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Understanding New Data Driven HR Strategies Views

Understanding New Data Driven HR Strategies Views 


Theoretical Underpinnings

The theoretical underpinnings provide a platform for human resource professionals to envision, design, and implement data-driven initiatives. By combining these models, a thorough understanding of how data analytics may be aligned with HRM objectives is established, ensuring the ethical, efficient, and strategic use of data for human capital management inside organizations.

Resource-based View (RBV)

The organization places a strong emphasis on the resource-based view (RBV), which holds that a company's long-term competitive advantage stems from its unique resources and capabilities. When applied to data-driven HR initiatives, RBV is compatible with the premise that human capital, as a valued resource, significantly adds to an organization's competitive advantage (Otubanjo, 2010). Human resource departments may use data analytics to discover, grow, and maximize this priceless asset. HR analytics, for example, helps organizations assess and enhance their employees' competencies, knowledge, and potential (Wright, Dunford, and Snell, 2021). According to the resource-based view (RBV), data-driven human resource (HR) policies that prioritize human capital as a crucial asset may nurture long-term competitive advantage via the continuous improvement and exploitation of employee talents to achieve organizational goals.

Lewin’s Change Management Model

The unfreezing, altering, and refreezing stages of Lewin's change management model offer organizations significant insights into the implementation of data-driven HR initiatives. When data-driven techniques are adopted in human resource management, the first step of 'unfreezing' comprises educating stakeholders about the need for change (Szamosi and Duxbury, 2002). Employees must be educated about the importance of implementing data-driven initiatives, and HR departments must prepare them for the upcoming changes. The 'changing' phase includes the implementation of these initiatives, in which human resources professionals use data analytics to guide choices, optimize HR practices, and enhance employee experience (Straatmann et al., 2016). The 'refreezing' phase culminates with the organizational culture being strengthened and these data-driven practices being embedded so that the changes become the new norm. Lewin's approach provides HR directors with a methodical framework for navigating and supervising the implementation of data-driven HR initiatives while also ensuring sustainability and minimizing opposition (Stouten, Rousseau, and De Cremer, 2018).


Herzberg’s Two Factor Theory

Herzberg's Two-Factor Theory, often known as the Motivation-Hygiene Theory, identifies the factors that impact employee unhappiness and contentment. When data-driven HR policies are implemented, this theory highlights the importance of many elements, such as recognition, development opportunities, meaningful work, and job enrichment, in increasing employee motivation and happiness (Sanjeev and Surya, 2016). The use of HR analytics to study employee feedback, performance statistics, and engagement indicators may help identify these aspects. Through the use of data insights, HR departments may build targeted interventions that boost employee motivation and work satisfaction, leading to enhanced employee retention and performance (Nickerson, 2023). Herzberg's theory highlights the need to use data to understand and address the factors that influence employee happiness in enterprises.


People Analytics Value Chain

The value chain model for people analytics describes the sequential procedures that are used to derive value from individual data. This model exactly correlates to the execution of data-driven HR strategies, as it outlines the sequence of actions starting with data sourcing and ending with action implementation (Tursunbayeva, Di Lauro, and Pagliari, 2018). The statement emphasizes the importance of HR professionals collecting relevant data (including engagement surveys and employee performance metrics), using analytics tools to efficiently process and analyze this data, deriving actionable insights, making data-driven decisions, and implementing strategies accordingly (Veldsman, 2023). The People Analytics Value Chain serves as a practical guide for human resource professionals, highlighting the process of getting value from data at each stage and ensuring that data-driven HR policies are purposeful and aligned with the organization's objectives.

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