Abel are technically ine?cient. The technical ine?ciency is

Abel andBara (2017) discussed both theoretical and empirical literature on bankingsector efficiency in Zimbabwe. The theoretical literature dwells on thediscussion on e?ciency and how it impacts the banking system. The empiricalliterature discusses a number of studies that have looked at the technical,pure technical and scale e?ciency in the banking sector and the methodologiesused. The studyconcluded that managerial e?ciency scores were higher than technical e?ciencyscores, implying that commercial banks in Zimbabwe are technically ine?cient.The technical ine?ciency is a result of scale ine?ciency, i.

e. the majority ofbanks were operating at the wrong scale of operations. Speci?cally the bankswere operating under decreasing returns to scale, where there is stillopportunity to increase operations to obtain optimum scale. The study hencerecommended the banks to work on their pure technical efficiencies in order toincrease their efficiencies.

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Mahajan ,Nauriyal ,Singh (2014) measured the technical efficiency, input-output slacks,and ranking of individual firms as per the ownership type in order to find outif there are significant differences among the firms belonging to differenttypes of ownership. From the analysis, it is found that 9 firms are overalltechnical efficient, and 19 firms are pure technical efficient, while theremaining firms are inefficient. The average of PTE is worked out to be 0.858,which suggests that given the scale of operation, on an average, firms canreduce their inputs by 14.

2 percent of their observed levels without affectingoutput levels. The results also show that 9 firms are scale efficient, whileremaining 41 firms are scale inefficient. On the basis of super-efficiencyscores, firms have been ranked.Agha,Kuhail, Abdelnabi, Salem, Ghanim (2011) evaluated the relative technicalefficiencies of the academic departments of the Islamic University using DEAmodel and concluded that the average efficiency score is 68.5% and that thereare 10 efficient departments out of the 30 studied.

It is noted that departmentsin the faculty of science, engineering and information technology have togreatly reduce their laboratory expenses.Tavana ,Khakbaz and Songhori (2009) studied IT investment impacts on productivity in 20public conventional power plants built between 1967 and 2006 in Iran.1 Allpower plants are fossil fuel plants that burn diesel, oil and/or natural gas toproduce electricity. These power plants are designed on a large scale forcontinuous operations and provide most of the electrical energy in Iran.They used a two-stage DEA modelto decompose IT investment impacts on productivity in 20 public conventionalpower plants in Iran. The proposed model allowed the integration of productionand investment performance, and provided management with a comprehensiveperformance evaluation system.

The results from our correlation analysisindicated that the IT budget has the utmost impact on availability andproduction efficiencies. Our results indicate that IT plays an important rolein the effective and efficient generation of electricity in conventional powerplants.Sohn andMoon (2004) used a proposed approach in the context of efficiency measurementon 131 IT technology scenarios with six inputs, three outputs, and ninegrouping criteria considered as environment factors.

They obtained theposterior probability for the effective commercialization project when it hasonly the information about the environmental factors.Tehrani,Mehragan, Golkani (2012)  applied the financial ratios as theinput and output indices of the DEA model and evaluated the financial performance of companies 36companies in Iran and concluded that out of 36 companies examined through thestudy, 9 companies were regarded as efficient while the remaining 27 companieswere determined as inefficient implying that the model can accurately measurecompanies’ performance. The selected indexes and the derived model canefficiently investigate and compare the companies’ financial positions as well.Bahrani andKhedri (2013) used DEA model for selecting portfolio in Tehran Stock exchange.This technique enables us to overcome two drawbacks of Markowitz Model.

Data Envelopment Analysis method isthe comparison of inputs and outputs of a series of decision-making units withefficiency appraisal related to them. The results indicated that the portfolio created by dataenvelopment analysis offers a higher return than the average return of industry;the results show that BCC model of data envelopment analysis confirms the claimand the portfolio created using this model had a better performance usingSharpe criterion. However, the portfolio created by CCR model of dataenvelopment analysis was unable to create a return higher than the average ofindustry. It seems that it occurred due to the weakness of distinctive power inthe model. In the model, the higher the number of decision-making departmentsis, the higher the efficiency of the model will be.Sharma ,Momaya , Manohar (2010) applied Data Envelopment Analysis (DEA) to differentservice providers first and then to the area circles. From the results, BhartiAirtel, Vodafone Aircel and BSNL came out to be the most efficient serviceproviders. Another important but surprising insight is that MTNL, Reliance andTata Teleservices have shown the lowest efficiency levels.

Therefore there istremendous scope for improvement in resource utilization in these firms.Dubrovnik (2001)used the Data envelopment analysis to evaluate the efficiency of Croatian Bankingmarket from the period 1995 to 2000 using the years for which relativelyreliable balance sheets were available and also a period in which themacroeconomic environment was stable. They came to conclusion that foreignowned banks are more efficient followed by the new banks which are moreefficient than old ones. In terms of size they concluded that smaller banks areglobally efficient and larger banks are locally efficient.

Another conclusionreached was that, since 1995 there was strong equalization in terms of averageefficiencies happened in Croatian banking .On average the most slipperyterritory appeared to them was ,in which medium sized banks operates. Theirrelative inefficiency was attributable to more to the fact that they areregional banks, than their size.Pale?ková1(2015) used DEA model to examine the e?ciency of the banking sectors inVisegrad countries during the period 2009–2013. The results show that average e?ciency was slightlydecreasing within the period 2010–2011.A signi? cant decrease in e?ciency thatoccurred in 2012, was probably as a result of ? nancial crisis. After thataverage e?ciency increased in 2013.

This ?nding con? rms results that wereobtained by Anayiotos et al. (2010) who presented that banking e?ciencydecreased during the crisis period. The efficiency values that were obtainedfrom BCC model came out to be higher than that obtained by CCR model .This wasbasically obtained by eliminating the part of ine?ciency that is caused by alack of size of production units. We found that the Czech banking sector wasthe highest e?cient under the assumptions of constant return to scale. On theother hand, the Hungarian banking sector was the most e?cient under the variablereturn to scale.

Because the Hungarian banking sectors was the lowest e?cientin CCR model, it shows that the Hungarian commercial banks, especially largebanks in the market, have improperly chosen their scale size. The lowest e?cientwere the Polish and Slovak banking sectors. Our result is consistent with theconclusion of Stavárek and Polou?ek (2004), Stavárek (2005) or Melecký andStaní?ková (2012) who evaluated the Czech banking industry as the highest e?cient.

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