Using case-based decision support systems for accounting choices (CBDSS): an experimental...

7/30/2019 Using case-based decision support systems for accounting choices (CBDSS): an experimental investigation

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Using case-based decision support systemsfor accounting choices (CBDSS): an

experimental investigationN. Wongpinunwatana and K. Ratanaubol

Abs tractThe primary objective of this research is to investigate the impact of case-based decision support systems on

decision makers' certainty in their selection when using case-based decision support systems for accounting choices

emphasizing depreciation methods. Two case-based decision support systems (a plain case-based decision support system

and a traditional case-based decision support system) were developed. A laboratory experiment was performed with one

hundred and four graduate students studying for a Masters degree in accounting. The results suggested that the effects of

asymmetries in the distribution of information and in information processing on certainty in decision making was significant for a

traditional case-based decision support system. However, the decision makers perceived value of information moderated the

relationship between asymmetries in the processing of information and certainty in decision making.

Index Terms Information asymmetry; Accounting choices; Depreciation methods; Decision support systems; Case-based

reasoning; Case-based decision support systems

1 INTRODUCTION

revious research related to accounting choices havebeen conducted generally on the assumption thatmanagers make accounting choices in order to

influence an outcome beneficial to the firms or themselves[14]. Fields et al. [14] classify the accounting choiceliterature into three groups: agency costs, informationasymmetries, and externalities affecting non-contractingparties. First, agency costs are generally related tocontractual issues such as compensation plans, debtcontracts and bond covenants. The examples of researchin this agency costs are [9, 20, 22, 41, 47]. Second,information asymmetries generally refer to therelationship between the better informed (managers) andthe less informed (investors). In addition, the literatureexamines the way accounting methods affect equityvaluation or the cost of capital. Examples of research inthese information asymmetries are [2, 13, 35]. Finally,other externalities are generally related to third-partycontractual and non-contractual relations. Normally,firms select accounting methods not only to reduce or todefer taxes but also to avoid potential regulation orpolitical costs. Past research in these other externalities

has been done by [23, 26]. The research results, however,are of varying consistency.

For depreciation method choices, leverage and size arekey determinants of accounting choices [11, 22, 36, 47].The survey on depreciation methods of some companieslisted in the Stock Exchange of Thailand (SET) during

2001 and 2011 is showed in Table 1. This survey selectedonly 6 sectors: packaging, automotive, petrochemicalsand chemicals, construction materials, food and beverage,and home and office products. The selection criteria wereas follows: (1) the companies in these sectors had to haveinvested heavily in machinery to manufacture productsfor their normal operation (2) the machines should havebeen of high value with high depreciation, therebyaffecting companies operating income. In addition, onlylisted companies which had a complete 10-year record offinancial statements (from 2001-2011) were selected. Intotal, 74 of all 476 listed companies were selected. Thisresearch collected the companies depreciation methodsfrom notes to financial statements. The data in table 1shows that almost all listed companies adoptedstraight-line depreciation (SLD) as a depreciationaccounting method in order to compile with depreciationannouncement from the Revenue Department of Ministryof Finance. In other words, they selected SLD to avoidregulatory intervention. However, some listed companiesapply other depreciation accounting methods (i.e., doubledeclining balance (DDB) and sum of the years digits

(SYD). Recently, Thai Financial Reporting Standard No.16 Property, Plant and Equipment issued by theFederation of Accounting Professions have suggested allcompanies in Thailand use depreciation methods whichreveals companies real performance.

N. Wongpinunwatana is with MIS department, Thammasat BusinessSchool, Thammasat University, Bangkok, 10200, Thailand.

K. Ratanaubol is with Accounting department, Thammasat BusinessSchool, Thammasat University, Bangkok, 10200, Thailand.

P

JOURNAL OF COMPUTING, VOLUME 5, ISSUE 3, MARCH 2013, ISSN (Online) 2151-9617

https://sites.google.com/site/journalofcomputing

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TABLE1PERCENTAGE OF COMPANIES USING EACH DEPRECIATION

METHOD BY SECTOR

Sector

Percentage of companiesusing each depreciationmethod by sector from

2001-2011

SLD DDB SYDAutomotive 90.48 4.76 4.76Construction Materials 100 - -

Food and Beverage 80.00 16.67 3.33Packaging 92.86 - -

Petrochemicals and chemi-cals

85.71 14.29 -

Home and office products 83.33 16.67 -

Notes: Most companies used DDB or SYD for assets bought before 1990.

Only one company, which was in the Food and Beverage sector, used SYD

for all of its machines.

The argument of this study is that if decision makers(who do not intend to manipulate financial statementinformation) have more information on their accountingchoices, they should employ the choice which willindicate true company performance. Furthermore, Francis[15] suggests that future research should (1) provide moreevidence on the costs and benefits of the implications ofeach accounting choice (2) appropriately examine onechoice to the exclusion of others and (3) improve researchdesigns and methods. Studies into this have been carriedout mainly by [5, 30]. In addition, few studies have beenconducted to investigate the effect of informationasymmetry and perceived value of information onaccounting choices. In this sense, the main aim of thisresearch is to determine whether information on

company performance provided by case-based decisionsupport systems (CBDSS) can have a significant impacton decision makers certainty in selecting accountingchoice, especially on depreciation methods. There are alsoa few studies on integrating case-based reasoning (CBR)into the architecture of decision support systems (DSS)[28]. The CBDSS, as an information system has been seenas a major means for companies to increase informativereal performance in the eyes of decision makers.Normally, information systems are designed,implemented or used to provide information [19].

2 THEORETICAL BACKGROUND

2.1 Case-based Decision Support System

A Decision Support System (DSS) is a system intended tosupport managerial decision-makers in semi-structureddecision scenarios [28, 40]. DSS typically (1) aim at theless well structured, under-specified problems thatupper-level managers typically face (2) combine the useof models or analytic techniques with traditional dataaccess and retrieval functions (3) focus on features thatmake them easy to use in an interactive non-computermode and (4) are flexible and adaptable to accommodatechanges in environment and decision making approaches

of the user [12]. The system has been recognized as ameans for formulating better problem-solving processesand improving decision quality during the process ofdecision making [3, 12, 17, 18, 28]. Four types of supportthat can be provided to decision makers are (1) passivesupport by allowing decision makers to makeautonomous decisions (2) traditional support by meshingwith decisions to improve decision making (3) extendedsupport by actively suggesting solutions for decisionmakers and (4) normative support by dominating thewhole decision process, while managers provide inputand specifications [12].

A Case-based reasoning system (CBR) is one type ofartificial intelligence system designed to perform like anexpert in a specific area [29]. Case-based reasoningrepresents knowledge in the form of cases. The contentsof the cases are stored directly in a case base [46]. To findsolutions, case-based reasoning performs analogicalreasoning by finding previous cases similar to the presentproblem and then adapting the previous solutions to fitthe current problem. The quality of case-based

reasoning's solutions depends on three fundamentalfactors [21, 27]: (1) the number of well-defined casesstored in the system; (2) the ability of the system to recallexperiences by using an index and to interpret the newsituation in terms of those experiences; and (3) theadaptation of an old solution to meet the demands of anew situation. Some case-based reasoning systems alsopossess the ability to evaluate and adapt experiences toavoid repeating past mistakes [27]. Meanwhile, acase-based decision support system (CBDSS) integratesCBR into DSS to enhance problem-solving and decisionmaking. CBR helps solve problems using reasonableknowledge from past cases while DSS is used to help

solve problems within a dynamic environment [28].

2.2 Information Asymmetry

Information asymmetry is a condition where one partyhas a greater quantity of, or more relevant, informationthan the other. Information asymmetry causes decisionmakers to become uncertain in their decisions, sincedecision makers do not have access to the informationthey need for their decision making processes.Information asymmetry can be classified as asymmetriesin the distribution and processing of information [5].

Asymmetries in the distribution of information aresets of information that has different decisionalimplications. This would mean that different informationis available to different parties [43]. Asymmetricalinformation distribution in the form of hiddeninformation is a necessary precondition for decisionmaking [5].Asymmetries in the processing of information are anindividuals interpretation and processing of informationthat can hamper decision making. Asymmetries inprocessing and task complexity are also interrelated. Thethree elements of task complexity as described by [4] areinput, processing, and output. Because of the greaterdemands placed on memory, the greater the extent ofeach of the three elements present and the more complex





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the task is considered to be [4]. The greater the clarity ofthe three elements, the less complex the task isconsidered. The amount of input is measured by thenumber of alternatives and the number of cues peralternative. Redundancy of the cues, however, can reducethe amount of input. The clarity of input refers to thespecification or measurement of the relevant cues. Thematching between presented cues and stored cues inmemory, and the presentation of information in the formof graphics can also increase the clarity of input. Theamount of processing is measured by the number of stepsor procedures that have to be executed to solve aproblem. Normally, the amount of processing alsodepends on the amount of input. The clarity of processingis determined by how well the procedure used to solvethe problem is specified. The amount of output is thenumber of goals achieved or solutions per alternative,while the clarity of output is the degree to which aspecific goal can be achieved. Depreciation choice taskstypically require only one judgment and have a definitegoal or standard by which to judge the output. For

example, the goal of the choice is to judge whether thedecision maker should select SLD, DDB, or SYD.Furthermore, the input for calculating return on asset(ROA) also is definite defined. The output and inputelement does not vary substantially in variousdepreciation choice tasks. Therefore, the output and inputelements are treated equally for the three depreciations inthis research. Only processing complexity varies.

2.3 Perceived Value of Information

Information is data organized and processed in a formthat is useful in decision making. The information isvaluable to decision makers because it reduces

uncertainty, increases knowledge in a particular area ofconcern and improves the decision making process [16,39]. The effectiveness of information can be measured interms of understandability, relevance, timeliness,predictive value, feedback value, verifiability, freedomfrom bias, comparability, consistency, and integrity (orvalidity, accuracy, and completeness) [16].

There are many terms for value of information (i.e.,the amount a decision maker would be willing to pay forinformation prior to making a decision). This researchdefines the value of information as the benefits producedby the information minus the costs of producing it. Themajor benefits of information are a reduction ofuncertainty, improved decisions, and a better ability toplan activities. The costs are the time and resources spentcollecting, processing, and storing data, as well asdistributing information to decision makers.Unfortunately, determining the value of information isnot easy [39]. However, there are three ways to assess thevalue of information: normative, realistic, and subjective[37]. The normative value of information is calculated byfocusing on expected utility. The utility, however, variesby person and circumstance. The realistic value ofinformation is ex-post evaluation or inspection paradox[45]. Therefore, this calculated method is inappropriatefor evaluating information content. Meanwhile, the

subjective value of information is based onrepresentativeness, availability, and adjustment andanchoring (or heuristics).

Perceived value of information is the worth thatinformation has in the mind of the decision maker. Inother words, the decision maker has an internal feelingfor how much certain information is worth to him/her.

2.4 Certainty in Decision MakingCertainty is defined as the state of having no doubtsabout something, e.g., users do not have any doubtsabout their solution to a problem [8]. Certainty indecision making refers to a belief that one has thecapability to make effective decisions for a specific task.In this research, certainty in decision making representsusers belief in their ability to make decisions after theyhave used case-based decision support systems fordepreciation methods.

3 RESEARCH MODEL AND HYPOTHESES

Fig. 1 illustrates the information asymmetries model ofcertainty in decision making of accounting choices.Asymmetries in the distribution of information are likelyto increase certainty in decision making. The research alsoanalyzes asymmetries in the processing of information oncertainty in decision making, which is moderated byperceived value of information.

Fig. 1. Information asymmetries model of certainty in decisionmaking of accounting choices

Information asymmetry [31] makes it very difficult forinvestors to assess true company performance beforeinvesting in those company stocks. Moreover, the moredifficult it is for investors to assess real companyperformance prior to investment and the less they knowabout the business of selling stocks, the more likely theyare to rely on signals to form expectations about realperformance [24, 38, 42]. CBDSS is one of the primarymeans that businesses can use to signal companies realperformance to investors. Some secondary data studieshave found that providing clear company performanceserves to reduce information asymmetry and increaseinvestment [25]. While interacting with the traditional





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case-based decision support system, the decision makerconveys an amount of information more precise than theplain case-based decision support system. With plaincase-based decision making, the decision maker requiresa higher level of information processing, which maymake it easier for him/her to exceed the limits of his/hercapacity. Hence, this study proposes the following:

H1: When presented with a case-based decisionsupport system, students using a traditionalcase-based decision support system have morecertainty in their decisions than students using a plaincase-based decision support system.

There are limits to the amount of information thehuman mind can effectively absorb and process [39]. Thedecision making quality declines while the decisionmaker has to process rich information, especially whenthose limits are exceeded (called information overload).To help decision makers to work their way throughexcessive information, the traditional case-based decision

support system provides information filteringmechanisms that filter out irrelevant information, leavingonly relevant data. Without such filtering systems, thedecision maker may perceive a higher degree ofinformation overload. Moreover, when individualreceives information from case-based decision supportsystems, he or she will perceive the value of theinformation which in turn will enhance their certainty intheir decisions. Hence, this study proposes that:

H2: When presented with case-based decision supportsystems, students certainty for both case-baseddecision support systems is negatively correlated with

asymmetries in the processing of information.

Perceived value of information can also influencecertainty in decision making. Decision makers withdifferent perceived value of information will havedifferent certainty in decision making. Therefore, offeringthe same case-based decision support system to differentdecision makers also will not lead to the same certainty indecision making. Thus, this study hypothesizes that:

H3: Students perceived value of information willmoderate the relationship between asymmetries in theprocessing of information and certainty in decision

making; with the same asymmetries in the processingof information, students with more perceived value ofinformation will have more certainty in decisionmaking.

4 RESEARCH METHOD

4.1 Research Design

A laboratory experiment was designed to gather data onwhich to test the hypotheses. The experimental designconsisted of a task type with one level and two levels ofcase-based decision support systems. The task type was

decision on depreciation method, while the twocase-based decision support systems were a plaincase-based decision support system (plain CBDSS) and atraditional cased-based decision support system(traditional CBDSS). Participants for depreciation taskswere divided into two groups: participants in plainCBDSS and participants in traditional CBDSS.

4.2 Construction of the Research InstrumentsMicrosoft Excel had been used to build two case-baseddecision support systems: a plain CBDSS and a traditionalCBDSS. Excel was chosen because of its built in featuresof various functionality with formulas. Furthermore,Excel is a simplistic type of DSS [12]. As personalityinfluences ones decision style [32], both systems in thisstudy have been designed to make them easy to use forindividuals to maximize the effectiveness of CBDSSs [12].The plain CBDSS provided book value of the machines ofeach listed company and other related information suchas accumulated depreciation of the machines, companyassets and liabilities, and company net profits (see fig. 2).

This information was collected from companies financialstatements for the year 2011. Students would be able touse calculation functions from the Excel to calculate theirown information to make decisions. Apart from usingMicrosoft Excel, the traditional CBDSS was also built byusing Visual Basic for Application (VBA). VBA is acomputer programming language which is used tocontrol Microsoft Excels functionality. This systemprovided two choices: type in decision makers own dataor use the systems cases, which were based on the realdata from listed companies financial statements in theyear 2011. Once the latter choice is selected, the systemprovided the companys net profit, the net profit after

three depreciations (SLD, DDB, and SYD) and forecasted5 years return on assets (ROA) for these threedepreciations. The system also compared those data forcompanies within the same categories (see fig. 3).Meanwhile participants had a choice to enter their owndata or use provided data. In addition, information onboth systems was categorized by six industrial sectors:packaging, automotive, petrochemicals and chemicals,construction materials, food and beverage, and home andoffice products.

As participants had to answer questions on thequestionnaire, a five-point scale from 1 for stronglydisagree to 5 for strongly agree was used for the ratingof certainty in their decisions, asymmetries in theprocessing of information, and perceived value ofinformation. The questions were adapted from [6]. Inaddition, the questionnaire also required participants toprovide their depreciation selection by sector.

To avoid bias and ambiguity in the researchinstruments, a pilot test was conducted with oneaccounting manager in one public organization inThailand. The manager has worked in his current positionfor almost 10 years. This manager took the two hour test.The test simulated the real experimental procedures.After the pilot test, the research instruments (plain andtraditional CBDSSs and questionnaire), as well as the





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procedures, were adjusted based on the managerscomments.

Fig. 2. Plain case-based decision support system

Fig. 3. Traditional case-based decision support system

4.3 Participants

One hundred and four graduate students studying for theMasters degree in accounting at three universities inThailand participated in this study. These students wereselected because they represent accountants. In addition,some of them also work as accountants in the private andpublic sectors. As accountants, they may have a chance toprovide information for accounting managers, who areresponsible for selecting accounting choices. Theexperiment was included in the course outline as aCBDSS exercise worth ten percent of the total coursegrade. Students were informed that their scores dependedon their participation in the CBDSS procedures for the fullone hour as well as the quality of their answers to the

questionnaire.

4.4 Experimental Procedures

The experiment was conducted in the computer rooms ofeach university. The experiment ran over a one-hourperiod and consisted of four sections: instructions (10minutes), using the case-based decision support system(40 minutes), and answering the questionnaire (10minutes). Participants interacted with a plain CBDSS or atraditional CBDSS. In addition, the participants answeredquestions relating asymmetries in the processing ofinformation, perceived value of information, certainty indecision making, and decision on depreciation method

for each sector.

5 RESULTS

This section reports the results of assessing the variableswith respect to the assumptions underlying the statisticaltests and also reports the research results. A statisticalpackage was used to evaluate the assumptions beforeproceeding with an analysis of variance (ANOVA) andinteraction effects in regression suggested by [1, 7, 33, 34].

5.1 Evaluation of Assumpt ions

One hundred and ten graduate students studying for aMasters degree in accounting participated in this study.The reason for limiting the study to these accountingstudents was that participants in this area normallyunderstand depreciation much better than participants inother areas. In addition, the participants were accountingstaff who have knowledge in depreciation. One hundredand four graduate students fully completed experimentalprocedures. Therefore, responses from 104 participants

were included in the analysis. Sample sizes of all datawere quite similar for the two groups: 56 participants inplain CBDSS and 48 participants in traditional CBDSS.With the ratio of 1.16: 1, the discrepancy in sample sizesdoes not invalidate the use of ANOVA [44]. Table 2presents demographic data for the participants whoprovided usable results in each group. Results of theevaluation of assumptions of normality, homogeneity ofvariance, linearity, and multicollinearity of eachdependent variable were satisfactory.

TABLE2PERCENTAGE OF DEMOGRAPHIC DATA FOR THE PARTICIPANTS

WHO PROVIDED USABLE RESULTS

Items PlainCBDSS

TraditionalCBDSS

Gender:MaleFemale

25.075.0

10.489.6

Age:21-2627-3233-38>39

67.926.85.4-

27.118.843.810.4

Career:AuditEducation

AccountingInternal auditingBankOther

8.95.4

44.61.8

16.123.2

2.114.6

37.54.26.235.4

5.2 Analysis and Discussion

One-way analysis of variance (One-way ANOVA) wasused to analyze the effect of asymmetries in thedistribution of information on the data of 104participants. The analysis was performed on dependentvariables (certainty in decision). The independentvariable was effects of two CBDSSs within depreciationmethods. A p-value of 0.05 was used as the significant





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level in the statistical tests. A p-value between 0.05 and0.10 is generally deemed as marginally significant.

H1 suggests that certainty in decision making is higherfor traditional CBDSS than for plain CBDSS. The resultsof a one-way ANOVA in Table 3 indicate significance forthe effect of CBDSS on the certainty of decision with ap-value of 0.031. Detail analysis of mean of certainty indecision making shows that the mean value for plainCBDSS was 3.268. Meanwhile, the mean value fortraditional CBDSS was 3.688. The mean of certainty indecision making for traditional CBDSS was higher thanthat for plain CBDSS. The CBDSS appears to affect users'certainty in decision making. Thus, H1 is supported. Thisresult is consistent with that obtained by [5] who foundthat asymmetries in the distribution of informationaffected decision quality.

TABLE3EFFECTS OF CASE-BASED DECISION SUPPORT SYSTEMS ON

CERTAINTY IN DECISION

Source Sum

ofSquare

df Mean

Square

F

Value

Sig

Betweengroup

4.552 1 4.552 4.772 0.031*

Within group 97.295 102 0.954Total 101.846 103

Note: * p < 0.05

H2 suggests that students certainty for both CBDSSsis negatively correlated with asymmetries in theprocessing of information. Statistical significant supportwas obtained for the effects of asymmetries in theprocessing of information on the certainty in decision

making with a p-value of 0.000 (see Table 4).

TABLE4EFFECT OF INTERACTION EFFECTS ON CERTAINTY IN

DECISION MAKING

Source Sumof

Square

df MeanSquare

FValue

Sig

Betweengroup

28.082 3 9.361 12.690 0.000*

Within group 73.764 100 0.738

Total 101.846 103Note: * p < 0.05, R2 = 0.276

Detailed information of t test on asymmetries in theprocessing of information showed statistical significancewith p = 0.023. In addition, the relationship betweenasymmetries in the processing of information andcertainty in decision making is negative. Therefore, theresults provide strong support for H2.

H3 suggests that students perceived value ofinformation will moderate the relationship betweenasymmetries in the processing of information andcertainty in decision making; with the same asymmetriesin the processing of information, students with more

perceived value of information will have more certaintyin decision making. The interpretation effects of twocontinuous predictor variables (asymmetries in theprocessing of information and perceived value ofinformation) in Table 5 and fig. 4 indicate that certainty indecision making was affected by the interaction effect ofperceived value of information on asymmetries in theprocessing of information. All the p-values ofasymmetries in the processing of information, perceivedvalue of information, and interaction effect of those twovariables were 0.023, 0.000, and 0.007 respectively. Inaddition, the simple slope test of the variables indicates asignificant relationship for those two variables. Hence, theresults provide strong support for H3.

TABLE5COEFFICIENTS OF INTERACTION EFFECTS ON CERTAINTY IN

DECISION MAKING

Model UnstandardizedCoefficients

t Sig

Constant 3.482 41.186 0.000*

Zscore (Asymmetriesin the processing ofinformation)

-0.196 -2.310 0.023*

Zscore (Perceivedvalue of information)

0.358 4.145 0.000*

Zscore (Asymmetriesin the processing ofinformation) * Zscore(Perceived value ofinformation)

0.232 2.758 0.007*

Note: * p < 0.05

Fig. 4. Plot of interaction effects on decision making

Detailed analysis was performed with data on theselection of depreciation method choice. The resultsshowed that 65.21 percent selected DDB and SYD insteadof continuing to use SLD (the depreciation methodapplied by most listed companies) while 34.79 percentstill continued to use SLD. A plausible reason may be that





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human decision makers generally do not make decisionsbased on the probability of success (or high return onassets (ROA) in this study), because the penalty of a vitaldecision that turns out to be wrong is normallysubstantial [12]. Though many participants found thattraditional CBDSS provided useful information (ROAclassified by company) for making decisions, some ofthem still wanted more information such as return oninvestment and other financial ratios.

6 DISCUSSION AND CONCLUSIONS

6.1 Conclusions

To conclude, this research has contributed to knowledge inthe following ways. First, it has developed an informationasymmetries model of certainty in decision making ofaccounting choices, emphasizing depreciation methods.Second, it extended prior research on accounting choices byanalyzing two types of asymmetries (asymmetries in thedistribution of information and asymmetries in the

processing of information). Third, this research investigatedthe perceived value of information which moderates therelationship between asymmetries in the processing ofinformation and certainty in decision making. Finally, theresults obtained from this research have statisticalsignificance, supporting the hypothesis that asymmetries inthe distribution of information affect the users' certainty indecision making. The research results also provide evidencethat the perceived value of information moderates therelationship between asymmetries in the processing ofinformation and certainty in decision making.

6.2 Limitations of the Research

The results of this study are limited to only the graduatestudents so they may not be generalized beyond thesestudents (i.e., the accounting manager). In addition, theresearch is applicable only to students studying for aMasters degree in accounting in three universities. Theresearch may not be generalized to all accountants incountries where they may place emphasis on differentaccounting aspects. Moreover, this experiment concernsonly a depreciation method choice. Therefore, the resultsof the research in this study should be generalized withcare.

As the experiment was conducted in a short period oftime (one hour), with the participants using the CBDSSsfor the first time, the participants may have not been able

to learn how to use the system properly because of a lackof time for learning. As a result, the findings of thisresearch cannot be generalized for situations where theparticipants used the system for a long period of time.Furthermore, the proposed CBDSSs may not be the bestsystems; therefore, different methods could yielddifferent results.

6.3 Directions for Future Research

The results of this study suggest several areas for futureresearch. First, although the laboratory experimentprovides an efficient mechanism for controlling theindependent variables, it lacks realism. There is a risk that

other important variables such as compensation andvalue of taxes have been omitted from the experimentalmanipulations. These variables may contribute to the lowR2 obtained in this research. In addition, the subjects usedin this experiment may have affected the empiricalfindings. Accounting managers working in listedcompanies may be more suitable subjects than graduatestudents studying for a Masters degree in accounting.Future research, therefore, should attempt differentresearch methods.

Second, studies could be conducted in areas related tothe impact of CBDSS on decision making effectiveness.Some research suggests that there is some effect oncertainty in decision making and the quality of decisionmaking.

Finally, other research methods such as case studyresearch may be conducted to explain the willingness tochange depreciation choices to reveal real performance.

ACKNOWLEDGMENT

The authors would like to thank Research Center ofThammasat Business School for financial support.

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Nitaya Wongpinunwatana got Ph.D. in field of information systemat the University of Queensland, Australia 1998. Her researchinterests include decision support systems, e-commerce, computersecurity, and online-learning. Currently she is working on theresearch title of role-playing game on information system audit.

Kobkaew Ratanaubol is associated professor in accounting atAccounting department, Thammasat Univerisity. Her researchinterests include accounting and auditing.




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