Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 492

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Influence of new-age technologies on marketing: A research agenda

Kumar, V., Ramachandran, Divya, Kumar, Binay·2021·Journal of Business Research

Abstract

Abstract This study focuses on four key new-age technologies – the Internet of Things, Artificial Intelligence, Machine Learning, and Blockchain – and their respective roles in marketing. Specifically, the study examines each of these four technologies in depth, to understand their key elements, the domains in which they operate, and current use cases pertaining to these technologies. The adoption of these new-age technologies is expected to have an impact on the outcomes for firms and customers. However, these are dynamically evolving technologies that have not yet been completely explored and whose full potential has yet to be uncovered. This study presents some research questions that are pertinent to key entities (firms, customers, intermediaries, developers, and regulators), and which merit deeper investigation in the future. This study also highlights major areas that need managerial focus in the adoption of these new-age technologies.

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 · 10.1016/j.jbusres.2020.01.007

Cites

2 of 2 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

Cited by

See all 492

Metadata

Authors
Kumar, V., Ramachandran, D., & Kumar, B.
Venue
Journal of Business Research
Publisher
Elsevier BV
Published
2021-03
Volume
125
Pages
864-877
ISSN
0148-2963 (ISSN-L)
OpenAlex
W3001124561
Cited by
492
References
2
FWCI
44.79
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.