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Journal articles on the topic 'Cost efficiency analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

Ngo, Thanh, and David Tripe. "Stochastic cost frontier analysis – a sensitivity analysis on cost measures." Pacific Accounting Review 28, no. 4 (November 7, 2016): 401–10. http://dx.doi.org/10.1108/par-03-2016-0028.

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Purpose This paper aims to examine alternative methods for recording and treating costs in studies of bank efficiency. Design/methodology/approach This study used stochastic frontier analysis (SFA) models with core costs and total costs to estimate the cost efficiency of banks in two different economies, Vietnam where the banking system is under-developed (and thus is dominated by traditional banking activities) and New Zealand where the banking system is well-developed (and thus non-traditional banking activities play an important role). Findings The authors found that models using total cost tend to underestimate the banks’ cost efficiency. This underestimation relates to the extent of modern activities in a banking system: it is larger in an advanced banking system (i.e. New Zealand) and smaller in a less-developed banking system (i.e. Vietnam). Research limitations/implications Research is limited to two countries, and it would be useful to apply the same technique to other data sets. Practical implications The paper suggests a new approach to cost SFA studies in banking. Originality/value The paper provides a much more searching analysis of costs in banking than has generally been seen in previous research.

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2

Nure, Gazmend. "Cost Efficiency Analysis in the Banking Industry." International Journal of Corporate Finance and Accounting 7, no. 2 (July 2020): 1–20. http://dx.doi.org/10.4018/ijcfa.2020070101.

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The efficiency of commercial banks is essential for the stability of banks by implying that banks that take higher risks are more inefficient. This paper builds on a stochastic heteroscedastic boundary model, where one will analyze a sample of 70 banks in Western Balkan countries such as Albania, Northern Macedonia, Serbia, Kosovo, and Montenegro for the period 2007-2017, highlighting determinants of bank cost efficiency. Banks with less liquidity, with a lower solvency rate and a higher credit risk, are more ineffective than prudent credit institutions. The paper also aims to address the relative lack of studies on the efficiency of banks in the region through the use of the stochastic frontier analysis (SFA).

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3

Clinch, J. Peter, and John D. Healy. "Cost-benefit analysis of domestic energy efficiency." Energy Policy 29, no. 2 (January 2001): 113–24. http://dx.doi.org/10.1016/s0301-4215(00)00110-5.

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4

Cesaroni, Giovanni. "Industry cost efficiency in data envelopment analysis." Socio-Economic Planning Sciences 61 (March 2018): 37–43. http://dx.doi.org/10.1016/j.seps.2017.01.001.

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5

Kangaspunta, Jussi, Juuso Liesiö, and Ahti Salo. "Cost-efficiency analysis of weapon system portfolios." European Journal of Operational Research 223, no. 1 (November 2012): 264–75. http://dx.doi.org/10.1016/j.ejor.2012.05.042.

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6

Mamasoliev, G'ayratbek Mahamadyusupovich. "Methodological Approaches To Analysis Of The Food Industry Cost Chain And Estimation Of Its Efficiency." American Journal of Applied sciences 03, no. 04 (April 30, 2021): 138–44. http://dx.doi.org/10.37547/tajas/volume03issue04-19.

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In this article commented methodological approaches on analysis of value chain, and highlighted an aim and tasks of the methods of analysis, besides developed recommendations about using them on practice.

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7

Winkler, Harald, Randall Spalding-Fecher, Lwazikazi Tyani, and Khorommbi Matibe. "Cost-benefit analysis of energy efficiency in urban low-cost housing." Development Southern Africa 19, no. 5 (December 2002): 593–614. http://dx.doi.org/10.1080/03768835022000019383.

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8

Qing, Cheng Lin, JuMong Na, Seog Ju Chang, and Chang Uk Im. "The Cost Efficiency Analysis of JeollaNamdo Food Industry." Journal of the Korean society for quality management 43, no. 4 (December 31, 2015): 533–44. http://dx.doi.org/10.7469/jksqm.2015.43.4.533.

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9

Abel, Sanderson, Alex Bara, and Pierre Le Roux. "Evaluating Bank Cost Efficiency Using Stochastic Frontier Analysis." Journal of Economics and Behavioral Studies 11, no. 3(J) (July 18, 2019): 48–57. http://dx.doi.org/10.22610/jebs.v11i3(j).2868.

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The study seeks to assess the cost efficiency of the commercial banks in Zimbabwe using the stochastic frontier analysis. The cost efficiency of the Zimbabwean banks is estimated using the trans-log stochastic frontier approach. The Stochastic Frontier Analysis methodology is among the host of methods that has been used to measure banking sector efficiency. The analysis of cost efficiency of commercial banks has important implications for the economy since an efficient banking system has potential to reduce interest rates which can lead to increased investment and growth for the economy. The cost of doing business in Zimbabwe is perceived to be high hence improved bank efficiency has the potential to reduce the cost of doing business. The average cost efficiency scores for the Zimbabwean banks over the study period show that the banking sector in Zimbabwe experiencing 17 percent inefficiency. The efficiency levels have been declining over the years reflecting increased resource wastage in the system. The study recommends that the banking institutions should continue to innovate so as to reduce their inefficiencies.

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10

Zhu, Weiquan, Xiaoguang Yang, Hongwei Ge, and Binglei Xie. "Cost-efficiency of buses using stochastic frontier analysis." Proceedings of the Institution of Civil Engineers - Transport 170, no. 4 (August 2017): 185–93. http://dx.doi.org/10.1680/jtran.14.00084.

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11

Kim, S. K., M. S. Lee, W. I. Ko, Yanghon Chung, and Sungsig Bang. "Cost efficiency analysis of disposal alternatives in Korea." Progress in Nuclear Energy 53, no. 6 (August 2011): 722–28. http://dx.doi.org/10.1016/j.pnucene.2011.04.004.

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12

Ryan, Michael J. "Data envelopment analysis, cost efficiency and performance targetting." Journal of Information and Optimization Sciences 23, no. 2 (May 2002): 241–58. http://dx.doi.org/10.1080/02522667.2002.10698994.

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13

Buzad, Francis A., Louis M. Corne, Thomas C. Brown, Randy S. Fagin, April E. Hebert, Charles A. Kaczmarek, Angie N. Pack, and Thomas N. Payne. "Single-site robotic cholecystectomy: efficiency and cost analysis." International Journal of Medical Robotics and Computer Assisted Surgery 9, no. 3 (May 2, 2013): 365–70. http://dx.doi.org/10.1002/rcs.1507.

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14

Anjani, Andan, and Adirizal Nizar. "Inventory management and cost efficiency." International Journal of Research in Business and Social Science (2147- 4478) 10, no. 2 (March 21, 2021): 217–27. http://dx.doi.org/10.20525/ijrbs.v10i2.1042.

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This research aims to observe current inventory management applied by one of the medical equipment distributors specializing in eye health devices, and provide recommendations for an optimal inventory management system to achieve cost efficiency afterward. The method used in this research is quantitative, focusing on processing and analyzing numerical data obtained from the company to calculate safety stock and the number of orders. In addition, ABC classification is also used in data processing to group items based on their value. Items belonging to class A (having a value of 80% for the company) will be used in the data processing. The next step is to conduct forecasting simulations for demand forecasts. The results of the forecast will be used for calculating the safety stock and order quantity. The output from the results of data processing and analysis in this study shows 67 items included in class A, which will then be processed using Minitab software for forecasting. Based on comparing the four forecasting methods, the one with the lowest error value is Trend Analysis. If the company can apply the storage method according to the fixed-time period model calculation, there is a potential savings of US $ 717,133 or 63% of the total overstock

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15

정수관 and Sangmok Kang. "Efficiency Analysis of Public Library by DEA Cost Minimization." Journal of Cultural Policy 27, no. 2 (August 2013): 144–63. http://dx.doi.org/10.16937/jcp.27.2.201308.144.

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16

Akyazıcı, Fahrettin, and Mehmet Fatih Altan. "Efficiency and Cost Analysis of Natural Masonry Construction Manufacturing." Journal of Sustainable Construction Materials and Technologies 2, no. 2 (December 31, 2017): 108–19. http://dx.doi.org/10.29187/jscmt.2018.15.

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17

Crespo, C., A. Monleon, W. Díaz, C. Rodriguez, M. Brosa, and M. Rios. "Comparative Efficiency Research: Meta-Analysis of Cost-Effectiveness Studies." Value in Health 16, no. 7 (November 2013): A617. http://dx.doi.org/10.1016/j.jval.2013.08.1794.

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18

Philippon, Thomas. "Efficiency and Benefit-Cost Analysis of the Financial System." Journal of Legal Studies 43, S2 (June 2014): S107—S120. http://dx.doi.org/10.1086/676685.

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19

Kashian, Russell, Elaine Yiling Lin, and Yuhan Xue. "Cost Efficiency Analysis of Local Commercial Banks in Taiwan." Asian Economic Journal 33, no. 1 (March 2019): 87–108. http://dx.doi.org/10.1111/asej.12170.

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20

Merdad, M., NE Wolter, SL Cushing, KA Gordon, and BC Papsin. "Surgical efficiency in bilateral cochlear implantation: A cost analysis." Cochlear Implants International 15, no. 1 (November 25, 2013): 43–47. http://dx.doi.org/10.1179/1754762813y.0000000042.

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21

Koop, Gary, Jacek Osiewalski, and Mark F. J. Steel. "Bayesian efficiency analysis through individual effects: Hospital cost frontiers." Journal of Econometrics 76, no. 1-2 (January 1997): 77–105. http://dx.doi.org/10.1016/0304-4076(95)01783-6.

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22

Farsi, Mehdi, and Massimo Filippini. "An analysis of cost efficiency in Swiss multi-utilities." Energy Economics 31, no. 2 (March 2009): 306–15. http://dx.doi.org/10.1016/j.eneco.2008.11.009.

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23

Lozano, Sebastián. "Scale and cost efficiency analysis of networks of processes." Expert Systems with Applications 38, no. 6 (June 2011): 6612–17. http://dx.doi.org/10.1016/j.eswa.2010.11.077.

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24

Sengupta, Jati K. "Cost Efficiency and Demand Fluctuation under Data Envelopment Analysis." OPSEARCH 39, no. 3-4 (August 2002): 161–76. http://dx.doi.org/10.1007/bf03398678.

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25

Jahanshahloo, G. R., M. Soleimani-damaneh, and A. Mostafaee. "On the computational complexity of cost efficiency analysis models." Applied Mathematics and Computation 188, no. 1 (May 2007): 638–40. http://dx.doi.org/10.1016/j.amc.2006.10.030.

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26

Welch, Eric, and Darold Barnum. "Joint environmental and cost efficiency analysis of electricity generation." Ecological Economics 68, no. 8-9 (June 2009): 2336–43. http://dx.doi.org/10.1016/j.ecolecon.2009.03.004.

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27

Fontani, Alessio, and Luca Vitali. "Cost Efficiency of Italian Commercial Banks:A Stochastic Frontier Analysis." Universal Journal of Industrial and Business Management 2, no. 3 (March 2014): 80–91. http://dx.doi.org/10.13189/ujibm.2014.020304.

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28

Hamilton, D., C. Hulme, L. Flood, and S. Powell. "Cost–utility analysis and otolaryngology." Journal of Laryngology & Otology 128, no. 2 (February 2014): 112–18. http://dx.doi.org/10.1017/s0022215114000048.

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AbstractAs providers of health care, we face increasing demand on our limited, indeed diminishing, resources. Economic appraisal of our interventions means assessing the trade-off between effectiveness, efficiency and equity. When rationing becomes inevitable, calculation of utility values is a valuable decision-making tool. This paper reviews objective measures of patient benefit, such as quality of life, and focuses on their application within otolaryngology.

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29

Mishra, Mukesh Kumar, and Aditya Trivedi. "Spectral Efficiency and Deployment Cost Efficiency Analysis of mmW/UHF-Based Cellular Network." IEEE Transactions on Vehicular Technology 68, no. 7 (July 2019): 6565–77. http://dx.doi.org/10.1109/tvt.2019.2914699.

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30

Kyshakevych, Bohdan, and Dmytro Mazharov. "A STOCHASTIC FRONTIER ANALYSIS OF UKRAINIAN BANKS EFFICIENCY." Scientific Journal of Polonia University 31, no. 6 (December 20, 2018): 27–40. http://dx.doi.org/10.23856/3103.

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The authors have developed a model for assessing the Ukrainian banks cost efficiency by means of Stochastic Frontier Analysis, in which the banks are considered as financial intermediaries. On the basis of the likelihood ratio test results the trans-logarithmic function steps forward in the role of the bank’s cost function. In the role of input variables we used the value of fixed assets, the price of labor and price of loanable fund, in the role of output − total loans, other assets and total cost.

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31

Bhatia, Aparna, and Megha Mahendru. "Cost Efficiency Vis-à-Vis Revenue Efficiency Analysis of Indian Scheduled Commercial Banks in a Dynamic Environment." NMIMS Management Review 29, no. 02 (April 12, 2021): 53–72. http://dx.doi.org/10.53908/nmmr.290204.

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The paper endeavours to analyze Cost Efficiency vis-à-visRevenue Efficiency of Scheduled Commercial Banks (SCBs) as well as across ownership in India. Data Envelopment Analysis (DEA) has been employed to calculate the efficiency scores of SCBs over five points of time i.e. 2000-01, 2004-05, 2008- 09, 2012-13 and 2016-17. The differences in the efficiency scores are examined by applying Analysis of Variance (ANOVA). The results of Cost and Revenue Efficiency of Indian Scheduled Commercial Banks highlight that the highest level of inefficiency subsist on the cost side as Scheduled Commercial Banks have higher Revenue Efficiency scores in comparison to Cost Efficiency scores. Cost Efficiency across ownership shows that Public Sector Banks have higher Cost Efficiency in 2000-01. Private Sector Banks are cost efficient in 2004-05 while Foreign Sector Banks show higher Cost Efficiency scores in 2008-09, 2012-13 and 2016-17. Revenue Efficiency scores shows that Public Sector Banks have higher scores as compared to Private and Foreign Sector Banks in the 2000-01 and 2004-05. Foreign Sector Banks are revenue efficient in 2008-09 and 2016-17 with Private Sector Banks taking the lead in 2012-13. The results of ANOVA reveal that there exists a statistically significant difference in Cost Efficiency and Revenue Efficiency among banks in different sectors over different points of time.

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32

Kyrö, Riikka, Antti Peltokorpi, and Karlos Artto. "Connectivity, cost-efficiency, community and collaboration." Facilities 34, no. 13/14 (October 3, 2016): 873–90. http://dx.doi.org/10.1108/f-05-2015-0032.

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Purpose This paper aims to increase understanding on how co-locating in a multi-firm campus setting could be of value to healthcare organizations. Design/methodology/approach The paper presents a qualitative case study of two health campuses in Finland. The data comprises interviews with different organizations operating on the campuses, complemented by onsite observations, and analysis of archival data. Findings Based on the empirical analysis, the value of co-locating as perceived by the organizations operating on campus is grouped into four categories: connectivity, cost-efficiency, community and collaboration (or the “four Cs”). Research limitations/implications The study does not aim at statistical genaralizability but rather seeks to draw analytical generalizations based on identified empirical regularities. The developed value framework, the four Cs, contributes to current scholarly knowledge on location strategies. Practical implications Furthermore, the managerial implications of the four Cs entail a new twofold role for property management: the traditional facilitator role, which is suitable for delivering the two tangible values of connectivity and cost-efficiency, and the modern era integrator, a community builder that is able to deliver community and collaboration. Originality/value Previous literature on healthcare facilities has focused on the technical performance of the buildings, while previous literature on the collaborative value of co-location has studied mainly single-firm corporate campuses. This study uniquely explores the potential value of health campuses, where different private, public and third sector organizations co-locate.

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33

Sakouvogui, Kekoura. "A comparative approach of stochastic frontier analysis and data envelopment analysis estimators: evidence from banking system." Journal of Economic Studies 47, no. 7 (May 4, 2020): 1787–810. http://dx.doi.org/10.1108/jes-01-2019-0051.

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PurposeThe consistency of stochastic frontier analysis (SFA) and data envelopment analysis (DEA) cost efficiency measures using a sample of 650 commercial and domestic banks in the United States is investigated based on cluster analysis while accounting for the yearly variation in banks.Design/methodology/approachDue to the importance of efficiency measures for policy and managerial decision-making, the cost efficiency measures of SFA and DEA estimators are examined according to four criteria: levels, rankings, stability over time and stability over clustering groups. In this paper, we present two clustering methods, Gap Statistic and Dindex, that involve SFA and DEA cost efficiency measures. The clustering approach creates homogeneous groups of banks offering a similar mix of efficiency levels. Hence, each evaluated bank knows the cluster to which it belongs. Furthermore, this paper provides nonparametric statistical tests of SFA and DEA cost efficiency measures estimated with and without a clustering approach.FindingsThe results suggest that the clustering approach plays a considerable role in the rankings of US banks. Furthermore, the average SFA and DEA cost efficiency measures over time of the homogeneous US banks are substantially higher than those of the heterogeneous US banks.Originality/valueThis research is the first to provide comparative efficiency measures needed for desirable policy conclusions of heterogeneous and homogeneous US banks.

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34

Picken, David H., and Stephen Mak. "Risk analysis in cost planning and its effect on efficiency in capital cost budgeting." Logistics Information Management 14, no. 5/6 (December 2001): 318–29. http://dx.doi.org/10.1108/eum0000000006244.

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35

Horne, Jocelyn, and Baiding Hu. "Estimation of cost efficiency of Australian universities." Mathematics and Computers in Simulation 78, no. 2-3 (July 2008): 266–75. http://dx.doi.org/10.1016/j.matcom.2008.01.018.

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36

Aghayi, Nazila, and Samira Salehpour. "A generalized fuzzy cost efficiency model." RAIRO - Operations Research 54, no. 6 (September 16, 2020): 1775–91. http://dx.doi.org/10.1051/ro/2019102.

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The concept of cost efficiency has become tremendously popular in data envelopment analysis (DEA) as it serves to assess a decision-making unit (DMU) in terms of producing minimum-cost outputs. A large variety of precise and imprecise models have been put forward to measure cost efficiency for the DMUs which have a role in constructing the production possibility set; yet, there’s not an extensive literature on the cost efficiency (CE) measurement for sample DMUs (SDMUs). In an effort to remedy the shortcomings of current models, herein is introduced a generalized cost efficiency model that is capable of operating in a fuzzy environment-involving different types of fuzzy numbers-while preserving the Farrell’s decomposition of cost efficiency. Moreover, to the best of our knowledge, the present paper is the first to measure cost efficiency by using vectors. Ultimately, a useful example is provided to confirm the applicability of the proposed methods.

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37

BROWN, T. M., M. M. BECK, D. D. SCHULTE, D. D. JONES, J. H. DOUGLAS, and S. E. SCHEIDELER. "Nipple Waterers for Chick Batteries: Design, Efficiency, and Cost Analysis." Poultry Science 74, no. 3 (March 1995): 457–62. http://dx.doi.org/10.3382/ps.0740457.

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38

López-Neblina, F., H. Alvarez Jiménez, and I. Finkelstein London. "High efficiency kidney transplantation: concept, technique, results, and cost analysis." Transplantation Proceedings 31, no. 7 (November 1999): 3025. http://dx.doi.org/10.1016/s0041-1345(99)00652-1.

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39

Lopez-Neblina, F., J. H. Alvarez, and L. I. Finkelstein. "High-efficiency kidney transplantation: concept, technique, results, and cost analysis." Transplantation Proceedings 32, no. 1 (February 2000): 141–42. http://dx.doi.org/10.1016/s0041-1345(99)00925-2.

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40

Dempsey-Brench, Zara, and Nicola Volta. "A cost-efficiency analysis of European air navigation service providers." Transportation Research Part A: Policy and Practice 111 (May 2018): 11–23. http://dx.doi.org/10.1016/j.tra.2018.02.019.

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41

Ghosh, Simantika, and Jyotsna Goswami. "Cost-efficiency Analysis of USG-guided Transversus Abdominis Plane Blocks." International Journal of Perioperative Ultrasound and Applied Technologies 2 (2013): 115–17. http://dx.doi.org/10.5005/jp-journals-10027-1048.

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42

Merkert, Rico, Andrew S. J. Smith, and Chris A. Nash. "Benchmarking of train operating firms – a transaction cost efficiency analysis." Transportation Planning and Technology 33, no. 1 (February 2010): 35–53. http://dx.doi.org/10.1080/03081060903429330.

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43

Akkaya, Onur. "Cost efficiency analysis of Swedish financial enterprises: An empirical investigation." European Research on Management and Business Economics 22, no. 1 (January 2016): 31–37. http://dx.doi.org/10.1016/j.iedee.2013.10.001.

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44

Abid, Ihsen, and Mohamed Goaied. "Consideration of technological and environmental heterogeneity in cost efficiency analysis." International Review of Applied Economics 29, no. 5 (June 29, 2015): 650–76. http://dx.doi.org/10.1080/02692171.2015.1054368.

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45

Mostafaee, A., and F. H. Saljooghi. "Cost efficiency measures in data envelopment analysis with data uncertainty." European Journal of Operational Research 202, no. 2 (April 2010): 595–603. http://dx.doi.org/10.1016/j.ejor.2009.06.007.

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46

Reyna, Ana María, and Hugo J. Fuentes. "A cost efficiency analysis of the insurance industry in Mexico." Journal of Productivity Analysis 49, no. 1 (December 1, 2017): 49–64. http://dx.doi.org/10.1007/s11123-017-0521-7.

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47

King, Bonnie M., Paul Arents, and Nathalie Moreau. "Cost/efficiency evaluation of descriptive analysis panels — I. Panel size." Food Quality and Preference 6, no. 4 (January 1995): 245–61. http://dx.doi.org/10.1016/0950-3293(95)00024-0.

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48

Lotfi, F. Hosseinzadeh, A. Amirteimoori, Z. Moghaddas, and M. Vaez-Ghasemi. "Cost efficiency measurement with price uncertainty: a data envelopment analysis." Mathematical Sciences 14, no. 4 (August 26, 2020): 387–96. http://dx.doi.org/10.1007/s40096-020-00349-2.

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49

Huljak, Ivan. "Cost Efficiency of Banks in Croatia." Croatian Review of Economic, Business and Social Statistics 1, no. 1-2 (December 1, 2015): 12–26. http://dx.doi.org/10.1515/crebss-2016-0002.

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Abstract Foreign and larger banks in Croatia are generally considered to be more cost efficient compared with domestic and smaller banks. However, those views are often based on data from financial statements that can be misleading due to simultaneous consolidation process on the market and the existence of economies of scale. To contribute to the Croatian banking efficiency literature, we construct a panel of individual bank data for 1994-2014 period and conduct a frontier analysis to calculate bank specific X-efficiency. Our results suggest that efficiency scores depend on the cost definition as domestic and smaller banks are more efficient in managing administrative costs compared with foreign and larger banks but equally efficient in managing total costs. Results indicate that average bank relative efficiency increased on two occasions: one in the late 90s in the period of banking crisis and subsequent “market cleansing” and to a lesser extent in the period marked with financial crisis. Although the differences between bank cost efficiencies seem small, we conclude that the area is worth further research as significant gains in bank earnings could be achieved by increasing efficiency.

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50

Lukić, Radojko, and Dragana Vojteski-Kljenak. "Analysis of food retail efficiency in Serbia." Poslovna ekonomija 14, no. 2 (2020): 1–18. http://dx.doi.org/10.5937/poseko18-26786.

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It is well known that the problem of analyzing the efficiency of all companies, including trading companies, is very topical, significant and complex. In the context of the above mentioned, mathematical methods and models are increasingly being applied. With this in mind, this paper explores the effectiveness of food retailers in Serbia using AHP and DEA methods. Of all the observed optimization criteria (cost of goods sold, operating costs, gross margin and net profit), the most significant one was the cost of goods sold. The most efficient food retailer in Serbia is Delhaize Serbia. Mercator-S is inefficient according to DEA analysis. In order to improve the efficiency of food retailers in Serbia, it is necessary to look up to the Western models and apply new business models (private brand, multichannel sales, organic food sales and others) and new concepts of strategic management accounting as well as strengthen the digitalization of business.

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