Factors impacting adoption of people analytics – application of interpretive structural modeling

Niharika Atchyutun, Vignana Jyothi Institute of Management, Andhra Pradesh, India
P Vijay Kumar, Jawaharlal Nehru Technological University, Andhra Pradesh, India
Abstract
People Analytics has transformed the way the human resources of an organization are utilized and managed. The fast-changing field of technology has also contributed to the advancement of the people analytics evolution. The pace of adoption and application of people analytics across organizations has, however, not matched the initial expectations. Though the reasons for this slow adoption have been discussed on some platforms, there have not been many comprehensive analyses of the same. The factors are intricate and the interrelationships, complex. In this paper, Interpretive Structural Modelling (ISM), a qualitative technique has been applied to gain clarity on the prioritization and categorization of factors and also define their interrelationships. The categorization of factors as driving factors, dependent factors and linking factors can help organizations design effective strategies for smoother adoption and implementation of people analytics. This can result in improved performance of organizations not only in terms of people management but also in enhancing business performance.

People Analytics has transformed the way the human resources of an organization are utilized and managed. The fast-changing field of technology has also contributed to the advancement of the people analytics evolution. The pace of adoption and application of people analytics across organizations has, however, not matched the initial expectations. Though the reasons for this slow adoption have been discussed on some platforms, there have not been many comprehensive analyses of the same. The factors are intricate and the interrelationships, complex. In this paper, Interpretive Structural Modelling (ISM), a qualitative technique has been applied to gain clarity on the prioritization and categorization of factors and also define their interrelationships. The categorization of factors as driving factors, dependent factors and linking factors can help organizations design effective strategies for smoother adoption and implementation of people analytics. This can result in improved performance of organizations not only in terms of people management but also in enhancing business performance.

Keywords: People Analytics, HR Analytics, Factors, Interpretive Structural Modelling, MICMAC Analysis: People Analytics, HR Analytics, Factors, Interpretive Structural Modelling, MICMAC Analysis.

Suggested citation: Atchyutuni, N. & Kuma, P.V. (2019). Factors impacting adoption of people analytics –application of interpretive structural modelling. Skyline Business Journal, 15(2), 41-52. https://doi.org/10.37383/SBJ14021904

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Notes on contributors
Niharika Atchyutuni is working as Assistant Professor with Vignana Jyothi Institute of Management, Hyderabad. She coordinates student training in the institute. She is currently pursuing research in the area of HR/People Analytics. An alumna of MDI Gurgaon, she worked in the industry for around 8 years before getting into teaching 5 years ago. She is an avid reader and strongly believes that learning is a continuous process. The achievements of her students define success for her. Her other areas of interest include ‘India’s position on Happiness Index’, ‘Technological Advancements for People Management’ and ‘Sustainable Development’.

Dr. P.Vijaya Kumar is Former Director, School of Management Studies, Jawaharlal Nehru Technological University, Kakinada (AP), India. He has rich academic and administrative experience and has guided many research scholars. He guided many PhD students and published numerous research articles. His areas of specialization include Finance and Human Resource Management. He continues to be actively involved in research.
Suggested citation

Atchyutuni, N. & Kuma, P.V. (2019). Factors impacting adoption of people analytics –application of interpretive structural modelling. Skyline Business Journal, 15(2), 41-52. https://doi.org/10.37383/SBJ14021904