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2006bai6085.doc

  1. 1. MORE INFORMATION TECHNOLOGY INVESTMENTS, MORE PERFORMANCE IN BANKING? Chu-Fen Li Department of Finance, National Formosa University, 64, Wun-Hua Road, Huwei, Yunlin 632, Taiwan ROC cfli@nfu.edu.tw ABSTRACT This study investigates the relationship between information technology (IT) and operational performance based on a sample of Taiwan’s banks. Using data envelopment analysis (DEA) and stochastic frontier approach (SFA), this study finds some interesting points. First, low operating efficiencies exist in the banking Industry during the study period. These inefficiencies are in nature ascribable to a combination of both wasteful overuse of IT resources and inappro- priate scale of IT investments. Second, operating efficiencies measured by DEA and SFA present a significant strong relationship. Third, for an inefficient indi- vidual bank, the performance can be enhanced if the amount of IT investment is enlarged. Fourth, the difference of the ownership type has a significant effect upon bank performance with respect to the contribution of IT investment to op- erating performance. Fifth, as a whole, both number of ATMs and diversifica- tion of IT services contribute positively to bank performance. Sixth, the opera- tional efficiency of the mutually-owned banks is positively affected by number of ATMs and diversification of IT services while negatively affected by number of IT staff. Finally, number of ATMs has a positive impact on the performance of the privately-owned banks. Keywords: Banking, Data Envelopment Analysis (DEA), Information Technology (IT), Operating Efficiency, Performance, Stochastic Frontier Approach (SFA)
  2. 2. C.-F. Li / More Information Technology Investments, More Performance In Banking 1. INTRODUCTION Over the past few decades, modern business organizations have been in- creasingly investing substantial amounts of money in information technology (IT) with the objective of improving their operational efficiency and competitive ability in the industry. The important role IT plays in contemporary business is unquestionable. IT is regarded as a critical factor for business enterprises to sur- vive and to grow up further; however, empirical evidence to support these antici- pated benefits has been mixed. Some researchers asserted that the IT investments can really promote the enterprises' operational performance by means of reduc- ing costs, raising profit margin, upgrading production levels, increasing service quality, advancing customer satisfaction, and improving overall operations. In contrast, other researchers did not demonstrate the positive effect of IT invest- ment and concluded that IT spending brought no significant contributions to the enterprises’ operations, and so the “IT productivity paradox” has been an issue debated continuously for decades. The differences among research objects, methodologies, and performance indices result in inconsistent conclusions obtained in the literature. In this re- spect, the lack of good quantitative measures for the output and value created by IT has made the studies on justifying IT investments particularly difficult. Bryn- jolfsson (1993) proposed the following four explanations for IT productivity paradox: (1) mismeasurement of inputs and outputs; (2) lags between cost and benefit; (3) redistribution and dissipation of profits; (4) mismeasurement of in- formation and technology. Obviously, a new research method should be able to eliminate or overcome above-mentioned defects. To explore the effects of IT spending on organizational performance, this study provides a framework for performance evaluation using reliable quantita- tive tools, not only nonparametric data envelopment analysis (DEA) but also parametric stochastic frontier approach (SFA). The data set is based on a sample of Taiwan’s banks. The banking industry has been particularly information inten- sive. In history, banking has always been a crucial area for IT to be implement- ed. That is, an area where the advantage from using IT is so considerable that the state-of-the-art IT is developed almost as soon as it becomes available. A widely held belief is that IT is absolutely vital to a bank’s survival and growth. In this regard, it seems especially meaningful to link this issue with banking institu- tions. To sum up, the purpose of this study is to assess the impact of IT invest- ment on organizational performance that accomplishes the following five main objectives. First, we attempt to study the relevant theories and thereby to devel- op more complete models for performance evaluation of banks. Second, we mea- sure operational performance for each individual bank and analyze the main sources of operating inefficiency. Third, we investigate the impact of IT invest- ment on bank operational performance and measure its magnitude. Fourth, we compare and interpret the effects of IT investment on performance of different types of banks. Fifth, we contrast alternative approaches to the measurement of IT value. 2
  3. 3. C.-F. Li / More Information Technology Investments, More Performance In Banking The remainder of this paper is organized as follows. Section 2 reviews the previous empirical research at the firm level. Section 3 describes the analytical techniques applied——DEA and SFA, as well as the empirical data. Section 4 re- ports and discusses the empirical results from three aspects: (1) evaluation of op- erational efficiency; (2) comparison of efficiency differences between various types of banks; and (3) impact of IT investment on operational efficiencies. Sec- tion 5 sums up the main findings and presents the conclusions. 2. LITERATURE REVIEW Since the relevant empirical literature is very rare in the field of banking, this section also describes existing studies relating to other service organizations, manufacturing firms and hospital institutions. Cron and Sobol (1983) examined the relationship between computerization and several measures of overall firm performance based on a sample of 138 medical wholesalers. Using correlation analysis, the results showed that comput- erization was related to overall performance. Non-users tended to be small firms with about average overall performance. On the other hand, firms owning com- puters and making extensive use of them in a variety of ways tended to be either very high or low performers. Bender (1986) surveyed 132 life insurance companies in 1983 to investigate the financial impact of IT on firms in this industry. Organizational performance was measured in terms of the ratio of total operating expense to total premium income. The IT impact was represented by the ratio of information-processing expense to total general expense (IPE/EXP ratio). The results revealed that an appropriate level of investment in IT could have a positive impact on total ex- pense. A range in the IPE/EXP ratio of 15% to 25% seemed to produce optimum results in the life insurance industry. Contrarily, a company that had an IPE/EXP ratio of less than 15% was mostly likely not sufficiently automated to combat the escalation costs of doing business. Alpar and Kim (1990) utilized 424-759 U. S. banks during 1979-1986 to analyze the impact of IT on economic performance. Applying cost function ap- proach they found that IT was able to reduce operating costs, increase capital ex- penditures of banks, save personnel costs, reduce demand deposits, and increase time deposits. Strassman (1990) investigated the relationship between IT and return on in- vestment in a sample of 38 service sector firms using correlation analysis. He found that some top performers invested heavily in IT, while some did not. He concluded that there was no correlation between spending for computers, profits and productivity. Weill (1992) studied 33 medium and small-scaled valve manufacturing companies to explore the relationship between the IT investments and organiza- tional performance using hierarchical regression. Although transactional IT in- 3
  4. 4. C.-F. Li / More Information Technology Investments, More Performance In Banking vestment was found to be strongly related to superior organizational perfor- mance, there was no evidence that strategic IT investment, on a long-term basis, would increase or decrease organizational performance. However, the results im- plied that strategic IT investment was beneficial to relatively poor performing firms in the short run. Mahmood and Mann (1993) utilized canonical correlation analysis to ex- plore the organizational impact of IT investment of 100 U. S. listed companies. The results indicated that economic performance measures such as sales by em- ployee, return on sales, sales by total assets, return on investment, and market to book value were affected by IT investment measures such as IT budget as per- centage of revenue, percentage of IT budget spent on training of employees, number of PCs per employee, and IT value as a percentage of revenue. The orga- nizational performance measure growth in revenue and IT investment measure percentage of IT budget spent on staff were not significantly related to other measures and therefore were not indicated to be useful for investigating possible effects of IT investment on organizational economic performance. Loveman (1994) utilized OSL regression to assess the productivity impact of IT based on a sample of 60 manufacturing firms during 1978-1984. The re- sults showed that during the five-year period, the contribution of IT investment to the output of manufacturing firms was nearly zero. There existed no sufficient evidence to support the benefit of IT from productivity enhancement. Berndt and Morrison (1995) explored relationships between industry perfor- mance measures and investments in high-tech office and IT capital for two-digit manufacturing industries during 1968-1986. They found limited evidence of a positive relationship between profitability and the share of high-tech capital in the total physical capital stock (OF/K). They also found that increases in OF/K were negatively correlated with multi-factor productivity and tended to be labor- using. Furthermore, they found some evidence that industries with a higher pro- portion of high-tech capital had higher measures of economic performance, al- though within industries increasing OF/K did not appear to improve economic performance. Kivijarvi and Saarinen (1995) used a sample of 36 Finnish firms to probe the relationship between IT investments and of firm financial performance. Uti- lizing regression analysis, the results demonstrated that IT investments had no direct relationship with financial performance. However, IT investments were able to improve firm performance in the long term. Brynjolfsson and Hitt (1996) used 367 large firms during 1987-1991 as a sample to study the benefits of information systems (IS) spending. The results indicated that IS spending had made a substantial and statistically significant contribution to firm output. It was also found that the gross marginal product (MP) for computer capital was at least as large as the MP of other types of capi- tal investment and that IS labor spending generated at least as much output as spending on non-IS labor and expenses. Hitt and Brynjolfsson (1996) applied OLS regression and iterated seeming- 4
  5. 5. C.-F. Li / More Information Technology Investments, More Performance In Banking ly unrelated regression (ITSUR) to explore the business value of IT based on a sample of 370 large firms. The findings indicated that IT had increased produc- tivity and created substantial value for consumers. However, they did not find evidence that these benefits had resulted in supranormal business profitability. Mitra and Chaya (1996) used a sample of over 400 large and medium-sized U.S. corporations to analyze the performance impact of IT investment. They found that higher IT investments were associated with lower average production costs, lower average total costs, and higher average overhead costs. They also found that larger companies spent more on IT as a percentage of their revenues than smaller companies. However, they did not find any evidence that IT re- duced labor costs in organizations. Byrd and Marshall (1997) investigated the relationship between IT invest- ment and organizational performance using a sample of 350 public companies during 1989-1991. Applying correlation analysis, they found that the number of PCs and terminals as a percentage of employees was significantly and positively related to sales by employee. The value of supercomputers, mainframes, and minicomputers as well as the percentage of IT budget spent on IT staff were sig- nificantly and negatively associated with the sales by employee. The IT budget as a percentage of revenue was significantly and negatively associated with sales by total assets. The percentage of IT budget spent on IT staff training was not re- lated to any performance variable. Rai et al. (1997) employed Cobb-Douglas cost function approach to probe the relationship between IT investment and business performance based on a sample of 497 firms during 1994. The results suggested that IT investments could make a positive contribution to firm output and labor productivity. Howev- er, various measures of IT investment did not appear to have a positive relation- ship with administrative productivity. Furthermore, IT was likely to improve or- ganizational efficiency, its effect on administrative productivity and business performance might depend on such other factors as the quality of a firm’s man- agement processes and IT strategy links, which could vary significantly across organizations. Devaraj and Kohli (2000) examined monthly data collected from 8 hospitals over a recent three-year time period to study the relationship between IT and performance. Applying correlation analysis, the results provided support for the relationship between IT and performance that is observed after certain time lags. Such a relationship may not be evident in cross-sectional data analyses. Also, re- sults indicated support for the impact of technology contingent on business pro- cess reengineering practiced by hospitals. Lee and Menon (2000) used DEA and Cobb-Douglas cost function approach to analyze the financial data on the hospitals during 1976-1994. They found that hospitals that were characterized by high technical efficiency also used a greater amount of IT capital than firms that exhibited low technical efficiency and that a group of hospitals exhibiting high technical efficiency also exhibited low alloca- tive efficiency, indicating that, while processes might have been efficient, re- source allocation and budgeting between various categories of capital and labor 5
  6. 6. C.-F. Li / More Information Technology Investments, More Performance In Banking had not been efficient. Moreover, they found that IT labor had a negative contri- bution to productivity and that non-IT capital had a greater contribution to pro- ductivity than IT capital. Sircar, Turnbow and Bordoloi (2000) explored the relationship between firm performance and IT investments based on a sample of 624 firms. They used canonical correlation analyses as a research method and found that IT invest- ments had a strong positive relationship with sales, assets, and equity, but not with net income. Spending on IS staff and staff training was positively correlated with firm performance, even more so than computer capital. Shao and Lin (2001) investigated the relationship between IT investments and technical efficiency of 370 large U.S. firms during 1988-1992. Using both Cobb-Douglas and Translog cost functions and hypothesis test, the results indi- cated that IT had a significantly positive effect on technical efficiency and, hence, contributed to the productivity growth in organizations. Osei-Bryson and Ko (2004) employed the same data set used by Brynjolfs- son and Hitt (1996) to explore the relationship between IT investments and firm performance using regression splines analysis. The results exhibited that depend- ing on the conditions that applied, an unbiased observer could either conclude that investments in IT had a positive statistically significant effect on productivi- ty, or that there was a ‘productivity’ paradox. This suggested that the relationship between IT investments and organizational performance is much more complex than that found in some other studies. Ham, Kim and Joeng (2005) examined the effect of IT applications on per- formance based on a sample consisted of 13 five-star hotels and 8 four-star ho- tels in Korea. Using hypothesis test, the results supported the relationship be- tween IT usage and the performance of lodging operations. Furthermore, they found that front-office applications, restaurant and banquet management sys- tems, and guest-related interface applications significantly and positively affect- ed performance of lodging operations; however, guest related interface applica- tions were not significant. Andersen and Foss (2005) investigated the role and effects of information and communication technology in multinational enterprises. They suggested that the attendant cost–benefit tradeoff could be influenced by computer-mediated communication. Based on a sample of 88 organizations in the computer products industries, they found that multinationality in itself did not guarantee a higher level of strategic opportunity. Instead, use of information technology to facilitate communication among managers across functional and geographical boundaries enhanced coordination of multinational activities in the development of strategic opportunity, which in turn was associated with superior performance. 3. METHODOLOGY This paper attempts to investigate the impact of information technology on 6
  7. 7. C.-F. Li / More Information Technology Investments, More Performance In Banking operational performance of banking firms by mean of both DEA and SFA. This section introduces the methodology, where the research methods and the empiri- cal data are described, respectively, as follows. 3.1 The Research Methods In the past several years, the assessment of operational performance has re- ceived much attention in academia and in business circles due to increased com- petition in the market. While evaluating the performance of a decision making unit (DUM), it is indispensable to use reliable approaches. Up to the present, the three prevailing techniques developed for efficiency measurements in both the industrial and academic worlds are traditional financial ratio analysis, economet- ric approach and linear programming approach. The financial ratio analysis shows the relations between two financial figures after being compared, such as return on assets, return on investments, etc. It is based on financial statements and has been widely used not only for financial and production management but also for marketing, purchasing and personnel management throughout all sectors of business and commerce. Without a doubt, these ratios do convey some finan- cial information about firm performance. It is particularly meaningful when they are compared with those ratios of prior periods or of other firms. The popularity of financial ratio analysis lies perhaps in the simplicity and ease of calculation. However, a critical limitation is that financial ratio analysis fails to consider the multiple input–output characteristics of business enterprises and cannot give an overall clear picture of organizational operations, because firm performance may exhibit considerable variation depending on the indicator chosen. In the recent banking literature, much attention mostly directed to the latter two techniques of frontier efficiency analysis──econometric approach and linear programming ap- proach that are able to provide comprehensive insights beyond those available from financial ratio analysis for evaluating and improving banking efficiency. Since the seminal study by Farrell (1957), methodological development in frontier efficiency analysis has been continued at a rapid pace. To date, there are a multitude of techniques, parametric and nonparametric, stochastic and deter- ministic. The essential differences among these techniques primarily reflect dif- fering assumptions used in estimating the shape of frontier and the distributional assumptions imposed on the random error and inefficiency. There are at least five different types of approaches in the literature that have been employed in measuring the banking efficiency. Of those, three econo- metric approaches, such as stochastic frontier approach (SFA), distribution-free approach (DFA) and thick frontier approach (TFA) are parametric, and two lin- ear programming approaches are nonparametric, such as data envelopment anal- ysis (DEA) and free disposal hull (FDH). Each of the approaches necessarily has weaknesses as well as strengths relative to the other. The literature has not yet come to a consensus about the preferred approach for determining the best-prac- tice frontier against which relative efficiencies are measured. In general, parametric approaches are stochastic, and so attempt to distin- guish the effects of inefficiency from the effects of noise. A key drawback to parametric approaches is that they usually specify a particular functional form 7
  8. 8. C.-F. Li / More Information Technology Investments, More Performance In Banking that presupposes the shape of the frontier. If the functional form is misspecified, measured efficiency may be confounded with the specification errors. In sharp contrast to parametric approaches, nonparametric approaches are inherently bounding techniques, and so they impose less structure on the frontier. They are deterministic and do not allow for random error owing to luck, data problems or other measurement errors. If random errors do exist, measured efficiency may be confounded with these random deviations from the true efficiency frontier. So the former's limitations is exactly the latter's advantages and vice versa. Conse- quently, we employ both nonparametric DEA and parametric SFA as research methods at the same time. The DEA and SFA are among the most popular of frontier efficiency analysis. Thus we can not only analyze the impact of IT in- vestment on banking operational performance but also contrast the results of both approaches. DEA and SFA are described as follows. 3.1.1 Data Envelopment Analysis The data envelopment analysis (DEA) is basically a mathematical program- ming technique initially developed by Charnes, Cooper and Rhodes (1978) based on the basic concepts of relative efficiency and nonparametric frontier of Farrell (1957). DEA extends the notion of Farrell's productive efficiency from single- output case to multiple-output case. Unlike parametric frontier approaches, DEA does not require any assumptions about the functional form. The DEA frontier is formed as the piecewise linear combinations that connect a set of the best-prac- tice DMUs, which is obtained from the observed sample, yielding a convex pro- duction possibilities set. Thus a maximal efficiency measure for each DMU rela- tive to all other DMUs in the observed data set can be calculated only with the requirement that each DMU lies on or below the external frontier. The most important characteristics of the DEA methodology can be present- ed by the CCR model. For the discussions to follow, let us suppose that there are k DMUs to be evaluated. Each DMU j (j=1, 2,…, k ) consumes varying amounts of n inputs to produce m outputs and each has at least one positive input and one positive output. The primal input-oriented CCR model is formulated as follows: m ∑u r y ro max E o = r =1 n u, w ∑w i =1 i x io subject to m ∑u r y rj r =1 n ≤1 for j = 1, 2, L , k ∑w x i =1 i ij u r ≥ ε , r = 1, 2, L , m 8
  9. 9. C.-F. Li / More Information Technology Investments, More Performance In Banking w i ≥ ε , i = 1, 2,L , n where subscript ( o ) = the DMU being evaluated from the observed data; E 0 = the efficiency rating of the DMU being evaluated; Yrj = the observed amount of output r for DMU j, yr j ≥ 0 X ij = the observed amount of input i for DMU j, xi j ≥ 0 ur = the weight for output r; wi = the weight for input i; and ε= a non-Archimedean infinitesimal constant. 3.1.2 Stochastic Frontier Approach The stochastic frontier approach (SFA) specifies a functional form for the cost, profit, or production relationship among inputs, outputs and environmental factors, and allows for random error. It was first proposed by Aigner, Lovell and Schmidt (1977), Battese and Corra (1977) and Meeusen and van den Broeck (1977) simultaneously on three different continents. Its residuals contain two er- ror terms, one for inefficiency that is assumed to be either nonpositive or non- negative relying on its distributional assumption and another for noise or random error that is unrestricted to be positive or negative. The former represents factors that can be controlled by DMUs, while the latter represents those effects which cannot be controlled by the DMUs, including quality or measurement errors. The inefficiency term is supposed to follow a one-sided distribution, usually the asymmetric half-normal; whilst noise component is supposed to follow a sym- metric distribution, usually the standard normal. Let us consider that a specific DMU j (j=1, 2,…, k ) uses n inputs x = ( x1 , x2 ,L , xn ) ∈ R+ to produce scalar output y ∈ R+ , the production function n can be expressed as follows. n ln Y j = β 0 + ∑ β i ln X ij − u j + v j ; for j = 1, 2,L , k 1 i =1 where Yj = the observed amount of output for DMU j; X ij = the observed amount of input i (i =1, 2, …, n) for DMU j ; 1 If it is assumed that ui = 0 , the problem simplifies to one of OLS estimation of the parameters of a production function with no inefficiency; while if it is assumed that vi = 0 the problem simplifies to one of estimating the parameters of a deterministic production frontier with no noise. In the former case there is no efficiency measurement problem to worry about; while in the latter case there is no decomposition problem to worry about. 9
  10. 10. C.-F. Li / More Information Technology Investments, More Performance In Banking ui = the measure for technical inefficiency, ui ≥ 0 ; v i= a random error term indicating the usual statistical noise, v i ~ N (0, σ i ) . 2 3.2 The Data The research object of this study is all banks operating in Taiwan during 1996- 2000. The definition of a bank here adopts in a broader sense; it is re- ferred to as a financial institution able to create deposit currency, including gen- eral banks, and community financial institutions, such as credit cooperatives, credit departments of farmers' and fishermen's association. The empirical data comes from the following two sources. The first is primary data that mainly pro- vides information about IT investments and spending, which is taken from the results based on a questionnaire survey to current directors in charge of informa- tion unit or center of each bank. The second is secondary data that chiefly pro- vides financial information, which is taken from the financial statements of each bank announced annually. For general banks, the financial statements are collect- ed from the publication “Financial Statistics” by the Bureau of Monetary Affairs of Ministry of Finance, R.O.C. For community financial institutions, the finan- cial statements are collected from their annual reports. To improve survey re- sults, a pretest is carried out before the questionnaire survey, and the question- naire is revised accordingly. Altogether, 74 copies of questionnaires are distributed to the participants in various ways such as mail, email, Internet, facsimile transmission, etc. Of the 74 copies, 46 are sent to general banks, 22 to credit cooperatives and 6 to credit de- partments of farmers' and fishermen's association. The participants of the self- administered survey are the directors in charge of the information unit or center.2 Finally, 41 responses are received after follow-ups. Among them, 11 are invalid and 30 are effective. The effective response rate is up to 41%. Thus, the total sample for this study consists of all 30 individual banks, which can be catego- rized into three groups based on their business ownership form——5 publicly- owned banks (17%), 12 privately-owned banks (40%) and 13 mutually-owned banks (43%). According to the relevant theories and literature, six variables are used for this study, namely, number of IT staff, number of ATMs, number of PCs and ter- minals, number of financial cards issued, diversification of IT services and pre- tax income. Among those, the output is pre-tax income and the inputs are the other five variables. The value of each variable is defined as an annual average value during the study period. Since a strong correlation between input and out- put variables should be suggested in principle, this study conducts a Pearson cor- relation analysis and the result demonstrates a highly significant positive corre- lation between both input and output variables at the 1% significance level. The 2 We contacted nearly 90% of the directors by telephone in advance. If they were not willing to fill out the questionnaire or unable to offer us the relevant information in the questionnaire, we deleted their banks from our sampling population.. 10
  11. 11. C.-F. Li / More Information Technology Investments, More Performance In Banking Pearson correlation coefficients are between 0.749 and 0.862, implying that the input and output variables chosen are quite suitable. In addition, this study also examined collinearity among input variables. The results show that the Pearson correlation coefficient ranges between 0.168 and 0.437, suggesting that tolerance is sufficient and collinearity does not affect the predictive ability of a regression equation. Table 1 summarizes the arithmetic mean, standard deviation, minimum and maximum for each input and output variable mentioned earlier. Table 1 The Data Summary Mean S. D. Min. Max. IT staff 53.65 42.20 5.20 180.40 ATMs 117.48 140.27 12.00 483.80 PCs and terminals 955.17 1,107.54 54.20 4,251.20 Financial cards issued 383.23 533.93 17.40 2,186.68 Diversification of IT services 180.27 47.27 98.20 245.40 Pre-tax income 1,782.90 3,663.78 32.00 17,778.00 Note: Except that number of financial card issued is measured in thousands and pre-tax income in million NT dollars, the other variables are measured in units. 4. RESULTS AND DISCUSSION In the study, both DEA and SFA techniques are utilized at the same time to measure the operating efficiency based on a sample of Taiwan’s bank. The em- pirical results of this study are described and interpreted as follows, from three aspects: (1) evaluation of operational efficiency; (2) comparison of efficiency differences between various types of banks; and (3) impact of IT investment on operational efficiencies. Table 2 presents the results related to various efficiency indices for each bank and also provides some summary statistics in the form of arithmetic mean, standard deviation, minimum and maximum values. The first column in the table is bank code. The second and the third columns, DEATE and DEAPTE, indicate technical efficiency and pure technical efficiency derived from CCR and BCC models of DEA, respectively. The last column, SFATE, represents technical effi- ciency value using SFA model. Here, we evaluate the operational performance of each bank with technical efficiency, which measures the ability of a bank in uti- lizing IT resources to create profit. The fourth column, DEASE, represents scale efficiency, which is calculated as a ratio of DEATE to DEAPTE. DEASE is used 11
  12. 12. C.-F. Li / More Information Technology Investments, More Performance In Banking to examine whether a bank is operating at economies of scale. An efficiency val- ue less than unity implies that a bank is not on the frontier and thus operating in- efficiently, while that equal to unity implies quite the opposite. As shown in Ta- ble 2, low operating efficiencies do exist in banking during the study period. On average, the values of technical efficiency by DEA and SFA are 40.9% and 39.5%, respectively. The figures seem to be quite close to each other. The coeffi- cients of Pearson and Spearman’s rank correlations between DEA and SFA effi- ciency indices are 0.632 and 0.712, respectively. We further conduct a test of hy- potheses and find that the null hypothesis H 0: ρ=0 is rejected at the 0.1% signifi- cance level. The correlation results suggest that there is a significant strong posi- tive relationship between both DEA and SFA indices. Table 2 The DEA and SFA Efficiency Indices with a Statistical Summary DEATE DEAPTE DEASE SFATE B1 0.443 0.562 0.788 0.264 B2 1.000 1.000 1.000 0.697 B3 0.601 0.714 0.841 0.321 B4 0.723 0.725 0.998 0.572 B5 0.339 0.596 0.569 0.241 B6 0.562 0.570 0.986 0.445 B7 0.118 0.467 0.252 0.153 B8 0.353 0.487 0.724 0.901 B9 0.185 0.504 0.368 0.350 B10 0.407 0.502 0.810 0.999 B11 0.017 0.400 0.043 0.080 B12 0.155 0.482 0.322 0.228 B13 0.025 0.513 0.049 0.035 B14 0.674 0.678 0.994 0.769 B15 0.218 0.552 0.394 0.204 B16 0.211 0.485 0.435 0.187 B17 0.025 0.562 0.045 0.022 B18 0.913 1.000 0.913 0.636 B19 0.465 0.736 0.631 0.461 B20 0.413 1.000 0.413 0.247 B21 0.516 1.000 0.516 0.650 B22 0.649 0.808 0.804 0.843 B23 0.549 1.000 0.549 0.501 B24 0.448 0.773 0.579 0.193 B25 0.676 1.000 0.676 0.285 B26 0.444 0.851 0.522 0.282 12
  13. 13. C.-F. Li / More Information Technology Investments, More Performance In Banking B27 0.274 0.685 0.400 0.263 B28 0.403 0.745 0.540 0.253 B29 0.365 0.653 0.560 0.655 B30 0.093 0.559 0.167 0.100 Mean 0.409 0.687 0.563 0.395 S. D. 0.253 0.193 0.288 0.268 Min. 0.017 0.400 0.043 0.022 Max. 1.000 1.000 1.000 0.999 Note: The study also examines the returns to scale patterns for each bank by DEA and SFA, the results indicate that all banks are operating at IRS except that B2 exhibits CRS. Furthermore, we explore the sources of operating inefficiencies through the relationship among DEATE, DEAPTE and DEASE. For a technically inefficient bank, both of its DEAPTE and DEASE are found to be very low at the same time apart from a few exceptions. That is, these operating inefficiencies are in nature ascribable to a combination of both pure technical inefficiencies and scale ineffi- ciencies. The former arises from wasting IT resources, while the latter results from not operating at an optimal scale. To explore the causes of scale inefficien- cies, the study also examines the returns to scale patterns for each bank by both DEA and SFA. The results indicate that increasing returns to scale (IRS) is the exclusive cause of scale inefficiency. Specifically, all banks are operating at IRS except that the large bank B 2, which is operating efficiently, exhibits constant re- turns to scale (CRS). That is, for an inefficient bank, the performance is able to further enhanced if the amount of its IT investment can be enlarged. As mentioned previously, the sample banks are classified into three types, publicly-owned, privately-owned and mutually-owned, according to their busi- ness ownership form. Table 3 further compares the efficiency differences be- tween various types of banks. It can be seen in the table, except for pure techni- cal efficiency, publicly-owned banks have the highest efficiency values consis- tently, followed by mutually-owned banks; the privately-owned banks have the lowest efficiency values. The ANOVA results do not support that there are no differences between the average efficiency values of different types of banks ex- cept SFATE, indicating that the difference of the ownership type has a signifi- cant effect upon the performance of the bank with respect to the contribution of IT investment to operating performance. Moreover, the results of both Scheffe’s pairwise and Turkey’s HSD multiple comparison tests suggest that the publicly- owned banks are significantly superior to the privately-owned banks no matter in technical, pure technical or scale efficiency. On average the mutually-owned banks are higher than the privately-owned banks with respect to each efficiency index, however, this relationship significantly exists only for both technical and pure technical efficiencies by DEA. 13
  14. 14. C.-F. Li / More Information Technology Investments, More Performance In Banking Table 3 Efficiency Differences between Various Types of Banks with a Statistical Summary [1] Public [2] Private [3] Mutual Bank (N=5) (N=12) (N=13) Scheffe’s / F Type Tukey’s Mean S. D. Mean S. D. Mean S. D. DEATE 0.621 0.258 0.246 0.213 0.478 0.200 6.75*** [1>2] [3>2] DEAPTE 0.719 0.172 0.517 0.069 0.832 0.156 18.28*** [1>2] [3>2] DEASE 0.839 0.178 0.452 0.350 0.559 0.185 3.81** [1>2] SFATE 0.419 0.204 0.364 0.342 0.413 0.226 0.12 Note: ** significant at 5% level; *** significant at 1% level. Table 4 investigates the impact of IT investment on operating performance applying a Tobit regression model. In the regression model, technical efficiency of banks by DEA and SFA is the independent variable and all input variables are regarded as the dependent variables. In the first row of the table, the designation of regression models consists of the following two parts: (1) Alphabets A, B and C represent that the bank data are taken from all banks (N =30), privately-owned banks (N =12) and the mutually-owned banks (N =13), respectively 3; (2) Arabic numerals 1 and 2 indicate that DEA and SFA models are used to evaluate the in- dependent variable, i.e., technical efficiency of banks, respectively. In addition, this study also carries out residual analysis and demonstrates the regression does not suffer from problems of autocorrelation, heteroskedasticity and residual non- normality, implying that the regression assumptions are not violated. As exhibited in Table 4, only three variables, namely, number of ATMs, di- versification of IT services and number of IT staff are significantly related with operational performance of banks, while the other independent variables fail to attain statistical significance even at a 90% confidence level. Among the three variables, number of ATMs and diversification of IT services are positively asso- ciated with operational efficiency by DEA for all banks and also positively relat- ed with operational efficiency by SFA for the mutually-owned banks. Further- more, number of ATMs is positively correlated with operational efficiency by DEA for privately-owned banks; while number of IT staff is negatively related with operational efficiency by DEA only for the mutually-owned banks. The re- sults reveal that to improve operational efficiency, banks are suggested to install more ATMs and provide customers with convenient access to a wide variety of IT services. Besides, the mutually-owned banks also require a cutback in IT per- sonnel to increase operational performance. 3 The regression results of the publicly-owned banks are not listed in Table 4, since the type of banks is not suitable for the regression model even through the procedure of adjusting. 14
  15. 15. C.-F. Li / More Information Technology Investments, More Performance In Banking Table 4 The Impact of IT Investment on Operating Performance in Banking Model A1 A2 B1 B2 C1 C2 Constant term 0.8155*** 0.3378 -1.0650 -1.5373 0.7858 -0.2888 IT staff -0.0011 -0.0017 0.0057 0.0152 -0.0191* 0.0055 ATMs 0.0030** 0.0007 0.0041* 0.0003 0.0011 0.0246** PCs/terminals -0.0000 -0.0000 0.0001 -0.0001 0.0011 -0.0008 Financial cards -0.0004 -0.0000 -0.0005 0.0001 -0.0020 0.0032 issued Diversification 0.0030** 0.0005 0.0033 0.0052 0.0003 0.0097** of IT services N 30 30 12 12 13 13 F 2.980** 1.997 3.584* 1.669 4.548** 8.087*** R2 0.383 0.294 0.749 0.358 0.765 0.852 Adj. R 2 0.255 0.147 0.540 0.177 0.597 0.747 DW 2.399 2.333 1.817 2.134 1.719 2.298 Note: * significant at 10% level; ** significant at 5% level; *** significant at 1% level. 5. CONCLUSIONS The study has dealt with the impact of information technology (IT) invest- ment on the operational performance of banks. By comparison with the existing literature, this study is characterized by the combination of nonparametric DEA and parametric SFA techniques to measure operational performance. Our major findings and suggestions are outlined as follows. First, low operating efficiencies exist in the banking industry during the study period. On average, the technical efficiencies by DEA and SFA are 40.9% and 39.5%, respectively. According to the Pearson and Spearman’s rank correla- tion results, there is a significant strong positive relationship between both DEA and SFA indices. The bank management will be more confident of the evaluation results when adopting both approaches at the same time. Second, generally speaking, the operational inefficiency may be caused mainly by a combination of both pure technical inefficiency and scale inefficien- cy. Therefore, in order to enhance operational performance, bank management has to solve the problems of wasting IT resources and operating at an inappropri- ate scale. Since IRS is the dominant source of scale inefficiency, bank manage- ment is suggested to enlarge the amount of IT investment to improve perfor- mance. 15
  16. 16. C.-F. Li / More Information Technology Investments, More Performance In Banking Third, the difference of the ownership type has a significant effect upon bank performance with respect to the contribution of IT investment to operating performance. Moreover, the results of both Scheffe’s pairwise and Turkey’s HSD multiple comparison tests suggest that the publicly-owned banks are significant- ly superior to the privately-owned banks no matter in technical, pure technical or scale efficiency. A possible explanation is that the publicly-owned banks are rel- atively large-scaled in IT investments and applications; they are hence able to achieve greater economies of scale and scope, so their operational performance is detected to be the best. Fourth, the regression results indicate that both number of ATMs and diver- sification of IT services contribute positively to the performance of banks. Con- sequently, banks are suggested to install more ATMs and provide customers with convenient access to a wide variety of IT services to improve performance. Fifth, the regression results reveal that the operational efficiency of the mu- tually-owned banks is positively affected by number of ATMs and diversification of IT services while negatively affected by number of IT staff. To promote per- formance, the mutually-owned banks require installation of more ATMs and pro- vision of a wide variety of IT services and cutbacks in IT personnel at the same time. Finally, according to the regression results, number of ATMs has a positive impact on the performance of the privately-owned banks. Thus, the privately- owned banks are suggested to increase operating efficiency by installing more ATMs. ACKNOWLEDGEMENT The author is grateful to National Science Council of the Republic of China for its financial support under grant number NSC 90-2416- H-309-010. REFERENCES [1] Aigner, D. J., Lovell, C. A. K. and Schmidt, P. (1977), “Formulation and Es- timation of Stochastic Frontier Production Function Models,” Journal of Econometrics, 6 (1), July, 21-37. [2] Alpar, P. and Kim, M. A. (1990), “A Microeconomic Approach to the Mea- surement of Information Technology Value,” Journal of Management Infor- mation Systems, 7 (2), 55-69. [3] Andersen, T. J. and Foss, N. J. (2005), “Strategic Opportunity and Econom- ic Performance in Multinational Enterprises: The Role and Effects of Infor- mation and Communication Technology,” Journal of International Manage- ment, 11 (2), June, 293-310. 16
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