategic HR collaboration increases the likelihood of evidence-based HR choices.
Parallel research by Bos-Nehles et al. (2018) found that evidence-based HR practices improved strategic HR collaboration in health-care businesses. HR data analytics and strategic business are linked by organisational EBP. Several research studies show that organisational evidence-based approaches mediate HR data analytics and strategic business. In UK industrial companies, evidence-based HR policies mediate the link between HR data analytics and strategic business, according to Kinnie et al. (2016). According to Chen et al. (2018), evidence-based HR practices moderate the interaction between HR data analytics and strategic business in Chinese firms. Several studies link HR data analytics with HR technology accessibility. Bondarouk et al. (2017) found that HR data analytics is more likely to assist decision-making in companies with better HR technology capability.
HR data analytics supports EBPs and strategic business collaborations and HR technology is favourably connected with it. Figure 2 represents author’s own model focusing on HR data analytics and EBP as a strategic business partner. This research examines how HR technology, HR data analytics, EBPs and strategic business partnerships relate positively.
Objectives and hypothesis
To study the relationship among HR data analytics, technology and EBP as a strategic business partner.
Hypothesis:
EBP integrates significantly with HR data analytics.
Enterprise EBP and HR data analytics have a significant relationship.
Organisational EBP is significantly correlated with strategic business partners.
HR data analytics and HR technology are correlated.
HR technology adoption is associated with HR data analytics, which facilitates EBP and results in strategic business partners.
Research methodology
The research uses a semi-systematic methodology, which includes the use of a structured survey questionnaire to acquire quantitative data. Using SPSS, the data are examined. The questionnaire was distributed to organisations in various industries, including banking, information technology and hospitality. The majority of respondents were HR professionals in a variety of positions, HR administrators, managers and strategic planners. Random sampling method is used, a Google Form questionnaire was distributed to around 320 participants, out of which we have received approximately 300 responses. Nevertheless, only 250 responses were deemed legitimate and valid for the research. Fifty-three per cent of participants were male. Seventy-six per cent held HR manager/director or senior manager positions. Eighty-eight per cent of respondents represented private businesses. Thirty per cent of the companies were involved in ICT, 25% in financial services and 13% in professional services (accounting, architecture, consulting and law firms). The study used particular measurements to evaluate a variety of factors: based on the work of Delaney and Huselid in 1996, the business strategy was evaluated based on seven elements. On a five-point Likert scale with a 0.83 Cronbach’s alpha reliability coefficient, respondents ranked their company’s performance relative to competitors. This aspect was evaluated using six queries based on Rousseau’s (2006) work and Barends et al.’s (2014) EBM concept. Using a five-point Likert scale (1–5), respondents indicated whether they agreed or disagreed with various propositions. This measurement had a Cronbach’s alpha reliability coefficient of 0.93. In the absence of a reliable HR data analytics scale, published scale questions were used to depict Minbaeva’s theoretical framework. Using five questions from Pipino et al. (2002), the dimension of high-quality data was evaluated with a Cronbach’s alpha reliability coefficient of 0.73. This aspect of the research was based on three previously published entries modified by Aral et al. (2012). Table 1 represents the standard coefficients such as mean and standard deviation of variables considered for the study. ANOVA is used in research to compare two or more groups or treatments. This is particularly helpful when comparing group averages for statistically significant differences. The ANOVA showed significant variations in firm age, staff count and industry categorisation. This study used structural equation modelling (SEM). SEM is a powerful statistical approach for studying and validating complicated interactions between numerous variables. SEM lets researchers investigate a data set’s core structure by analysing measurement models (how variables relate to latent components) and structural models, providing a complete examination of latent and observable variable correlations (Kline, 2015).
Data analysis and interpretation
Table 2 represents the hypothesis testing with its p-value and status accordingly. HR data analytics correlated positively with organisational EBP in H1’s structural equation modelling. The standardised coefficient of organisational EBP on HR data analytics was positive and significant (5 0.30, p = 0.05). H1 was proven. The second theory linked EBP to important business partners. Figure 3 shows the strategic business coefficient on EBP was 0.48 (p = 0.001). H2 was verified. EBP mediated HR data analytics and the strategic business partner in H3. EBP mediated HR data analytics and strategic business, supporting H3. H4: HR technology improves HR data analytics. The HR technology–HR data analytics coefficient is positive and statistically significant (5 0.81, p = 0.001). H5 advocated a chain model linking HR technology to strategic business via HR data analytics and organisational EBM. H1 to H4 show that HR technology has a significant influence on HR data analytics (5 0.81, p = 0.001), which enables organisational EBP (5 0.30, p = 0.05) and leads to strategic business practises (5 0.41, p = 0.001). Strategic business indirectly affected HR technology via HR data analytics and organisational EBP at 0.06 (p = 0.05) with a 95% confidence range of 0.0013–0.117. The HR technology–HR data analytics–organisational EBM–organisational performance chain concept supports H5. Figure 3 represents the SEM model with the corresponding p-value.
Table 3 depicts the fits comparison measurements with model comparison. Each model is compared to the comprehensive measurement model:
HR data analytics and evidence-based factors combined together.
HR data analytics and technology as a singular factor.
Combining HR data analytics, evidence-based research and technology into a single factor.
Factors of evidence-based management and strategic business combined into one.
HR data analytics and strategic business rolled into one factor.
HR data analytics, evidence-based and strategic business factors are combined into one, and all five factors are added together to form a single factor.
Discussion
This research examines HR data analytics’ influence on strategic business. Due to rising interest in HR data analytics, firms have created HR data analytics teams to leverage workforce data for strategic workforce decisions. There is little evidence that HR data analytics improves strategic business. Despite the excitement for HR data analytics, McIver et al. (2018) found that organisations are unaware of how to use it to improve performance. Despite HR data analytics’ growing popularity. King (2016) added that HR data analytics is becoming more popular, but firms should only invest in programs that improve performance. Even though HR data analytics is growing in popularity. This study shows that HR data analytics improves strategic business, meeting demands. This effort links HR technology and analytics to assist HR data analytics research. Recent study reveals HR data analytics need HR technology. HR technology collects, manipulates and reports structured and unstructured workforce data for HR data analytics. The third research examines how HR data analytics influences strategic business via EBM. HR data analytics performance research is scarce. HR data analytics cannot affect strategic business theoretically or empirically; thus, intervening variables must be examined. This study shows the correlation between HR data analytics and organisational performance, but it is only the beginning.
Finally, this research finds HR data analytics as an antecedent of EBP and validates its performance impact. EBP is becoming widespread in academia and practice. Nothing has been done to directly address EBP’s performance impact on management, which is “of the utmost importance” (yet the organisational aspects that drive EBP are unknown). This paper extends EBP research by recognising HR data analytics as an organisational component that supports EBP.
Theoretical contribution
This study examines the theoretical effects of HR data analytics on strategic business operations to advance knowledge. In recent years, HR data analytics has gained interest. Current research, such as McIver et al. (2018), has indicated a lack of understanding of how HR data analytics might improve organisational performance. This association is examined to improve the theoretical understanding of HR data analytics and its role in strategic decision-making. This research highlights the importance of HR technology in enabling and improving analytics efforts. Recent study shows that HR technology helps capture, manage and report structured and unstructured worker data, allowing HR data analytics. The theoretical relationship emphasises the necessity for integrated HR technology solutions in data-driven HR operations. This article investigates the comparatively understudied influence of HR data analytics on strategic company operations from an EBP approach. Theoretical and experimental studies imply that HR data analytics alone may not affect strategic business outcomes.
Relevance in practice
The study’s practical implications demonstrate HR data analytics’ role in strategic decision-making for businesses. HR data analytics helps organisations understand worker characteristics, optimise HR operations and integrate HR with business objectives. HR technology investments benefit firms by providing HR data analytics insights. This emphasises the necessity to invest in modern HR technology solutions that can acquire, analyse and display data, improving HR operations and commercial success. Understanding the relationship between HR data analytics, EBP and strategic business outcomes may help firms integrate EBP into their decision-making processes. This may lead to more efficient HRs initiatives, improving corporate performance. Organisational learning fosters a culture of continual learning and adaptability in enterprises. Recognising HR data analytics and EBP in strategic corporate goals may lead to continual improvement, well-informed experimentation and increased HR methodology flexibility.
Conclusion
HR data analytics is a new field of study that is growing in prominence. HR data analytics and evidence-based practise have become indispensable for HR professionals to become strategic business partners. By leveraging data and evidence-based approaches, HR can make better decisions that are in line with the overall business strategy and contribute to the success of the organisation. HR data analytics is the process of collecting, analysing and interpreting data to identify trends and patterns that can inform HR policies and practises. HR professionals can make informed decisions, optimise workforce administration and identify areas for improvement by using data-driven insights. Evidence-based practise is the utilisation of research and empirical evidence to guide HR decisions. HR professionals can ensure that their decisions are effective, efficient and in line with the organisation’s objectives by relying on scientific evidence and best practises. HR data analytics and evidence-based practise together can assist HR professionals in becoming strategic business partners. HR can demonstrate its value to the organisation and contribute to its overall success by relying on data and evidence when making decisions.
Thus, researchers and practitioners may study how HR’s digitisation and rising people data affect HR decision-making and organisational results. This study shows how HR data analytics affects strategic business. We expect further study will be done to understand how HR data analytics benefits firms.
Limitation and future research
Despite its importance to HR data analytics theory and practise, this research has limitations. This study’s cross-sectional design limits causality testing. Secondly, its small sample size, poor response rate and surroundings hinder the research. Research should gather longitudinal data from diverse industries, sectors and nations. HR technologies and analytics need more study. HR technology as the fourth aspect of HR data analytics? Does it just aid HR data analytics? As shown, firms use several HR technologies for HR data analytics. Excel and HRIS, such as Workday, SuccessFactors and BambooHR, are used by HR departments for reporting, metrics and dashboards. To perform predictive and prescriptive data analysis, HR departments that have reached an analytically mature stage will combine these platforms with current HR technologies such as business intelligence tools, AI-enabled platforms and open-source statistical packages. This raises the issue of whether or not sophisticated HR technology makes HR data analytics more effective. These discoveries compared to those from fundamental technologies, how significant are they? HR data analytics teams, not individual workers, are responsible for doing the analytics (also known as the transformation and interpretation of high-quality workforce data into organisational insights). There has been a lack of attention paid to the makeup of HR data analytics teams as well as their effect on HR practises and strategic business.
Figures
Standard coefficients
| SL no. | Considered variables | Mean | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Strategic business partner | 3.7 | 0.64 | |||||||||
| 1 | Based on evidence practice | 3.8 | 0.69 | 0.43** | |||||||
| 2 | HR data analytics | 3.3 | 0.82 | 0.36** | 0.36** | ||||||
| 3 | Utilisation of HR technology | 3.8 | 0.91 | 0.20* | 0.22** | 0.68** | |||||
| 4 | Size (organisation) | 1.5 | 0.73 | 0.01 | –0.07 | 0.21* | 0.07 | ||||
| 5 | Age (organisation) | 3.3 | 0.98 | –0.11 | –0.24** | 0.06 | –0.03 | 0.44** | |||
| 6 | Sector | 0.9 | 0.34 | 0.05 | 0 | –0.14 | –0.01 | –0.17* | –0.14 | ||
| 7 | Type of organisation | 0.6 | 0.5 | –0.13 | –0.05 | 0.04 | –0.07 | 0.06 | –0.09 | –0.03 | |
| 8 | Type of industry | 2.8 | 1.24 | –0.11 | –0.03 | –0.06 | –0.08 | 0.08 | 0.09 | –0.05 | 0.06 |
**p < 0.01 and *p < 0.05
Source: Authors’ own work
Hypothesis testing
| Code | Hypothesis | p-value | Status |
|---|---|---|---|
| H1 | EBP integrates with HR data analytics | 0.005 | Significant |
| H2 | Enterprise EBP and HR data analytics have a significant relationship | 0.001 | Significant |
| H3 | Organisational EBP is significantly correlated with strategic business partners | 0.001 | Significant |
| H4 | HR data analytics and HR technology are correlated | 0.001 | Significant |
| H5 | HR technology adoption is associated with HR data analytics, which facilitates EBP and results in strategic business partners | 0.005 | Significant |
Source: Authors’ own work
Fits comparison measurements
| Models | χ2/df | CFI | TLI | RMSEA | SRMR | Δχ2 | Δdf |
|---|---|---|---|---|---|---|---|
| Measurement – full | 246.3/132 | 0.96 | 0.94 | 0.07 | 0.07 | ||
| Model 1 | 498.1/145 | 0.84 | 0.79 | 0.13 | 0.13 | 261.78*** | 3 |
| Model 2 | 371.3/145 | 0.87 | 0.87 | 0.1 | 0.09 | 134.90*** | 4 |
| Model 3 | 842.4/146 | 0.63 | 0.57 | 0.19 | 0.17 | 606.71*** | 4 |
| Model 4 | 412.2/147 | 0.88 | 0.83 | 0.11 | 0.12 | 174.69*** | 3 |
| Model 5 | 459.0/147 | 0.83 | 0.81 | 0.12 | 0.15 | 223.87*** | 4 |
| Model 6 | 669.0/147 | 0.73 | 0.69 | 0.15 | 0.17 | 422.37*** | 5 |
| (Harman’s test) | 1,011.7/138 | 0.45 | 0.39 | 0.18 | 0.17 | 753.94*** | 5 |
***p < 0.001; χ2: 5 chi-square discrepancy, df = 5 degrees of freedom; CFI = 5 comparative fit index; TLI = 5 Tucker–Lewis Index; RMSEA = 5 root mean square error of approximation; SRMR = 5 standardised root mean square residual; 5 difference in chi-square, 5 difference in degrees of freedom. In all measurement models, error terms were free to covary to improvefit and help reduce bias in the estimated parameter values
Source: Authors’ own work
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Further reading
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