Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 424

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

The Importance of Learning Style in End-User Training1

Bostrom, Robert P., Olfman, Lome, Sein, Maung K.·1990·MIS Quarterly

Abstract

The importance of effective training in ensuring the success of end-user computing (EUC) has been emphasized by several researchers in information systems. A vast amount of evidence from research in related areas such as educational psychology suggests that individual differences, such as learning style, may affect users learning about a new EUC software package. This article reports the findings of a series of studies that examine the influence of a novice’s learning style in learning typical EUC tools such as spreadsheets and electronic mail. A consistent pattern of findings emerges that indicates that learning modes is an important predictor of learning performance, both by itself and in interaction with training methods. The findings suggest that in the design of training, it is essential to match training methods to individual difference variables. Based on these findings, guidelines are recommended for IS professionals involved in EUC training and further research directions are discussed.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.2307/249313

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

Metadata

Authors
Bostrom, R. P., Olfman, L., & Sein, M. K.
Venue
MIS Quarterly
Publisher
MIS Quarterly
Published
1990-03-01
Volume
14
Issue
1
Pages
101-119
ISSN
  • 0276-7783 (ISSN-L)
  • 2162-9730
OpenAlex
W1552210990
Cited by
424
References
1
FWCI
15.47
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.