Managing Accounting Information Quality: An Australian Study

Association fo Infomation Systems AIS Electonic Libay (AISeL) ICIS 2000 Poceedings Intenational Confeence on Infomation Systems (ICIS) Decembe 2000 Managing Accounting Infomation Quality: An Austalian Study Hongjiang Xu Univesity of Southen Queensland Follow this and additional woks at: http://aisel.aisnet.og/icis2000 Recommended Citation Xu, Hongjiang, "Managing Accounting Infomation Quality: An Austalian Study" (2000). ICIS 2000 Poceedings. 67. http://aisel.aisnet.og/icis2000/67 This mateial is bought to you by the Intenational Confeence on Infomation Systems (ICIS) at AIS Electonic Libay (AISeL). It has been accepted fo inclusion in ICIS 2000 Poceedings by an authoized administato of AIS Electonic Libay (AISeL). Fo moe infomation, please contact elibay@aisnet.og.

MANAGING ACCOUNTING INFORMATION QUALITY: AN AUSTRALIAN STUDY Hongjiang Xu Depatment of Infomation Systems Univesity of Southen Queensland Austalia Abstact The quality of the data povided is impotant to the success of accounting infomation systems. The evidence indicates that oganizations have data quality poblems. Accounting infomation systems ae one of the coe systems in the oganization; theefoe, knowledge of how to manage the quality of accounting infomation has become citical. The eseach poposed hee will develop and test a model that identifies the citical success factos influencing data quality in accounting infomation systems. The eseach will involve case studies of accounting infomation quality in Austalian oganizations in pactice and then will use case study findings to modify the initial eseach model and identify the possible set of success factos. This pape descibes the oveall objectives of this eseach and the methodology to be employed. Keywods: Accounting infomation quality, data quality, citical success factos 1. INTRODUCTION Global, national, and local oganizations ae all opeating and competing in today s infomation society. An oganization s basis fo competition, theefoe, has changed fom tangible poducts to intangible infomation. Poo quality infomation can have significant social and business impacts (Stong et al. 1997). Thee is stong evidence that data quality poblems ae becoming inceasingly pevalent in pactice (Redman 1998; Wand and Wang 1996). Most oganizations have expeienced the advese effects of decisions based on infomation of infeio quality (Huang et al. 1999). In paticula, accounting infomation systems (AIS) maintain and poduce the data used by oganizations to plan, evaluate, and diagnose the dynamics of opeations and financial cicumstances (Anthony et al. 1994). Poviding and assuing quality data has been the pimay objective of accounting since the inception of the field. With the advent of AIS, the taditional focus on the input and ecoding of data needs to be offset with the ecognition that the systems themselves may affect the quality of data (Fedoowicz and Lee 1998). Empiical evidence suggests that data quality is poblematic in AIS (Johnson et al. 1981). AIS data quality is concened with detecting the pesence o absence of taget eo classes in the accounts (Kaplan et al. 1998). Thus, knowledge of the citical factos that influence the data quality in AIS will assist oganizations to ensue and impove thei accounting infomation systems data quality. While many AIS studies have looked at intenal contol and audit, data quality (DQ) studies focused on measuement of DQ outcomes, but it appeas that vey little attempt has been made to identify the citical success factos (CSFs) fo impoving data quality in AIS. The eseach poposed hee will develop and test a model that identifies the citical success factos influencing data quality in accounting infomation systems. 2. RESEARCH PROBLEM In bief, it appeas that thee has been little discussion in the liteatue of the impact of CSFs on the data quality of AIS. Theefoe, this eseach addesses the poblem: 628

Managing Accounting Infomation Quality RP: Thee is lack of knowledge of citical success factos fo ensuing data quality in accounting infomation systems. Two eseach questions ae deived fom the poblem above: RQ 1. What ae the factos that affect the vaiation of data quality in accounting infomation systems? RQ 2. Which of these factos ae citical to ensuing high quality of data in accounting infomation systems and why? Specific objectives of this eseach include: Poposing a list of factos influencing the data quality of AIS fom the liteatue Conducting pilot case studies, using the findings fom the pilot study togethe with the liteatue to identify possible citical success factos fo AIS s DQ Examining AIS DQ s citical success factos in eal-wold pactice Compaing similaities and diffeences between poposed citical success factos with eal-wold citical success factos Identifying a set of citical success factos fo AIS DQ fom eseach findings Detemining the sub-factos fo each of the identified citical success factos 3. PRELIMINARY LITERATURE REVIEW Taditionally, data quality has only been descibed fom the pespective of accuacy. Reseach and pactice indicates that data quality should be defined as beyond accuacy and is identified as encompassing multiple dimensions. Howeve, no standad data quality definition exists today (Huang et al. 1999). In accounting and auditing, whee intenal contol systems equie maximum eliability with minimum cost, the key data quality dimension used is accuacy, defined in tems of the fequency, size, and distibution of eos in data (Wang et al. 1995). In assessing the value of accounting infomation, eseaches also identified elevance and timeliness as desiable attibutes (Feltham 1968). In ode to ensue the data quality in AIS, it is impotant to undestand the undelying factos that influencing the AIS s DQ. The knowledge of the citical factos that contibute to the success of AIS with high quality data is desiable but is still unclea at this time. Thee have been some studies of citical success factos in quality management, such as total quality management (TQM) and iust-in-time (JIT). Some of the data quality liteatue also addessed the citical points and steps fo DQ. Table 1 shows the elated eseach effots and eflects whethe these eseach effots addessed cetain issues o elements of citical success factos of quality o data quality management. 4. RESEARCH APPROACH AND METHODOLOGY 4.1 Initial Exploatoy Reseach The fist phase will involve the development of the eseach model epesenting possible citical success factos fo data quality in AIS. The conceptual study eseach method will be used togethe with the pilot case study in ode to build the eseach model. Fist, a list of factos that influence data quality in AIS will be poposed by synthesizing citical success factos, data quality, and accounting infomation systems concepts fom the quality management, DQ, and AIS liteatue. A conceptual study method will be used, because it can captue and aticulate the eseaches views and it is effective in developing new concepts and insights (Gallies 1991). Two o thee pilot case studies will be conducted. The pilot study will assist in efining data collection plans with espect to both the contexts of the data and the pocedues to be followed (Yin 1994). It is expected to povide a boad pictue of data quality issues in AIS and the evidence of accepting o ejecting initial poposed factos fom the liteatue. The pilot study may uncove some new factos influencing accounting infomation quality that wee not included in the poposed list. 629

Table 1. Summay of Liteatue Review Identifying Factos Influencing (Data) Quality Johnson (1981) Goome and Muthy (1989) Yu and Nete (1973) Facto Wang Cushing (1974) (1998), Zhu and Fields et al. Saaph et al. (1989) English (1999) Fith (1996) Huang et al. (1999) Segev (1996) Meedith (1995) O (1998) Bikett (1986) (1986) Nichols (1987) Role of top management (Data) Quality polices and standads Role of (data) quality and (data) quality mange Taining Oganizational stuctue (communication) Natue of the system Poduct/sevice design Appoaches (contol and impovement) Pocess management Employee/pesonnel elations Supplie quality management Pefomance evaluation and ewods (esponsibility fo DQ) Manage change Extenal factos Evaluate cost/benefit tadeoffs Audits Intenal contol (systems, pocess) Input contol Custome focus (use involvement) Usage of data ( use-based DQ) Bowen (1993)

Managing Accounting Infomation Quality Data Quality (DQ) Pilot Case Studies AIS DQ s CSFs Accounting Infomation Systems (AIS) CSFs in Quality Management Figue 1. Aeas That May Contibute to the Model Building of this Reseach Development of the Reseach Model. The findings fom the pilot study will be used togethe with the available liteatue to build the eseach model, which will include the possible citical success factos fo DQ in AIS. Figue 1 shows how diffeent aeas of liteatue and the pilot study contibute to the model building of this study. The initial exploatoy eseach will also be used to e-design the inteview potocol and data collection pocedues, which will be used in next phase. 4.2 Test the Reseach Model: Multiple Case Studies The second phase of this eseach will be to examine the applicability of the poposed factos compaed to the factos that impact data quality in AIS in pactice. The case study eseach method will be used in this phase. Case study eseach is used to study the contempoay phenomenon in its eal-life context (Yin 1994) and it can be used whee the eseach and theoy ae at thei ealy, fomative stages (Benbasat et al. 1987). Given that little eseach has been conducted on DQ citical success factos in AIS, thee is a need to examine the eal wold AIS DQ citical success factos and modify the initial poposed citical success factos based on eal-life pactice. Theefoe, the case study method seems appopiate fo this phase. Case Studies. The case studies will be conducted within Austalian oganizations in elation to the factos that impact on data quality in thei accounting infomation systems. It will lead to a deepe undestanding of citical success factos on accounting infomation quality. Multiple case studies. Afte the pilot study, multiple case studies will be conducted as the methodology to futhe investigate the citical success factos fo accounting infomation quality. The evidence fom multiple cases is often consideed moe compelling and the oveall study is theefoe egaded as being moe obust (Heiott and Fiestone 1983). Sample selection. Cases fo the study will be caefully selected, so that each case eithe pedicts simila esults (a liteal eplication) o poduces contasting esults but fo a pedictable eason (a theoetical eplication) (Yin 1994). The numbe of case studies included in this study will be six to ten, as the ability to conduct that numbe of case studies, aanged effectively within a multiple-case design, is analogous to the ability to conduct the same numbe of expeiments on elated topics (Yin 1994). Within those six to ten cases, half will be chosen fom lage oganizations and the othe half will be small to medium (SMEs) oganizations. This design will allow fo the investigation as to whethe oganizational size influences the citical success factos and whethe it is possible to geneate some common citical success factos fo oganizations of diffeent sizes. Due to funding constaints, the selected oganizations will be fom cities on the easten coast of Austalia. Data collection and unit of analysis. Semi-stuctued and unstuctued inteviews with key AIS people will be conducted, including accounting and finance manages, finance systems manages, senio managements, intenal auditos, and data manages. In data quality studies, fou types of stakeholdes have been identified: data poduces, data custodians, data consumes, and data manages (Stong et al. 1997; Wang 1998). In this study, stakeholdes of data quality in AIS ae defined as follow: Data poduces ae those who ceate o collect data fo the AIS Data custodians ae those who design, develop, and opeate the AIS Data consumes ae those who use the accounting infomation in thei wok activities Da manages ae those esponsible fo managing the entie data quality in AIS Because auditos play a vey impotant ole in monitoing AIS data quality, the study will also include the oganization s intenal auditos. It is likely that SMEs have less pesonnel involved in thei AIS, theefoe, it is possible that the study will inteview fewe stakeholdes in SMEs than the lage oganizations. Table 2 shows the details of inteview plans fo this study. 631

Xu Table 2. Planned Case Study Inteviews Position within the oganization Stakeholdes Categoy Lage oganizations SMEs (3 to 5) (3 to 5) Data poduces Accounting manages Accountants Data custodians IS manages IS pesonnel Data consumes Senio manages Senio manages Data manages Data manages (if applicable) N/A Intenal auditos Intenal auditos N/A Data collection souces will also include elevant documents, such as position desciption, policy manuals, oganizational stuctue chats, and taining documents, as well as some published infomation about the oganizations, such as thei financial statements and annual epots. Documents can be used to cooboate and augment evidence fom othe souces and they play an explicit ole in the data collection pocess in doing case studies (Yin 1994). Thee will be two diffeent units of analysis in the case studies. The individual oganization is the unit of analysis when we compae diffeence types and sizes of oganizations. The individual stakeholde is the unit of analysis when we compae the views of diffeent stakeholdes. The pupose of the case study is to investigate key stakeholdes peceptions of citical success factos of AIS DQ and to detemine the empiical validity of the poposed citical success factos concepts, leading to the identification of CSFs fo data quality in AIS. 4.3 Identification of CSFs fo Data Quality in AIS The thid phase is to identify a set of citical success factos fo AIS DQ. Fom the analysis of the case study, some of ou poposed citical success factos will be modified in ode to make them moe appopiate fo epesenting eal-life situations. Theefoe, a set of possible citical success factos fo AIS DQ will be able to be identified. Thee ae many diffeences between oganizations in elation to size, fom of owneship, and industy secto. The degee to which these will affect the CSFs will vay between oganizations and industies and at diffeent peiods of time. Howeve, thee ae also many similaities among oganizations; theefoe, the majo factos that detemine the AIS s data quality ae likely to be common to most oganizations. It may be possible to identify a geneal set of CSFs that influences the data quality of AIS. This phase will also involve the identification of the sub-factos fo each of the citical success factos based on the case studies findings. Futhemoe, the phase may equie going back to the case study oganizations to veify and seek moe data. This step might enhance the validity of the eseach findings. 5. DATA ANALYSIS The data gatheed fom case studies need to be compiled and examined in ode to addess and answe the eseach questions. Case studies analysis will discuss the similaities and diffeences between the poposed citical success factos and actual factos that influence the data quality in AIS and how well the poposed citical success factos match the actual situations. The modes of analysis that will be used in this study ae descibed below. Patten-matching. Compaison will be made between the findings of the case studies and the poposed factos. The compaison is between the findings of the case studies, as empiically based pattens, and poposed factos as pedicted pattens. If the pattens coincide, the esults can help case studies stengthen thei intenal validity (Yin 1994). 632

Managing Accounting Infomation Quality Clusteing. The simila comments and ideas fom case study findings will be gouped. The citical success factos of AIS will be used to cluste the findings fom case studies, whee cluste means inductively foming categoies and iteative soting of events, factos, settings, and sites into those categoies (Miles and Hubeman 1994). If clusteed, the findings can be easily compaed among diffeent stakeholdes and oganizations. Compaing. The compaison will include diffeent stakeholdes pespective within one case study and the similaities and diffeences of CSFs acoss cases. It is likely that diffeent stakeholdes will have diffeent pespectives as to what constitutes the CSFs fo data quality in AIS. The study will compae diffeent stakeholdes viewpoints and, futhemoe, will investigate whethe thee ae some common factos that all stakeholdes deem to be citical. As the case study will involve diffeent sizes of oganizations and oganizations fom diffeent industies, the study will also compae and contast the similaities and diffeences of CSFs in diffeent sizes of oganizations and acoss diffeent cases. Geneal factos vesus sub-factos. The case studies findings could povide some possible sub-factos that constitute each of the CSFs. These sub-factos will be gouped unde thei elevant CSFs. This step is necessay, as thee is a need fo a genealised set of CSFs, as well as sub-factos within each CSF, which could povide moe detailed guidelines to pactitiones. 6. CONTRIBUTION OF THE RESEARCH Given that little eseach has been conducted to identify the CSFs fo data quality in AIS, the poposed eseach will make both theoetical and pactical contibutions to the field of data quality and accounting infomation systems in the following ways: 1. This eseach will make a contibution to the body of knowledge of data quality and AIS by identifying the citical success factos fo AIS s data quality. 2. The eseach could help oganizations to focus on only the impotant factos, theeby obtaining bette benefit fom less effot. Such an outcome will be helpful to both Accounting and IT pofessionals in obtaining a bette undestanding of data quality issues in AIS. 7. LIMITATIONS Fist, AIS has lage numbe of stakeholdes and diffeent stakeholdes may have diffeent pespectives of the impact of citical success factos on data quality. This study will only include the majo stakeholdes in AIS. Howeve, the pespectives of othe, mino stakeholdes may also be impotant and, theefoe, futhe eseach should be conducted. Second, the study will be constained to oganizations in Austalia, especially fom easten coast of Austalia. Theefoe, the conclusions dawn fom this study may have a potential poblem with genealizability. It is acknowledged that cultue diffeences may impact upon the esults, a point beyond the scope of this eseach, and those issues could be addessed by futhe eseach. Refeences Anthony, R. S., Reese, J. S., and Heenstein, J. H. Accounting Text and Cases, Homewood, IL: Richad D. Iwin, 1994. Benbasat, I., Goldstein, D. K., and Mead, M. The Case Study Reseach Stategy in Studies of Infomation Systems, MIS Quately (11), 1987, pp. 369-386. Bikett, W. P. Pofessional Specialization in Accounting IV: Management Accounting, Austalian Accountant, Septembe 1986, p. 78. Bowen, P. Managing Data Quality Accounting Infomation Systems: A Stochastic Cleaing System Appoach, Unpublished Ph.D. Dissetation, Univesity of Tennessee, 1993. Cushing, B. E. A Mathematical Appoach to the Analysis and Design of Intenal Contol Systems, The Accounting Review (49:1), 1974, pp. 24-41. English, L. P. Impoving Data Waehouse and Business Infomation Quality: Methods fo Reducing Costs and Inceasing Pofits, New Yok: John Wiley & Sons, Inc., 1999. 633

Xu Fedoowicz, J., and Lee, Y. W. Accounting Infomation Quality: Reconciling Hieachical and Dimensional Contexts, Poceedings of the Fouth Ameica s Confeence on Infomation Systems, E. Hoadley and I. Benbasat (eds.), Baltimoe, MD, August 1998, pp. 9-11. Feltham, G. The Value of Infomation, The Accounting Review (43:4), 1968, pp. 684-696. Fields, K. T., Sami, H., and Sumnes, G. E. Quantification of the Audito s Evaluation of Intenal Contol in Data Base Systems, The Jounal of Infomation Systems (1:1), 1986, pp. 24-77. Fith, C. Data Quality in Pactice: Expeience fom the Fontline, in Poceedings of the Confeence on Infomation Quality (IQ 96), Massachusetts Institute of Technology, Sloan School of Management, Cambidge, MA, Octobe 25-26, 1996. Gallies, R. D. Choosing Infomation Systems Reseach Appoaches, in Infomation Systems Reseach: Contempoay Appoaches Emegent Taditions, H-E. Nissen, H, K. Klein, and R. A. Hischheim (eds.), Amstedam: Noth-Holland, 1991, pp. 327-346. Goome, S. M., and Muthy, U. S. Continuous Auditing of Database Applications: An Embedded Audit Module Appoach, The Jounal of Infomation Systems (3:2), 1989, pp. 53-69. Heiott, R. E., and Fiestone, W. A. Multisite Qualitative Policy Reseach: Optimizing Desciption and Genealizability, Educational Reseache (12), 1983, pp. 14-19. Huang, H-T., Lee, Y. W., and Wang, R. Y. Quality Infomation and Knowledge, Uppe Saddle Rive, NJ: Pentice Hall, 1999. Johnson, J. R., Leitch, R. A., and Nete, J. Chaacteistics of Eos in Accounts Receivable and Inventoy Audits, The Accounting Review (56:2), 1981, pp. 270-293. Kaplan, D., Kishnan, R., Padman, R., and Petes, J. Assessing Data Quality in Accounting Infomation Systems, Communications of the ACM (41:2), Febuay 1998, pp. 72-78. Miles, M. B., and Hubeman, A. M. Qualitative Data Analysis: An Expended Souce Book, Thousand Oaks, CA: Sage, 1994. Nichols, D. R. A Model of the Audito s Peliminay Evaluations of Intenal Contol fom Audit Data, The Accounting Review (62), 1987, pp. 183-190. O, K. Data Quality and Systems Theoy, Communications of the ACM (41:2), Febuay 1998, pp. 66-72. Redman, T. C. The Impact of Poo Data Quality on the Typical Entepise, Communications of the ACM (41:2), Febuay 1998, pp. 79-82. Saaph, J. V., Benson, P. G., and Schoede, R. G. An Instument fo Measuing the Citical Factos of Quality Management, Decision Sciences (20:4), 1989, pp. 810-829. Segev, A. On Infomation Quality and the WWW Impact: A Position Pape, in Poceedings of the Confeence on Infomation Quality (IQ 96), Massachusetts Institute of Technology, Sloan School of Management, Cambidge, MA, Octobe 25-26, 1996. Stong, D. M., Lee, Y. W., and Wang, R. Y. Data Quality in Context, Communications of the ACM (40:5), 1997, pp. 103-110. Wand, Y., and Wang, R. Y. Anchoing Data Quality Dimensions in Ontological Foundations, Communications of the ACM (39:11), 1996, pp. 86-95. Wang, R. Y., A Poduct Pespective on Total Data Quality Management, Communications of the ACM (41:2), 1998, pp.58-65. Wang, R. Y., Lee, Y. L., Pipino, L., and Stong, D. M. Manage You Infomation as a Poduct, Sloan Management Review (39:4), 1998, pp. 95-105. Wang, R. Y., Stoey, V. C., and Fith, C. P. A Famewok fo Analysis of Data Quality Reseach, IEEE Tansactions on Knowledge and Data Engineeing (7:4), 1995, pp. 623-640. Yin, P. K. Case Study Reseach: Design and Methods (2 nd ed.), Thousand Oaks, CA: Sage, 1994. Yu, S., and Nete, J. A Stochastic Model of the Intenal Contol System, Jounal of Accounting Reseach (11:3), 1973, pp. 273-295. Zhu, Z., and Meedith, P. H. Defining Citical Elements in JIT Implementation: A Suvey, Industial Management & Data Systems (95:8), 1995, pp. 21-29. 634