Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 215

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Big Data and corporate reporting: impacts and paradoxes

Al-Htaybat, Khaldoon, von Alberti-Alhtaybat, Larissa·2017·Accounting, Auditing & Accountability Journal

Abstract

Purpose: The purpose of this paper is to investigate the phenomenon of Big Data and corporate reporting, and to determine the impact of Big Data and the current Big Data state of mind with regard to corporate reporting, what accountant and non-accountant participants’ perceptions are of the phenomenon, what the accountants’ role is and will be in this regard, and what opportunities and risks are associated with Big Data and corporate reporting. Furthermore, this study seeks to identify the inherent technological paradoxes of Big Data and corporate reporting. Design/methodology/approach: The current study is qualitative in nature and assumes an interpretive stance, investigating participants’ perceptions of the phenomenon of Big Data and corporate reporting. To this end, interview data from 25 participants, video and text material, were analysed to enhance and triangulate findings. A four-fold sampling strategy was employed to ensure that any collected data would contribute to the findings. Data were analysed on the basis of open and selective coding stages. Data collection and analysis took place in two stages, in 2014 and in 2016. Findings: Three topics, or categories, emerged from the data analysis, which have sufficient explanatory power to illustrate the phenomenon of Big Data and corporate reporting, namely the Big Data state of mind and corporate reporting, accountants’ role and future related to Big Data, and perceived opportunities and risks of Big Data. Features of a new approach to corporate reporting were identified and discussed. Furthermore, four paradoxes emerged to express inherent opposing positions of Big Data and corporate reporting, namely empowerment vs enslavement, fulfilling vs creating needs, reliability vs timeliness and simplicity vs complexity. Originality/value: The original contribution of the study lies in the empirical investigation of the phenomenon of Big Data and corporate reporting as one of the most recent and praised developments in the accounting context. The dual communication flows of corporate reporting with Big Data is an important element of the findings, which can enhance the prospective financial statements significantly. Finally, technological paradoxes of Big Data and corporate reporting are discussed for the first time, two of which are based on the literature and the remaining two are inherent in the phenomenon of Big Data and corporate reporting.

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/aaaj-07-2015-2139

Cites

7 of 7 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 7 references

Cited by

See all 215

Metadata

Authors
Al-Htaybat, K., & von Alberti-Alhtaybat, L.
Venue
Accounting, Auditing & Accountability Journal
Publisher
Emerald
Published
2017-05-15
Volume
30
Issue
4
Pages
850-873
ISSN
0951-3574 (ISSN-L)
OpenAlex
W2604482855
Cited by
215
References
7
FWCI
33.69
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: Big Data and corporate reporting: impacts and paradoxes. https://app.cochat.ai/papers/10.1108/aaaj-07-2015-2139
The paper
Al-Htaybat, K., & von Alberti-Alhtaybat, L. (2017). Big Data and corporate reporting: impacts and paradoxes. Accounting, Auditing & Accountability Journal. https://doi.org/10.1108/aaaj-07-2015-2139

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.