Table of content
1. A design science research methodology and its application to accounting information systems
research (Geerts, 2011)
1. Big Data in Accounting: An Overview (Vasarhelyi, Kogan, Tuttle, 2015)
2. Toward Blockchain-based accounting and assurance (Dai, Vasarhelyi, 2017)
3. Continuous auditing in ERP system environments: The current state and future directions (Kuhn,
Sutton, 2010)
3. Designing confidentiality-preserving Blockchain-based transaction processing systems (Wang,
Kogan, 2018)
4. Impact of business analytics and enterprise systems on managerial accounting (Appelbaum, Kogan,
Vasarhelyi, Yan, 2017)
4. Reducing false positives in fraud detection: Combining the red flag approach with process mining
(Baader, Kremar, 2018)
5. A taxonomy to guide research on the application of data mining to fraud detection in financial
statement audits (Gray, Debreceny, 2013)
6. Consequences of XBRL standardization on financial statement data (Vasarhelyi, Chan, Krahel 2012)
7. Robotic Process Automation (Aalst, Bichler, Heinzl, 2018)
,1. A design science research methodology and its application to accounting information systems
research (Geerts, 2011)
Natural science research papers; why things work the way they do, adhere to a structure that
consists of: problem definition, review, hypotheses, data collection, analysis, results and discussion
helps with production and reproduction.
Important characteristics of design science artefacts; relevance (solve important problems) and
novelty (differentiate from routine designs unsolved or unique).
Lack of stereotypical template similar to natural science research is important concern in design
science research for AIS research DSRM activities;
- Problem identification and motivation
- Definition of the objectives of a solution
- Design and development
- Demonstration
- Evaluation
- Communication
Objectives of DSRM (design science research methodology);
- Provide a nominal process for the conduct of DS research
- Build upon prior literature about DS and IS and reference disciplines
- Provide researchers with a mental model or template for a structure for research outputs
Short; aims at improving production, presentation and evaluation of design science while being
consistent with principles and guidelines of previous studies.
Observations from examinations of IS/AIS research;
- Most design science research papers lack structured discussion of how it’s done. Retroactive
analysis in this papers shows it’s also true for AIS
- Awareness of an interesting problem can come from multiple sources such as new
developments in industries or reference disciplines. Retroactive analysis AIS design
strongly driven by needs of accounting practice. Call is answered about dealing with rapid
change in and expansion of performance and reporting standards. Solution is provided for
need that automating the confirmation process should enhance confirmation’s effectiveness
, by improving respondent authentication, which itself reduces opportunity for confirmation
fraud.
- New artefacts were created by applying emerging technologies. XML to improve processing
of semi-structured data. Semantic web technologies to enable network-based and semantic
interoperability among independent and geographically distributed partners. Exception-
employ web technologies as XML, XBRL and others to create artefacts.
- Retroactive analysis was challenging; lack of uniformity and structured discussions of how
tools are selected and applied puts extra burden on evaluating contributions.
REA model example; didn’t include demonstration and evaluation; common, causes;
- Substantial time lag often occurs between DSRM activities
- Some of the activities require very different skills sets
, 1. Big Data in Accounting: An Overview (Vasarhelyi, Kogan, Tuttle, 2015)
Meaning of Big Data differs across domains (accounting firms vs. NASA). This paper;
- The amount of data at or beyond the limit of what the relevant information systems can
store and/or process.
Features of Big Data challenging capabilities of modern information systems;
- Huge volume
- High velocity
- Huge variety
- Uncertain veracity
Volume and velocity continuous auditing relevant for enabling automated and real-time analysis
also gaps between present analytics and requirements for Big Data Analytics also challenges
beyond capability of current batch oriented systems in CA.
Storage capabilities vary from a terabyte to hundreds. Accounting and auditing may use and benefit
from external data stores that are closely or loosely linked or local stores, but located elsewhere.
Processing capacity is much more difficult wide variety of computational tasks in accounting and
auditing summations, comparisons and others scale with size of dataset and therefore what is
considered big from processing viewpoint depends on storage capacity. Estimations of complex
analytical models may require computation that increases exponentially with size of dataset Big
Data can lead to high computational times that are impractical or infeasible. Greater computer
complexity needed bigger dataset will look. If incomputable draw sample that is acceptable
does diminish the benefit inherent in complete data. Sources of Big Data can be internal and
external. Internally grow big if there are large volumes of transactions that are captured
automatically (RFID streams/call records), may also be external (depending on the view).
Enterprise data ecosystem exponentially expending dynamically changing set of characteristics
that require development of enhanced theory of information requires recognition of nature of
data capture (manual/automatic), volume, efficiency of integration, efficiency of transformation to
information, granularity of data, types of operations/decisions supported and others. Corporations
want competitive advantages progressively expanded scope of IS from traditional data to
automating data capture, relying on automated sensing to automate management and production
support systems.
Accounting is seen as financial reporting result of transaction registrations cost/benefit
considerations and their results. Chronological events of evolution technology and accounting:
- Before computers; summary accounting data retained via chart of general ledger, individual
transactions not accessible or with great effort
- Advances made possible to store all transactions what can be gained by this?
- Technology enabled global businesses, expansions in volume, velocity and variety of
accounting data. Recording, storage and reporting of transactions resolved many differences
- Enterprise systems integrated accounting data with nonfinancials expansion of volume
and variety. Allows creation of continuity equations with lags linked to (non-)financials
- Expansion with new types of data fields (automated sensors)
- Automatic capture of data through sensors, RFID and GPS data allows increased frequency
and large sets of data, also for audit evidence
Accounting measurement has lost informational value with reduction of market value explanation by
accounting variables pronounced for new knowledge-intensive firms with higher intangible
intensity growing share in economy. Economy supported by real-time processes measurement
quarterly not accurate. Loss of accounting information value progressed companies expanded
traditional data stores large databases in ERP systems with minimal portion directly related to
