Earticle

초록

영어

One of the challenges faced by Talent Acquisition teams today pertains to the acquisition of human resources by matching job descriptions and skillsets desired. It is more so in the case of competitive sectors like the Indian IT sector. There can be various channels for Talent Acquisition and accordingly, the cost and benefits might vary. However, the consequences of a mismatch have an impact on the quality of deliverables, high recruitment expenses and loss of revenue for the organization. With increased and diverse sources of data that are available to organizations today, there is ample opportunity to apply analytics for informed decision making in this field. This paper reveals useful insights that help streamline the Talent Acquisition process in the Indian IT Industry. The paper adopts a data-centric approach to examine the critical determinants for efficient and effective Talent Acquisition process in IT organizations. Selected supervised machine learning algorithms are applied for the analysis of the dataset. The study is likely to help organizations in reassessing their talent acquisition strategy with respect to key parameters like expected cost to company (CTC), candidate sourcing channels and optimal joining period.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Literature Review
2.1. Hypothesis Formulation:
Ⅲ. Research Methodology
3.1. Tools Used:
Ⅳ. Data Analysis, Results and Discussion:
4.1. Exploratory Data Analysis (EDA):
4.2. Results:
4.3. Discussion on Hypothesis Support
Ⅴ. Contribution and Implications
5.1. Limitations and Future Research
Ⅵ. Conclusion
Acknowledgement

키워드

저자

• Avik Ghosh [ Active Researcher, Analytics and Information Systems, Xavier Institute of Management, Bhubaneswar, India. ] Corresponding Author
• Bhaskar Basu [ Professor, Information Systems, Xavier Institute of Management, Bhubaneswar, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658