Necmi K. Avkiran on the Network Data Envelopment Analysis (NDEA)
Fast Breaking Commentary, August 2010
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Article Title: Opening the black box of efficiency analysis: An illustration with UAE banks
Authors: Avkiran, NK |
Necmi K. Avkiran talks with ScienceWatch.com and answers a few questions about this month's Fast Breaking Paper paper in the field of Economics & Business.
Why do you think your paper is highly
cited?
At the time of publishing, none of the existing applications of Network Data Envelopment Analysis (NDEA) had used the Slacks-based Measure, which sets this paper apart from extant literature as the first illustration of a non-oriented, non-radial measure in NDEA. Also, as a first study of its kind in Banking and Operations Research literature, the paper illustrates an application of NDEA using simulated profit center data that, in turn, relies on actual aggregate data on domestic commercial banks in the United Arab Emirates (UAE).
UAE is a region of rapid economic growth supported by an extensive banking system, strategically positioned at the entrance to the Persian Gulf. Another reason for choosing the UAE banking sector for the empirical example was the small amount of attention it had received in refereed journals. Focusing on UAE contributes to banking literature because this dynamic part of the Middle East is seldom studied in frontier efficiency literature.
The new approach developed and applied in this paper has global appeal. It can be used in those countries where an industry is known to suffer from relatively high levels of inefficiency, as well as in countries where the overall measured inefficiencies are low but more focused strategies are needed to identify and eliminate the remaining inefficiencies.
"As world population grows in an environment of global warming, scarce resources will be more intensely contested..."
Developing reliable performance measures could also assist in managerial decision-making involving mergers where executives typically search for synergies that are often enjoyed by acquiring divisions that provide complementary services.
Does it describe a new discovery, methodology, or
synthesis of knowledge?
The study contributes to a perennial research problem, namely, inability of the outside researcher to access internal organizational data for developing or testing new methods. Methodology for the NDEA illustration using UAE banks included developing an innovative approach to divisional data simulation.
Empirical tests use a combination of actual data sourced from financial statements on core profit efficiency variables for UAE domestic commercial banks, and simulated data on their key profit centers because the latter are not available in the public domain.
In summary, the paper first estimates the proportion of total outputs corresponding to each profit centre by allowing the proportions to vary randomly in the designated ranges subject to the constraint that they add up to 1.00. Then, the ratios that emerge from the actual observed data at the bank level are used to further disaggregate the simulated profit centre data into interest income and non-interest income.
A similar procedure is followed in the estimation of total inputs corresponding to each profit centre by allowing the proportions to vary in the designated ranges, followed by disaggregating the simulated data to estimate profit centre interest expenses and non-interest expenses. This approach brings a certain level of realism to the simulation exercise.
Would you summarize the significance of your paper
in layman's terms?
In the dynamic, innovative, global environment of today, organizations are invariably complex. Nevertheless, they need to be versatile enough to deliver their promised outcomes to various stakeholders. Furthermore, in the presence of cyclical economic conditions and uncertain federal budgets, identifying inefficiencies becomes more critical for long-term survival. The need for identifying inefficiencies holds equally true for non-profit, as well as for profit-making organizations.
Another argument in support of identifying inefficiencies is its contribution to organizational learning. An organization that is not constantly acquiring knowledge, sourced internally from various divisions, or externally, is condemned to lose its competitive advantage. The focus of managers is seen to be shifting from strategic planning to organizational learning—that is, nurturing skills that would help an organization to respond to change quickly and effectively.
The study posits that responding to change can be more effective if management can track where the operational inefficiencies are located. Thus, efficiency analysis of organizational divisions can be an integral part of organizational learning and a source of competitive advantage.
In an age of global economy characterized by cross-border, fierce competition, business organizations are increasingly forced to look for new ways of identifying inefficiencies in order to sustain their competitive advantage in the market. The paper illustrates how NDEA can address the above need by providing insight to the specific sources of organizational process inefficiency in the context of UAE domestic commercial banks.
"...none of the existing applications of Network Data Envelopment Analysis (NDEA) had used the Slacks-based Measure, which sets the this paper apart from extant literature as the first illustration of a non-oriented, non-radial measure in NDEA..."
By helping management open the "black box" of production, NDEA gives access to the underlying diagnostic information in divisions (i.e., profit centers) that would otherwise remain undiscovered.
How did you become involved in this research, and
how would you describe the particular challenges, setbacks, and
successes that you've encountered along the way?
Part of my motivation in writing this paper was the unfolding Global Financial Crisis (GFC). I wanted to contribute to the organizational toolkit for operating more efficiently, in particular, during harsh economic times. Another motivation was, of course, the fact that no one had previously published an application of NDEA in banking. The biggest challenge was devising a method that would link actual observed data with simulated data in a meaningful fashion.
Where do you see your research leading in the
future?
I continue to research into bank performance and challenges facing management. Some of my more recent work includes network efficiency and accounting for the impact of the environment on performance, and combining Data Envelopment Analysis (DEA) in innovative ways with other methods to predict bank stock performance.
I always try and identify the managerial implications of methodologies I develop or apply, which, unfortunately, is not common practice in mathematical Operations Research literature. I see performance analysis and productivity benchmarking of the service sector a fertile and worthwhile field for research.
The service sector holds substantial challenges for productivity analysis because most service delivery is often heterogeneous, simultaneous, intangible, and perishable. Nevertheless, the prospects for future studies are promising as we continue to gently push the DEA research envelope by using more innovative research designs that include synergistic partnerships with other methods and disciplines, as well as delve deeper into organizational networks.
Do you foresee any social or political
implications for your research?
As world population grows in an environment of global warming, scarce
resources will be more intensely contested. Operating efficiently
will continue to become increasingly important to organizations of all
kinds if we are to maintain the level of prosperity we take for granted in
developed countries. As expectations about quality of life rise among the
populations of developing countries, the need for efficient operations will
gain universal prominence.
Necmi K Avkiran, Ph.D., SA Fin, MASOR
Senior Lecturer in Financial Studies
UQ Business School
The University of Queensland
Brisbane, Australia
KEYWORDS: NETWORK DEA, EFFICIENCY, SIMULATION, BANKING, DATA ENVELOPMENT ANALYSIS, COMMERCIAL BANKS, PERFORMANCE, MODELS, UNITS, IMPACT.