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
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.
Integrity signals
Registry
Cites
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.
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
Users of the world, unite! The challenges and opportunities of Social Media
Kaplan, A. M., & Haenlein, M. (2010) · Business Horizons · doi:10.1016/j.bushor.2009.09.003 · Cited by 17,875
Qualitative Analysis for Social Scientists
Strauss, A. L. (1987) · doi:10.1017/cbo9780511557842 · Cited by 12,277
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250
Beyond the hype: Big data concepts, methods, and analytics
Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226
Sampling in Interview-Based Qualitative Research: A Theoretical and Practical Guide
Robinson, O. C. (2014) · Qualitative Research in Psychology · doi:10.1080/14780887.2013.801543 · Cited by 3,232
Big data in accounting: an overview
Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015) · Accounting Horizons · doi:10.2308/acch-51071 · Cited by 641
Digitisation, Big Data and the transformation of accounting information
Bhimani, A., & Willcocks, L. (2014) · Accounting and Business Research · doi:10.1080/00014788.2014.910051 · Cited by 542
Cited by
The digital transformation of external audit and its impact on corporate governance
Manita, R., Elommal, N., Baudier, P., & Hikkerova, L. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2019.119751 · Cited by 533
The Data Analytics Journey: Interactions Among Auditors, Managers, Regulation, and Technology*
Austin, A. A., Carpenter, T. D., Christ, M. H., & Nielson, C. S. (2021) · Contemporary Accounting Research · doi:10.1111/1911-3846.12680 · Cited by 172
Explaining the (non-)adoption of advanced data analytics in auditing: A process theory
Krieger, F., Drews, P., & Velte, P. (2021) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2021.100511 · Cited by 133
Digital transformation and the public sector auditing: The SAI’s perspective
Otia, J. E., & Bracci, E. (2022) · Financial Accountability & Management · doi:10.1111/faam.12317 · Cited by 124
A Review of Corporate Reporting Transparency Quality: Current Status and Future Directions
Hewage, Y. M., Cooray, A., & Ahmed, R. (2026) · Australian Accounting Review · doi:10.1111/auar.70019 · Cited by 3
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.