EVALUATE THE IMPACT OF CONTEMPORARY INFORMATION SYSTEMS AND THE INTERNET ON IMPROVING BANKING PERFORMANCE

VOLUME 2, 2011 EVALUATE THE IMPACT OF CONTEMPORARY INFORMATION SYSTEMS AND THE INTERNET ON IMPROVING BANKING PERFORMANCE Akram Jalal Karm, Allam M. Hamdan Ahla Unversty, Kngdom of Bahran Ths paper examnes the affects of nformaton technology (IT) on the Jordanan bankng ndustry for the perod of 2003 2007. The research examne the level of usng IT by 15 Jordanan Banks for a perod of fve years, then explore the mpresson on mprovng the performance of two forms of matrx. The frst s matrx of fnancal performance whch comprses Market Value Added (MVA), Return on Investment (ROI) and Earnng per Share (EPR) and the second s matrx of operatonal performance, whch ncludes the Net Proft Margn (NPM), Operatng Return on Assets (ORA) and Proftablty of Employee (PE). Utlzng IT by Jordanan banks wll be measured by testng the level of nvestment n Hardware, Software, Internet Bankng, Phone bankng, number of ATMs, use of Cyber branches and Bankng va SMS. The results of our measurements ndcated that there s an mpact on the use of MIS n Jordanan banks n the market value added (MVA), Earnngs Per Share (EPS), Return on Assets (ROA), Net Proft Margn (NMP). < M15 < Bankng < Informaton Systems < Internet In recent years, the utlzaton of nformaton technology has been magnfcently ncreased n servce ndustres, partcularly, the bankng ndustry, whch by usng Informaton Technology related products such as nternet bankng, electronc payments, securty nvestments, nformaton exchanges (Berger, 2003), fnancal organzatons can delver hgh qualty servces to clent wth less effort. Whtten et al. (2004, p.12.), stated that "nformaton s an arrangement of people, data, process, and nformaton technology that nteract to collect, process, store and provde as output the nformaton needed to support an organzaton," whch ndcates that nformaton system s an arrangements of groups, data, processes and technology that act together to accumulate, process, store and provde nformaton output needed to enhance and speed up the process of decson makng. Durng last decade, hgh percentage of fnancal organzatons are frequently utlze computers technology to facltate provdes servces; and that the speed of adopton s expected to grow further as the technology expands. The ntroducton of electronc ndcates of data accumulaton, accessng and manpulaton assstng the process of bankng decson makng. In a Bank's nformaton system, there are always potentals of crss whch make the bank endure an nsuffcency; thus, advanced nformaton system supported by a superor mechansm control s requred to make certan that an nformaton system has acheved the requred processes. Surprsngly, some lteratures defense the dea of Solow Paradox n concludng that Informaton Technology may essentally affect negatvely on banks effcency and may reduce productvty. Ths noton was noted by Solow (1987), "you can see the computer age everywhere these days, but n the productvty statstcs". The paradox has been defned by E. Turban, et al. (2008) as the "dscrepancy between measures of nvestment n nformaton technology and measures of output at the natonal level." Snce 1970s to the tme Solow was clamng that there was a huge decelerates n growth as the technologes were becomng ubqutous. In an artcle by Shu and Strassmann (2005), a survey was conducted on 12 banks n the US for the perod of 1989 1997. They notced that even though Informaton Technology has been one of the most essental dynamc factors relatng all efforts, t cannot mprove banks' earnngs. However, conversely, there are many lteratures approvng the postve mpacts of Informaton Technology expenses to busness value. Kozak (2005) nvestgates the nfluence of the evoluton n Informaton Technology on the proft and cost effectveness of the bankng zone durng the perod of 1992 2003. The study ndcates optmstc relatonshp among the executed Informaton Technology and together productvty and cost savngs. Brynjolfsson and Htt (2000) ndcates that "Informaton Technology contrbute sgnfcantly to frm level output." They determne that Informaton Technology captal contrbutes an 81% margnal ncrease n output, whereas non Informaton Technology captal contrbutes 6%. Lkewse they llustrate that Informaton System professonals are more than twce as productve as non Informaton System professonals. Ordnarly, recent lteratures showed that the relatonshp concernng Informaton technology and banks' performance have two encouragng outcomes. Frstly, Informaton technology can brng down the operatonal costs of the banks (the cost advantage). For nstance, nternet technology facltates and speeds up banks procedures to accomplsh standardzed and low value added transactons such as bll payments and balance nqures processes va onlne network. Consequently, ths technology wll helps banks concentratng ther captals on exceptonal, hgh value added transactons such as personal trust servces and nvestment bankng va branches. The second encouragng outcome s that Informaton Technology can promote transactons between customers wthn the same network (the network effect) (Farrell and Saloner, 1985; Economdes and Salop, 1992). In an artcle by Saloner and Shepard (1995), data for Unted States commercal banks for the perod 1971 1979 was conducted and llustrated that the nterest of network effect s sgnfcant n utlzng an Automated Teller Machnes 33

Evaluate the Impact of Contemporary Informaton Systems and the Internet on Improvng Bankng Performance (ATMs). Mlne 2006 also encourages the noton of the above authors. The modernzaton of IT has set the stage for extraordnary mprove n bankng procedures throughout the world. For nstance the development of worldwde networks has consderably decreased the cost of global funds transfer. The task of Informaton Technology n the communty, restrcted and foregn sector banks s to evaluate and measure the observaton of the Bank Employees towards the Implementaton of Informaton Technology n the Banks also to assst the awareness and fulfllment of the clents wth the banks. Informaton Technology has been gvng clarfcaton to fnancal organzaton to take care of ther accountng and back offce requrements. It also facltates and speeds up the automaton of modern supply controls, such as Automated Teller Machnes, Net Bankng, Moble Bankng and the akn to. Snce organzatonal performance cannot be shaped only by IS applcatons, other factors such as busness strateges and organzatonal culture should also be taken nto consderaton whle measurng the mpact of IT on overall performance Ths study ams to explore the extent of utlzng Informaton Technology by Jordanan banks n ther bankng management and then search the mpact on mprovng the performance of the matrx, whch s dvded nto a matrx of fnancal performance, whch ncludes the Market Value Added (MVA), Return On Investment (ROI) and Earnng Per Share (EPR), and a matrx of operatonal performance, whch ncludes the Net Proft Margn (NPM), Operatng Return on Assets (ORA) and Proftablty of Employee (PE). The extent of utlzng banks of nformaton technology wll be measured by the volume of nvestment n equpment, and the volume of nvestment n the software and the use of Internet Bankng, Phone Internet, the number of ATM, the use of Cyber Branch and Bankng va SMS. The collecton of data on a sample study of Jordanan banks n all 15 of the Banks over a perod of fve years from 2003 2007 wll be collected. Snce the study sample s a group of banks whch s the nature of scan data va a set of past data, approprate regresson model to measure ths relatonshp s the Pooled Data Regresson. Ths paper s structured as follow; n the next secton (II. Methodology) descrbes the methodology used to reach the best soluton for the research under the study, followed by testng hypothess and result analyss of the current challenge n secton (III). Fnally the concluson reached from ths research paper wll be dscussed n secton (IV). Determnng the methodology of the study s the most mportant stages of scentfc research due to ts great mpact on the valdty and accuracy of the results that are reached, havng been talkng about the problem of the study, objectves, and then dscuss the most mportant prevous studes that examned the same subject; In ths part of the study, we buld the methodology of the study, whch s characterzed n terms of tools, and method of measurement and testng to the problem of the study, takng advantage of methods and tools of prevous studes, n order to ensure access to the methodology to obtan the most accurate and best results. Key varables wll be measured n an attempt to practce dentfyng dependent and ndependent varables and to explan how the ndependent varable affects the dependent varable. Research Model Fg (1) detaled structural varables of the ndependent and dependent varables and the control study, whch forms the model of ths study: Fgure 1. Detaled structure of varables The mathematcal model of the study s developed as follows: éperf. Matrx = ê ê êë Where: + + + Software + 1 PhBank + 4 SMS + 7 ATM + Sze + 8 Hardware + Deposts + Credt + l Perf. Matrx (the dependent varable): the bankng performance of the proposed matrx s subdvded nto fve fnancal ndcators. 5 1. Software: It s the net nvestment bank n the software durng the perod. It s one of the ndependent varables. 2. Hardware: It s the net nvestment bank n the computer hardware and equpment n the perod. It s one of the ndependent varables. 3. InBank: It s how the Internet used frequently by the Bank from a varable. It s dummy varable, so that f the bank apples ths property n the perod gve (1), otherwse gves (0). It s one of the ndependent varables. 4. PhBank: It s how the dea of Bank answers phone frequently used by the banks. It s a dummy varable, so that f the bank apples ths property n the perod gve (1), otherwse t gves (0). It s one of the ndependent varables. 5. ATM: The number of ATMs owned by the bank n the perod. It s one of the ndependent varables. 2 9 6 CyBranch 3 InBank 10 ù ú ú úû 34

Evaluate the Impact of Contemporary Informaton Systems and the Internet on Improvng Bankng Performance 6. CyBranch: Is the possblty of utlzng the bank branches electronc property n the perod. It s a dummy varable, so that f the bank apples ths property n the perod gve (1), otherwse (0). It s one of the ndependent varables. 7. SMS: It s how to use the bank to bank messagng feature. It s a dummy varable, so that f the bank apples ths property n the perod gves (1), otherwse (0). 8. Sze: It s referred to the sze of the bank, measured by total assets n the perod. It s a control varable. 9. Deposts: It s a rato of deposts to assets n the perod. It s a control varable. 10. Credt: Is the proporton of credt facltes to the assets n the perod. It s a control varable. However, ths model wll also dvde nto fve sub models concerned wth measurng the mpact of ndependent factors of each of the ndcators of performance matrx: 1. The frst Model s the Market Value Added (MVA) : 2. The second model s the Return on enqury (ROE): 3. The thrd model s the Earnng Per Share (EPR): 4. The fourth model s the Net Proft Margn (NPM): 5. The ffth model s the Operatng Return on Assets (ROA): The man Hypotheses There s no statstcally sgnfcant mpact on the use of MIS to mprove the matrx performance n Jordanan banks. H01: There s no statstcally sgnfcant mpact on the use of MIS n Jordanan banks n the MVA. H02: There s no statstcally sgnfcant mpact on the use of MIS n Jordanan banks n the ROE. H03: There s no statstcally sgnfcant mpact on the use of MIS n Jordanan banks n the EPS H04: There s no statstcally sgnfcant mpact on the use of MIS n Jordanan banks n the NPM H05: There s no statstcally sgnfcant mpact on the use of MIS n Jordanan banks n the ROA Research Socety and Sample The study populaton conssts of all Jordanan banks lsted on the Amman Fnancal Market, whch s about (15) Banks, whch consttute the study sample, so that the study data 35 were collected for the perod from 2003 and untl 200 therefore t s a Cross Sectonal Data n nature (CSD) for a range of years. It s also a Tme Seres Data (TSD); therefore the data used for ths research s a Pooled Data. Ths Secton ncludes three man topcs, the frst s concernng n testng the relablty of statstcal analyss for the data by dentfyng how ths data s close to normal dstrbuton, f the data dd not have normal dstrbuton, then t should be a subject to necessary treatment so we could use t correctly to test the hypothess, snce the study ams to examne the effect of many ndependent varables on dependent varable. Whch means usng regresson model, therefore the relablty of the used model should be examned, by testng the study model to make sure that ths model s free from Multcollnearty between the same ndependent varables and also the Autocorrelaton whch wll appear n the model f the near observatons are connected wth each others, snce that would affect the relablty of the study model as that would create unspecfed effect to the ndependent varables over the dependent varable. The second topc s about descrptve statstcs of the study varables through varous descrptve statstcal measures, such as: Central tendency measures, Dsperson measures, Mean, Medan, Range and Standard Devaton; to descrbe the study varables. The thrd topc represents testng the study hypotheses, and computng the regresson model nformaton. Frst stage: test the valdty of data for statstcal analyss: Before data analyss and hypothess testng, we must frst dentfy the characterstcs of data to make sure of the appropraton of the model by makng the followng test: Normal Dstrbuton Test: In order to test the data f t`s subject to normal dstrbuton therefore t s requred to select the sutable statstcal procedures to test the hypotheses. In order to acheve that, the (Jarque Bera) test wthn the package of E vews program was used, the decson about f the data s subject to normal dstrbuton f the (J B) test s greater than 0.05 (Gujarat, 2003). And the proxmty of the testng data from the normal dstrbuton for all varables related to the study (MVA, ROI, EPS, NPM, ROA, Software, Hardware), the remanng varables, are (Dummy Varables) they are not subject to the normal dstrbuton. Table 1. Tests for normal dstrbuton for the study varables Varables Jarque Bera Test From prevous table (1) whch s related to the normal dstrbuton to the study varables, we fnd from Jarque Bera Test that the statstcal value s hgh and sgnfcant value s J B MVA 2669,9 0 ROI 24,1 0 EPS 5489,7 0 NPM 1742,2 0 ROA 171,3 0 Software 437,1 0 Hardware 74,1 0 ATM's 16 0 Sze 447,3 0 Deposts to Assets 212,9 0 Sg Credt Facltes to Assets 3,4 0,183

Evaluate the Impact of Contemporary Informaton Systems and the Internet on Improvng Bankng Performance Table 2. Correlaton matrx between the ndependent varables Software 1 Hardware 0,821 1 Internet Bank 0,366 0,371 1 Phone Bank 0,355 0,504 0,375 1 ATM's 0,647 0,907 0,281 0,473 1 Cyber Branch 0,104 0,165 0,343 0,317 0,192 1 SMS Bank 0,534 0,497 0,655 0,377 0,38 0,112 1 Sze 0,816 0,798 0,426 0,476 0,71 0,19 0,515 1 Deposts to Assets 0,207 0,29 0,238 0,126 0,393 0,058 0,218 0,388 1 Credt to Assets 0,171 0,028 0,184 0,158 0,03 0,144 0,146 0,103 0,489 1 less than 5% for all ndependent varables whch means that t's not close from ts normal dstrbuton, Except (credt facltes to total Assets); so n order to avod ths problem we wll use the (Ln) for these varables so t wll be close from ts normal dstrbuton. Multcollnearty test: The strength General Lnear Model (G.L.M) s depends on the ndependence of each ndependent varable among each other and f ths condton s not found the varable wll lose ts ndependency and n ths case the varable won't be vald snce that would means there s a Multcollnearty problem, but the fnal test to fnd out the problem of the Multcollnearty s the measurement of (Collnearty Dagnostcs) to each varable of the ndependent varables,then to fnd Varance Inflaton Factor coeffcent (VIF). Correlaton matrx between ndependent varables Table (2) shows the results of Pearson correlaton matrx between each par of ndependent varables of the sample, and we note from ths, ths ndcates that there s no Multcollnearty between these varables, the correlaton between them s very weak and have no sgnfcant statstcally assocaton relatonshp and ts value do not exceed (0.60) whch means that the study model s effectve n explan and determne the effect on the dependent varable. Except n the relatonshp between: (Software, Hardware), (Hardware, ATM's) and (Software, ATM's), (Software, Sze), (Sze, Hardware) (Internet Bank, SMS Bank), (Sze, ATM's) as the value of the relatonshp between them s more than (0.60). Table 3. Varance Inflaton Factor test Tolerance VIF Software 0,167 5,983 Hardware 0,086 11,629 Internet Bank 0,495 2,018 Phone Bank 0,594 1,685 ATM's 0,129 7,77 Cyber Branch 0,628 1,593 SMS Bank 0,475 2,107 Sze 0,218 4,585 Deposts to Assets 0,398 2,511 Credt Facltes to Assets 0,498 2,01 Varance Inflaton Factor test (VIF) To test the prevous result, we wll use (Collnearty Dagnostcs) test to support the credblty of the results, as we wll test that there s no problem of Varance Inflaton Factor test between varables, f the value of VIF s less than (10), (Gujarat, 2003). From table No. (3) We note that the factor (VIF) (Varance Inflaton Factor) of all ndependent varables s under (10), except (Hardware) as the value of (VIF) s more than (10); so n order to avod ths problem we wll delete ths varable. The results shows that VIF, whch support the prevous correlaton matrx whch shows that there s weak correlaton between the explanatory varables and the ndependent varables; therefore we consder that there s no effect of Varance Inflaton Factor problem on the relablty of the study model. Autocorrelaton Test: To examne the exstence of ths problem n the models we use some tests as (Durbn Watson Test), ths test consdered one of the most frequently used methods of economcs, and by testng the Autocorrelaton problem of n the model t reveals the results shows n the followng table (4). Table 4: Autocorrelaton Test 1 1,46 2 1,29 3 1,06 4 0,969 5 1,426 From the above table we note that the D W for model No. 1 and 5, located between maxmum and mnmum of the scheduled lmts. Also t`s near to (2) (Basher, 2003) whch means that there s no Autocorrelaton problem n the study model. The rest of the models do not, so n order to avod ths problem we used (Lag. 1). Heteroskedastcty Test. Mnmum (DL) = 1.369 Maxmum (DU) = 1.901 Number of observaton (N) = 75 Number of varables (K) = 9 We found Heteroskedastcty problem n models, In order to test the Heteroskedastcty, and avod ths problem we used to (Whte test) wthn the package of E vews. 36

Evaluate the Impact of Contemporary Informaton Systems and the Internet on Improvng Bankng Performance Second Stage, Descrptve Statstcs. Dependent Varable. Table (5) reports descrptve statstcs for Performance Matrx of the Jordanan banks. Table 5. Performance Matrx of the Jordanan banks. 2003 30 8,373 1,051 2,195 0,901 2004 106 13,255 0,773 4,323 1,585 2005 372 19,461 0,481 3,356 2,805 2006 119 13,552 0,289 2,121 2,02 2007 231 11,638 0,281 3,592 1,853 2003 60 6,602 3,491 2,302 1,02 2004 199 5,686 2,057 7,433 0,841 2005 839 6,951 0,283 2,744 1,825 2006 295 3,154 0,159 3,146 1,111 2007 614 4,627 0,234 3,865 1,441 2003 228 20,37 13,66 7,42 2,63 2004 806 22,38 8,19 30,41 3,44 2005 3 39,84 1,14 12,37 8,1 2006 1 20,85 0,74 6,79 5,74 2007 2 20,46 0,94 17,3 6,74 2003 42 2,24 0,03 0,01 1,01 2004 8 5,68 0,08 0 0,3 2005 13 10,51 0,09 0 0,99 2006 3 8,51 0,12 7,75 1,06 2007 1 5,5 0,07 1,51 0,55 Table 6. Report descrptve statstcs for the ndependent varables Table number 5 showed that the economc value added of Jordanan banks reached the hghest value n the years 2005 and 2007 whle the lowest value was n the year 2003. The year 2005 was the most devaton n the data whch reachng the largest value (3,380,000,000) among all years, and less value was 13,122,096. Return on equty (ROE) was marked by the year 2005 the hghest revenue between the years of the sample, as t reaches the hghest return 39,840% but the standard devaton was great. The year 2003 marked the lowest return on equty, amountng to ( 2,240%). Ether wth regard to revenue per share (EPS) was characterzed n years 2004 and 2005 as bggest earnngs per share durng the study perod for all companes, whle the hghest value was n the year 2003 as well as the lowest value, ndcatng a hgh dsperson and hgh standard devaton of ths year. As well as the year 2004, s the largest n terms of average Net Proft Margn (NPM). It s the same year whch has the largest value and the hghest standard devaton, whle the least (NPM) of the share was n year 2006. Return on assets (ROA) was marked by the year 2005 the hghest return on assets, and the largest value of 6,740% of the share of the same year, whle the lowest value ( 1,010%) was n the year of 2003. From Table number (6), whch llustrates the use of Jordanan banks for Informaton Technology, we notes that the average cost of software n Jordanan banks reached the hghest average n the year 2007 and ths year was the largest n the standard devaton, as well as the year that contans the largest cost for nvestment, whch refers to the rse n nvestment software by Jordanan banks. It s also noted that nvestment n software runs n parallel wth nvestment n equpment and computers; for ths reason a sgnfcant correlaton has been appeared between them, 2003 2,298,528 6,385,204 46 2003 6,224,576 10,009,979 46 2004 2,458,426 6,667,136 47 2004 6,545,368 10,177,119 47 2005 2,734,913 7,478,274 48 2005 6,861,340 10,663,573 47 2006 3,098,384 8,457,107 49 2006 7,185,433 11,469,042 48 2007 3,511,784 9,151,003 51 2007 7,561,902 12,514,660 48 2003 24,646,833 35,209,761 160 2003 26,538 170,945 0 2004 25,944,035 37,062,907 160 2004 48,252 221,067 0 2005 27,309,510 39,013,586 163 2005 87,73 232,702 0 2006 28,746,853 41,066,932 164 2006 104,911 228,338 0 2007 30,259,845 43,228,350 164 2007 110,433 518,794 0 Table 7. Report descrptve statstcs for the control varables 2003 1 72,188 39,258 2003 3 21,773 11,863 2004 1 70,725 40,907 2004 4 21,35 11,174 2005 2 69,487 44,609 2005 4 20,755 10,923 2006 2 68,493 44,053 2006 4 20,469 15,875 2007 2 68,428 49,009 2007 5 20,184 10,45 2003 15 90,36 58,89 2003 66 0,26 19,43 2004 16 90,79 62,88 2004 101 0,35 23,43 2005 16 89,65 65,86 2005 115 0,26 26,41 2006 18 86,66 66,58 2006 137 0,23 0,28 2007 21 85,44 71,34 2007 130 0,19 37,13 Source of tables: Authors 37

Evaluate the Impact of Contemporary Informaton Systems and the Internet on Improvng Bankng Performance thus creatng the problem of Multcollnearty and ths was to delete ths varable from the form to solve ths problem. Regardng the number of ATMs owned by the Jordanan banks, we notce that they are on the ncrease from year to year and the average n the last year of the study was 51 ATMs. However, the largest number of ATMs was 164 for the Arab Bank, and notes that some banks do not have ATM, such as Industral Development Bank. From the prevous table whch llustrate the control varables that was entered, we note that the frst varable (the sze of the bank), whch was measured by total assets, one of the factors that have proven ther mpact on the Bank's performance n prevous studes, we note that the average sze of Jordanan banks n the rse from year to other. The second varable whch s the rato of deposts to assets s n the swng from year to year and n general t refers to a declne n the rato of deposts to assets n recent years. The net assets of the facltes are on the rse from year to year and reached the hghest rate n the last year 2007. Thrd Stage: Test of Hypotheses: Snce the study sample s a group of banks whch were cross sectonal n nature (CSD) across a range of years (2003 to 2007) and a tme seres data (TSD), then the regresson model approprate to measure ths relatonshp s the jont regresson (Pooled Data Regresson) usng Pooled Least Square Manner. The table (8) shows the tests of the fve study models. Table 8. Test of study models Frst (MVA) 0,541 8,834 0 Second (ROE) 0,061 0,473 0,87 Thrd (EPS) 0,422 5,289 0 Fourth (NPM) 0,641 10,09 0 Ffth (ROA) 0,291 3,714 0,04 F n (df for denomnator N β 1 = 75 9 1 = 65) and (df or nnumerator = β = 9) = 2.04 Measurng the frst model As t showed n table (8), t can test the frst hypothess of whch llustrate the effect of the use of MIS n Jordanan banks n the market value, where the value of R 2 s 54%, whch ndcates range of the change n MVA, whch explaned the change n the ndependent varables, whch s acceptable and ndcates the strength model. When testng the frst hypothess that can be expressed mathematcally as follows: Null hypothess H0: β = 0 aganst the alternatve hypothess Ha: β 0 and when to test ths hypothess found that the value F s 8,834, whch s the largest of ts value at the scheduled value 5 % (2.04) and ts tolerated (Sg.) s less than 5 % whch means we reject the Null hypothess and accept the alternatve hypothess as there s an mpact on the use of MIS n Jordanan banks n the market value added (MVA). Measurng the second model And when we tested the second model, whch examnes the mpact of the use of MIS n Jordanan banks n Return on Equty (ROE), t was found that R 2 = 6% whch s very low, whch means the weakness of ths model, and test the Null hypothess H0: β = 0 aganst the alternatve hypothess Ha: β 0 found that the value of F s 0.473 whch s less than the scheduled value at 5 % (2.04) and tolerated (Sg. = 0.870) s greater than 5 %, whch means acceptance of the Null hypothess and rejecton of the alternatve hypothess 38 where the alternatve does not affect the use of MIS n Jordanan banks to mprove the return on equty ROE. Ths s due to the ncreased costs of nvestment n nformaton technology whch mght work to reduce the return on the property. Measurng the thrd model The thrd model, whch examnes the mpact of the use of MIS n Jordanan banks n the earnngs per share (EPS), was found to be R 2 = 42% whch s good result and ndcates the strength model, and by testng the Null hypothess H0: β = 0 aganst the alternatve hypothess Ha: β 0 found that the value F 5,289 whch s the largest of ts scheduled value and ts tolerated s less than 5 % whch t can be sad that the use of MIS n Jordanan banks affect the Earnngs Per Share (EPS). Measurng the fourth model Agan, Table No. (8) can test the fourth model, whch examnes the mpact of the use of MIS n Jordanan banks n the Net Proft Margn (NPM), where the value of R 2 s 64% whch s acceptable and ndcates the strength of the model, and when testng the fourth hypothess that can be expressed mathematcally as follows: Null hypothess H0: β = 0 aganst the alternatve hypothess Ha: β 0, and when to test ths hypothess found that the value of F s 10,090, the largest of ts value at the scheduled 5 % (2.04) and ts tolerated (Sg.) s less than 5 % whch means reject the Null hypothess and accept the alternatve hypothess whch means there s an mpact on the use of MIS n Jordanan banks n the Net Proft Margn (NMP). Measurng the ffth model But by testng the ffth model, whch works to study the mpact of the use of MIS n Jordanan banks n Return on Assets ROA, t found that the value of R 2 s 29 %, and when you test the Null hypothess H0: β = 0 found that the value of F s 3.714, whch means the largest of ts value as scheduled and tolerable (Sg. = 0.040) less than 5 %, thus t can be sad that the use of MIS n Jordanan banks affect the Return on Assets (ROA). The bankng ndustry has changed ncessantly; the role of nformaton technology applcatons n the World Banks s examned, together wth the economc return on such nvestments and ways to enhance ther development mpact. Results ndcate that nformaton technology s now a very dynamc busness area for the Bank. It s qute apparent from our study that enhancng technology n bankng ndustry s a must n a rapdly changng market place, as the IT revoluton has set the stage for exceptonal ncrease n fnancal actvty across the globe. Ths paper s nvolved wth the nfluence of nformaton technology on the Jordanan bankng, as banks are the exhaustve consumers of IT. The research used a Pooled Data Regresson usng Pooled Least Square Manner to measure the level of nvestment n ITs on mprovng the matrx of fnancal and operatonal performances. The results of our measurements usng test of hypothess showed that there s an mpact on the use of MIS n Jordanan banks n the market value added (MVA), Earnngs Per Share (EPS), Return on Assets (ROA), Net Proft Margn (NMP). However, the test of hypothess also showed that there no mpact of the use of MIS n Jordanan banks to mprove the Return On Equty ROE. Ths s probably due to the ncreased costs of nvestment n nformaton technology whch mght work to reduce the return on the property.

Evaluate the Impact of Contemporary Informaton Systems and the Internet on Improvng Bankng Performance Berger A. N. 2003. The economc effects of technologcal progress: evdence from the bankng ndustry. Journal of Money, Credt, Bankng, 35 (2): 141 176. Brynjolfsson E. and Htt L.M. 2000. Beyond computaton: nformaton technology, organzatonal transformaton and busness performance. Journal of Economc Perspectves, 14(4): 23 48. E. Turban, et al., Informaton Technology for Management: Transformng Organzatons n the Dgtal Economy, 6th edton (John Wley & Sons, 2008), ISBN 9780471787129. Economdes N. and Salop S. 1992. Competton and ntegraton among complements, and network market structure. The Journal of Industral Economcs, XL (1): 105 123. Farrell J. and Saloner G. 1985. Standardzaton, compatblty and nnovaton. RAND Journal of Economcs, 16 (1): 70 83. Kozak S. 2005. The role of nformaton technology n the proft and cost effcency mprovements of the bankng sector. Journal of Academy of Busness and Economcs, February 2005. Mlne A. 2006. What s n t for us? Network effects and bank payment nnovaton. Journal of Bankng & Fnance, 30 (6): 1613 1630. Saloner G. and Shepard S. 1995. Adopton of technologes wth network effects: an emprcal examnaton of the adopton of automated teller machnes. RAND Journal of Economcs, 26(3): 479 501. Solow R. 1987. Wed better watch out. New York Tmes Book Revew, July 12. Shu W. and Strassmann P.A. 2005. Does nformaton technology provde banks wth proft?. Informaton & Management, 42: 781 787. Whtten J. L., Bentley L.D. and Dttman K.C. 2004. System Analyss and Desgn Methods, eds ke 6 Mc.Graw Hllk, New York. 39