Зеленская_резюме_англ (Оценка эффективности деятельности учреждений культуры (на примере театральных учреждений)), страница 3

This fact signifies thatefficiency of theatrical institutions does not depend strongly on variables analyzed and moregenerally speaking on the aspect of efficiency under analysis.Table 2. Correlation matrix between efficiency indicators within Cluster 3 (variable returnsto scale)Efficiency ModelsModels ‘Attendance’ and‘Revenues’Models ‘Attendance’ and‘Work with Children’Models ‘Revenues’ and‘Work with Children’20120.6748*20130.6858*20140.5546*20150.5196*20160.5506*2012-20160.5709*0.5418*0.4451*0.5078*0.5864*0.7261*0.5413*0.6978*0.7081*0.4030*0.7480*0.7238*0.6619** Correlation is significant at the 5% level and lower.

Source: own elaboration.Table 3. Correlation matrix between efficiency indicators within Cluster 2 (variable returnsto scale)11Efficiency ModelsModels ‘Attendance’ and‘Revenues’Models ‘Attendance’ and‘Work with Children’Models ‘Revenues’ and‘Work with Children’20120.6795*20130.5999*20140.6461*20150.6001*20160.7405*2012-20160.6488*0.7033*0.7083*0.8386*0.6109*0.8005*0.7242*0.8060*0.7482*0.7639*0.7616*0.7191*0.7587** Correlation is significant at the 5% level and lower. Source: own elaboration.Ninthly, conclusions regarding the influence of internal factors on theatres’ efficiencylevels were made.

We found out that the influence of different factors depends on the cluster whatsignifies a high level of heterogeneity of theatrical institutions. Theatres with higher values ofperformance indicators (Cluster 3) are characterized by statistically significant negative influenceof the number of new and re-staged performances on efficiency, statistically significant positiveinfluence of the number of performances at home stage(s) on efficiency and no significantinfluence of indicators that concern tours abroad. Theatres of the smaller Cluster 2 arecharacterized by statistically significant negative influence of the number of performances at homestage(s) and indicators that concern tours abroad.Moreover, the influence of theatre characteristics on efficiency was identified. Theatrelocation (region) has statistically significant influence on efficiency in Models ‘Revenues’ and‘Work with children’. Federal status has significant negative influence on efficiency in terms ofrevenues.

Opera and ballet genres have significant negative influence on efficiency in each modelin both clusters.The above-mentioned results are depicted in Tables 4 and 5.12Table 4. Results of regression analysis on Cluster 3VariablesNumber of new performancesNumber of re-staged performancesNumber of performances at home stage(s)Number of performances at other stages withinthe local areaNumber of tours outside the local area in RussiaNumber of tours abroadNumber of performances given during toursabroadNumber of children performancesRegionFederal statusTheatres for childrenOpera and ballet theatresYear 2013Year 2014Year 2015Year 2016ConstantNumber of observationsR-squared(1)Model 1‘Attendance’(2)Model 2‘Revenues’(3)Model 3 ‘Workwith children’-0.0204***(0.00614)-0.0301(0.0346)0.000587**(0.000239)0.000988(0.000804)0.00249(0.00162)-0.0184(0.0154)0.00173(0.00110)0.000278(0.000293)-0.0111(0.00709)-0.00793(0.0495)-0.380***(0.143)-0.161**(0.0643)-0.0883(0.0696)-0.0518(0.0679)0.0121(0.0744)-0.0691(0.0759)0.781***(0.0988)1230.441-0.00349(0.00575)-0.0111(0.0324)0.000164(0.000224)-0.00160**(0.000753)-0.00221(0.00151)0.0203(0.0144)-0.000296(0.00103)0.000825***(0.000274)-0.0121*(0.00664)-0.117**(0.0464)-0.469***(0.134)-0.213***(0.0602)-0.0441(0.0652)-0.148**(0.0635)-0.131*(0.0696)-0.127*(0.0711)0.953***(0.0925)1230.432-0.00897(0.00655)-0.0573(0.0369)-6.23e-05(0.000255)-6.46e-05(0.000857)-0.00384**(0.00172)-0.0110(0.0164)0.000268(0.00117)0.000963***(0.000312)-0.00595(0.00756)-0.0735(0.0528)-0.166(0.153)-0.267***(0.0685)-0.0579(0.0742)-0.238***(0.0724)-0.119(0.0793)-0.125(0.0809)0.989***(0.105)1230.411Note: standard errors in parenthesis, significance levels – ***p<0.01, **p<0.05, *p<0.1.Source: own elaboration.13Table 5.

Results of regression analysis on Cluster 2VariablesNumber of new performancesNumber of re-staged performancesNumber of performances at home stage(s)Number of performances at other stages withinthe local areaNumber of tours outside the local area in RussiaNumber of tours abroadNumber of performances given during toursabroadNumber of children performancesRegionFederal statusOpera and ballet theatresTheatres for childrenYear 2013Year 2014Year 2015Year 2016ConstantNumber of observationsR-squared(1)Model 1‘Attendance’-0.00596(0.00368)0.0122(0.0114)-0.000810***(0.000120)-0.000306(0.000410)-8.62e-05(0.00109)-0.0164*(0.00903)-0.00174*(0.000903)0.000746***(0.000124)-0.000226(0.000525)-0.441***(0.0521)-0.350***(0.0383)0.0356(0.0354)0.00374(0.0311)0.101***(0.0309)0.0320(0.0314)0.0360(0.0310)0.935***(0.0417)9480.228(2)Model 2‘Revenues’-0.00871**(0.00379)0.00893(0.0118)-0.00112***(0.000124)-0.000192(0.000422)-0.000997(0.00112)-0.0170*(0.00931)-0.000589(0.000931)0.00111***(0.000128)-0.00103*(0.000542)-0.383***(0.0537)-0.372***(0.0395)-0.0129(0.0365)-0.00737(0.0320)0.00491(0.0319)-0.0100(0.0324)-0.0334(0.0320)1.058***(0.0430)9480.227(3)Model 3 ‘Workwith children’-0.000723(0.00332)0.00857(0.0103)-0.000677***(0.000109)-0.00114***(0.000369)-0.00102(0.000979)-0.0141*(0.00814)-0.000597(0.000814)0.000672***(0.000112)-0.00101**(0.000474)-0.436***(0.0470)-0.361***(0.0345)-0.0493(0.0319)-0.00411(0.0280)0.0120(0.0279)-0.0104(0.0284)-0.00354(0.0280)1.010***(0.0376)9480.222Note: standard errors in parenthesis, significance levels – ***p<0.01, **p<0.05, *p<0.1.Source: own elaboration.Tenthly, the author has shown that efficiency level of theatrical institution has statisticallysignificant positive influence on consumer satisfaction level with a two- and three-year lags(Tables 6 and 7).14Table 6.

Influence of efficiency (Model 1 ‘Attendance’) on consumer satisfactionVariablesEfficiency indicator (2016)(1)0.0452(0.0620)Efficiency indicator (2015)(2)(3)(4)(5)0.0397(0.0601)Efficiency indicator (2014)0.194**(0.0793)Efficiency indicator (2013)0.105*(0.0609)Efficiency indicator (2012)0.0221(0.0586)Capacity of the main stage -0.000289*** -0.000299*** -0.000282*** -0.000286*** -0.000316***(8.38e-05)(9.04e-05)(8.86e-05)(9.23e-05)(9.35e-05)Total number of employees 0.000450*** 0.000392*** 0.000561*** 0.000489*** 0.000460***(0.000143)(0.000145)(0.000145)(0.000142)(0.000139)Share of budget funding-0.00326**-0.00288**-0.00288**-0.00309**-0.00294**(0.00132)(0.00146)(0.00141)(0.00145)(0.00144)Status (federal / local)-0.00970-0.05380.01990.01250.00329(0.0769)(0.0908)(0.0749)(0.0778)(0.0777)ConstantNumber of observationsR-squared4.707***(0.121)2274.705***(0.131)2004.521***(0.146)1934.638***(0.128)1824.712***(0.126)1860.0780.0700.1150.1030.087Note: standard errors in parenthesis, significance levels – ***p<0.01, **p<0.05, *p<0.1.Source: own elaboration.Table 7.

Influence of efficiency (Model 2 ‘Revenues’) on consumer satisfactionVariablesEfficiency indicator (2016)Efficiency indicator (2015)Efficiency indicator (2014)Efficiency indicator (2013)Efficiency indicator (2012)Capacity of the main stageTotal number of employeesShare of budget fundingStatus (federal / local)ConstantNumber of observationsR-squared(1)0.0596(0.0557)(2)(3)(4)(5)0.0233(0.0616)0.110*(0.0647)0.113*(0.0635)0.0849(0.0624)-0.000283*** -0.000302*** -0.000281*** -0.000284*** -0.000301***(8.40e-05)(9.06e-05)(9.07e-05)(9.24e-05)(9.27e-05)0.000450*** 0.000388*** 0.000505*** 0.000484*** 0.000495***(0.000139)(0.000148)(0.000143)(0.000141)(0.000140)-0.00318**-0.00295**-0.00333**-0.00311**-0.00293**(0.00132)(0.00146)(0.00141)(0.00145)(0.00143)-0.00629-0.05270.003230.006660.00988(0.0759)(0.0910)(0.0750)(0.0776)(0.0775)4.688***4.725***4.642***4.630***4.645***(0.120)(0.129)(0.129)(0.129)(0.129)2272001931821860.0800.0690.1000.1040.096Note: standard errors in parenthesis, significance levels – ***p<0.01, **p<0.05, *p<0.1.Source: own elaboration.15Contribution1.

A complex multi-stage methodological approach to relative efficiency measurement ofcultural organizations is proposed and tested in the dissertation. The approach is based on theapplication of econometric methods, the key method being data envelopment analysis. To theauthor’s knowledge this method has never been previously applied to cultural organizations byRussian scholars. The author shows that different specifications of the methodological approachcan be successfully applied to efficiency measurement of cultural organizations in order tocalculate different aggregated efficiency indicators depending on the input and output indicatorsused in analysis.

The variable-returns-to-scale DEA model is viewed as the most appropriate onefor the cultural sphere.2. The whole population of Russian theatres that are part of the network governed by theRussian Ministry for Culture was investigated with the use of cluster analysis. As a result, fourdistinct groups (clusters) of theatres were distinguished, and their characteristic features weredescribed.3. The author proposed a set of performance indicators that represent a useful standardizedresearch instrument for efficiency measurement of cultural organizations based on non-parametricmethods such as data envelopment analysis which are sensitive to the ratio between the number ofobjects and variables analyzed.4.

To the author’s knowledge, for the first time efficiency of a large sample of Russiancultural organizations has been measured based on econometric methods. Efficiency indicators ofeach analyzed object were elaborated, necessary measures of improvement of particular indicatorswere calculated, and benchmarks were identified.5. According to the suggested methodological approach efficiency indicators are used asboth exogenous and endogenous variables. We identified the influence of efficiency level onconsumer satisfaction and investigated the influence of different internal factors (measured in thisresearch through outputs) on efficiency levels of theatres.

Thus, the proposed methodology isbased on both objective statistical data and subjective opinion of key stakeholders of culturalorganizations – consumers.Original methodological approach and the main findings of the study may be used by publicauthorities in the cultural sphere for well-grounded allocation of budget resources to particularorganizations and in order to identify the main avenues for development of cultural policy andtargets of governmental programmes. Managers of cultural organizations may use the results ofthe study to learn about production processes practiced by benchmark theatres and further integratetheir practices in order to increase one’s own efficiency level.