An Efficiency based approach to measure return on investment in Information technology S G S D Jayasekara
Abstract Evaluation of return on investment in information technology is a long discussed inconclusive topic because returns are very difficult to measure and inherently intangible. Therefore, conventional investment appraisal techniques are not suitable to evaluate investment in information technology. However, information technology may improve the overall performance of organizations as a result of streamlining of operations reducing overall costs and improving the quality. This paper investigates whether the investment in information technology improves the efficiency of financial institutions that have invested substantial amount of assets in information technology. Efficiency of major financial institutions in Sri Lanka was evaluated using Data Envelopment Analysis under the assumption of constant return to scale for five-year period from 2009 to 2013 and scores were compared with the investment in information technology as a proportion of total fixed assets. The results revealed that licensed commercial banks, which have invested higher proportion of fixed assets in information technology, were more efficient over the other financial institutions. Key words: Financial Institutions, Return on investment in information technology, Data Envelopment Analysis, Efficiency, Sri Lanka
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FCA, FCCA, ACMA, BBM, MSc, MBA Director- Finance, Defence Headquarters Complex Project Akuregoda, Battaramulla, Sri Lanka E-mail :
[email protected]
1. Introduction
argue that inability of measuring intangible benefits of IT has caused nformation Technology the equivocal results. Improvement (IT) is an essential business of quality, variety, timeliness and infrastructure of the customisation are some of intangible current complex business benefits, which are very difficult to environment. However, be appropriately measured (Im et return on investment in information al., 2001). Therefore, conventional technology (ROIT) has become performance measures are not a complex inconclusive topic as a suitable to measure the performance result of the difficulty of measuring of investment in IT. IT infrastructure investment related cash inflows performs a supporting role in many which are intangible in nature and business models of which direct indicated through the improved influence on the performance is efficiency of organisations. very difficult to quantify. In reality, Therefore, evaluation of investments various supporting factors influence in IT is very difficult since there are the performance of businesses. no direct methods to justify ROIT. One supporting factor or even a Investment in IT may streamline combination does not necessarily operations reducing overall costs. provide a conclusive picture of Many previous studies on the the performance of a business. value of investment in IT have These measurement issues make it not provided conclusive results extremely difficult to establish the indicating how the investment causality between investment in IT in IT provides pay- off. Im et and firm level output performance. al., (2001) argue that studies relating to investment in IT and 2. Efficiency and investment in IT organisational performance have Major objectives of this study are to been equivocal. Brynjolfsson & Hit (2003) states that the contradictory investigate whether investment in IT improves the efficiency of licensed perspectives have been attributed financial institutions (LFIs) in Sri primarily to the inadequacies of Lanka and to propose an efficiency productivity measurement as well based approach to measure ROIT. as time lags due to learning effect IT is a very useful tool, which is of IT or a time consuming period used to improve productivity of of complementary changes in organisations. However, improper organizations. Some researchers
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or incorrect use can lead for problems or complications than solutions. Therefore, companies have to evaluate expected performance benefits of proposed investment in order to justify investment. Improvement of overall productivity, which is generated through labour productivity, as a result of uniformity of operations is considered as a key indicator of justifying investment in IT. Linking the performance of companies with investment in IT is still a major concern of evaluation. In general practice, LFIs have invested substantial amount of their assets in IT infrastructure over the other organisations in Sri Lanka. Therefore, changes in performance through improvement of efficiency arisen as a result of investment in IT can be easily evaluated through LFIs. Average IT investment in IT as a percentage of total value of fixed assets of major LFIs is shown at table 1. Table 1: Average investment in IT of major licensed financial institutions
Industry
Investment in IT as a percentage of total value of fixed assets1 (%)
Licensed Commercial Banks ( As at 31.12.2013)
27.00 - 47.00
Licensed Specialized Banks (As at 31.12.2013)
18.00 - 29.00
Licensed Finance Companies(As at 31.03.2014)
12.00 - 26.00
Source: Compiled by the author using annual reports of LFIs The above information reveals that investment in IT of LFIs is substantial in relation to the total value of fixed assets. Such investments have facilitated online real time transactions allowing for faster processing of data, easier retrieval of information reducing interaction of physical employees and in return generating opportunities to improve efficiency through the reduction of cost and time. Therefore, this study evaluates whether the investment in IT has improved the efficiency of LFIs. The efficiency of LFIs measures the relative ability of utilizing resources in an efficient manner in generating outputs. IT infrastructure may facilitate the process of utilising resources to generate improved output. Therefore, efficiency is a better measure to capture the quality of LFIs and their functions in an economy. The efficiency can be used to evaluate and compare the performance of an LFI in relation to the performance of another LFI, particularly with compared to a best practice. Efficiency measures provide a numerical
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efficiency value, which facilitates ranking LFIs against each other, and an LFI is considered efficient when it uses a right proportion of appropriate amount of inputs in the intermediation process. 3. Methodology There are different approaches to measure the efficiency of financial institutions. The history of efficiency measurement begins with Farrell (1957) who defined a simple measure of firm efficiency, which could account for multiple inputs. Since then, at least four different approaches have evolved to analyse the efficiency of financial institutions. These approaches differ in assumptions placed on the probability distributions of the X-efficiency (deviations from the efficient frontier) differences and unrelated random errors. These are: (i) the econometric frontier approach; (ii) the thick frontier approach; (iii) the distributionfree approach; and (iv) the data envelopment analysis.
The econometric frontier approach assumes a two-component error structure such that the inefficiencies follow an asymmetric half-normal distribution and the random errors are normally distributed (Ferrier and Lovell, 1990; Bauer et al., 1993; Esho and Sharpe, 1996; Bonin et al., 2005). The thick frontier approach posits that deviations from predicted costs within the lowest average-cost quartile of financial institutions are the result of random error, whilst differences between the highest and lowest average-cost quartile reflect inefficiencies plus exogenous differences in output quantities and input prices (Berger and Humphrey, 1991; Bauer et al., 1993). The supporters of the distribution-free approach argue that efficiency differences are stable over time, whilst random errors average out over time (Bauer et al., 1993; Berger, 1993). Finally, the Data Envelopment Analysis (DEA) assumes that all deviations from the estimated frontier represent inefficiency (Elysiani and Mehdian, 1990; Miller and Noulas, 1996; Drake and Weyman-Jones, 1996; Sufian et al., 2010; Chan, 2011; Jayasekara.2014 & 2015).
Today, DEA has become a widely used measure of efficiency. The literature suggests two approaches, production approach and intermediation approach, to identify input and output variables in order to measure efficiency. Production approach considers financial institutions as producers of services for their account holders (Benston, 1965). Berger and Humphrey (1997) suggest that the production approach is more appropriate for measuring branch efficiency, as branches are mainly engaged in processing documents and mobilising deposits for account holders. Intermediation approach considers financial institutions as mere intermediaries between depositors (savers) and borrowers. Therefore, efficiency is measured considering loans/ investments as outputs and deposits/ labour and capital as inputs at the intermediary process. This study will use the intermediation approach to define inputs and outputs. Three input variables and three output variables were selected to provide a parsimonious model to evaluate the efficiency of LFIs. All variables are measured in terms of millions of Sri Lankan Rupees (LKR). LFIs in Sri Lanka, Licensed Commercial Banks (LCBs), Licensed Specialised Banks (LCBs) and Licensed Finance Companies (LFCs), are modelled as multi-product firms, which produce three outputs by employing three inputs. Accordingly, total loans (y1), interest income (y2), non-interest income (y3) are outputs, which are generated by employing Deposits (x1), interest expense (x2) and fixed assets (x3). Efficiency scores of LFIs are measured in terms of constant return to scale as follows (Cooper et al., 2000).
Efficiency of FIo ( θ) = Virtual output Virtual input = u1y1o+ u2y2o+ u3y3o v1x1o+ v1x1o+v3x3o 4. Results The efficiency scores of LFIs, which are summarised at table 2, were measured on annual basis. The results show a decreasing trend of mean technical efficiency of LCBs and LFCs during the period. Mean technical efficiency of LSBs has fluctuated during the period. The highest mean technical efficiency of LCBs was 96.44% in 2011 and it has gradually decreased to 92.81% by 2013. Most notable observation in
respect of LCBs is that the variation of efficiencies among LCBs was low in contrast to LSBs and LFCs. The highest mean technical efficiency of LSBs was 88.41% in 2011 and it has reached to 87.55% by 2013. However, both LCBs and LSBs were technically efficient over the LFCs during the period of study. Overall, higher efficiency scores of licensed banks can be interpreted as generated by higher proportion of investment in IT over LFCs. Further, decreasing trend of efficiency scores shows the short-term maturity of investment in IT due to innovations. Therefore, LFIs require to invest modern IT infrastructures to improve efficiency.
Table 2: Average Technical efficiency of licensed financial institutions Category of Financial Institutions 2009 2010 2011 2012 2013 Licensed Commercial Banka 0.9574 0.9615 0.9644 0.9483 0.9281 Licensed Specialised Banks
0.8659 0.8849 0.8841 0.8255 0.8755
Licensed Finance Companies 0.7555 0.6840 0.6678 0.6464 0.6392 Source: Compiled by the author Efficiency scores of LCBs shows stable results. However, efficiency scores of LSBs and LFCs show a greater volatility over the period of study. LCBs have invested higher proportion of fixed assets in IT, which has resulted in higher stable efficiency scores over the other financial institutions Movement of standard deviation of efficiency scores are shown at table 3. Table 3: Standard Deviation of Average Technical efficiency of financial institutions Category of Financial Institutions 2009 2010 2011 2012 2013 Licensed Commercial Banka 0.0570 0.0451 0.0352 0.0596 0.0668 Licensed Specialised Banks
0.2136 0.1804 0.1709 0.2051 0.1734
Licensed Finance Companies 0.2352 0.2381 0.2660 0.2380 0.2563 Source: Compiled by the author The results indicate that the investment in IT reflects in the performance of financial institutions. LCBs operate in large scale over the other LFIs. Therefore, Investment in IT has improved efficiency of LCBs mixing with competitive advantages arisen as a result of size, economies of scale,
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geographical distribution of branch network, capital structure, and high-quality staff of LCBs over the other LFIs. However, other financial institutions do not have such advantages in short -run and as a result of that they have become inefficient at the competition. Assuming that the efficiency of LFIs is mainly due to the investment in information technology, the following process is proposed to measure ROIT.
Example: ABC Company
identifying cash inflows related to the investment in IT. Therefore, ROA t0 =2.5, ROAt1 =2.65, this study evaluated whether the Efficiency =0.8564, adjusted profit investment in IT has improved before tax= Rs.80.0 mn, investment the efficiency of LFIs using data in information technology Rs.100.0 envelopment analysis with three mn inputs and three outputs for the period from 2009 to 2013. Step 1: Basic Return on Investment in IT = (ROA t1 – ROAt0) x Efficiency The efficiency scores of LFIs were compared with the average Score investment in IT as a proportion = (2.65-2.5) X0.8564 of the value of total fixed assets. = 12.85% -------- (A) The results revealed that the LCBs Step 2: Gross return on investment in , which have invested higher IT = (Profits before tax + Step 1: Basic Return on Investment in proportion of fixed assets in IT = (ROA t1 – ROAt0) x Efficiency Depreciation and other noninformation technology, were more cash adjustments)x (A) ------ (B) Score ----- (A) efficient over the other financial = 80.0 X12.85% institutions. These results indicate = 10.28 Step 2: Gross return on investment in that efficiency is associated with IT = (Profits before tax + Return on investment in IT investment in IT. Most notable Depreciation and other non= (B÷ IT investment) X 100 observation of the study was that cash adjustments) x(A) -------- (B) = (10.28/100) X 100 average efficiency scores of LCBs Return on investment in IT = (B÷ =10.28% and LFCs were decreasing while IT investment) X 100 mean technical efficiency scores ROA t0 = Return on assets prior to 5. Conclusion of LSBs were fluctuating during the investment in IT the period. Decreasing trend of Return on investment in ROAt1 = Return on assets after the information technology is a long efficiency scores shows the shortinvestment in IT term maturity of investment in IT discussed matter and still it is inconclusive since other investment due to innovations in IT industry. Therefore, LFIs require investing appraisal methods are not suitable in IT in line with the development to evaluate returns on investment of modern IT infrastructures to in IT due to inherent difficulty of improve the overall efficiency. References Annual reports of financial institutions, http://www.cse.lk/company_info.do?id Bauer, P.W., Berger, A.N., Ferrier, G.D. and Humphrey, D.B. (1993), Consistency Conditions for Regulatory Analysis of Financial Institutions: A Comparison of Frontier Efficiency Methods, Journal of Economics and Business, 50 (2), 85-114. Benston, G. (1965), Interest Payments on Demand Deposits and Bank Investments Behaviour, Journal of Political Economy, 72 Berger, A.N. (1993), Distribution-free estimates of efficiency in the US banking industry and tests of the standard distributional assumptions, Journal of Productivity Analysis, 4, 261–292. Berger, A.N. and Humphrey, D.B. (1991),The dominance of inefficiencies over scale and product mix economies in banking, Journal of Monetary Economics, 28, 117–148. Berger, A. N., and Humphrey, D. B.(1997), Efficiency of Financial Institutions: International Survey and Directions for Future Research, European Journal of Operation Research, 98, 175–212. Bonin, J., Hasan, I. and Wachtel, P. (2005), Bank Performance, Efficiency and Ownership in Transition Countries, Journal of Banking and Finance, 29, 31-53. Brynjolfsson E. & Hitt L. (2003), Computing Productivity: Firm Level Evidence, Review of Economics and Statistics, November, 1-27. Chan S.G. (2011), Technical Efficiency of Commercial Banks in China: Decomposition in to Pure Technical and Scale Efficiency, International Journal of China Studies,2 (1), 27 -38 Cooper, W.W.,Seiford,L.M., and Tone,K.(2000), Data Envelopment Analysis, Kluwer Academic Publishers, London. Drake, L. and Weyman-Jones, T.G. (1996), Productive and allocative inefficiencies in UK building societies: A comparison of non-parametric and stochastic frontier techniques, The Manchester School, 64, 22–37.
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