Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 219

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Profiling potential adopters and non‐adopters of an interactive electronic shopping medium

Eastlick, Mary Ann, Lotz, Sherry·1999·International Journal of Retail & Distribution Management

Abstract

A mail survey was conducted to identify personal characteristics, shopping patterns, and attitudes of potential innovators and non‐adopters of an interactive electronic shopping innovation. Differences among all potential adopter groups were also examined. A national sample of 2,500 US cable television subscribers were surveyed. Results suggested that strongest predictors of potential innovator and non‐adopter group memberships were perceived characteristics of the interactive shopping innovation including relative advantage over other shopping formats and compatibility with lifestyles. Also important were consumers’ prior shopping experiences with other nonstore retailers. Among potential adopters (i.e., innovators, early adopters, and followers), similar characteristics were exhibited, differing only by intensity. Managerial implications 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 · 10.1108/09590559910278560

Cites

3 of 3 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

Cites

Show all 3 references

Cited by

See all 219

Metadata

Authors
Eastlick, M. A., & Lotz, S.
Venue
International Journal of Retail & Distribution Management
Publisher
Emerald
Published
1999-08-01
Volume
27
Issue
6
Pages
209-223
ISSN
0959-0552 (ISSN-L)
OpenAlex
W2090776796
Cited by
219
References
3
FWCI
20.78
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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Profiling potential adopters and non‐adopters of an interactive electronic shopping medium. https://app.cochat.ai/papers/10.1108/09590559910278560
The paper
Eastlick, M. A., & Lotz, S. (1999). Profiling potential adopters and non‐adopters of an interactive electronic shopping medium. International Journal of Retail & Distribution Management. https://doi.org/10.1108/09590559910278560

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.