# Vol. 4, No. 3 Mar 2013 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.

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3 6. RESULTS The analysis is reported in three sections: description of analysis, participants, and results of analysis. 6.1 Description of Analysis We use t-tests to test our research questions and hypotheses. Since the data of the current study was not normally distributed and since responses were recorded on 5 point Likert scales, we use the Kruskal-Wallis oneway analysis of variance by ranks test (Siegel and Castellan 1998). This test was deemed to be appropriate for testing whether or not the mean responses are significantly different at the 5% confidence level. Table 1: Demographic Information Furthermore, we analyze the effects of the independent variables (compatibility, flexibility, Control) on firm size. The rejection of the null hypothesis indicates that there is a significant difference between one or more groups considered in the test. 6.2 Participants One 106 business professionals (with 1-20 years of business experience) participated in the study. Table 1 provides demographic information of the participants. The demographic information indicates that the respondents were qualified to provide the information requested in the questionnaire. Frequency Percent Frequency Details Male Female to to to to to to More than Accounting Economy Management Associate Diploma Bachelor Degree Mater Degree to to Up to 150 Variables Gender Age Class Business Experience Area of Specialization Education Level Firm Size Explanation 6.3 Results of analysis accepted and H 1 is rejected at error level of 0.05 (Table 2). Therefore, we can state that AIS is compatible with Test of Hypotheses firm activities at a reasonable degree. To test all hypotheses, the following Claim and Reject hypotheses have been offered: Test of hypothesis 2: According to value of statistic T (1.121), degree of freedom (105) and Sig= 0.257, more than 0.05 of H 0 is Test of hypothesis 1: According to value of statistic T (6.818), degree of freedom (105) and Sig= 0.00, less than 0.05 of H 0 is rejected and H 1 is accepted at error level of 0.05 (Table 2). Therefore, we can state that AIS and its tools do not have enough flexibility against future changes of the manufacturing firms. 292

4 Table 2: Main Analysis Test Value = 3 Hypotheses t Mean df Sig. (2-tailed) Mean Difference Result H1 (Compatibility) Accept H2 (Flexibility) Reject H3 (Control) Reject Test of hypothesis 3: According to value of statistic T (1.324), degree of freedom (105) and Sig= 0.188, more than 0.05 of H 0 is rejected and H 1 is accepted at error level of 0.05 (Table 2). Therefore, we can state that AISs do not have appropriate control programs. 6.4 Firm Size and Independent Variables To examine the impact of firm size on independent variables (compatibility, flexibility and control), Kruskal-Wallis test has been applied. Table 3 presents the number and ranking average of each independent variable divided by firm size. Table 4 shows the results of Kruskal-Wallis test. It can be concluded from Table 4 values that significance of variables flexibility is equal based on medium, large and mega size, but degree of compatibility and control different. Independent Variables Compatibility Flexibility Control Table 3 Firm Size N Mean Rank 10 to to Up to to to Up to to to Up to Table 4: Result of Kruskal-Wallis Test Result Independent Variables Compatibility Flexibility Control Chi-square Df P-Value Asymp. Sig. Difference is not Difference is Difference is not 6.5 Equality Testing of Independent Variables In order to prioritize and determine the significance level of each independent variable, Friedman Test has been applied. This test states that either among preventing factors, one factor is more significant of the rest or all are of the same significance. To test this, the researcher has used the following claim or reject hypotheses: H 0 : Independent variables are of the same significance of impact. H 1 : Independent variables are not of the same significance of impact. According to Table 5, P-Value=0.006, df=2 and Chi-Square= It can be concluded that H 0 is accepted and H 1 is rejected at error level of This means that independent variables are not of the same degree of impact. Priority order of independent variables are offered in Table 6. Table 5: Friedman Test Statistics N 106 Chi-Square Df 2 P-value

5 Table 6: Mean Ranking of Variable Independent Variable Compatibility Flexibility Control Mean Rank [6] Evans, J. S. (2007). Strategic flexibility for high technology manoeuvres: a conceptual framework. Journal of management studies, 28(1), [7] Genus, A. and K. Dickson, 1995, Technological Analysis and Strategic Management 7(3), FINDINGS OF THE RESEARCH The results yield the existence of compatibility between AISs and activities of manufacturing firms, as Ghaemi et al (2012) proved a positive relationship between AIS appropriateness for firm functions. Although Ramazani et al (2013) study showed an incompatibility between AISs and activities of production cycle. We can just reach an accurate and efficient evaluation if we examine the subject of AIS compatibility in different departments of a firm, like the study of Ramazani et al in production cycle activities. In flexibility and control programs domain, lack of enough flexibility of AISs and their tools against future changes of under-study firm and weakness of the plans refer to control and security. Kruskal-Wallis Test results presented a significant Flexibility, that show the equality of the views among firms of different sizes but in compatibility and control variable there is no significant difference. Friedman Test results proved the inequality of the significance of the three variables among which flexibility was of the highest significance to the manufacturing firms. REFERENCES [1] Abernethy, M. A., & Vagnoni, E. (2004). Power, organization design and managerial behavior. Accounting, Organizations and Society, 29(3), [2] Byrd, T. A., & Turner, D. E. (2000). Measuring the flexibility of information technology infrastructure: Exploratory analysis of a construct. Journal of Management Information Systems, 17(1), [3] Chapman, C. S., & Kihn, L. A. (2009). Information system integration, enabling control and performance. Accounting, Organizations and Society, 34(2), [4] Davis, G.B. and Olson, M.H. (1985). Management Information Systems: Conceptual Foundation:, Structure, and Development. New York: McGraw- Hill. [5] Eardley, A., Avison, D., & Powell, P. (1997). Strategic information systems: An analysis of development techniques which seek to incorporate strategic flexibility. Journal of Organizational Computing, 7(1), [8] Ghaemi, Mohammad Hosseinet al (2012) Adjusting AIS Capabilities with Information Demands and Impact on Companies Functions, Journal of Danesh-e Hesabrasi, No. 46, pp [9] Herbert, G., Ray Gullet., (2002), Management, 7th ED. McGraw-Hill, pp.582. [10] Hilton; R.W. Maher; M.W Selto; F.H. (2000). Cost Management: Strategies for Business decision, New Jersy, Mc-Fraw Hill. [11] Jan Eppink, D. (1978). Planning for strategic flexibility. Long Range Planning, 11(4), [12] Jensen, MC., (1983). Organization theory and methodology. Account Rev, (53), pp [13] Ramazani, M. (2012). Compatibility of Accounting Information Systems (AISS) with Activities in Production Cycle. Available at SSRN [14] Ramazani, M., Allahyari, A., Vali Moghaddam Zanjani, F., & Askari, R. (2013). Compatibility of Accounting Information Systems (AISs) with Activities in Production Cycle. Management Science Letters, 3(1). [15] 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). [16] Rezaeian, A. (2001), Management Information System (Information modalization), SAMT, Tehran. [17] Romney, M. B., Steinbart, P. J., & Cushing, B. E. (2000). Accounting information systems. Upper Saddle River, NJ: Prentice Hall. [18] Rushinek, A., & Rushinek, S. F. (1995). Accounting software evaluation: hardware, audit trails, backup, error recovery and security. Managerial Auditing Journal, 10(9), [19] Sajady, H., Dastgir, M., & Nejad, H. H. (2012). Evaluation Of The Effectiveness Of Accounting Information Systems. International Journal of Information Science and Management (IJISM), 6(2),

6 [20] Sajjadi Seyed Hosein, Tabatabaei nejad Seyed Mohsen (2006), Accounting Information Systems, Chamran University, Ahvaz, Iran. [21] Simons, R. (1987). Accounting control systems and business strategy: an empirical analysis. Accounting, Organizations and Society, 12(4), [22] Zimmerman, J. L. (2000). Accounting for decision making and control. Irwin/McGraw-Hill. 295

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