“Understanding the Adoption of HR Analytics in Indian Corporations”:  A Case Study on Selected Indian Private Multinational Company

Mrs. Malini N

Research Scholar, Management Science

Abstract

We live in what could be termed the Age of Analytics. Information itself is so cheap and abundant. What we need now is not more data but better ways to make sense of it.  This puts a premium on business analysis and interpretation, especially in the area of human resources. Talent represents the true competitive advantage for today’s organizations, yet HR is an area that has traditionally lagged behind other business functions in terms of analytics. This Research Paper focuses its understanding to know the adoption of hr analytics in Indian corporations.

There is an attempt made in the study to bring about the evolution of HR analytics and also the challenges faced by most of the organizations in the adoption of analytics in the area of HR. The study also will make an attempt to describe how organizations can make all that important connection between employee and decision making.

The study will be conducted in six organizations of IT sector, through the research tool as questionnaire and later the data collected will be analysed and interpreted with the help of required statistical tools

Keywords: HR Analytics, Metrics, Adoption, Human Resources.

Introduction

The term “HR analytics” means different things to different people. To some, the term only means a process for systematically reporting on different aspects of HR metrics—time to hire, turnover, compensation, employee engagement.HR analytics is an approach for making better decisions on the people side of the business, it consists of an array of tools and technologies, ranging from simple reporting of HR metrics all the way up to predictive modeling. Davenport, Harris and Shapiro help to provide clarity in this area by laying out the range of applications that constitute “talent analytics,” their phrase for HR analytics, from simplest “human-capital facts” to most sophisticated analytics that help optimize the “talent supply chain” (Davenport, et al., 2010).Some well-known and highly regarded HR practitioners argue that they have no need for HR analytics because their senior executives don’t require or expect it of them. This misguided point of view is the result of a fundamentally incorrect understanding of the purpose of HR analytics. Using HR analytics as a means of proving the value of the HR function is misguided. It is a misuse of analytics that fails to create any lasting value for an organization. “From a practical perspective, it immediately calls into question the credibility of any findings, insights, and recommendations that emerge. In short, if executives believe the HR function is embarking on an analytics project to justify itself, the outcomes from the project will be viewed with suspicion even if the analysis is done well. More substantively, such a perspective fails to capitalize on the tremendous value that can be created for an organization as a whole from the effective application of HR analytics” (Bassi, et al., 2010).The purpose of HR analytics is to improve individual and organizational performance. So it needs to be done, even if the CEO doesn’t require it. Although it is not its purpose to prove the worth of HR, analytics can certainly enhance the credibility of the function and the profession by improving the effectiveness of HR policies and practices and contributing to the competitive advantage of organizations that develop it as a core competency. HR analytics can help where effort, resource and budgets are not producing their intended impacts, and in so doing reduce the workload while improving the effectiveness of HR. HR functions and professionals develop new skills and capabilities so that they can effectively partner with and lead IT and finance on HR analytics initiatives. Mastering the art and science of HR analytics takes effort. But it can result in an elevation of the status of the profession and its practitioners by helping them to guide their organizations.

Literature Review

The core functions of HR are ultimately to optimize the workforce through adroit processes to optimize, acquire, develop, and pay the workforce, while complying with statutory requirements. Each of these processes has a set of business objectives; sometimes there are issues in meeting those objectives.

To meet the business objectives of developing workforce capabilities while increasing workforce engagement, a learning strategist can assess the organizational skills and experience levels and organizational strengths and weaknesses. Starting with an organizational skill map, the strategist can assess the effectiveness of learning programs, development plans, and employee potentials to help employees understand the development and career opportunities available to them to optimize the overall skill portfolio. To ensure engagement, the strategist can also determine the top performers and identify which development activities helped them reach that level then use that information to develop a top performer profile. The development group can use that performer profile to make recommendations for new hire training that builds a new set of top performers.

From the HR Performance dashboard, operational managers can correlate financial measures with key workforce metrics to demonstrate HR’s strategic value in the workforce. Managing the effectiveness of an organization’s workforce is one of the most critical performance management objectives for this decade – and, we anticipate, for future decades as well. Strategic analysis of information about an organization’s workforce can improve its performance. It is important to understand that this is not a narrowly focused task for the human resources department; the entire organization can achieve maximum effectiveness from its employees by applying workforce management across all functions and organizational groups.

Objectives:                        

  1. To study the adoption of HR analytics in Firms.

2. To understand HR analytics & present scenario

Figure 1: Stages of Data Collection followed by Author

An exhaustive empowerment questionnaire was put to test. Several HR Analytics angles were probed and a total of nearly 30 odd areas were identified, which were apt, valid and relevant on five point scale, viz: Strongly agree; Agree; can’t say; Disagree; and strongly disagree.  Such areas put to test includes understanding the system of analytics within the organisation, level of adoption , communication process adopted, decision making process, delegation and shared responsibility, power distribution, degree of trust & loyalty, employee participation and the like were put to test.

Hypothesis:

H1 – The understanding of adoption of HR Analytics is low in Indian Companies

H2 – The application of HR Analytics is significantly low in Indian Companies

H3 – Middle Management involvement in HR Analytics is significantly low in Indian companies.

Data Collection & Analysis

  1. A) Selection of organisation
  • 25 Indian private Multinational companies are selected on the basis of top 50 Indian companies in Forbes Global lists of various years between 2003-2014.
  • These companies are also selected on the basis of top 100 companies ranked by HR Analytics Association
  1. B) Sampling population
    • As many as 100 samples were included as part of data for the study. These samples were collected from Top , middle management executives & also at supervisory level
    1. C) Data collection
    • An exhaustive questionnaire was prepared and data was collected with regard to employee retention and loyalty.

    Cronbach alpha Test:

    Cronbach’s alpha is a statistic. It is generally used as a measure of internal consistency or reliability of a psychometric instrument , It was used to test the reliability of statements measuring perception

     

    Cronbach’s alpha Internal consistency
    α ≥ 0.9 Excellent (High-Stakes testing)
    0.7 ≤ α < 0.9 Good (Low-Stakes testing)
    0.6 ≤ α < 0.7 Acceptable
    0.5 ≤ α < 0.6 Poor
    α < 0.5 Unacceptable

     

     

    Reliability Statistics
    Cronbach’s Alpha N of Items
    .78 15

    The Cronbach alpha value is .78, which says questionnaire is Good (Low stakes testing)

     

    Results:

     

    The following Tables show the opinion of the participants. SPSS software package is used for statistical analysis.

     
    Table 1:  Metrics & Analytics are being adopted by respondent organization in the following functional areas
      Frequency Percent Valid Percent Cumulative Percent
    Valid Finance & Accounts 19 19.0 19.0 19.0
    Human Resources 9 9.0 9.0 28.0
    Production 9 9.0 9.0 37.0
    R & D 36 36.0 36.0 73.0
    Marketing 9 9.0 9.0 82.0
    CRM 9 9.0 9.0 91.0
    Supply Chain 9 9.0 9.0 100.0
    Total 100 100.0 100.0  
       

    Analysis & Interpretation: The frequency of  adoption of Metrics & Analytics by respondent organization in the following functional areas are Finance & Accounts 19%, HR 9%, Production 9%, R&D 36%, Marketing 9%, CRM 9% & supply chain 9%.

     

    Table 2: Managerial level function to which the application of Metrics & Analytics in respondent Organization are categorised

     
        Frequency Percent Valid Percent Cumulative Percent  
      Valid Top Level Management 21 21.0 21.0 21.0  
      Middle Level Management 58 58.0 58.0 79.0  
      Low Level Management 21 21.0 21.0 100.0  
      Total 100 100.0 100.0    

    Analysis : From the above table it shows that, Managerial level function to which the application of Metrics & Analytics in respondent Organization are categorised as 21%  top level function, 58% in middle level function & 21% in Low Level function.

     

    Table 3: Extent to which employees are capable of using Metrics & Analytics in respondent organization
      Frequency Percent Valid Percent Cumulative Percent
    Valid Very High 11 11.0 11.0 11.0
    High 12 12.0 12.0 23.0
    Moderate 45 45.0 45.0 68.0
    Low 20 20.0 20.0 88.0
    Least 12 12.0 12.0 100.0
    Total 100 100.0 100.0  

    Analysis : : Extent to which employees are capable of using Metrics & Analytics in respondent organization are 11% very high, 12% High, 45% moderate, 20% low & 12% Least.

     

    Table 5: Eligibility criteria in an Analyst who handles Metrics & Analytics in respondent organization
      Frequency Percent Valid Percent Cumulative Percent
    Valid Knowledge of Excel 21 21.0 21.0 21.0
    Knowledge of SPSS 19 19.0 19.0 40.0
    Knowledge of SAS 19 19.0 19.0 59.0
    Knowledge of Oracle 23 23.0 23.0 82.0
    Knowledge of R 18 18.0 18.0 100.0
    Total 100 100.0 100.0  

    Analysis : From the above table it shows that 21% of the respondents say that Knowledge of Excel is an eligibility criteria  in an Analyst who handles Metrics & Analytics in respondent organization, 19% say Knowledge of SPSS, 19% say Knowledge of SAS,23% say Knowledge of Oracle, 18% say Knowledge of R.

     

    Table 6: Quantifying the data required for Metrics & Analytics analysis
      Frequency Percent Valid Percent Cumulative Percent
    Valid Very Huge 29 29.0 29.0 29.0
    Not much Huge 15 15.0 15.0 44.0
    Neutral 14 14.0 14.0 58.0
    Very Little 28 28.0 28.0 86.0
    Least 14 14.0 14.0 100.0
    Total 100 100.0 100.0  

     Analysis : The frequency of Quantifying the data required for M&A analysis in respondent organisation is 29% very huge, 15% Not much huge, 14% Neutral, 20% Very little, 14% Least.

     

    Table 7: Extent to which decisions are based on Metrics & Analytics in respondent organization

      Frequency Percent Valid Percent Cumulative Percent
    Valid Great extent 25 25.0 25.0 25.0
    Higher extent 37 37.0 37.0 62.0
    Neutral 13 13.0 13.0 75.0
    Some extent 13 13.0 13.0 88.0
    Least 12 12.0 12.0 100.0
    Total 100 100.0 100.0  

    Analysis : The frequency of extent to which decisions are based on M&A in respondent organisation is 25% to great extent, 37% to higher extent, 13% neutral, 13% Some extent, 12% Least.

     

    Table 8: Level of sophistication of data analytics application that respondent organization address with HR Analytics
      Frequency Percent Valid Percent Cumulative Percent
    Valid Data & Basic Reporting 28 28.0 28.0 28.0
    Descriptive 14 14.0 14.0 42.0
    Multi Variate analysis 14 14.0 14.0 56.0
    predictive analysis 44 44.0 44.0 100.0
    Total 100 100.0 100.0  

    Analysis : From the above table it shows that 28% of the respondents use Data & Basic Reporting as level of sophistication of data analytics  application to address with Employee Training as HR functions, 14% MultiVariate analysis & descriptive, 44% Predictive analysis

     

    Table 9 : Frequency of generating analytical Reports in respondent Organization
      Frequency Percent Valid Percent Cumulative Percent
    Valid Daily 12 12.0 12.0 12.0
    Weekly 17 17.0 17.0 29.0
    Fortnight 17 17.0 17.0 46.0
    Monthly 17 17.0 17.0 63.0
    Quarterly 15 15.0 15.0 78.0
    Half Yearly 11 11.0 11.0 89.0
    Yearly 11 11.0 11.0 100.0
    Total 100 100.0 100.0  

     Analysis : From the above table it shows that 12% of respondents frequently generate analytical reports on daily basis,17% on weekly, fortnight, monthly, 11% half yearly & yearly and 15% quarterly.

     The Data collected has been primarily tabulated & Master table was prepared. Sample was tested for reliability using Cronbach’s  alpha. Percentage analysis is the basic tool for analysis. Regression analysis a statistical process for estimating the relationships among variables, is used.

    Regression Analysis

    Model Summary -1
    Model R R Square Adjusted R Square Std. Error of the Estimate
    1 .788a .620 .619 .594

     

    a. Predictors: (Constant), Is there any kind of adoption of metrics & analytics applied in your organization?

    Dependent Variable (X): Extent of usage of metrics & analytics applied

    Independent Variable(Y): Adoption of metrics & analytics applied

    R2, the Coefficient of Determination, tells how many points fall on the regression line. In Model Summary 1 – 0.62 means that 62% of the variation of y-values around the mean is explained by the x-values. In other words, 62% of the values fit the model.

    H0 – The understanding of adoption of HR Analytics is significantly low in Indian Companies

    H1 – The understanding of adoption of HR Analytics is significant in Indian Companies

     

    Alternate Hypothesis is accepted.

     

    Model Summary – 2
    Model R R Square Adjusted R Square Std. Error of the Estimate
    1 .810a .657 .655 .525

     

    a. Predictors: (Constant), Our firm is a socially responsible firm

    Dependent Variable(X): Employees engaged in the usage of Metrics & Analytics

    Independent Variable(Y): Kind of adoption of metrics & analytics applied.

    In Model Summary 2 – 0.81 means that 81% of the variation of y-values around the mean is explained by the x-values. In other words, 81% of the values fit the model.

    H0 – The application of HR Analytics is significantly low in Indian Companies

    H2 – The application of HR Analytics is significant in Indian Companies

    Alternate Hypothesis is accepted

     

    Model Summary -3
    Model R R Square Adjusted R Square Std. Error of the Estimate
    1 .841a .708 .707 .522

     

    a. Predictors: (Constant), Our firm is a socially responsible firm

    Dependent Variable(X): Managerial level function

     Independent Variable(Y): Adoption of metrics & analytics applied.

    H0 – Middle Management involvement in HR Analytics is significantly low in Indian companies.

    H3 – Middle Management involvement in HR Analytics is significant in Indian companies.

    In Model Summary 3 – 0.84 means that 84% of the variation of y-values around the mean is explained by the x-values. In other words, 84% of the values fit the model. Alternate Hypothesis is accepted

    Conclusion:

    The organisations are in the process of creating global systems that serve the mission and vision and incredible things happen and this is how HR Analytics has been created at work place in the organisation.  The leader with the vision and mission can turn the very face of organisation and great results can be achieved as has been seen in the organisation under the preview of the study.

     

    It should also be noted that, HR analytics is a holist approach and all the systems, practices, people, leadership, culture, ethos, policies and principles should together join hands in working at it.

     

    • The understanding of adoption of HR Analytics is significant in Indian Companies
    • The application of HR Analytics is significant in Indian Companies
    • Middle Management involvement in HR Analytics is significant in Indian companies.

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