IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Expanding the Kirkpatrick Evaluation Model-Towards More Efficient Training in the IT Sector

Expanding the Kirkpatrick Evaluation Model-Towards More Efficient Training in the IT Sector
View Sample PDF
Author(s): Neetima Agarwal (Jaypee Institute of Information Technology, India), Neerja Pande (Indian Institute of Management Lucknow, India)and Vandana Ahuja (Jaypee Institute of Information Technology, India)
Copyright: 2019
Pages: 18
Source title: Human Performance Technology: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8356-1.ch054

Purchase

View Expanding the Kirkpatrick Evaluation Model-Towards More Efficient Training in the IT Sector on the publisher's website for pricing and purchasing information.

Abstract

The purpose of this paper is to investigate how the Kirkpatrick Learning Evaluation Model (1950) can be augmented to make it more credible and successful evaluation parameter in the changing times. Since the advent of Information Technology industry the rigid structures of organizations are replaced by the Flat/Matrix structures, removing the bars of time and place. This paper is an attempt to include three gaps identified in the Kirkpatrick Model, Training motivation, Organization citizenship behaviour and the Assessment of both the individual and the Organization simultaneously. Through co-relation and regression analysis these gaps were tested on the data obtained from 461 employees. The data support the various relationships to be included in Kirkpatrick Model and it identifies that for an effective training program it's essential to perform both pre-training and post-training analysis using the four parameters of Kirkpatrick Model viz. Reaction (changed to Motivation), Learning, Behaviour (or Performance) and Results.

Related Content

Maja Pucelj, Matjaž Mulej, Anita Hrast. © 2024. 29 pages.
Hemendra Singh. © 2024. 26 pages.
Nestor Soler del Toro. © 2024. 27 pages.
Pablo Banchio. © 2024. 18 pages.
Jože Ruparčič. © 2024. 26 pages.
Anuttama Ghose, Hartej Singh Kochher, S. M. Aamir Ali. © 2024. 28 pages.
Bhupinder Singh, Komal Vig, Pushan Kumar Dutta, Christian Kaunert, Bhupendra Kumar Gautam. © 2024. 23 pages.
Body Bottom