Operational-Profitability-Quality Performance of Dubai s Banks Parallel Data Envelopment Analysis

Transcription

1 Journal of Management Research Vol. 13, No. 1, January March 2013, pp Operational-Profitability-Quality Performance of Dubai s Banks Parallel Data Envelopment Analysis Attiea Marie, Amad Al-Nasser and Mohamed Ibrahim Abstract A parallel data envelopment analysis model of operational-profitability and operational-quality indicators was applied to the banking sector in Dubai. Information was gathered about the 18 main banks which clustered into Islamic and commercial banks. Data were obtained from two sources; for the operationalprofitability segment, data were obtained from the formal financial statements of banks in Dubai, and the data for the operational-quality segment were collected randomly from 450 customers by using a well-prepared SERVQUAL questionnaire. The technical efficiencies for both models were computed; and comparisons were made between the Islamic and the commercial banks within both models. The findings are that there are no statistical differences between the Islamic and the commercial banks in the operational-profitability model. However, the Islamic banks dominate the commercial banks in the operational-quality model. Based on an in-depth analysis, the operational-quality in Islamic banks depends on the assurance, responsiveness, and reliability factors. Keywords: Efficiency, financial and non-financial indicators, SERVQUAL, Islamic and commercial banks INTRODUCTION Banks are a nation s money stock and are considered as the core resources of financial intermediation. Evaluating their performance and monitoring their service quality is important to governments, customers, and managers. It is well known among researchers and practitioners that the efficiency of the financial performance of banks is reflected in operating profit and earnings per share that are governed by many factors listed in the income statement. The best financial measures used to evaluate the bank s performances are ROE (return on equity) and ROA (return on assets) (Sinkey, 2002). Also, the efficiency of banks Attiea Marie Amad Al-Nasser Mohamed Ibrahim (Corresponding Author) Dean College of Business Administration University of Dubai, Dubai, UAE can be viewed as the quality of services provided, which reflects the customers conviction (beliefs) and satisfaction. In Dubai, as well as other parts of the world, the negative effects of the global financial crisis since 2008 have comprised all aspects of economic, social and political life. The Dubai government has taken to free up prices and wages, rationalize indirect taxes, deregulate the financial system, promote FDI (foreign direct investment), and encourage all economic entities to use new information and communication technology. As a result, this study employed PDEA (Parallel Data Envelopment Analysis) to examine the efficiency and quality of services provided by 18 commercial and Islamic banks working in Dubai. In many of the banking efficiency studies, the researchers consider the system as the composition of several stages or processes that can have a series structure. Such consideration is needed because the classical one-stage DEA (Data Envelopment

2 Analysis) method is incapable of reflecting the reality of the bank performance. In the series structure, the production process can be divided into two or more sequential stages (Tsai, Cheng-Ru and Ya-Mei, 2011). The DEA is then applied on the stages continuously such that the output of the first stage will be treated as an input for the next stage. Seiford and Zhu (1999) first suggested the use of the two-stage DEA method by dividing a commercial bank s production process into two stages. The first stage measured marketability (they considered three inputs: employees, assets and shareholder equity compared with two outputs: revenue and profit). However; the second stage proposed to measure bank s profitability (where the inputs variable were exactly the same as the outputs of the first stage, and the outputs: market value, total investment return and earnings per share). In this paper, combinations of financial and nonfinancial (service quality) factors were considered to enhance the performance of banks in Dubai. Two disoint parallel steps technique is used to evaluate Dubai s banks. In the first step, DEA was used to obtain a measure of operational quality of the bank s performance (external efficiency) based on non-financial factors. Secondly, the DEA was used to measure the profitability by measuring the technical efficiency of operational profitability by considering the financial factors: ROA and ROE. The remainder of this paper is organized as follows. Section 2 provides a literature review. Section 3 illustrates the DEA methodology. Section 4 describes data, input and output variables and discusses the research model. The empirical results are given in Section 5. REVIEW OF LITERATURE DEA approach is considered as a relative measure of efficiency as it identifies the best DMU (decision making unit) such as a bank in a group according to their observed activities in terms of outputs and inputs. Numerous studies have examined individual banks performance using DEA across the globe (Chunhachinda and Li, 2010; Sufian, 2009; Heffernan and Fu, 2008; Debasish, 2006; Stiroh and Rumble, 2006; Casu and Molyneux, 2003; Sufian and Noor, 2009; Li, 2005; Sturm and Williams, 2004; Isik and Hassan, 2002). The discussion in most of the earlier studies focused on the choice of the input/output variables that should be included in the DEA model. Fethi and Pasiouras (2009) mentioned 164 studies; and Eken and Kale (2011) discussed 49 studies that used DEA in order to select the suitable input/output variables to measure the bank s performance. The following discussion refers, but not limited to, some of the other studies that have been conducted in context of the issue under consideration. Badreldin (2009) emphasized the importance of ROA and ROE measures for evaluating bank performance. Spathis, Doumpos and Zopounidis (2002) presented strong linkages between bank size (non-financial measure) and performance efficiency. Seven financial ratios were selected for examination. The results indicated that large banks were more efficient than the small ones and can be characterized by high capital yield, high interest rate yield, high financial leverage, and high capital adequacy. Meanwhile, large banks can be considered as having high asset yield (ROA) and low capital and interest rate yield. Kalhoefer and Salem (2008); and Collier and McGowan (2010), looked at the evaluation of banks through the usage of the Du Pont System for Financial Analysis. The evaluation of performance was separated into three elements: net profit margin, total asset turnover, and the equity multiplier. European Central Bank (2010) suggests using a system of financial ratios composed of three categories: traditional measures of performance ratios such as ROA and ROE, cost-to-income ratio and net interest margin. The economic measures of performance include economic value added and risk-adusted return on capital. Market based measures of performance characterize the activity of a company valued by capital markets compared with the estimated accounting or economic value. 26 Journal of Management Research

3 Hassan (2005) examined the relative cost, profit, efficiency, and productivity of the world Islamic banking industry. The results also show that all five efficiency measures are highly correlated with ROA and ROE, suggesting that these efficiency measures can be used concurrently with the conventional accounting ratios in determining Islamic bank performance. Most of the research focussed on evaluating a bank s performance based on financial aspects, whereas the non-financial aspects were ignored. Manandhar and Tang (2002) considered financial and non-financial aspects by including internal service quality, operating efficiency, and profitability as dimensions of bank performance. Hays, De Lurgio and Gilbert (2009) analyzed five main segments of bank operation: CAMEL (capital adequacy, asset quality, management quality, earnings ability and liquidity). They presented the enhancement of the CAMEL model by federal banking regulators in order to assess the overall performance of commercial banks. Akroush and Khatib (2009) conducted an empirical study in the banking sector of Jordan and found that the service quality dimensions (functional and technical) have positively and significantly affected banks performance assessed based on financial performance and customer indicators. On the other hand, many investigations have been made by considering the Islamic bank sector. Ahmad, Noor and Sufian (2010) investigate the efficiency of the Islamic banking sectors in four Asian countries during the period The results imply that the Islamic banks in the Asian countries could have produced the same amount of outputs by using only 86.5% of inputs they employed. Jabnoun and Khalifa (2005) modified the SERVQUAL dimensions and added two more dimensions: values and image. They examined their new model in conventional and Islamic banks operating in UAE. They found that there were four SERVQUAL dimensions: personal skills, reliability, values, and image. Al-Tamimi, Lafi and Hamid Uddin (2009) investigated how bank customers in the UAE view Islamic banks versus conventional banks and whether this image affects customer loyalties or selection of a bank. They found that most UAE bank customers prefer banking with Islamic banks, although they are not satisfied with the quality of products and services. Consequently, it can be said that the maority of empirical research in the area of service quality has employed the functional and technical quality dimensions as the maor variables for examining their effect on business performance. It is also clear that the literature analysis on measurement of bank performance shows different techniques and methods that can be applied in this field. In this article, the main purpose is to include financial and non-financial measures that would reflect the most accurate view of Dubai banking Islamic and conventional banks in order to evaluate their performances. NON PARAMETRIC DEA METHODOLOGY Farrell (1957) addressed the relative efficiency of multiple input and output by assigning weights to the variable so that the relative efficiency scores are a ratio of the weighted sum of outputs to weighted sum of inputs. weighted sum of output Efficiency = weighted sum of input Note that it is common to apply the same sets of weight to the inputs or outputs of all DMUs. Consider we have n DMU with m inputs and s outputs. Let x i ; (i = 1, 2,..., m) be the inputs and y r ; (r = 1, 2,..., s) be the outputs of a DMU ; ( = 1, 2,..., n). The mathematical programing of the above model would be as follows (Charnes et al., 1978). Max s k = 1 m = 1 u y k v x ko o s u y subect to i n k ki k = 1 : 1. = 1,2,...,. m v x i = 1 Volume 13, Number 1 January March

4 uk 0, kk, = 1, 2,..., s. v 0,, = 1, 2,..., m. where : th yki : amount of k output produced by DMU i. th x i : amount of input utilized by DMU i. th uk : weigthed given to k ouput. th v : weigthed given to input. Here, the unknown parameters are u and v. They are vectors of weighted input and weighted output, respectively. The DEA formulation for each DMU determines weights (v and u) for inputs and outputs that would maximize efficiency of the DMU. While the first constraint limits the maximum efficiency of each DMU to 1, the value of each weight is restricted in the problem so that no weight can be less than zero from the last constraint, because the obective is to maximize efficiency. The fractional program as shown above can be converted to a LP (linear programming) system. The CCR-efficiency model was formulated as an LP problem with row vector v for inputs and row vector u for outputs. Both u and v are treated as decision variables in the following primal LP system. s Max u y k = 1 subect to : v x = 1 o = 1 s m uk yki v x i k = 1 = 1 k ko 0. u k 0, k, k = 1, 2,..., s. v 0,, = 1, 2,..., m. m This maximization model is used to calculate the artificial weight of inputs and output to show the contribution of each in the DMU efficiency or the contribution of inputs and outputs in the proposed model for each DMU (Cooper et al., 2000). The primal model is converted to the dual form to measure the efficiency for multiple-input, and multiple-output DMUs that attempt to minimize their inputs for given outputs. The dual problem of linear programming system is expressed with a real variable θ and a non-negative vector λ = (λ 1,..., λ n ) t of variables as follows. min θ subect to: θx 0 Xλ > 0 Yλ > y 0 λ > 0 If an optimal solution of the above model satisfies ˆ θ = 1 and is zero-slack, then the DMU o is called a CCR-efficient, otherwise the DMU o is called CCRinefficient. The mathematical programming could be repeated with additional set of outputs separately to end up with the parallel DEA formulation. The CCR model assumes constant return to scale while determining the efficiency of DMUs. Banker et al (1984), which is considered in this research, modify the CCR model by adding a constraint to account for the variable return to scale. The envelopment from the BCC model would be the same as the dual for the CCR model but with an additional constraint. n eλ = λ = 1. = 1 i Where e is a unit vector of size n x 1. Then, the BCC model can be expressed as an LP system. Min θ subect to : λ X X, n = 1 n = 1 o o = 1 λ = 1. = 1,2,..., n. λ 0.. λ Y Y, n o RESEARCH MODEL DESIGN Measuring the relative efficiency of Dubai s banks with different models will give an insight into the performance of these banks on various dimensions. This research contributes to propose a methodology for using parallel DEA for operational-quality-profitability evaluations of the banks in Dubai, where the operational factors including customer accounts, operating expenses, 28 Journal of Management Research

5 Figure.1 Parallel DEA Model for Dubai s Banks fee and commission income and net interest income represent the inputs of the DEA model. The first model is the operational-quality efficiency model which is suggested to evaluate the external efficiency. Quality in many areas is critical and it is one of the key success factors in the banking sector, but it is not included in DEA models. Therefore, a quality dimension should be incorporated into the DEA model in order to identify best practices that work efficiently and at the same time provide services with high quality. The quality of services is measured on an average level by the overall customer satisfaction derived from the SERVQUAL dimensions. The second model is the classical DEA model for evaluating the bank s performances, which is the operational profitability efficiency model (internal efficiency). This model measures the operational factors with the two main profitability factors: ROA and ROE. The two specifications developed are presented in Figure1. The main idea of the parallel model is to study the DMU in more detail by eliminating the effects of some homogeneous outputs variables. In general, most of the DEA applications for banks considered operational-profitability model (Berger and Humphrey, 1997). However, the overall performances are not evaluated by operationalprofitability only, but also by considering the service quality which reflect the reality of the banks performances. It is important to note that there is no unique way for building these models (Soteriou and Zenios, 1997). The two output sets, which are considered in the parallel DEA model, (service quality and profitability), are uncorrelated and disproportionate factors. However; each of them reflects different side of the banks efficiency. The linkage between these outputs could be setting by the operational factors such as customer s deposits, expenses, and revenue. DUBAI S BANKS DATA AND EMPIRICAL RESULTS Two different types of data were collected from the main 18 local banks in Dubai in 2011 to demonstrate the applicability of the proposed model. The operational and profitability data were gathered from a formal recourse of each bank and from the Dubai financial market web page. However, the quality services data gathered from 450 customers; 204 (45.3%) from the main four Islamic banks and 246 (54.7%) customers from fourteen commercial banks; by using a well prepared questionnaire adapted from SERVQUAL (Parasuraman, Zeithaml and Berry, 1988). The bank s customers were asked to indicate on a 5- point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree), the extent to which they had a satisfaction on the services of the bank. The survey contains the six dimensions of SERVQUAL. However, for this analysis, the average of the overall satisfaction level by considering all service quality dimensions was used. The descriptive statistics for input/output variables are given in Table 1 and Table 2. Efficiency analysis of Dubai banks performances; quality and profitability; approach with parallel DEA by using CCR model in Table 2 shows that 8 (44.4%) banks are technically operational-quality efficient with an average of Seven (38.9%) Volume 13, Number 1 January March

6 Table 1: Summary Statistics of Inputs/Outputs Variables External Statistic Model Inputs Internal Outputs Output CA OE FC NI ROA ROE CS Max Min Average SD Table 2: Efficiency Analysis and Banks Classifications Bank Type Bank Operational Operational Average Overall Classification Quality Profitability Efficiency Efficiency Efficiency Efficiency Islamic R HH R HL R HH R HH Commercial R LL R HH R HH R HL R LL R LL R LL R LL R LH R LL R LL R LL R HH R LL Average No of Efficient DMU No.of Inefficient DMU Journal of Management Research

7 banks are technically operational profitability efficient with an average of In spite of high average efficiency, 10 (55.6%), and 11(61.1%) of the 18 banks are not technically quality and profitability efficient, respectively. This indicates that the inefficient banks should increase their activities to produce significant outputs at a given set of inputs to become an efficient bank. Moreover, the efficiency analysis is performed by considering one-stage model. By considering both quality and profitability variables as one set of outputs of the proposed model, the results shows that 9 (50%) with an average (0.9361), banks are technically efficient. It is worth saying that when the bank has an efficient score in one of the two parallel models, it will be an efficient unit in the one-stage model. In a similar classification of Zervopoulos and Palaskas (2011), the parallel DEA models locate the efficiency results of the operational quality and operational profit models into four quadrants. a) high-quality-high-profit (H-H): an efficient DMU in both models. b) low-quality-high-profit (L-H): efficient DMU in operational profit model only. c) low-quality-low-profit (L-L): inefficient DMU in both models. d) high-quality-low-profit (H-L): efficient DMU in operational quality model only. Noting that the efficient DMU is classified as high (quality/profit) whenever the efficiency score is unity, it is otherwise classified as low (quality/ profit) inefficient DMU. Based on the suggested bank classification, the results in Table 2 and Figure 2 show that there are six banks classified as HH (3(75%) Islamic banks; and 3(21.4%) commercial banks). However, there are 2 banks classified as HL (one Islamic bank and one commercial bank); one commercial bank as LH and 9 commercial banks as LL. Table 3: Comparisons between Islamic and Commercial Banks Bank Type Islamic Banks Commercial Banks Quality Profitability Overall Quality Profitability Overall No. of efficient DMU (H) No. of inefficient DMU (L) Average Score Table 4: Mann-Whitney U-test Results Islamic Banks Commercial Banks U test N Mean Minimum N Mean Minimum P value Z value Decision Quality The operational quality distribution is not the same across the banks. Profitability The operational profitability distribution is the same across the banks. Volume 13, Number 1 January March

8 High Quality R. 8 R. 2 R.1 R.7 R.3 R.4 R.5 R.17 R R R. 11 R. 10 R. 12 Quality R Low Quality R. 16 R R Low Profit High Profit Figure 2: Operational Quality and Operational Profitability Classification of Dubai Banks By comparing the bank types, the results in Table 3 shows that all Islamic banks and only 28.6% (4 out of 14) commercial banks are technically operational quality efficient. This confirmed that the avoidance of interest and other religious factors; which is the core difference between the Islamic and the commercial banks, are the most important reasons that increase the operational quality Islamic banks in Dubai. Also, 75% of the Islamic banks and 28.6% of the commercial banks are technically operational profitability efficient. These results confirm Dubai financial market results as the Islamic banks have always been awarded as the best banks in Dubai for the last 10 years. CONCLUSION The current study uses PDEA (Parallel Data Envelopment Analysis) to analyze efficiency of financial markets. Further, the study breaks down the efficiency into internal efficiency (called operational profitability) and external efficiency (called operational quality) for both Islamic and commercial banks located in Dubai. This is to facilitate proper conduct of banks operations and help the policy-makers in designing suitable policy actions to manage and control internal and external performance in Dubai s banks. The technical efficiency of the proposed model gave Islamic banks an advantage over the commercial banks in terms of average efficiency score. To have more 32 Journal of Management Research

9 insight about such a conclusion, Table 4 shows the results of Mann-Whitney test which was used to test the differences between the efficiency scores based on the bank type. That is, The distribution of the quality/profitability is the same across different bank types. Therefore, the results showed an efficient relationship between customer satisfaction and operational factors. This study considered the SERVQUAL dimensions namely: empathy, assurance, compliance, responsiveness, and reliability. The effect of each of these dimensions on the customer satisfaction is given in Table 5. It can be concluded from the external efficiency that there are three significant dimensions assurance, responsiveness, and reliability affect the satisfaction of customers of Islamic banks. However, only two significant dimensions (reliability and compliance) increase the satisfaction level of commercial bank customers in order to make the bank an efficient DMU. Table 5: Customer Satisfaction Comparisons between Islamic and Commercial Banks Dimensions Islamic Commercial Empathy CS Assurance CS.124*.054 Compliance CS * Responsiveness CS.398*.015 Reliability CS.296*.166* R Significant at 0.05 level REFERENCES Ahmad, N. H. bt., Noor, M. A. N. M. and Sufian, F. (2010), The Efficiency of Islamic Banks: Empirical Evidence from the Asian Countries Islamic Banking Sectors, J. International Business and Entrepreneurship Development, 5(2): Al-Tamimi, H., Lafi, A. and Md Hamid Uddin (2009), Bank Image in the UAE: Comparing Islamic and Conventional Banks, Journal of Financial Services Marketing, 14(3): Akroush, M. and Khatib, F. (2009), The Impact of Service Quality Dimensions on Performance: An Empirical Investigation of Jordan s Commercial Banks, Journal of Accounting, Business and Management, 16(1): Badreldin, A. M. (2009), Measuring the Performance of Islamic Banks by Adapting Conventional Ratios, German University in Cairo Working Paper No. 16. Available on Banker, R. D., Charnes, A. and Cooper, W. W. (1984), Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis, Management Science, 30(9): Berger, A. N. and Humphrey, D. B. (1997), Efficiency of Financial Institutions: International Survey and Directions for Future Research, The Wharton Financial Institutions Center WP Casu, B. and Molyneux, P. (2003), A Comparative Study of Efficiency in European Banking, Applied Economics, 35(17): Charnes, A., Cooper, W. W. and Rhodes, E. (1978), Measuring the Efficiency of Decision Making Units, European Journal of Operational Research, 2(4): Chunhachinda, P. and Li, L. (2010), Efficiency of Thailand Commercial Banks: Pre vs Post-1997 Financial Crisis, Review of Pacific Basin Financial Markets and Policies, 13(3): Collier, H. W. and McGowan, C. B. (2010), Evaluating the Impact of a Rapidly Changing Economic Environment on Bank Financial Performance Using the DuPont System of Financial Analysis, Asia Pacific Journal of Finance and Banking Research, 4(4): Cooper, W., Seiford, L. and Tone, K. (2000), Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Kluwer Academic Publishers. Debasish, S. S. (2006), Efficiency Performance in Indian Banking: Use of Data Envelopment Analysis, Global Business Review, 7(2): Volume 13, Number 1 January March

10 Eken, M. and Kale, S. (2011), Measuring Bank Branch Performance Using Data Envelopment Analysis (DEA): The Case of Turkish Bank Branches, African Journal of Business Management, 5(3): European Central Bank ( 2010), Beyond Roe How to Measure Bank Performance. Available on beyondroehowtomeasurebankperformance201009en.pd Farrell, J. M. (1957), The Measurement of Productive Efficiency, Journal of the Royal Statistical Society, 120: Fethi, M. D. and Pasiouras, F. (2009), Assessing Bank Efficiency and Performance with Operational Research and Artificial Intelligence Techniques: A survey, University of Bath WP Hassan, M. K. (2005), The Cost, Profit and X-Efficiency of Islamic Banks, paper presented at the 12th ERF Annual Conference, December 19-21, Egypt. Hays, F. H., De Lurgio, S. A. and Gilbert, A. H. (2009), Efficiency Ratios and Community Bank Performance, Journal of Finance and Accountancy, 1: Heffernan, S. and Fu, M. (2008), The Determinants of Bank Performance in China, EMG Working Paper Series. Isik, I. and Hassan, M. (2002), Technical, Scale and Allocative Efficiencies of Turkish Banking Industry, Journal of Banking and Finance, 26: Jabnoun, N. and Khalifa, A. (2005), A Customized Measure of Service Quality in the UAE, Managing Service Quality, 15(4): Li, Y. (2005), DEA efficiency measurement with undesirable outputs: An application to Taiwan s commercial banks. International Journal Services Technology and Management, 6(6), Manandhar, R. and Tang, C. S. (2002), The Evaluation of Bank Branch Performance Using Data Envelopment Analysis, The Journal of High Technology Management Research, 13(1): Parasuraman, A., Zeithaml, V. A. and Berry, L. L. (1988), SERVQUAL: A Multi-item Scale or Measuring Customer Perceptions of Service Quality, Journal of Retailing, 64(1): Kalhoefer, C. and Salem, R. (2008), Profitability Analysis in the Egyptian Banking Sector, German University in Cairo, Faculty of Management Technology Working Paper No. 7. Available on Sinkey, F. (2002), Commercial Bank Financial Management, Prentice-Hall, Englewood Cliffs, NJ. Seiford, L. M. and Zhu, J. (1999), Profitability and Marketability of the Top 55 US Commercial Banks, Management Science, 45(9): Soterious, A. and Zenios, S. (1997), Efficiency, Profitability and Quality in the Provision of Banking Services, working paper, Department of Public and Business Administration, University of Cyprus, Nicosia, CYPRUS. Spathis, C., Doumpos, M. and Zopounidis, C. (2002), Detecting Falsified Financial Statements: A Comparative Study Using Multicriteria Analysis and Multivariate Statistical Techniques, Eur. Account. Rev., 11(3): Stiroh, K. J. and Rumble, A. (2006), The Dark Side of Diversification: The Case of US Financial Holding Companies, Journal of Banking and Finance, 30(8): Sturm, J. E. and Williams, B. (2004), Foreign Bank Entry, Deregulation and Bank Efficiency: Lessons from the Australian Experience, Journal of Banking and Finance, 28(7): Sufian, F. and Noor, M. A. (2009), The Determinants of Islamic Banks Efficiency Changes Empirical Evidence from the MENA and Asian Banking Sectors, International Journal of Islamic and Middle Eastern Finance and Management, 2(2): Sufian, F. (2009), Determinants of Bank Efficiency During Unstable Macroeconomic Environment: Empirical Evidence from Malaysia, Research in International Business and Finance, 23: Tsai, Hui-Yin, Cheng-Ru, Wu and Ya-Mei, Wang (2011), Operating Strategies of Banks in China: Analysis of Operational and Profitability Efficiency, African Journal of Business Management, 5(4): Zervopoulos, P. and Palaskas, T. (2011), Applying Quality-driven, Efficiency-adusted DEA (QE-DEA) in the Pursuit of Highefficiency High-quality Service Units: An Input-oriented Approach, IMA J Management Math, 22(4): Journal of Management Research

11 Copyright of Journal of Management Research ( ) is the property of South Asia Publications and its content may not be copied or ed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or articles for individual use.