E‐learning in a large organization

Abstract

Purpose The purpose of this paper is to provide new insights into the implementation of large‐scale learning projects; thereby better understanding the difficulties, frustrations, and obstacles encountered when implementing enterprise‐wide e‐learning as a tool for training and organization transformation in a complex organization. Design/methodology/approach Information‐sharing disturbances, one of five categories of disturbances that emerged from a grounded theory open coding procedure applied to empirical data collected through a longitudinal field research, are in focus. Third generation activity theory, specifically the notions network of activity systems, disturbances, tensions and contradictions, is used as an analytical lens through which to understand the role of information sharing in a large‐scale implementation. Findings The study has identified how information sharing disturbances became a critical factor in the implementation of e‐learning in a large company. A number of tensions that point to potential contradictions have been identified. Ways in which to deal with such tensions in future implementations of e‐learning have been suggested. Research limitations/implications Activity‐theoretical discussion of e‐learning in a large organization, identifying underlying tensions, is of relevance to large organizations introducing new technologies for working and learning. Practical implications The study has identified the causes of an important type of problem that can slow down or hinder e‐learning adoption in an organization, and thereby pointing out shortcomings of standardised e‐learning applications. Originality/value Empirical studies of enterprise‐wide implementations of e‐learning have rarely been reported on in the research literature.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 40 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.