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

Abel and
Bara (2017) discussed both theoretical and empirical literature on banking
sector efficiency in Zimbabwe. The theoretical literature dwells on the
discussion on e?ciency and how it impacts the banking system. The empirical
literature discusses a number of studies that have looked at the technical,
pure technical and scale e?ciency in the banking sector and the methodologies
used. The study
concluded that managerial e?ciency scores were higher than technical e?ciency
scores, 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 of
banks were operating at the wrong scale of operations. Speci?cally the banks
were operating under decreasing returns to scale, where there is still
opportunity to increase operations to obtain optimum scale. The study hence
recommended the banks to work on their pure technical efficiencies in order to
increase their efficiencies.

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 out
if there are significant differences among the firms belonging to different
types of ownership. From the analysis, it is found that 9 firms are overall
technical efficient, and 19 firms are pure technical efficient, while the
remaining 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 can
reduce their inputs by 14.2 percent of their observed levels without affecting
output levels. The results also show that 9 firms are scale efficient, while
remaining 41 firms are scale inefficient. On the basis of super-efficiency
scores, firms have been ranked.

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Agha,
Kuhail, Abdelnabi, Salem, Ghanim (2011) evaluated the relative technical
efficiencies of the academic departments of the Islamic University using DEA
model and concluded that the average efficiency score is 68.5% and that there
are 10 efficient departments out of the 30 studied. It is noted that departments
in the faculty of science, engineering and information technology have to
greatly reduce their laboratory expenses.

Tavana ,
Khakbaz and Songhori (2009) studied IT investment impacts on productivity in 20
public conventional power plants built between 1967 and 2006 in Iran.1 All
power plants are fossil fuel plants that burn diesel, oil and/or natural gas to
produce electricity. These power plants are designed on a large scale for
continuous operations and provide most of the electrical energy in Iran.
They used a two-stage DEA model
to decompose IT investment impacts on productivity in 20 public conventional
power plants in Iran. The proposed model allowed the integration of production
and investment performance, and provided management with a comprehensive
performance evaluation system. The results from our correlation analysis
indicated that the IT budget has the utmost impact on availability and
production efficiencies. Our results indicate that IT plays an important role
in the effective and efficient generation of electricity in conventional power
plants.

Sohn and
Moon (2004) used a proposed approach in the context of efficiency measurement
on 131 IT technology scenarios with six inputs, three outputs, and nine
grouping criteria considered as environment factors. They obtained the
posterior probability for the effective commercialization project when it has
only the information about the environmental factors.

Tehrani
,Mehragan, Golkani (2012)  applied the financial ratios as the
input and output indices of the DEA model and evaluated the financial performance of companies 36
companies in Iran and concluded that out of 36 companies examined through the
study, 9 companies were regarded as efficient while the remaining 27 companies
were determined as inefficient implying that the model can accurately measure
companies’ performance. The selected indexes and the derived model can
efficiently investigate and compare the companies’ financial positions as well.

Bahrani and
Khedri (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 is
the comparison of inputs and outputs of a series of decision-making units with
efficiency appraisal related to them. The results indicated that the portfolio created by data
envelopment analysis offers a higher return than the average return of industry;
the results show that BCC model of data envelopment analysis confirms the claim
and the portfolio created using this model had a better performance using
Sharpe criterion. However, the portfolio created by CCR model of data
envelopment analysis was unable to create a return higher than the average of
industry. It seems that it occurred due to the weakness of distinctive power in
the model. In the model, the higher the number of decision-making departments
is, the higher the efficiency of the model will be.

Sharma ,
Momaya , Manohar (2010) applied Data Envelopment Analysis (DEA) to different
service providers first and then to the area circles. From the results, Bharti
Airtel, Vodafone Aircel and BSNL came out to be the most efficient service
providers. Another important but surprising insight is that MTNL, Reliance and
Tata Teleservices have shown the lowest efficiency levels. Therefore there is
tremendous scope for improvement in resource utilization in these firms.

Dubrovnik (2001)
used the Data envelopment analysis to evaluate the efficiency of Croatian Banking
market from the period 1995 to 2000 using the years for which relatively
reliable balance sheets were available and also a period in which the
macroeconomic environment was stable. They came to conclusion that foreign
owned banks are more efficient followed by the new banks which are more
efficient than old ones. In terms of size they concluded that smaller banks are
globally efficient and larger banks are locally efficient. Another conclusion
reached was that, since 1995 there was strong equalization in terms of average
efficiencies happened in Croatian banking .On average the most slippery
territory appeared to them was ,in which medium sized banks operates. Their
relative inefficiency was attributable to more to the fact that they are
regional banks, than their size.

Pale?ková1
(2015) used DEA model to examine the e?ciency of the banking sectors in
Visegrad countries during the period 2009–2013. The results show that average e?ciency was slightly
decreasing within the period 2010–2011.A signi? cant decrease in e?ciency that
occurred in 2012, was probably as a result of ? nancial crisis. After that
average e?ciency increased in 2013. This ?nding con? rms results that were
obtained by Anayiotos et al. (2010) who presented that banking e?ciency
decreased during the crisis period. The efficiency values that were obtained
from BCC model came out to be higher than that obtained by CCR model .This was
basically obtained by eliminating the part of ine?ciency that is caused by a
lack of size of production units. We found that the Czech banking sector was
the highest e?cient under the assumptions of constant return to scale. On the
other hand, the Hungarian banking sector was the most e?cient under the variable
return to scale. Because the Hungarian banking sectors was the lowest e?cient
in CCR model, it shows that the Hungarian commercial banks, especially large
banks in the market, have improperly chosen their scale size. The lowest e?cient
were the Polish and Slovak banking sectors. Our result is consistent with the
conclusion of Stavárek and Polou?ek (2004), Stavárek (2005) or Melecký and
Staní?ková (2012) who evaluated the Czech banking industry as the highest e?cient.

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