In this world of information, where all business entities want to build a competitive edge in the elevating global market (Kumar et al., 2020), the success of organizations depends on learning and adopting new processes and techniques in all business departments. The various internal and external forces have impacted businesses today in adopting new systems and technology; hence, this has increased pressure on human resources professionals to adopt new technologies to match the increased competition and rapid economic development in the era of industry. An organization can achieve better progress by acquiring HR analytics (Raj & Lalhall, 2024) and various metrics and creating a competitive edge in society. Bondarouk (2017) has defined HR analytics as the "systematic identification and quantification of people drivers of business outcomes." According to Jain (2020), "HR analytics is the art of collating, categorizing and scrutinizing the data related to HR functions & using several types of software and analytical processes to ensure better decision making within the organization to improve organizational performance."

Information technology (IT) encompasses computer hardware and software creation, application, and upkeep. IT-enabled services (ITES) are not core IT but rely on IT assistance (Afzal, 2019; Varun, 2020). Most of it involves soft communication skills where input of data is necessary. It is separated into three categories: legal process outsourcing (LPO), knowledge process outsourcing (KPO), and business process outsourcing (BPO). The IT industry depends heavily on its human resources. This skilled workforce gives this sector a competitive edge. Its creative work culture, which thrives on adopting new tools and applications like AI and HR Analytics, and its focus on competitiveness, strategic advantage, flexibility, and individualization make the IT-ITES sector the most vibrant by nature (Varun, 2020). Several factors aid in the adoption of HR analytics. However, other factors act as a hindrance to adoption (Shaik, 2023). This study tries to discover the most impactful factor that helps in HR analytics adoption in the IT industry.

Gentle (2015), in his study on ‘the Provocation Series Paper,' posited that HR analytics is expected to have a significant negative impact on HR practitioners. Several experts on HRA comment that organizations must adapt to emerging technologies that are entering the market due to technological advancements and the growing use of big data (Afzal, 2019). However, due to many individual and organizational level factors, HR experts are hesitant to adopt these cutting-edge tools. Products available in the market, such as Oracle's Taleo talent management suite and SAP's Success Factor, provide features like consolidating many HR-related datasets into a single cloud-based data warehouse. Although costing the firms a significant amount of money, these tools can enable HR data to make strategic decisions and integrate them into other business areas (Angrave, 2016; Kumar, 2021). Therefore, examining the factors that may enable or may become resistance to adopting HRA becomes imperative. Different studies (Raj & Lalhall, 2024) (Raj et al., 2024),      (Raj & Lalhall, 2024),   on adopting HR analytics have categorized these into personal and organizational factors. A review of such studies and the categorization of the factors are as follows:

Personal factors

Self-efficacy

The foundation of self-efficacy (Albert, Self-efficacy: Toward a unifying theory of behavioral change, 1977) is the conviction that one can succeed and reach a specific performance level. Thus, one's confidence in one's abilities determines whether or not to embrace HRA. Tool availability is defined as having the skill sets necessary to identify what data is needed and the capacity to analyze and understand data. Individuals with the requisite skill sets to use software to evaluate the essential dataset in the system are also equally vital (Johnson, 2011).

Quantitative self-efficacy 

 For the sake of this study, mathematical self-efficacy will be referred to as quantitative self-efficacy. People with higher levels of self-efficacy are more likely to participate in and do better on tasks that expand their knowledge and comprehension, supporting a more comprehensive learning process (Ozgen, 2013). Earlier research (Albert, 1982), (Schunk, 2014), and (Zimmerman, 2000) also supports the factor of quantitative self-efficacy affecting the adoption of HRA. Birigin (2009) and Ozgen (2013) presented similar ideas, stating that individuals who "had benefits and made life easier at work" also "believed that connections between mathematics and the real world increased their success in the workplace."

Individual level adoption

Innovations, in general, must be acknowledged for businesses to succeed in the current global environment and obtain a competitive advantage (Bersin, 2013; Giuffrida, 2013). Research indicates that both domestically and internationally, CEOs consider human resources among the most critical elements in establishing and preserving a competitive edge through analytics (Lesser & Hoffman, 2012; Financieras, 2013). A person goes through the adoption stage until they determine the time to institutionalize the innovation's use, put it to full use, and go on to the implementation stage. Alternatively, a person can choose not to move forward to passively reject the innovation or actively decide not to adopt it during the adoption procedure. The degree of adoption refers to the extent of the adoption procedure. An individual has made progress toward deciding whether or not to adopt the novel idea (Greenwood, 2018). Innovation adoption can be a complex process since it involves people participating in ways that can influence other people's decisions to accept or reject the innovation (Jeyaraj & Sabherwal, 2008). The willingness of an individual to adopt and use innovation can significantly impact a business's competitive advantage (Conrad, 2013).

Organizational factor

Tool availability

Tool availability is defined as having the skill sets necessary to identify what data is needed and the capacity to analyze and understand data. Software and system updates are also seen as crucial. More quickly and with greater data storage capacity, people can now access computers, and networks and connections have also improved. Thanks to modern HRIS and enhanced technology, organizations now have a different perspective on capital management (Carlson & Kavanagh, 2011). Undoubtedly, the creation of the HR systems in use now should have taken advantage of the computers and infrastructures in use today (Carlson & Kavanagh, 2011).

Data availability

Data availability is the accumulated information maintained in the HR division and the company. According to (Carlson & Kavanagh, 2011), reporting and benchmarking are the two HR practices most frequently employed when metrics and workforce analytics are concerned with administrative process efficiency. HR systems must now be built with computers and available infrastructures (Carlson & Kavanagh, 2011). Reliable data relevant to important performance areas can give businesses essential insights. Human resources managers can use analytics to provide value to each process step, from hiring to retaining people. The selection of data sources and metrics is crucial since businesses need to know what to measure to evaluate business success (Kumar, 2021).

Objective

The primary goal of this study is to identify the factors that are most important in adopting HR analytics in human resource management. In addition, the study looks for significant problems and obstacles that arise when HR analytics are adopted in the IT industry.

Rationale of study

HR analytics is an innovative approach to enhance and utilize HR for organizational benefits. HR analytics has been embraced by the Western IT industry thus far, but it is also making inroads into the Indian IT sector as of late. The current study thoroughly examines HR analytics in the Indian IT industry and how it approaches the creation and use of analytics. Consequently, there is a great deal of promise for HR analytics in the Indian IT sector, but there are gaps in HR systems, teams, and people skills (Boudreau, 2017). Since we firmly believe that imaginative and creative minds can produce the most significant economies, having logical HR professionals on staff is essential for strategic decision-making and maintaining a competitive edge (Bondarouk, 2017). In order to guarantee that such talent exists within the company, we also require robust assessment tools.

Research design

In this study, both descriptive and quantitative research methods were applied. A questionnaire designed in part using the UTAUT paradigm is used to collect the data. There are 25 questions in the questionnaire, some of which are based on organizational aspects and the UTAUT model, and others of which are meant to help understand the demographic profiles of the respondents. Only the Indian states were included in the study. This study is heavily field-based, with all the data used for analysis derived from survey responses obtained in the field. This study's unit of analysis is employees of the IT sector.

Sample frame

Since this research aims to examine responses from all over India, the Internet was thought to be the most practical and efficient way to collect data to save time and effort. In order to collect the answers, a Google Spread Sheet version of the survey was created and distributed via emails and several social media platforms. Over five to six months, 633 replies were collected, of which 596 were determined to be accurate and comprehensive. Thus, 596 people make up the study's sample size. The respondents were selected randomly from among the 29 states in India, and their demographic characteristics, including gender, age, and monthly income, were carefully considered. Both snowball sampling and convenience sampling were used to gather the data.

Brief Profile of Respondents

Table 1 displays the respondents' profiles. The respondents' demographic and economic characteristics are considered when tabulating the profiles. The study did not include the answers provided by those who declined to respond to this question.

Table 1

Reliability Analysis

One of the main issues during the research process was ensuring the questionnaire was reliable. A trustworthy questionnaire is one that consistently measures the concept it is intended to measure. The reliability of the questionnaire utilized in a given study is demonstrated by the respondents' ratings staying relatively constant after multiple measurements. Cronbach (1951) developed the most widely used scale reliability metric, Cronbach α (alpha). As per Cronbach's (1984) assertion, one may assess a questionnaire's internal consistency and reliability by computing its Cronbach's α. According to Nunnally (1978), Cronbach's α, dependent on the number of variables/items in a questionnaire and the correlations between the variables, is considered the most significant measure of the account reliability index.

The Cronbach's alpha coefficient (α) is used to evaluate the index alpha. Following a comprehensive analysis of all 25 questions in the structured questionnaire, the overall questionnaire's α value was determined to be 0.805 (refer to Table 2). A Cronbach's α value of 0.7 or above indicates that a factor is regarded as dependable, according to Kline (1999). The internal consistency of the questionnaire is assessed as "good" because its α = 0.805 value is within an acceptable range (0.7 ≤ α < 0.9 is judged good). The results imply that the structured questionnaire used in this study is reliable and has internal consistency.

Table 2

Table 3.

KMO and Bartlett’s Test

Kaiser–Meyer–Olkin and Bartlett’s Test

A measure of sample adequacy called Kaiser-Meyer-Olkin (KMO) and Bartlett's test of sphericity is advised to verify the data-to-variable ratio for the analysis being carried out. KMO and Bartlett's tests are crucial in most studies to approve the adequacy of the sample. According to Hutcheson and Sofroniou (1999), values between 0.5 and 0.7 are mediocre, values between 0.7 and 0.8 are good, values between 0.8 and 0.9 are great, and values beyond 0.9 are superb. Kaiser (1974) has indicated that a minimum of 0.5 is acceptable. Bartlett's test of sphericity evaluates the hypothesis that the correlation matrix is an identity matrix. This test must be significant, meaning its significance value should be less than 0.05. It follows that Bartlett's test of sphericity must always be less than 0.05 for factor analysis to be appropriate; otherwise, the data will not be deemed viable for use.

The Kaiser-Meyer-Olkin measure of sampling adequacy value for this dataset, according to KMO analysis, is 0.773 (Table 3), falling within the range of "good." As a result, the study's sample size is likely appropriate for factor analysis. Factor analysis is appropriate because Bartlett's test has also been determined to be highly significant (p < 0.001). Significantly less than 0.05, at 0.000 (Table 3), is the value for this investigation. Therefore, this study easily satisfies the need to perform a factor analysis.

Principal Component Analysis

The underlying presumption of principle component analysis (PCA) is that all Variance is common. For this reason, every commonality is one before the extraction (Table 4, column 2). The commonalities in Table 4 (column 3) represent the common Variance. It can be challenging for a variable to load on any given component when its commonality is between 0.0 and 0.4. Table 4 demonstrates that the extracted communalities meet the requirements to be included in the factor solution. Except for Q25, where the value is 0.387, the values recovered following analysis fall between 0.409 and 0.696; nonetheless, as the value is so near to 0.4, this anomaly is not considered very relevant.

Table 4.

 After the values were retrieved in PCA, it was discovered that the first three factors or components in the initial solution table had an eigenvalue more significant than 1, meaning that they validated the majority of the observed variation in developing workers' views regarding the adoption of HR analytics (Table 5). Factors with less than one would not be considered when determining how the study should be interpreted, as the Kaiser criterion specifies that only factors with eigenvalues more significant than one should be regarded as primary factors. Tabachnick and Fidell (2007) state that PCA is a statistical tool for data reduction used to extract each component's maximum Variance from a dataset. Dividing them into smaller, more manageable components simplifies the researchers' usage of enormous quantities of variables. Before employing a "true" factor analysis method, the researchers use PCA to minimize or mine the data.

The factors are then rotated using the Varimax with Kaiser normalization approach for better understanding because the unrotated factors are highly confusing and difficult to understand.

Factor loading has been used to quantify the association between the factors and the variables. Loading numbers near 1 suggests a strong correlation between the variable and the factor, while loading values closer to 0 indicate a poor correlation. As a result, for interpretation, only factors with values less than 0.4 are considered; those with values more than 0.4 are considered inconsequential. Five component or factor groupings could be identified from a thorough analysis of Table 5's total Variance explained, which included twenty-five items. Before rotation, the component matrix is shown in Table 6.

The loadings of every variable onto every factor are contained in this matrix. Table 7 provides further clarity on the factor structure's rotation, which aids in comprehending and identifying the affecting factors. According to Dutta, 2019 The factor transformation matrix, which shows how much the factors were rotated to get a solution, is the last component of the result (Table 8).

Table 5. Total Variance Explained (Extraction Method: Principal Component Analysis)

Table 6. Component Matrix

Table 7. Rotated Component Matrix

Table 8. Component Transformation Matrix

Following extensive analytical observation, it was determined that adopting HR Analytics in the IT business is primarily influenced by three out of five aspects. Each element is described separately, chronologically, according to priority.

Organizational Factors

Tool availability

Availability of tools was the highest-ranked component that affected the adoption of HR analytics in the IT industry. Table 5 shows that the first component, the availability of tools, has the highest eigenvalue, 4.686. For the adoption of HR analytics in the IT industry, the availability of tools is essential as it helps the organization to adapt to changes in technology and upgrade itself to fit in with emerging trends and technology, as well as gain a competitive edge. Adopting HR analytics depends on the availability of tools (Afzal, 2019). The findings show that the IT industry should make tools viable for employees to work with (Kremer, 2018). The employees should be responsive and receptive while interacting with the analytical applications. They should be eager to use the tools and make tasks more effective (Mittal, 2021).

Data availability

Once the company's dataset has the necessary data, the availability of HR tools becomes essential (Afzal, 2019). As per Table No. 5, the second component, which has a value of 2.237, is data availability. Data availability makes it easier to work with, making the tasks effective (Bowiya, 2017; Giuffrida, 2013). The organization should work on gathering, storing, and using data through datasets datasets for further decisions so data availability influences data availability influences the adoption of HR analytics.

Personal Factors

Individual Level Adoption

The third most important factor under Personal Factor classification in Table No. 5 with Eigenvalue 1.6 is individual-level adoption. Different studies have shown that although data and tools are available in the organization, adoption still needs to be improved (Mittal, 2021; Afzal, 2019). Also, some studies suggest that the adoption of HR analytics at the individual level is significant (Greenwood, 2018). Despite the availability of tools and data the organizations need to inculcate the instinct of adopting HR Analytics at individual levels (Vicence Fernandez, 2020).

Self-Efficacy and Quantitative Self-efficacy

The other two factors in personal factors, namely self-efficacy and quantitative self-efficacy, also depicted lower eigenvalues of 1.234 and 1.145, respectively, from Table No. 5. This value is also significant. It can be considered as the factor that influences the adoption of HRA, as the impact of self-efficacy and quantitative self-efficacy can be seen in various papers. Various authors (Albert, 1982; Chau, 2001; Ozgen, 2013) have also mentioned in their studies that self-efficacy is an important factor for adopting HR analytics.

In this research, it has been noted that the availability of tools is the most influential factor, followed by data availability and individual-level adoption. With accessible data and tools within an organization, individual adoption becomes significantly more accessible and convenient (Greenwood, 2018). Organizations should invest additional efforts in building employees' self-belief and confidence in their ability to utilize quantitative methods (Ozgen, 2013). Employees must have a clear and positive perception regarding the use of analytics in the HR department. Additionally, organizations should ensure that resources are readily available to employees to facilitate efficient work and the adoption of new technologies.

Considering the rapid advancements in technology, tools, and techniques, the HR department of the IT industry must embrace HR analytics in order to attain a competitive advantage (Kumar, 2021).

1. Afzal, M. (2019). HR ANALYTICS: CHALLENGES AND PROSPECTS OF INDIAN IT SECTOR. International Journal of Management, IT & Engineering, pp. 404–415.
2. Albert, B. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, pp. 191–215.
3. Albert, B. (1982). Self-efficacy mechanism in human agency. Amareican Pschycology.
4. (2016).
5. Balasubrahmanya, P. R. (2017). The rise of IT services clusters in India: A case of growth by replication. Telecommunications Policy, 90-105.
6. Bersin, J. (2013). Predictions for 2014: Building a strong talent pipeline for the global economic recovery. Deloitte.
7. Birgin, A. B. (2009). Conceptions of high school students about mathematical connections to the real life . Procedia Social and Behavioral Sciences.
8. Bondarouk, S. V. (2017). The rise (and fall?) of HR analytics: A study into the future application, value, structure, and system support. Journal of organizational effectiveness.
9. Boudreau, J. H. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 3-26.
10. Bowiya, R. A. (2017). Adoption Factors Impacting Human Resource Analytics Among Employees. International Journal of Economic Research.
11. Chau, P. Y. (2001). Influence of computer attitude and self-efficacy on IT usage behavior. Journal of End User Computing, pp. 26–33.
12. Dutta, S. S. (2019). "Factors Influencing Service Quality Perception in Indian Life Insurance Sector." Global Business Review.
13. Gartner. (2020). Retrieved from www.gartner.com.
14. Gentle, S. (2015). Big Data is changing the business landscape – but failing to change the workplace.
15. Giuffrida, M. (2013). HR can’t ignore big data. Talent Management Magazine, 16-19.
16. Greenwood, R. V. (2018). Individual adoption of HR analytics: a fine-grained view of the early stages leading to adoption. The International Journal of Human Resource Management, pp. 3046–3067.
17. Johnson, M. J. (2011). HR metrics and workforce analytics. Human Resource Information Systems: Basics applications and future directions, pp. 150–174.
18. Michele Kacmar, W. S. (2014). A Short and Valid Measure of Work-Family Enrichment. Journal of Occupational Health Psychology, 32-45.
19. Kremer, K. (2018). HR ANALYTICS AND ITS MODERATING FACTORS. Bupapest Management Review, 62-68.
20. Kumar, N. A. (2021). A ROADMAP TO EMPLOYING HR ANALYTICS AS A STRATEGIC TOOL. International Journal of Multidisciplinary Educational Research.
21. Levenson, A. (2005). Harnessing the power of HR analytics. Strategic Human Resource Review, pp. 28–31.
22. Mittal, M. A. (2021, November 5). EVALUATION OF HR ANALYTICS ADOPTION IN INDIAN BANKING AND FINANCIAL SERVICES INDUSTRY: THE MODERATING EFFECT OF EMPLOYEES’ RESISTANCE TO CHANGE. SPAST Abstracts, p. 1.
23. Opatha, H. P. (2020). HR Analytics: A Literature Review and New Conceptual Model. International Journal of Scientific and Research Publications, pp. 130–141.
24. Ozgen, K. (2013). Self-efficacy beliefs in mathematical literacy and connections between mathematics and the real world: The case of high school students. International Journal of Education Research.
25. Raj, R., & Lalhall, A. (2024.). Key Performance Indicators in Human Resource Analytics. Notion Press Media Pvt Ltd.
26. Raj, R., & Lalhall, A. (2024). Competitive Advantage among Organizations through Human Resource Analytics, A Theoretical Approach. HermeneuticS A Biannual Peer Reviewed Referred International Journal of Business and Social Studies, 14(01).
27. Raj, R., Lalhall, A., & Chaurasiya, S. (2024). Hindrance while Adopting and Implementing HR Analytics in the IT industry. Pragyaan: Journal of Management, 22(1).
28. Raj, R., & Lalhall, A. (2024). Evolution of HR Analytics in India – A Detailed Analysis. Journal of Economics & Management, 4(1).
29. Schunk, D. H. (2014). Learning theories: From an educational perspective.
30. Shaik, S. K. (2023). Exploring the Adoption of Human Resource Analytics among Human Resource Professionals. International Journal of Organizational Leadership.
31. Varun, T. (2020). INFORMATION TECHNOLOGY INDUSTRY IN INDIA: AN EX-POST STUDY. Dogo Rangsang Research Journal, 297-319.
32. Vicence Fernandez, E. G. (2020). HR analytics conceptualization and adoption: key issues.
33. Yamin, M. (2019). Information technologies of the 21st century and their impact on society. International Journal of Information Technology, pp. 759–766.
34. Zimmermen, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 82–91.