Keywords: Accounting Software, Analytic Network Process, Accounting Information Systems. 1. INTRODUCTION. Business world, today, is facing a rapid change ...
Vol. 5, No. 5 May 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences ©2009-2014 CIS Journal. All rights reserved. http://www.cisjournal.org
Application of ANP in Evaluating Accounting Softwares based on Accounting Information Systems Characteristics 1
Morteza Ramazani, 2 Reza Askari, 3 Ebrahim Fazli
1
Management and Accounting Department, Zanjan Branch, Islamic Azad University, Zanjan, Iran 2 Financial Management Department, Tehran University, Tehran, Iran 3 3 Department of Information Technology Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran
ABSTRACT In today’s commercial world, accounting softwares are of great importance for purposes of collecting, classifying and yielding financial reports; thus, it is an essential necessity for every firm to select appropriate and highly efficient software in order to reach their organizational goals. This study applies Analytic Network Process (ANP) method for classifying the features of accounting softwares based on characteristics of accounting information systems (AIS). The results prove the great importance of compatibility, training and integrity variables from users’ point of view in paired comparison. Keywords: Accounting Software, Analytic Network Process, Accounting Information Systems.
1. INTRODUCTION Business world, today, is facing a rapid change; among the most significant reasons are: globalization, increasing growth of IT, more investments on different IT grounds and also its combination with research and development costs (Frishamar, 2002). One of the growth grounds of IT is to develop applied softwares in different fields like corporation organizations. Organizational services are increasingly relying on softwares. In many cases, organizational success depends on the success of the softwares being applied by the organization for doing everyday tasks. As a profession in business environments, accounting has a wide interrelationship with IT; as a result, it is largely dependent on applied softwares of the field. Firms and organizations have grown up, they bear a relatively higher load of financial and accounting data; thus, traditional systems are not responsive anymore and corporations have to apply state-of-the-art IT tools and accounting softwares so that they can survive, save their position, and create competitive advantage in the current competitive atmosphere. Accounting softwares, being a focal financial database, record, classify, summarize the financial activities of firms and finally gives out a financial report to be decided upon. Accountants, as the users of these softwares, wish to do their job by a software having high consistency with AIS characteristics. Compatibility, flexibility and integrity consist the most significant characteristics of accounting information systems. In order to enhance the evaluation of accounting softwares, features such as general characteristics, training and report capability can also be used. This study has been carried out on the base of a previous research by Ramazani (2012), on the examination of the existing gap between actual and ideal conditions of accounting softwares. Below one can see six characteristics posed by him: a.
b.
General characteristics: including features related to guarantee, popularity, after-sale services, price, user-friendliness, etc. Compatibility: is related to compatibility of the software with obligatory and optional necessities
c. d.
e. f.
of explicit and implicit requirements, efficiency of the software, proper database, etc (Pressman, 1987). Flexibility: the ability of the software to adapt itself to upcoming changes of the firm. Security: the ability of the software in protecting the data against hackers, backup and items related to control and protection. Training: high capability of Help section of the software in guiding the user for the best use. Reporting capability: the ability of the software in giving out comparative reports with clearer graphics and desirable for users.
2. RESEARCH OBJECTIVES This study aims at examining the interdependency of accounting software’s characteristics by applying ANP method, in order to classify the characteristics based on the existing interdependency for the purpose of users’ purchase decision and the software manufacturers.
3. RESEARCH METHODOLOGY The study is of survey type and applied in purpose. Questionnaires are used to collect the primary data (paired comparison) and articles, books etc. for collecting secondary data. The statistical population consists of 200 Iranian users of the software. ANP method is used to analyze the data. Because of the solidarity among the users about the interdependency of characteristics, Super Decision Software was finally chosen to analyze ANP.
4. RESEARCH MODEL In this network model, six criteria are defined for selecting the accounting software: general characteristics, compatibility, flexibility, control (integrity), reporting capability, and training, all of which are interdependent. We are going to examine and classify the significance level of the criteria, according to the availability of interdependency among software’s criteria.
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Vol. 5, No. 5 May 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences ©2009-2014 CIS Journal. All rights reserved. http://www.cisjournal.org
General Features Compatibility Flexibility Accounting Software Select
Control (Integrity) Reporting Capability Training
Fig 1: Research Model 4.1 Analytic Network Process Due to shortcomings of Analytic Hierarchy Process (AHP) and incapacity of this approach in considering the interdependency between criteria and items, professor Sa’ati(1996) developed another approach known as ANP. The advantage of ANP over AHP is that it takes the dependency of the criteria into account (Yüksel, 2007).AHP organizes the components of a system in the form of a hierarchy, so that every component can be dependent on the component at the higher level and this dependency continues to the highest component in a linear mode. In other words, the dependency must occur in a linear mode in an ascending or descending order. If the dependency is mutual, i.e. weight of criteria is dependent on weight of options and weight of options is dependent on weight of criteria, the situation is not of AHP mode anymore and a “network” or
a nonlinear system or a feedback system will be formed (Kurttila, 2000). In the new system, rules and formulas of hierarchy cannot be used to calculate the weight of components, but Network Theory must be used (Saati, 1986)
5. ANALYSIS AND RESULTS At the first step, the interdependency of variables was defined according to the following table. As it is evident, the researcher assumes an interdependency among variables. To prove the existence of dependency among variables, the questionnaire data should be analyzed.
Table 1: Determining the interdependency among variables Criteria General Features Compatibility Flexibility Control Reporting Capability Training
General Features * * * * *
Compatibility * * * * *
At the second step, the scores are inserted in Super Decision Software, according to the geometrical average of paired comparison questionnaires like this:
Flexibility * * * * *
Control * * * * *
Reporting Capability * * * *
Training * * * * *
*
Paired comparison matrix of the research variables (unweighted super matrix) of the above scores are also presented in the following table:
359
Vol. 5, No. 5 May 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences ©2009-2014 CIS Journal. All rights reserved. http://www.cisjournal.org
Fig 2 Table 2: Paired comparison matrix of the research variables
Compatibility
1.00
6.00
1.00
General Features 0.25
Control
0.17
1.00
7.00
0.50
0.50
0.14
Flexibility
1.00
0.14
1.00
0.50
0.00
0.00
General Features Reporting Capability Training
4.00
2.00
2.00
1.00
0.00
0.00
0.33
2.00
0.00
0.00
1.00
0.00
5.00
7.00
0.00
0.00
0.00
1.00
Characteristics
Compatibility
Control
Flexibility
In the following chart, the results of paired comparisons with inconsistent coefficient of 0.2422 are described. As can be seen, judgment of the users suggest
Reporting Capability 3.00
Training 0.20
that training is of a great importance, but the ultimate standpoint about the importance is stated by final axis of the network.
Compatibility 1
0.8 Training
0.6
Control
0.4 0.2 0
Reporting Capability
Flexibility
General Features
Fig 3
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Vol. 5, No. 5 May 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences ©2009-2014 CIS Journal. All rights reserved. http://www.cisjournal.org
At the third step, weighted super matrix is calculated to reach a general conclusion. The following
table shows a weighted super matrix.
Characteristics
Compatibility
Control
Flexibility
General Features
Reporting Capability
Training
Goal
Table 3: Weighted super matrix
Compatibility
0.15
0.33
0.00
0.00
0.00
0.00
0.17
Control
0.11
0.33
0.00
0.00
0.00
0.00
0.17
Flexibility
0.06
0.33
0.00
0.00
0.00
0.00
0.17
General Features
0.21
0.00
0.00
0.00
0.00
0.00
0.17
Reporting Capability
0.03
0.00
0.00
0.00
0.00
0.00
0.17
Training
0.44
0.00
0.00
0.00
0.00
0.00
0.17
Goal
0.00
0.00
0.00
0.00
0.00
0.00
0.00
As we represent the Limit Super Matrix below:
Compatibility
Control
Flexibility
General Features
Reporting Capability
Training
Goal
Table 4: Limit Super Matrix
0.24 0.21 0.19 0.11 0.04 0.22 0.00
0.24 0.21 0.19 0.11 0.04 0.22 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.24 0.21 0.19 0.11 0.04 0.22 0.00
Characteristics
Compatibility Control Flexibility General Features Reporting Capability Training Goal
Lastly, normalized Limit Super Matrix is calculated in order to get accidental/ probable mode. Table 5: Normalized axis matrix Variable
Normalized by Cluster
Limiting
1
0.237176
Control
0.877748
0.208181
Flexibility
0.79105
0.187618
General Features
0.461255
0.109399
Reporting Capability
0.158223
0.037527
Training
0.928005
0.22
Compatibility
Total
1
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Vol. 5, No. 5 May 2014
ISSN 2079-8407
Journal of Emerging Trends in Computing and Information Sciences ©2009-2014 CIS Journal. All rights reserved. http://www.cisjournal.org
Normals
Compatibility 0.3
Training
0.2
Control
0.1 0
Normals
Reporting
Flexibility
Capability
General Features Fig 4 According to the above diagram, compatibility (0.237), training(0.22), and control(0.208) possess, respectively, the highest importance and impact in priority of using softwares, based on users’ viewpoints.
[2]
Kurttila, M., Pesonen, M., Kangas, J., &Kajanus, M. (2000). Utilizing the analytic hierarchy process (AHP) in SWOT analysis—a hybrid method and its application to a forest-certification case. Forest Policy and Economics, 1(1), 41-52.
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Pressman, R. S., &Jawadekar, W. S. (1987). Software engineering. New York 1992.
[4]
Ramazani, M., Zanjani, M., &Vali, F. (2012). Accounting Software Expectation Gap Based on Features of Accounting Information Systems (AISs). Journal of Emerging Trends in Computing and Information Sciences, 3(11).
[5]
Saaty, T. L., & Takizawa, M. (1986). Dependence and independence: From linear hierarchies to nonlinear networks. European Journal of Operational Research, 26(2), 229-237.
[6]
Yüksel, İ., &Dagdeviren, M. (2007). Using the analytic network process (ANP) in a SWOT analysis–A case study for a textile firm. Information Sciences,177(16), 3364-3382.
6. RESEARCH FINDINGS The resulting quantitative analysis represents the significance of criteria from the accounting software users’ viewpoints that developers must take into account when promoting applied softwares. The suggested criteria are among nonoperational requirements of this type of software products and the customers might not point to them explicitly while they are in the phase of relating and extracting the product requirements. But developers must take into account these criteria in all phases of the project in order to attract the attention of the customers and promote the product quality, because quality is not something they can add after completion of the product.
REFERENCES [1]
Frishammar, J. (2002). Characteristics in information processing approaches. International Journal of Information Management, 22(2), 143156.
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