Skip to main content
Log in

Assessing the productivity of the Italian hospitality sector: a post-WDEA pooled-truncated and spatial analysis

  • Published:
Journal of Productivity Analysis Aims and scope Submit manuscript

Abstract

This paper analyses the productivity of the hospitality sector (hotel and restaurants) in Italy at a regional level by using a mix of non-parametric and parametric approaches. A novel pooled-truncated and spatial analysis is employed, based upon a window data envelopment analysis (WDEA), where pure technical efficiency is computed. The WDEA results show that Lombardy is the best relative performer. However, overall Italian regions reveal important sources of inefficiency mostly related to their inputs. As a post-WDEA, the pooled-truncated estimation indicates that the rate of utilisation and regional intrinsic features positively affect hospitality efficiency. Nevertheless, the spatial analysis does not support evidence of spill-over effects amongst Italian regions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. As argued by Diewert and Mendoza (1995), DEA is highly sensitive to data errors and outliers. Notably, since aggregation alleviates measurement errors at the individual level, using regional data allows one to reduce such biases.

  2. The solution of Eq. (1) is given by either a maximisation or a minimisation approach when either one input or one output is used. However, in the presence of a multivariate input–output framework, the problem can be solved with either an output-oriented method (O-OM), by maximising the numerator while keeping the denominator constant, or an input-oriented (I-OM) method, by minimising the denominator while keeping the numerator constant. Within the O-OM, no DMU in the sample, with the same type of inputs, is able to derive a higher quantity of output. In general, this setting is employed for planning and strategic objectives. For example, it is used when a DMU needs to understand whether an expansion of its capacity is feasible, as long as the existing infrastructure has already been used at its maximum capacity given the level of the inputs (Cullinane et al. 2004).

  3. Standard DEA scores range between zero and one, as mentioned in Sect. 3.1. Unfortunately, double bounds of the dependent variable impose a more complex procedure in solving Eq. (4). In order to simplify the econometric model, we follow the suggestion of Simar and Wilson (2007), who propose to use the inverse of standard DEA scores. This way, the new dependent variable has a single lower bound equals to one.

References

  • Anselin L (1988) Spatial econometrics: methods and models. Kluwer, Dordrecht

    Book  Google Scholar 

  • Assaf AG, Agbola FW (2011) Modelling the performance of Australian hotels: a DEA double bootstrap approach. Tour Econ 17(1):73–89

    Article  Google Scholar 

  • Assaf AG, Cvelvar LK (2010) The performance of the Slovenian hotel industry: evaluation post-privatisation. Int J Tour Res 12(5):462–471

    Google Scholar 

  • Assaf A, Barros CP, Josiassen A (2010) Hotel efficiency: a bootstrapped metafrontier approach. Int J Hosp Man 29(3):468–475

    Article  Google Scholar 

  • Assaf AG, Deery M, Jago L (2011) Evaluating the performance and scale characteristics of the Australian restaurant industry. J Hosp Tour Res 35(4):419–436

    Article  Google Scholar 

  • Balcombe K, Fraser I, Latruffe L, Rahman M, Smith L (2008) An application of the DEA double bootstrap to examine sources of efficiency in Bangladesh rice farming. App Econ 40(15):1919–1925

    Article  Google Scholar 

  • Banker PC, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Man Sci 30(9):1078–1092

    Article  Google Scholar 

  • Barros CP (2005) Measuring efficiency in the hotel sector. Ann Our Res 32(2):456–477

    Google Scholar 

  • Barros CP, Alves FP (2004) Productivity in the tourism industry. IAER, August, 10(3). Technical University of Lisbon, Portugal

  • Barros CP, Dieke PUC (2008) Technical efficiency of African hotels. Int J Hosp Man 27(3):438–447

    Article  Google Scholar 

  • Barros CP, Santos CA (2006) The measurement of efficiency in Portuguese hotels using data envelopment analysis. J Hosp Tour Res 30(3):378–400

    Article  Google Scholar 

  • Barros CP, Botti L, Peypoch N, Robinot E, Solonandrasana B, Assaf G (2011) Performance of French destinations: tourism attraction perspectives. Tour Man 32(1):141–146

    Article  Google Scholar 

  • Bernard AB, Jensen JB, Redding SJ, Schott PK (2007) Firms in international trade. Working Paper 13054, http://www.nber.org/papers/w13054. National Bureau of Economic Research, Cambridge

  • Bernini C, Guizzardi A (2010) Internal and location factors affecting hotel industry efficiency: evidence from Italian business corporations. Tour Econ 16(4):883–913

    Article  Google Scholar 

  • Biagi B, Pulina M (2009) Bivariate VAR models to test granger causality between tourist demand and supply: implications for regional sustainable growth. Pap Reg Sci 88(1):1–14

    Article  Google Scholar 

  • Bimonte S, Brida JG, Pulina M, Punzo L (2012) Tourism and growth: stories of the two continents, Chapter 16. In: Punzo L, Feijo C, Puchet Anyul M (eds) Beyond the global crisis: structural adjustments and regional integration in Latin America and Europe. Routledge Studies in the Modern World Economy, USA, pp 252–268

    Google Scholar 

  • Brida JG, Deidda M, Pulina M (2012) Investigating economic efficiency in Italy: a regional comparison. Int J Rev Manag 6(3/4):175–198

    Google Scholar 

  • Brown JR, Dev CS (1999) Looking beyond RevPAR; productivity consequences of hotel strategies. Corn Hot Rest Admin Quart 40(2):23–33

    Google Scholar 

  • Brown JR, Dev CS (2000) Improving productivity in a service business: evidence from the hotel industry. J Serv Res 2(4):339–354

    Article  Google Scholar 

  • Bruni ME, Guerriero F, Patitucci V (2011) Benchmarking sustainable development via data envelopment analysis: an Italian case study. Int J Environ Res 5(1):47–56

    Google Scholar 

  • Chang TC (1999) Local uniqueness in the global village: heritage tourism in Singapore. Prof Geogr 51(1):91–103

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444

    Article  Google Scholar 

  • Charnes A, Clark CT, Cooper WW, Golany B (1985) A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the US air forces. Ann Oper Res 2(1):95–112

    Article  Google Scholar 

  • Cooper WW, Seiford LM, Tone K (2000) Data envelopment analysis: a comprehensive text with models, applications, references and DEA-Solver software. Kluwer, Boston

    Google Scholar 

  • Cortés-Jiménez I, Pulina M (2010) Inbound tourism and long-run economic growth of Spain and Italy. Curr Issue Tour 13(1):61–74

    Article  Google Scholar 

  • Cracolici MF (2008) Assessment of tourism competitiveness by analysing destination efficiency. Tour Econ 14(2):325–342

    Article  Google Scholar 

  • Cracolici MF, Nijkamp P (2006) Competition among tourist destination. An application of data envelopment analysis to Italian provinces. In: Giaoutzi M, Nijkamp P (eds) Tourism and regional development: new pathways. Ashgate, Aldershot, UK, pp 133–152

    Google Scholar 

  • Cracolici MF, Nijkamp P (2009) The attractiveness and competitiveness of tourist destinations: a study of Southern Italian regions. Tour Man 30(3):336–344

    Article  Google Scholar 

  • Cullinane K, Song D-W, Wang T-F (2004) An application of DEA windows analysis to container port production efficiency. Rev Net Econ 32:184–206

    Google Scholar 

  • Diewert WE, Mendoza MNF (1995) Data envelopment analysis: a practical alternative. UBC Departmental Archives 95-30, UBC Department of Economics

  • Emrouznejad A, Barnett RP, Tavares G (2008) Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Soc-Econ Plan Sci 42(3):151–157

    Article  Google Scholar 

  • Farrell J (1957) The Measurement of productive efficiency. J R Stat Soc 120(3):253–290

    Google Scholar 

  • Federalberghi-Mercury (2005) Rapporto 2005 sul sistema alberghiero in Italia. www.federalberghi.it. Accessed on 31 Jan 2013

  • Federalberghi-Mercury (2010) Sesto rapporto sul sistema alberghiero in Italia. www.federalberghiit. Accessed on 10 Jan. 2013

  • Federalberghi-Mercury (2012) DATATUR Trend e statistiche sull’economia del turismo. Federalberghi & Format, Roma

    Google Scholar 

  • Goh HN (2010) Recommending a productivity model for Singapore hotels: A critical review of productivity models adopted by researchers and hotel operators. UNLV Theses/Dissertations/Professional Papers/Capstones. Paper 685

  • Griffith DA (2003) Spatial autocorrelation and spatial filtering. Springer, Berlin

    Book  Google Scholar 

  • Helfand SM, Levine ES (2004) Farm size and the determinants of productive efficiency in the Brazilian Center-West. Agric Econ 31(2/3):241–249

    Article  Google Scholar 

  • ISTAT (2011) Sistema di indicatori territoriali. http://sitis.istat.it/sitis/html/index.htm. Accessed 12 Aug 2011

  • Kilic H, Okumus F (2005) Factors influencing productivity in small island hotels: evidence from Northern Cyprus. Int J Cont Hosp Man 17(4):315–331

    Article  Google Scholar 

  • Köksal CD and Aksu AA (2007) Efficiency evaluation of A-group travel agencies with data envelopment analysis (DEA): a case study in the Antalya region, Turkey. Tour Manag 28(3): 830–834

    Google Scholar 

  • Kravtsova V (2008) Foreign presence and efficiency in transition economies. J Prod Anal 29(2):91–102

    Article  Google Scholar 

  • Mandl U, Dierx A, Ilzkovitz F (2008) The effectiveness and efficiency of public spending. Eur Comm Econ Pap 301:1–36

    Google Scholar 

  • Manera Erbina C, Garau Taberner J, Molina de Dios R (2010) The tourism revolution in the Mediterranean, 1950–2005. Documentos de Trabajo, DT-AEHE 1014:1–17

    Google Scholar 

  • Melão N (2005) Data envelopment analysis revisited: a neophyte’s perspective. Int J Manag Dec Mak 6(2):158–179

    Google Scholar 

  • Min H, Min H, Joo SJ (2008) A data envelopment analysis-based balanced scorecard for measuring the comparative efficiency of Korean luxury hotels. Int J Qual Rel Man 25(4):349–365

    Google Scholar 

  • Molina-Azorin JF, Pereira-Moliner J, Claver-Cortés E (2011) The importance of the firm and destination effects to explain firm performance. Tour Man 32(1):22–28

    Google Scholar 

  • Moriarty JP (2010) Have structural issues placed New Zealand’s hospitality industry beyond price? Tour Econ 16(3):695–713

    Article  Google Scholar 

  • Neves JC, Lourenço S (2009) Using data envelopment analysis to select strategies that improve the performance of hotel companies. Inter J Cont Hosp Man 21(6):698–712

    Article  Google Scholar 

  • Olesen OB, Petersen NC (2002) The use of data envelopment analysis with probabilistic assurance regions for measuring hospital efficiency. J Prod Anal 17(1/2):83–109

    Article  Google Scholar 

  • Prado Lorenzo JM, Garcìa Sànchez IM (2007) Efficiency evaluation in municipal services: an application to the street lighting service in Spain. J Prod Anal 27(3):149–162

    Article  Google Scholar 

  • Pulina M, Detotto C, Paba A (2010) An investigation into the relationship between size and efficiency of the Italian hospitality sector: a window DEA approach. Eur J Oper Res 20(4):613–620

    Article  Google Scholar 

  • Reynolds D (2003) Hospitality-productivity assessment using data-envelopment analysis. Corn Hot Rest Admin Quart 44(2):429–449

    Google Scholar 

  • Reynolds D, Thompson GM (2007) Multiunit restaurant productivity assessment using three-phase data envelopment analysis. Int J Hosp Man 26(1):20–32

    Article  Google Scholar 

  • Robert WJA, Haug AA, Jaforullah M (2010) A two-stage double-bootstrap data envelopment analysis of efficiency differences of New Zealand secondary schools. J Prod Anal 34(2):99–110

    Article  Google Scholar 

  • Salazar NB (2010) The glocalisation of heritage through tourism: Balancing standardisation and differentiation. In: Labadi S, Long C (eds) Heritage and globalisation. Routledge, London, pp 130–147

    Google Scholar 

  • Sampaio De Souza MC, Cribari-Neto F, Stosic BD (2005) Explaining DEA technical efficiency scores in an outlier corrected environment: the case of public services in Brazilian municipalities. Braz Rev Economet 25(2):287–313

    Google Scholar 

  • Shuai JJ (2009) Web content and its influence on operational performance-case of the hotel industry. Industrial engineering and engineering management, IEEM, international conference, 8–11 December 2009, pp 885–889. doi:10.1109/IEEM20095372884

  • Shuai JJ, Wu WW (2011) Evaluating the influence of E-marketing on hotel performance by DEA and grey entropy. Exp Syst Appl 38(7):8763–8769

    Article  Google Scholar 

  • Sigala M (2004) Using data envelopment analysis for measuring and benchmarking productivity in the hotel sector. J Trav Tour Mark 16(2):39–60

    Google Scholar 

  • Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econom 13(6):31–64

    Article  Google Scholar 

  • Simar L, Wilson PW (2011) Two-stage DEA: caveat emptor. J Prod Anal 36(2):205–218

    Article  Google Scholar 

  • Sirirak S, Islam N, Khang DB (2011) Does ICT adoption enhance hotel performance? J Hosp Tour Tech 2(1):34–49

    Article  Google Scholar 

  • Sun DB (1988) Evaluation of managerial performance in large commercial banks by data envelopment analysis. IC2 Institute, Austin

  • Suzuki S, Nijkamp P, Rietveld P (2011) Regional efficiency improvement by means of data envelopment analysis through Euclidean distance minimization including fixed input factors: an application to tourist regions in Italy. Pap Reg Sci 90(1):67–89

    Article  Google Scholar 

  • UNWTO (2012) World tourism barometer, vol. 10, February 2012. http://www.unwto.org/. Accessed on 28 May 2012

  • Wang FC, Hung WT, Shang JK (2006) Measuring the cost efficiency of international tourist hotels in Taiwan. Tour Econ 12(1):65–85

    Article  Google Scholar 

  • Wanhill S (2011) What do economists do? Their contribution to understanding tourism. Estudios de Economia Aplicada 29(3):679–692

    Google Scholar 

  • Wilson PW (2008) FEAR: a software package for frontier efficiency analysis with R. Soc-Econ Plan Sci 42(4):247–254

    Article  Google Scholar 

  • WTTC (2010) Progress and priorities 2009–10 http://www.confederacaoturismoportugues.pt/downloads/get/id/192. Accessed on 14 Oct 2013

Download references

Acknowledgments

Manuela Pulina and Claudio Detotto acknowledge the financial support provided by the Banco di Sardegna Foundation (Prot. 1713/2010.0163). Juan Gabriel Brida acknowledges the financial support provided by the Free University of Bolzano, projects: “L’efficienza delle imprese turistiche in Italia” and “The Contribution of Tourism to Economic Growth”. Manuela Pulina acknowledges the financial support provided by the Free University of Bolzano (SECS-P/01—Economia Politica). The views expressed here are those of the authors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Claudio Detotto.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Detotto, C., Pulina, M. & Brida, J.G. Assessing the productivity of the Italian hospitality sector: a post-WDEA pooled-truncated and spatial analysis. J Prod Anal 42, 103–121 (2014). https://doi.org/10.1007/s11123-013-0371-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11123-013-0371-x

Keywords

JEL Classification

Navigation