Journal of Accounting Education 38 (2017) 81–93

Contents lists available at ScienceDirect

Journal of Accounting Education

journal homepage: www.elsevier .com/locate / jaccedu

Toward integration of Big Data, technology and informationsystems competencies into the accounting curriculumq

http://dx.doi.org/10.1016/j.jaccedu.2016.12.0080748-5751/� 2016 Elsevier Ltd. All rights reserved.

q This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.⇑ Corresponding author.

E-mail address: Deb.Sledgianowski@Hofstra.edu (D. Sledgianowski).1 Hunter College - City University of New York, 695 Park Avenue, New York, NY 10065, USA.2 Gartner Research. (2016) categorized analytics into four types: (1) descriptive (a retrospective inspection of data to find out ‘‘what happened?” us

such as visualization and narratives), (2) diagnostic (a retrospective analysis of data to find out ‘‘why did it happen?” using tools that enable drilling-dthe data, making correlations), (3) predictive (a prospective analysis to find out ‘‘what will happen?” using tools capable of statistical algorithms andlearning technique), and (4) prescriptive (a prospective analysis to find out ‘‘what should be done?” using tools capable of graph analysis, simulaoptimization, and other advanced techniques).

Deb Sledgianowski ⇑, Mohamed Gomaa, Christine Tan 1

Department of Accounting, Taxation and Legal Studies in Business, Frank G. Zarb School of Business, Hofstra University, Hempstead, NY 11549, USA

a r t i c l e i n f o

Article history:Received 1 July 2016Received in revised form 26 December 2016Accepted 26 December 2016Available online 10 January 2017

Keywords:Big DataTechnologyInformation systemsAccounting curriculumCompetency integration

a b s t r a c t

Recent initiatives of the American Accounting Association (AAA) and the Association toAdvance Collegiate Schools of Business International (AACSB) have emphasized the impor-tance of integrating Big Data and technology into the accounting curriculum. In response tothese calls and to identify a common body of instructional resources toward this purpose,our paper uses the lens of the Competency Integration for Accounting Education frame-work to provide examples of Big Data and information systems integration into instruc-tional resources. We loosely frame these instructional resources using accounting coursesubjects as the unit of analysis.

� 2016 Elsevier Ltd. All rights reserved.

1. Introduction

There is new impetus for the accounting profession to understand Big Data and business analytics, creating a growingopportunity for accounting educators to integrate these topics into the curriculum. Big Data generally describes datasets thatcontain volumes of differently structured data that traditional technology and information systems are inadequate to pro-cess and analyze (Cao, Chychyla, & Stewart, 2015; Vasarhelyi, Kogan, & Tuttle, 2015; Warren, Moffitt, & Byrnes, 2015). Morespecifically, Big Data is often characterized using the four Vs: volume (large volume of data), veracity (data from differentsources increasing the likelihood of uncertainty in the data), velocity (analysis of streaming data) and variety (analysis ofdifferent types of data structures, such as structured, semi-structured, and unstructured data) (Zhang, Yang, &Appelbaum, 2015). We define business analytics as the technology and information systems that enable Big Data analysisand reporting in businesses using different analytic techniques.2

Accounting firms and professional associations recommend that Big Data, technology, and information systems be inte-grated into accounting coursework to provide students with the necessary skills and knowledge to adapt to the data-centricenvironment. For instance, PricewaterhouseCoopers (2015) outlined recommendations for analyzing Big Data related to

ing toolsown intomachine-tion and

82 D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93

technical competencies in audit, tax, risk management, and consulting. The AAA facilitated the exploration of Big Data andanalytics in the accounting profession by hosting Big Data conferences that resulted in educational webinars, teaching mate-rials, and a call for collaboration between academics and practitioners. The AACSB emphasized the importance of integratingBig Data and business analytics into the accounting curriculum. Specifically, the AACSB’s revised accounting accreditationstandard A7 released in 2013 indicates that AACSB accredited accounting degree programs should include learning objec-tives to develop skills and knowledge related to the integration of information technology into accounting and business. Thisincludes the creation, sharing, and reporting of data, as well as data mining and analytics (AACSB, 2013).

There is a continuing call for information systems and technology competency integration in the accounting curriculum(e.g. AAA, 1986, AECC, 1990, AICPA, 1996; Behn et al., 2012; AACSB, 2013; Lawson et al., 2014). Apostolou, Dorminey, Hassell,and Rebele (2014) recommended that educators strive to describe and study a common body of AIS knowledge for account-ing majors and that AIS topics be integrated throughout the curriculum in a way that coordinates topics and reinforces theway that they are learned.

In response to these calls, we provide a method based upon Lawson et al.’s (2014) Competency Integration for AccountingEducation framework (hereafter, framework) for educators to integrate information systems and technology competenciesrelevant to Big Data and business analytics into the accounting curriculum. Additionally, we synthesize and organize theextant Big Data and business analytics instructional resources available (e.g. case studies, software tools, and data) intothe core groups of accounting competencies laid out by Lawson et al. (2014).

Lawson et al. (2014) describe an integrated competency-based framework of learning outcomes necessary for accountinggraduates’ success. Their framework categorizes competencies into accounting, foundational, or broad management (seeTable 1). The accounting competencies, ‘‘enable accountants to integrate management and analytical methods, supportedby technology, to assist an enterprise to formulate and execute its strategy successfully” (Lawson et al., 2014). These com-petencies are typically taught in required courses such as principles, intermediate, and advanced financial accounting; man-agement and cost accounting; accounting information systems (AIS); auditing; and taxation. While many informationsystems, such as enterprise resource planning (ERP), eXtensible Business Reporting Language (XBRL), information searchand retrieval, and data mining, may be covered in the AIS course, we suggest these competencies be integrated throughoutthe accounting curriculum.

Lawson et al.’s (2014) technological competency is their foundational competency that is most relevant to our paper. Itincludes outcomes such as knowledge of spreadsheet modeling; use of technology to access Big Data for financial analyses;use of communication technologies, such as interactive data visualization; knowledge of information systems design; knowl-edge of the purpose of information systems and Big Data, including the hardware and software that enable them to run; andrelated issues, such as computer security and business continuity.

Our paper exemplifies the competency integration framework proposed by Lawson et al. (2014) by providing examplesfor integrating information systems and technological competencies with discipline-based accounting competencies intocourse subjects required by most institutions. Discussing the information systems and technological competencies withina course context allows educators to identify applications of the competency where all accounting students in a programare exposed to specific learning opportunities, thus affording them the same opportunity to acquire the competency.

2. Curriculum integration methods

The approachwe develop integrates Big Data, information systems, and technologies into the accounting curriculum and isparticularly relevant to department curriculum committees. Our process recommends that faculty who are teaching the req-uisite courses for accounting majors be the ones to identify the desired level of integration. This should be based on criteriasuch as available resources, faculty competencies and interests, compliance with accreditation and licensing requirements,and input from advisory boards. The process could include conducting a gap analysis to identify the ‘‘as-is” current state ofintegration in the curriculum and the ‘‘to-be” state of the desired integration. Once a list of the two states is created, the dif-ference is analyzed to identify areas for improved integration. Faculty can then match the desired integration area with theavailable resources appearing in Table 2 of Appendix A. Table 2 in Appendix A provides a listing of representative articles ofinstructional cases and materials categorized by course and competency area using Lawson et al.’s (2014) framework to pro-vide suggestions on how these competencies can be integrated.We provide examples and suggestions based on an analysis ofteaching cases and other resources generally available to educators and of offerings from software vendor academic alliances(see Appendix B). The examples demonstrate the integration of typical discipline-based accounting competencies with tech-nological and information systems competencies emphasizing Big Data and business analytics.3

2.1. Financial accounting

Financial accounting is often taught at the introductory, intermediate, and advanced levels. The Pathways CommissionReport (Behn et al., 2012) recommends that the first accounting course, often the financial principles course, take advantage

3 We do not attempt to provide examples for every possible integration of competences that we found in the instructional resources. As Lawson et al. (2015)suggest, alternative approaches are possible and encouraged.

Table 1Lawson et al.’s (2014) competency integration framework for accounting education.

Accounting competencies Foundational competencies Broad management competencies

External reporting & analysis Communication LeadershipPlanning, analysis & control Quantitative EthicsTaxation, compliance, and planning Analytical thinking & problem solving Process management & improvementInformation systems Interpersonal Governance, risk & complianceAssurance & internal control Technological Additional core business competenciesProfessional values, ethics, & attitudes

D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93 83

of technology to convey the more strategic aspects of accounting. Given the changing role of accountants from transactionsprocessing to providing decision support to senior management due to the proliferation of large volumes of data and datacomplexity, educators could integrate into the first financial principles course the use of technology and large data sets,for instance interactive data visualization, to engage students in the study of accounting (Janvrin, Raschke, & Dilla, 2014).

Faculty can leverage technology and Big Data to engage accounting and non-accounting students to actively learnaccounting, Given the Securities and Exchange Commission’s (SEC) free and easy access to financial data, as described below,and the technology applications to consume and render this data, faculty can expose students to real-world applications.Students can spend more time focusing on data analytics instead of data transposition (manually collecting data and input-ting data into spreadsheets). By researching company filings (both in structured and unstructured formats) on the SEC’s Elec-tronic Data Gathering, Analysis and Retrieval (EDGAR) database, students are engaging in experiential learning whereby theyimitate the process and procedures business professionals go through to research company financial information.

At the introductory level, students view financial data from an applied perspective as consumers of information. In theintroductory accounting course, students could search volumes of financial data and company filings in various electronicfinancial databases and websites, such as EDGAR. In particular, the SEC makes available on EDGAR select key line itemsin the 10-Ks and 10-Qs filed by all SEC registrants going back to 2009 (see https://www.sec.gov/dera/data/financial-state-ment-data-sets.html). Students can freely download the data in zip files and calculate financial ratios and determine timeseries trends for thousands of companies or for a given industry using spreadsheet or statistical software tools.

Another way accounting educators can integrate technology into financial accounting is with spreadsheet software. Forinstance, Boyer and Lyons (2011) offer a teaching case that requires students to use spreadsheet software to enter journalentries for business transactions and to observe the effects on the firm’s financial statements and financial ratios for the prin-ciples course. This addresses Lawson et al.’s (2014) external reporting and analysis accounting competency and technologicalfoundational competency.

In 2009, the SEC mandated that all public companies tag each financial statement value using XBRL (SEC Release Nos. 33-9002; 34-59324; 39-2461; IC- 28609; File No. S&- 11-08). Debreceny and Farewell (2010) suggest that students in theaccounting principles courses should be introduced to the concept of XBRL and how it is becoming a common languagefor the transfer of financial data, especially since the SEC mandate is now part of the reality for external reporting profession-als. Their paper describes how XBRL can be integrated throughout the accounting curriculum and provides examples for eachcourse.

In the classroom, XBRL provides a powerful mechanism for students to quickly download vast amounts of financial datafrom the footnotes to analyze. It exposes students to new technology-specific software that is required to view and analyzethe XBRL data (e.g. see Taylor & Dzuranin, 2010). Applications like idaciti (see www.idaciti.com) provide a free and simpletool for students to query and visualize large amounts of the XBRL financial data. The complexities of the XBRL data are hid-den such that the students (user) can simply query the financial term and start analyzing and creating graphs. Students caninteract with large and complex datasets and discover new insights (Dilla, Janvrin, & Raschke, 2010).

In summary, XBRL makes possible the integration of Big Data analytics and technology into an introductory accountingclass. XBRL addresses Lawson et al.’s (2014) accounting competency of external reporting and analysis and foundationalcompetencies of technology, quantitative, and analytical. From a more transactional lens, Klamm and Segovia (2014),addressing Lawson et al.’s (2014) accounting competencies of information systems, assurance and internal control, as wellas the broad management competency of process management and improvement, use Microsoft Dynamics GP requiring stu-dents to prepare a report of the company’s value chain and business processes, conduct a risk assessment, and recommendmitigating controls. It introduces students to the complementary natures of technology and large volumes of data that canhelp with risk analysis of a company’s internal controls system.

In addition to teaching cases, educators can leverage software tools in the marketplace to integrate into the curriculum.For example, students can use financial accounting systems such as Microsoft Dynamics GP, Quick Books, Quicken, or SAPERP to enter manual voucher entries and see their effect on different general ledger accounts and view relevant financialstatements. Students may be assigned tasks requiring them to access financial data from accounting systems to use in prof-itability analysis and associated techniques to measure business value. The SAP University Alliance offers a tutorial assign-ment that requires students to determine appropriate voucher entries from a fact pattern, enter them into the ERP system,

84 D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93

and generate financial statements.4 Generally, educators can use these assignments with an emphasis on transactions process-ing to reinforce the importance of the integration of technology and Big Data to new accounting students.

In the intermediate courses, Big Data can play a central role in relating real-world examples to students. Educators canuse two key characteristics of Big Data--volume and variety--to quickly identify and choose from a large dataset of real-world disclosures to illustrate intermediate and advanced accounting principles. For instance, when covering pensions, stu-dents can collect the ‘expected return on plan assets’ from companies’ pension footnotes. Traditionally, this would take sometime to first discover which companies disclose this information that is buried in their pensions footnote, and then to collectthis information manually for a given company over time to conduct any trend analysis. However, the SEC XBRL mandaterequires companies to tag each footnote as a block of text with an appropriate element. With XBRL, students can easily col-lect this information by conducting a general query for the appropriate XBRL element and retrieve the expected return onplan asset amount for all companies over a period of time5. Alternatively, educators can quickly query and pull up certain foot-notes as entire blocks of text to provide examples of disclosures to students and they can pull up the pensions footnote for ahandful of companies using the relevant tag to show students a side-by-side comparison. Instructors can also integrate tech-nology and Big Data by querying the SEC XBRL data to plot pension expense on a graph of, for example, five companies inthe same industry with little effort using cloud-based software tools like idaciti. In any of these scenarios, educators can show-case volume and variety in a practical application, and demonstrate the accounting concepts learned in class as applied to real-world companies.

The intermediate financial accounting courses build on the introduction to use XBRL data for analysis in the foundationcourse by utilizing XBRL mapping software to actively learn how financial statements and footnotes are tagged and the pro-cess of generating interactive data files for SEC submission (Debreceny & Farewell, 2010).6 Fang (2014) describes a processthat students can use to retrieve this XBRL data and import it into spreadsheet software for viewing and further analysis.Gomaa, Markelevich, and Shaw (2011) offer a case where students extract financial statement data of firms in the same industryfrom the SEC’s database and import it into a spreadsheet in which they use spreadsheet software functions to compare the per-formance of the firms by applying ratio analysis. Taken together, these studies address the accounting competencies of externalreporting and analysis and information systems, and the foundational competencies of quantitative, analytical andtechnological.

Pedagogical opportunities integrating technology and Big Data also exist in intermediate accounting. For example, inte-grated reporting could be discussed within a context of how to facilitate the gathering and reporting of disparate data andinformation points about an organization’s governance, business model, risks, opportunities, strategy, and performanceusing an XBRL technology platform to prepare and communicate integrated reports (for example, see Monterio, 2013).

Gujarathi (2012) provides another example of how technology can be used to exercise and reinforce US GAAP researchskills. This may be particularly relevant for advanced accounting courses where more complex topics like business combi-nations, foreign currency transactions, and derivative accounting are covered. The electronic availability of the FASBAccounting Standards Codification (the Codification) allows students to quickly search through the vast amount of author-itative literature to identify specific US Generally Accepted Accounting Principles (GAAP) requirements. Students query theCodification database to research different accounting policies that were applied to the use-case company to see if the com-pany is compliant with US GAAP. Subsequently, students then use spreadsheet software to analyze relevant financial state-ment data to understand the implications of the company’s accounting policy choices on their performance.

2.2. Management and cost accounting

We suggest students gain an understanding of how business analytics can be used with structured and unstructured datato informmanagement decisions related to measuring, analyzing, and reporting information about the costs of acquiring andusing organizational resources, and for creating measures of operational profitability and performance. As described below,educators can facilitate their students’ understanding by providing examples, demonstrations, and practical application,using software, tutorials, and case studies when feasible.

Students can gain a conceptual understanding of business analytics usage in management and cost accounting throughdiscussion of topics such as how activity-based costing (ABC) systems use volumes of structured data (e.g. Collins, 2012;Kaplan & Anderson, 2004); assessment of a firm’s key performance indicators (KPIs) using volumes of structured andunstructured data (e.g. Warren et al., 2015); the use of predictive analytics to evaluate what-if scenarios (e.g. IMA, 2006);and demonstrations of the display of KPIs via interactive data visualization (e.g. Janvrin et al. 2014). Going beyond a discus-sion of conceptual topics to include hands-on education in environments where active learning is not feasible, instructorscan show and explain the results of in-class exercises to the students. Instructors can use diagnostic analytics using spread-sheet software and richer visualization software such as Tableau to demonstrate to their students how datasets can begraphically depicted to produce information in a format that may be more effective for decision making and communication.Janvrin et al. (2014) discuss interactive data visualization as a data analysis tool and method of communication for managers

4 The SAP University Alliance (https://go.sap.com/training-certification/university-alliances.html)5 The financial statement data sets provided freely by the SEC are curated from the XBRL filings submitted by companies.6 E.g. see Sledgianowski & Chen, 2013 for a tutorial using free training materials and EDGARsuite XBRL mapping software available from Advanced Computer

Innovations at http://sec-edgar-filing.com/download.htm.

D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93 85

and accountants and offer a simulated business case that gives instruction on how to create an interactive dashboard toassist management in making pricing and product-line strategy decisions from a dataset that contains detailed sales and pro-duct information. The case material includes instructions on how to navigate within the technology to select and displayappropriate data using pivot tables, among other functions.

Practical application of Big Data technology and information systems in the classroom is preferred, when time permits.Management accountants expect new hires to know how to use the pivot table feature of spreadsheet software to view sum-mary data (Bradbard, Alvis, & Morris, 2014). Convery and Swaney (2012) provide an instructional module that analyzes busi-ness issues using managerial accounting data to develop technological competency in using business analytic spreadsheetfunctions such as pivot tables, regression, and what-if analysis. Furthermore, students can practice predictive analytics usingstatistical methods and decision models using spreadsheet technology capable of linear programming and Monte Carlo sim-ulation, using examples relevant to analyzing cost accounting data (e.g. Togo, 2005).

Mensching, Adams, Gardiner, and Jones (2012)7 offer a hands-on case study that uses an object-oriented modeling tool todevelop stochastic budgeted financial statements and perform sensitivity analysis. They provide an example of using descriptiveand predictive analytic tools to develop budget forecasts and conduct sensitivity analyses, analyze profitability, build score-cards, and also exercise students’ communication and collaborative working skills. Mensching et al. (2012) suggest that theemerging availability of business intelligence systems in business provides the complex source of data required to make sim-ulation models practical. Igou and Coe (2016) offer a teaching case using Tableau and Microsoft Access software to analyze prof-itability and make recommendations based on the results.

Students in the cost accounting course could use business analytic tools to develop an activity based costing model andscorecard to communicate organizational goals and strategies. Teaching cases and instructional tutorials are available usingSAS Cost and Profitability Management software. Blocher, Stout, Juras, and Cokins (2016) cost accounting textbook providesaccess to the analytic software and instructions for its use, as well as mini-cases for students to solve using the software. Theinstructional material provided contains a tutorial showing students how to build a scorecard with appropriate performanceobjectives that measures and communicates goals, strategies, and objectives of an organization. Blocher, Shastri, Stout, andSwain (2009) offer a teaching case using the same software. Their instructional teaching case covers many issues relative toABC. For example, it requires students to analyze customer cost information and apply critical thinking to make recommen-dations for incentivizing customers to order less frequently and buy in larger volumes. Although this case uses a small data-set for students, organizations typically would apply this analysis to a larger dataset. The case assignment could be extendedto include additional competencies by requiring students to write about or give presentations addressing the organizationalissues that could arise from the implementation of a new costing system, including organizational commitment to the exist-ing system and other change management issues (IMA, 2006). The assignment could ask students to comment on how amanagement accountant would handle a situation in which an ABC analysis reveals a set of customers favored by the firm’smanagers but shown to be unprofitable to keep, which can serve to develop students’ interpersonal, ethics, and leadershipcompetencies. It could also require students to comment on how an organization can leverage a variety of structured andunstructured data coming from internal and external sources, such as Twitter feeds and internal email correspondence, toidentify key performance indicators, such as customer sentiment, to predict revenue churn.

2.3. Auditing

The auditing course content offers many opportunities for students to learn of the impact of information systems andtechnology on auditing. These include researching auditing standards and techniques, conducting audits, analyzing datasetsfor fraud, and assessing internal controls. However, given the vast amount of theoretical content that needs to be covered in atypical auditing course, many instructors feel that there is not much room to incorporate Big Data and data analytics con-cepts into their courses. This is a big misconception given the various resources available to support audit data analytics andthe importance Big Data and data analytics play in fraud detection in the current business environment.

Some auditing textbooks include access to ACL’s educational version of its audit software.8 This software, in conjunctionwith exercises from the textbook, allows students to practice audit testing (e.g. substantive testing, sampling) and tests for fraudusing a generalized audit software tool (GAS) that is similar to those used by CPA firms. Some publishers provide supplementalbooks that include the software, instructions, and assignments for using particular generalized audit software. For example,Arens, Elder, and Borsum (2013) provide students with case material and datasets focusing on teaching students how to useACL Data Analytics to solve audit problems.9

Teaching case studies that utilize information systems and technology to facilitate students’ active learning of variousaudit skills are available. These include activities such as creating work papers and researching audit techniques, as wellas assessing internal controls and applying substantive tests. Bagley and Harp (2012) offer a case that uses spreadsheet tech-nology to create electronic work papers. Worrell (2010) case requires students to research auditing and assurance resources

7 Faculty can request access to the Goldsim simulation software, instructions, assignments, and spreadsheet files from the AIS Educator Association websiteat http://www.aiseducators.com

8 Examples include Johnstone, Gramling, & Rittenberg, 2016; Louwers, Ramsay, Sinason, Strawser, & Thibodeau, 2015; and Whittington & Pany, 2015.9 Armond Dalton Publishing at http://www.armonddalton.com/publications/computerized-auditing-using-acl-data-analytics/http://www.armonddal-

ton.com/publications/computerized-auditing-using-acl-data-analytics/

86 D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93

including work programs, process flows, and checklists using Protiviti’s Knowledgeleader subscription-based online serviceavailable from its free University Program. Dow, Watson, and Shea (2013) provide a case simulating a transactions audit ofprocurement transactions collected from procurement cards (PCard). This case introduces students to an additional informa-tion technology that they may have been unfamiliar with but is widely used in the corporate world. Students, acting as audi-tors, assess internal control effectiveness conducting substantive tests of PCard transactions using generalized audit softwareto query for anomalies in the data. Matherly, Watson, and Ivancevich (2009) offer a case that can be completed using eitherACL or IDEA software, two popular generalized auditing software packages. Students collaborate in groups to import accountdata into the GAS system, analyze the data, and create confirmation letters using mail merge technology. Ernst & Young (EY)also provide a variety of resources for academics through their Academic Resource Center (EY ARC).10 EY developed an Ana-lytics Mindset framework providing cases that help faculty develop students’ analytics competencies. EY ARC also provides aPCard case, theirs utilizing a dataset with over 2.2 million transactions freely available from the state of Oklahoma government.

To supplement a discussion of computer assisted audit techniques (CAAT), students can complete the hands-on casestudy offered by Daigle, Daigle, and Lampe (2011). Their case study proposes to help students understand how ACL canbe used for continuous auditing to detect potential fraud and continuous monitoring to detect access control risk and secu-rity breaches.

The use of data visualization in auditing is becoming more common as auditors can generate valuable insights by capi-talizing on the vast wealth of data now available to them. A major vendor of audit software, Caseware, through its IDEA Aca-demic Partnership, provides low cost access to its IDEA data analysis and visualization software, including free curriculummaterial to faculty members. Although audit firms may use proprietary software instead, students learning how to use oneparticular product may find it helpful when learning to use similar custom and commercial products (Li & Chang, 2011;Richardson & Louwers, 2010). The IDEA Academic Partnership offers hands-on tutorials so students can learn to identifyfraud by querying transaction data (e.g. inventory and travel expense reporting fraud audit and payroll and accounts payablefraud audit) and visualizing the output. The academic partnership also includes educational material and case studies todemonstrate examples of continuous monitoring and IDEA’s Data Visualization feature.

Tableau Software provides several visualization and data analytics products. They offer their Tableau Desktop product,free to both students and instructors. Through their academic program, they provide instructors with free teaching materialsand resources to allow faculty to easily incorporate visual analytics into their courses. Students gain a valuable skill that canbe applied in practice to generate new insights from the data when they use visualization tools to analyze data from varioussources. Given the increased use of these tools in practice, it is essential that students be exposed to these tools in variouscourses throughout the accounting program.

2.4. Accounting information systems

The AIS course gained prominence after the 2002 mandate for SOX 404 compliance and more recently from professionaland accrediting bodies (e.g. AAA/AICPA Pathways Commission, AACSB) who have called for increased Big Data and othertechnology and information systems competencies for accounting students. It is here that the most comprehensive coverageof these topics can occur.

Instructors should utilize the information technology used in the previous accounting courses. For example, they couldelaborate on transaction processing exercises prepared in other courses to show how AISs facilitate segregation of dutiesand how application controls help reduce the risk of material misstatement and prevent fraud (e.g. see Jones &Mensching, 2007).

There are numerous teaching cases providing concrete examples of how technology and analytics can be integrated intoan AIS course. For instance, to support classroom discussions of the system development life cycle and facilitating informa-tion technologies used by accountants and auditors, faculty can assign students assignments and projects using softwaretools that corporations use throughout the SDLC. Microsoft’s Dreamspark program provides free access to some of their com-mercial software products such as Microsoft Project, Visio, and Access. Educators using free and discounted software fromvendor academic alliances could use this as a learning opportunity to discuss computer ethics issues such as ownershipof property, privacy and security of data, and misuse of systems.11

Premuroso, Hopwood, and Somnath (2011) offer an experiential learning case in which groups of students work on a sys-tem analysis and design project for the revenue transaction cycle using various software packages to complete the project.Their case requires students to use Microsoft Visio to prepare data flow diagrams and flowcharts, Microsoft Access to create adatabase structure, Microsoft Project to manage the team project, and Microsoft PowerPoint to present the proposal to thesteering committee.

Understanding the design and structure of transactional databases and the fundamentals of query writing is foundationalto learning the design and structure of analytical data warehouses and data mining. Some AIS textbooks include exercises forstudents to practice creating database structures and writing queries using Microsoft Access (e.g. Romney and Steinbart,2015; Simkin, Norman, & Rose, 2015). There are also supplemental books that include instructions, exercises, and data files

10 Ermst & Young Academic Resource Center (www.ey.com/us/arc)11 Microsoft’s Dreamspark program (www.dreamspark.com).

D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93 87

for Microsoft Access (e.g. Owen, 2015). Hall’s (2016, 480–483) AIS textbook describes the process of extraction, transforma-tion, and loading disparate data into a data warehouse, a necessary procedure for the integrity of data used for Big Dataanalytics.

Debreceny and Farewell (2010) suggest that the AIS course is where students can have a more detailed technical exposureto XBRL relative to their other accounting courses with an emphasis on creating and consuming of instance documents inXBRL. The AIS course could require students to use mapping software to tag financial statement data and notes to the USGAAP taxonomy to gain an understanding of the nuances of creating the various required instance documents for public fil-ings (e.g. see Elam, Wenger, & Williams, 2012).

Process mining, a relatively new concept to accounting, can potentially have a major effect on how auditing is per-formed.12 It could help identify inappropriate segregation of duties, fraudulent transactions, and business process inefficiencies.Available accounting-related instructional resources to demonstrate process mining are currently very limited. Educators par-ticipating in an academic educational alliance with a vendor that tracks event logs, such as SAP, can work with the alliance’scompetency center to retain the event log data and develop tutorials that have students enter transaction data and querythe associated event logs. Alternatively, the ProM framework is an open source access process mining platform for academics.13

Premuroso and Kirkham (2013) describe a process they use for teaching the AIS course that enables students to have achoice in deciding which group of software assignments they want to work on from an identified list based on the students’current career aspirations. Their assignments include using QuickBooks Pro 2010 Software or Microsoft Dynamics GP 10.0Software; Assessing Information Technology General Control Risk: An Instructional Case Study; and Interactive FinancialReporting: An Introduction to eXtensible Business Reporting Language. This pedagogical approach aligns with Lawsonet al.’s (2014) call to focus accounting curricula on students’ long-run career goals.

Online instructional resources are available for auditing and accounting information systems concepts that have no avail-able hands-on tutorials, or when there is not enough time for detailed coverage of each concept. These resources includeslide-decks, white papers, blogs, and audio/video files provided by software vendors, academic alliances, and consultingfirms. These online resources help explain how auditing and accounting techniques are implemented within various infor-mation systems. For example, the SAP community network website (https://go.sap.com/training-certification/university-alliances.html) publishes slide-decks and white papers that explain their continuous monitoring product called Process Con-trol Governance, Risk and Compliance (GRC). CaseWare and its IDEA Academic Partnership publish online videos and whitepapers describing success stories using continuous monitoring products in practice. KPMG’s Advisory Institute publishes freewebcasts by subject-matter experts discussing information technology aspects relevant to accounting and auditing.14 Otheravailable teaching resources include cases that develop knowledge of information technology without actually using a specifictechnology. For example, Cereola and Cereola (2011) offer an instructional case on knowledge development of privacy and secu-rity concerns related to internal controls of information systems. Their case looks at an instance of a real-world security breachexperienced by TJX Companies. A case by Bierstaker, Chung, Lee, and Sipior (2014) has students examine a real-world scenarioto assess internal controls of business processes within the final phase of an ERP system implementation.

Instructors can also use a number of different software tools for AIS courses. The financial accounting systems used inprevious courses, such as Microsoft Dynamics GP, QuickBooks, Quicken, and SAP ERP, contain programmed managementreports that students can run and examine to view the results of their transactions and also write their own ad-hoc queries.For example, a student could learn how to run a programmed accounts receivable aging report and then write a query todetermine the probability of bad debt and allowance for doubtful accounts based on the results of the aging report.15 Instruc-tors can use systems that students access remotely, such as the one provided by the SAP University Alliance University Com-petency Center or Teradata Academic Alliance, to explain cloud computing and virtualization and control issues relevant tousing processors, servers, and application software that are housed by a third-party. They can also use cases provided throughEY ARC’s Analytics Mindset framework, such as their TechWear case, to give students hands on experience with data collection,analysis and risk assessment for a firm.

Students can elaborate on concepts learned in the auditing and AIS courses by applying information technology tech-niques to facilitate their learning. For example, the SAP University Alliance provides a case study by Mensching (n.d.) thataccesses a large dataset provided by the Teradata University Network and accessed by components of the SAP Business Intel-ligence suite. This case study helps students gain an understanding of potential risks involved in retail returns fraud, andtheir control. Students learn about the data storage and data structure aspects of a data warehouse and how it differs fromrelational database storage and structure. They utilize multidimensional analysis tools and queries to investigate anomalies

12 Process mining is a form of data mining that leverages technology to query event logs of transactions that have transpired within ERP systems and otherorganizational systems that collect event data. Event data includes facts about the timing of the transaction and the identity of who is entering the transaction.The systems’ event logs can be mined to extract information useful for auditing, such as the ability to discover how business processes are actually being carriedout in an organization (compared to what the process documentation states) and for identifying transactions between specific organizational stakeholders(Jans, Alles, & Vasarhelyi, 2013).13 See http://www.processmining.org/ and Van Dongen, de Medeiros, Verbeek, Weijters, and Van der Aalst (2005) for a description of the framework.Although, ProM is primarily a resource for computer science and information systems researchers, the site does provide open access software tools to facilitateprocess mining activities and some tutorials that may be useful if adapted by accounting educators.14 E.g. see ‘‘How to Realize the Promise of Disruptive Big Data Platforms” at http://www.kpmg-institutes.com/institutes/advisory-institute/articles/2014/08/realize-big-data.html15 See Arens, Ward, Latham, and Copeland (2014) for a stand-alone textbook for Microsoft Dynamics GP that includes access to the software.

Table 2Summary of representative articles of instructional cases and materials categorized by competency area.

Competencies

Accounting Foundational Broad management

Course Reference Externalreporting&analysis

Planning,analysis& control

Taxation,compliance,& planning

Informationsystems

Assurance& internalcontrol

Professionalvalues,ethics, &attitudes

Communication Quantitative Analytical Interpersonal Technological Leadership Ethics & socialresponsibility

Processmanagement &improvement

Governance,risk &compliance

Additionalcoremanagement

Financialaccounting

Boyer and Lyons(2011)

X X X X

Debreceny andFarewell (2010)

X X X X X

Taylor andDzuranin (2010)

X X X X

Fang (2014) X XKlamm andSegovia (2014)

X X X X X

Managerialaccouning

Kaplan andAnderson(2004)

X X X

Warren andYoung (2012)

X X X X X

Janvrin et al.(2014)

X X X X

Warren et al.(2015)

X X X X X X X X

Convery andSwaney (2012)

X X X X

Cost accounting Lee (2009) X X XJanvrin et al.(2014)

X X X X X X

Togo (2005) X X X XMensching et al.(2012)

X X X X X

Igou and Coe(2016)

X X X X

Blocher et al.(2016)

X X X X X X X

Blocher et al.(2009)

X X X X X X X X X

Inermediatefinancialaccounting

Sledgianowskiand Chen (2013)

X X X

Gomaa et al.(2011)

X X X X

Monterio (2013) X X X XGujarathi(2012)

X X

Auditing Arens et al.(2013)

X X

Bagley and Harp(2012)

X X X

Worrell (2010) X X XDow et al.(2013)

X X X X X

Matherly et al.(2009)

X X X X X

Accounitnginformationsystems

Jones andMensching(2007)

X X X

Premuroso et al.(2011)

X X X X

88D.Sledgianow

skiet

al./Journalof

Accounting

Education38

(2017)81–

93

Table

2(con

tinu

ed)

Com

petencies

Accou

nting

Founda

tion

alBroad

man

agem

ent

Cou

rse

Referen

ceEx

tern

alrepo

rting

& analysis

Plan

ning,

analysis

&co

ntrol

Taxa

tion

,co

mpliance,

&plan

ning

Inform

ation

system

sAssurance

&intern

alco

ntrol

Profession

alva

lues,

ethics,

&attitude

s

Com

munication

Quan

titative

Analytical

Interperso

nal

Tech

nolog

ical

Lead

ersh

ipEthics&

social

resp

onsibility

Proc

ess

man

agem

ent&

improv

emen

t

Gov

ernan

ce,

risk

&co

mpliance

Add

itional

core

man

agem

ent

Elam

etal.

(201

2)X

X

Prem

uroso

and

Kirkh

am(201

3)X

XX

X

Daigleet

al.

(201

1)X

XX

XX

Cereo

laan

dCereo

la(201

1)X

XX

Bierstake

ret

al.

(201

4)X

XX

Men

sching(n.

d.)

XX

XX

X

Gardiner

(201

0)X

XX

XX

Taxa

tion

Stueb

set

al.

(201

2)X

XX

D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93 89

Table 3Available resources from vendor academic alliances.

Vendor academicalliance

Resource Web page link

Teradata UniversityAlliance

Large datasets; data analytic software tools such as Tableau, SAS, MicroStrategy,Teradata; instructional material

http://www.teradatauniversitynetwork.com/

SAP UniversityAlliance

Limited datasets; SAP ERP apps and proprietary data mining tools; instructionalmaterial; white papers

https://go.sap.com/training-certification/university-alliances.htmlhttps://go.sap.com/community.html

IDEA AcademicPartnership

CAAT software; instructional material, videos and white papers http://www.casewareanalytics.com/products/continuous-monitoring andhttp://www.audimation.com/caseware.html

KnowledgeLeaderUniversity Program

Podcasts and white papers about auditing topics and data analytics; syllabi; auditprograms, checklists, questionnaires, tools and templates; hot issues and bestpractices

http://knowledgeleader.com/KnowledgeLeader/Content.nsf/klUniversity.xsp

Tableau AcademicProgram

Data analytic and visualization software; instructional resources http://www.tableau.com/academic

Ernst & YoungAcademic ResourceCenter

Lecture Notes; Slides; Case Studies; Homework Problems; Videos. http://www.ey.com/us/arc

90 D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93

in the data, indicative of potential fraud (Mensching, n.d.). Gardiner (2010) documents the data structure for this large data-set using narrative, data flow diagrams, entity-relationship diagrams, and metadata descriptions. The dataset accessed byMensching’s (n.d.) retail fraud case is comprised of anonymized store-visit and product-related data from the retailer Sam’sClub. The data cubes, with over one million and three million records each, are a subset of data extracted from a larger data-base from the TUN lab. Gardiner (2010) suggests that the data is rich enough to support the creation of complementarycourse material, such as margin analysis.

2.5. Taxation

There are limited instructional resources for Big Data and business analytics in taxation. Technological competencies gen-erally applied in the taxation course include performing tax research online using software tools such as RIA Checkpoint,WestLaw, or LexisNexis; querying the online Internal Revenue code database and related online federal tax law sources;and using tax return preparation software. However, given the vast amounts of tax data being generated by organizationsand new data standards (e.g. XBRL), it is increasingly important for students to learn how to use tax analytics approachesto gain deeper insights from this data.

In the absence of instructional resources, instructors can discuss with students anecdotal descriptions of Big Data andbusiness analytics usage (e.g. New York State Department of Taxation, 2014 and Butler, 2012). The New York Departmentof Taxation and Finance (2014) describes how it redesigned its fraud recognition and detection process, explaining how pre-dictive business analytics is used by the department to facilitate the identification of income tax return fraud preemptivelybefore refunds are issued. The department explains its use of statistical analysis software and pivot tables to analyze largevolumes of tax return data and display the results of the analysis. The department also discusses how it used this informationsystem to identify a professional tax return preparer’s preparation of fraudulent tax returns, which clearly represents uneth-ical professional behavior in the preparer. The case demonstrates the integration of Lawson et al.’s (2014) framework’saccounting competencies of taxation, compliance and planning, assurance and internal control, professional values, ethicsand attitudes, and information systems; the foundational competency of technological; and the broad management compe-tency of process management and improvement.

Butler (2012) provides a presentation slide-deck of examples of business analytics technology and information systemsused by the Internal Revenue Service (IRS). The slide-deck demonstrates the IRS’s use of Big Data in terms of volume andvelocity and provides examples of how the IRS uses business analytics. These examples include the ability to predict theimpact of tax code changes on taxpayer behavior and predict patterns of payment compliance.

Stuebs, Wilkinson, and Arnold (2012) provide a teaching case study integrating the competencies of taxation and tech-nology by having students solve technical tax issues. In their case study, students are directed to query a tax law researchdatabase and apply that research to make appropriate decisions.

Powerlytics provides the academic community with fee-based access to its aggregated databases of individual, partner-ship and corporate tax returns.16 Instructors can use visualization software, such as Tableau, with tax data analytics techniquesto analyze the vast amount of data provided by Powerlytics. This provides instructors with an opportunity to teach studentshow to gain greater insights from the data available in these databases.

16 Powerlytics Inc http://www.powerlytics.com/

D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93 91

3. Conclusion

Our paper suggests a method for educators to facilitate integrating information systems and technological competenciesrelevant to Big Data and business analytics into the accounting curriculum. We present examples of instructional resourcesto be used as a reference point to facilitate this competency integration.

Our search of instructional resources related to Big Data and business analytics reveals a dearth of resources to supportintegration in the accounting curriculum. We found very limited instructional resources available for use in advanced finan-cial accounting and taxation courses. It may be that accounting educators are including Big Data and business analytics tech-nological and information system competencies in their curriculum, but the content is not being shared in a public forumwith other educators (Wixom et al., 2011). Educators developing educational resources for publication should considerthe competencies identified in Lawson et al.’s (2014) framework and how they are relevant, providing examples in theirmaterial suggesting how they can be integrated.

In support of Lawson et al.’s (2014) recommendations for integrating accounting competencies, we have a number of rec-ommendations for educators. First, we recommend that educators join the discussion of competency integration. Second,educators need to develop a fluid plan for their accounting programs that can be adjusted as competency emphasis shiftsto accommodate topical priorities (e.g. professional associations and accounting firms’ current emphasis on Big Data) or adepartment’s changing missions, resources, or faculty competencies. Finally, educators should implement the plan, eitherincrementally with individual faculty, as a cohesive group, or as an aligned combination. Department members workingtogether could initially conduct a ‘‘gap analysis” and use this to develop a competency roadmap or checklist, listing compe-tencies that are already integrated. They can use this roadmap to visualize missing competencies and identify those thatmake sense to integrate. The results of this ongoing planning process can be used as a roadmap to provide direction towardcompetency integration. This process should evolve with changing educational resources and shifts in competency emphasistoward ensuring a focus on curriculum requirements for the accounting student’s long-term career demands.

Acknowledgements

The authors thank the Editor-in Chief Natalie T. Churyk, Big Data special issue guest editor Diane Janvrin, and two anony-mous reviewers for comments and suggestions for improving the paper.

This research was sponsored by a Summer Research Grant from the Frank G. Zarb School of Business at Hofstra University.

Appendix A. Sources for instructional cases and materials

Table 2 provides a listing of representative instructional cases and materials discussed in our paper. In addition to theavailable academic journal databases, the following describes two additional methods for educators to find resources.

Meyer and Meyer (2014) maintain an open access website http://www.cases.ndacct.com/ with an index of instructionalcases that have been published in Issues in Accounting Education, the Journal of Accounting Education, and the IMA’s Edu-cational Case Journal. Articles are indexed by course name, and within a specific name are searchable by keywords. Articletitle, publication information, and abstract are accessible from the website. Article classification by relevant courses,accounting topics, and industries, is also provided. The authors claim they will update the index as new cases are publishedand will expand the set of source journals.

Apostolou, Dorminey, Hassell, and Rebele (2016) provide an accounting education literature review which is the latest ofa periodic series that reviews, summarizes, and categorizes the accounting literature published during the previous year insix accounting education journals. Their category section on ‘‘Instruction by Content Area” reviews and summarizes articlesthat may help to identify instructional resources relevant to the integration of information systems and technology into theaccounting curriculum. Their review also provides useful references to other educational resources.

Appendix B. A summary of vendor academic alliances

Table 3 provides a summary of resources from vendor academic alliances. Teradata University Network at http://www.teradatauniversitynetwork.com/) is a free resource for faculty and students and provides access to multiple large datasetsand data mining software tools such as Tableau, SAS, MicroStrategy, and Teradata. Instructional cases and resources areavailable to faculty members, with a limited amount of material currently available specific to the accounting curriculum,but some material may be adaptable. Faculty can request the alliance administrator to gain access, and a code is providedfor student access.

SAP University Alliance at https://go.sap.com/training-certification/university-alliances.html is a fee-based service thatprovides access to a full suite of SAP ERP and business intelligence software products. The access includes datasets andextensive instructional resources. University access is gained by approval of application request and then paying an annualfee. The Alliance follows a Software- as-a-Service model with assigned University Competency Centers serving as serviceproviders housing hardware, software, and data. The free SAP graphical user interface and Internet access are necessary

92 D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93

to access the systems. SAP also offers free resources such as white papers and other publications at their community websitehttps://go.sap.com/community.html.

Caseware Analytics, a major vendor of audit software, provides low cost access to its IDEA data analysis software, includ-ing free curriculummaterial to faculty members, through its IDEA Academic Partnership (see http://www.audimation.com/).The IDEA Academic.

Partnership offers hands-on tutorials to help students learn how to identify fraud by querying transaction data. The aca-demic partnership also includes educational material and case studies to demonstrate examples of continuous analyticalmonitoring.

Protiviti is a global business consulting and internal audit firm that provides access to an online collection of tools forinternal auditors and risk management professionals.

KnowledgeLeader is subscription-based online service available from its free University Program (see http://knowledge-leader.com/KnowledgeLeader/Content.nsf/klUniversity.xsp).

References

Accounting Education Change Commission (AECC) (1990). Objectives of education for accountants: Position statement number one. Issues in AccountingEducation, 5(2), 207–312.

American Accounting Association (AAA) (1986). Committee on the future structure, content, and scope of accounting education (Bedford Committee).Future accounting education: Preparing for the expanding profession. Issues in Accounting Education, 1(1), 168–195.

American Institute of Certified Public Accountants (AICPA) (1996). Information technology competencies in accounting profession: AICPA implementationstrategies for IFAC international education guideline no. 11. New York, NY: AICPA.

Apostolou, B. A., Dorminey, J. W., Hassell, J. M., & Rebele, J. E. (2014). A summary and analysis of education research in accounting information systems (AIS).Journal of Accounting Education, 32, 99–112.

Apostolou, B. A., Dorminey, J. W., Hassell, J. M., & Rebele, J. E. (2016). Accounting education literature review (2015). Journal of Accounting Education, 35,20–55.

Arens, A. A., Elder, R. J., & Borsum, C. J. (2013). Computerized auditing using ACL data analytics. Okemos, MI: Armond Dalton.Arens, A., Ward, D., Latham, C., & Copeland, M. K. (2014). Computerized accounting in the cloud using microsoft dynamics GP 2013 (7th ed.). Okemos, MI:

Armond Dalton.Association to Advance Collegiate Schools of Business International (AACSB) (2013). Eligibility procedures and accreditation standards for accounting

accreditation. Tampa, FL: AACSB.Bagley, P. L., & Harp, N. L. (2012). Shoe Zoo Inc: A practice in electronic work papers, tick mark preparation, and client communication through the audit of

property, plant, and equipment. Issues in Accounting Education, 27(4), 1131–1151.Behn, B., Ezzell, W. F., Murphy, L. A., Rayburn, J. D., Stith, M. T., & Strawser, J. R. (2012). The pathways commission on accounting higher education: charting a

national strategy for the next generation of accountants. Issues in Accounting Education, 27(3), 595–600.Bierstaker, J., Chung, Q., Lee, J., & Sipior, J. C. (2014). A real-world ERP pre-implementation case for the classroom. Communications of the Association for

Information Systems, 34, 105–150.Blocher, E., Shastri, K., Stout, D. E., & Swain, M. R. (2009). Instructional case: Blue Ridge revisited—integrating ABC and OROS Quick� software. Journal of

Accounting Education, 27(2), 85–103.Blocher, E. J., Stout, D. E., Juras, P. E., & Cokins, G. (2016). Cost management: A strategic emphasis (7th ed.). New York: McGraw-Hill/Irwin.Boyer, B., & Lyons, B. (2011). Rivera custom cabinetry: Financial statement analysis using Excel. Journal of Academy of Business Education, 12, 156–162.Bradbard, D. A., Alvis, C., & Morris, R. (2014). Spreadsheet usage by management accountants. An exploratory study. Journal of Accounting Education, 32(4),

24–30.Butler, J. (2012). Big data and analytics at the IRS.” TechAmerica Big Data Commission. Washington. DC. Available at: https://www-01.ibm.com/events/

wwe/grp/grp004.nsf/vLookupPDFs/Jeff%20Butler’s%20Presentation/$file/Jeff%20Butler’s%20Presentation.pdf (last accessed on October 10, 2016).Cao, M., Chychyla, R., & Stewart, T. (2015). Big data analytics in financial statement Audits. Accounting Horizons, 29(2), 423–429.Cereola, S. J., & Cereola, R. J. (2011). Breach of data at TJX: An instructional case used to study COSO and COBIT, with a focus on computer controls, data

security, and privacy legislation. Issues in Accounting Education, 26(3), 521–545.Collins, K. (2012). Total profitability management: A powerful framework. Financial Executive, 28(7), 58–61.Convery, S. P., & Swaney, A. M. (2012). Analyzing business issues–with Excel: The case of Superior Log Cabins, Inc.. Issues in Accounting Education, 27(1),

141–156. http://dx.doi.org/10.2308/iace-50095.Daigle, J. J., Daigle, R. J., & Lampe, J. C. (2011). Using ACL scripts to teach continuous auditing/monitoring: The Tremeg case. Journal of Forensic and

Investigative Accounting, 3(2), 277–389.Debreceny, R., & Farewell, S. (2010). XBRL in the accounting curriculum. Issues in Accounting Education, 25(3), 379–403.Dilla, W., Janvrin, D. J., & Raschke, R. (2010). Interactive data visualization: New directions for accounting information systems research. Journal of

Information Systems, 24, 1–37.Dow, K. E., Watson, M. W., & Shea, V. J. (2013). Understanding the links between audit risks and audit steps: The case of procurement cards. Issues in

Accounting Education, 28(4), 913–927.Elam, R., Wenger, M. R., & Williams, K. L. (2012). XBRL tagging of financial statement data using XML Spy: The small company case. Issues in Accounting

Education, 27(3), 761–781.Fang, J. N. (2014). Retrieving financial information in XBRL. CPA Journal, 84(11), 60–65.Gardiner, L. R. (2010). Sam’s club data and SAP BI infrastructure. Available at: <http://walton.uark.edu/enterprise/downloads/SAPua/

SamsClubSmallDWInfrastructure.pdf> (last accessed on June 17, 2016).Gartner Research. (2016). Gartner IT glossary. Available at: <http://www.gartner.com/it- glossary/?s=big+data> (last accessed on October 1, 2016).Gomaa, M. I., Markelevich, A., & Shaw, L. (2011). Introducing XBRL through a financial statement analysis project. Journal of Accounting Education, 29(2/3),

153–173.Gujarathi, M. R. (2012). Super Electronics Inc: Financial reporting of sales incentives and vendor allowances using FASB codification. Issues in Accounting

Education, 27(2), 461–474.Hall, J. (2016). Accounting information systems (9th ed.). Boston: Cengage Learning.Igou, A., & Coe, M. (2016). Vistabeans coffee shop data analytics teaching case. Journal of Accounting Education, 36, 75–86. http://dx.doi.org/10.1016/

j.jaccedu.2016.05.004.Institute of Management Accountants (IMA) (2006). Strategic cost management: implementing activity-based costing. Statements on management accounting.

Montvale, NJ: IMA.Jans, M., Alles, M. G., & Vasarhelyi, M. A. (2013). The case for process mining in auditing: sources of value added and areas of application. International Journal

of Accounting Information Systems, 14(1), 1–20.

D. Sledgianowski et al. / Journal of Accounting Education 38 (2017) 81–93 93

Janvrin, D. J., Raschke, R. L., & Dilla, W. N. (2014). Making sense of complex data using interactive data visualization. Journal of Accounting Education, 32(4),31–48.

Johnstone, K., Gramling, A., & Rittenberg, L. E. (2016). Auditing: A risk based-approach to conducting a quality audit (with ACL CD) (10th ed).Jones, N., & Mensching, J. (2007). A segregation of duties case study in the purchase-to-pay process with an SAP example. AIS Educator Journal, 2(1), 1–21.Kaplan, R. S., & Anderson, S. R. (2004). Time-driven activity-based costing. Harvard Business Review, 82(11), 131–138.Klamm, B. K., & Segovia, J. R. (2014). Creating financial statements using FRx within Microsoft Dynamics GP: Learning vs. teaching. Journal of Accounting

Education, 32(2), 171–184.Lawson, R. L., Blocher, E., Brewer, P. C., Cokins, G., Sorensen, J. E., Stout, D. E., ... Wouters, M. J. F. (2014). Focusing accounting curricula on students’ long-run

careers: Recommendations for an integrated competency-based framework for accounting education. Issues in Accounting Education, 29(2), 295–317.Lawson, R. A., Blocher, E. J., Brewer, P. C., Morris, J. T., Stocks, K. D., Sorensen, J. E., ... Wouters, M. J. F. (2015). Thoughts on competency integration in

accounting education. Issues in Accounting Education, 30(3), 149–171.Lee, R. (2009). Cost accounting exercises and case studies. SAP University Alliance. Available at <https://go.sap.com/training-certification/university-

alliances.html> (last accessed June 16, 2016).Li, Y., & Chang, K. C. (2011). Exploring the dimensions and effects of computer software similarities in computer skills transfer. Journal of Organizational &

End User Computing, 23(3), 48–66.Louwers, T. J., Ramsay, R. J., Sinason, D. H., Strawser, J. R., & Thibodeau, J. C. (2015). Auditing and assurance services (6th ed.). New York: McGraw Hill.Matherly, M., Watson, M. W., & Ivancevich, S. (2009). Implementing generalized audit software in the classroom. AIS Educator Journal, 4(1), 27–54.Mensching, J., Adams, S. J., Gardiner, L., & Jones, N. (2012). Modeling the budgeting process: Enriching the learning environment using Monte Carlo

simulations. AIS Educator Journal, 7(1), 53–67.Mensching, J. D. (n.d.). Retail returns fraud case. SAP University Alliance. Available at: <https://go.sap.com/training-certification/university-alliances.html>

(last accessed June 16, 2016).Meyer, M. J., & Meyer, T. S. (2014). Accounting case search: A web-based search tool for finding published accounting cases. Journal of Accounting Education,

32(4), 16–23.Monterio, B. J. (2013). Integrated reporting and the potential role of XBRL. Strategic Finance, 95(6), 62–65.New York State Department of Taxation and Finance. (2014). Leveraging business analytics to stop refund fraud. Available at: <https://www.tax.ny.gov/

about/fta-tech-award- nomination.pdf> (last accessed on October 10, 2016).Owen, G. (2015). Using Microsoft� Excel� and Access 2013 for accounting (4th ed.). Mason, OH: South-Western Cengage.Premuroso, R. F., Hopwood, W. S., & Somnath, B. (2011). Tasteless tea company: A comprehensive revenue transaction cycle case study. Issues in Accounting

Education, 26(1), 163–179.Premuroso, R. F., & Kirkham, T. (2013). Offering a ‘menu’ of software and case study options for the group project for students enrolled in the introductory

AIS course: An experimental application study. AIS Educator Journal, 4(1), 1–15.PricewaterhouseCoopers. (2015). Data driven: what students need to succeed in a rapidly changing business world. White Paper. <https://www.pwc.com/

us/en/faculty-resource/assets/PwC-Data-driven-paper-Feb2015.pdf> (accessed May 23, 2016).Richardson, R. C., & Louwers, T. J. (2010). Using computerized audit software to learn statistical sampling: an instructional resource. Issues in Accounting

Education, 25(3), 553–567.Romney, M. B., & Steinbart, P. J. (2015). Accounting Information Systems (13th edition). Pearson: Boston, MA.Simkin, M. G., Norman, C. S., & Rose, J. M. (2015). Core concepts of accounting information systems (13th ed.). Hoboken, NJ: Wiley.Sledgianowski, D., & Chen, Y. X. (2013). An active learning method for teaching XB RL and mapping concepts to accounting information systems students.

Proceedings for the Northeast Decision Sciences Institute, 33–38.Stuebs, M., Wilkinson, B., & Arnold, A. (2012). An ethical tax dilemma: support of hobby versus trade or business in the presence of competing incentives

and client pressure. Journal of Accounting Education, 30(3/4), 380–396.Taylor, E. Z., & Dzuranin, A. C. (2010). Interactive financial reporting: An introduction to extensible business reporting language (XBRL). Issues in Accounting

Education, 25(1), 71–83.Togo, D. F. (2005). Integrating operations management into cost systems: An accounting approach to linear programming. Journal of Business Case Studies, 1

(4), 27–32.Van Dongen, B. F., de Medeiros, A. K. A., Verbeek, H. M. W., Weijters, A. J. M. M., & Van der Aalst, W. M. P. (2005). The ProM framework: A new era in process

mining tool support. In G. Ciardo & P. Darondeau (Eds.), Application and theory of petri nets. Lecture notes in computer science (Vol. 3536, pp. 444–454).Berlin: Springer-Verlag.

Vasarhelyi, M. A., Kogan, A., & Tuttle, B. M. (2015). Big data in accounting: An overview. Accounting Horizons, 29(2), 381–396.Warren, J. D., Moffitt, K. C., & Byrnes, P. (2015). How big data will change accounting. Accounting Horizons, 29(2), 397–407. http://dx.doi.org/10.2308/acch-

51069.Warren, D. L., & Young, M. N. (2012). Integrated accounting principles: A best practices course for introductory accounting. Issues in Accounting Education, 27

(1), 247–266.Whittington, O. R., & Pany, K. (2015). Principles of auditing (20th ed.). New York: McGraw Hill.Wixom, B., Ariyachandra, T., Goul, M., Gray, P., Kulkarni, U., & Phillips-Wren, G. (2011). The current state of business intelligence in academia.

Communications of the Association for Information Systems, 29(16). Available at: (last accessed on June 16, 2016) <http://aisel.aisnet.org/cais/vol29/iss1/16/>.

Worrell, J. L. (2010). Blazer Communications: A procurement audit simulation. Issues In Accounting Education, 25(3), 527–546. http://dx.doi.org/10.2308/iace.2010.25.3.527.

Zhang, J., Yang, X., & Appelbaum, D. (2015). Toward effective big data analysis in continuous auditing. Accounting Horizons, 29(2), 469–476. http://dx.doi.org/10.2308/acch-51070.

本文献由“学霸图书馆-文献云下载”收集自网络，仅供学习交流使用。

学霸图书馆（www.xuebalib.com）是一个“整合众多图书馆数据库资源，

提供一站式文献检索和下载服务”的24 小时在线不限IP

图书馆。

图书馆致力于便利、促进学习与科研，提供最强文献下载服务。

图书馆导航：

图书馆首页 文献云下载 图书馆入口 外文数据库大全 疑难文献辅助工具