References
- Aalmo GO, Baardsen S. 2015. Environmental factors affecting technical efficiency in Norwegian steep terrain logging crews: a stochastic frontier analysis. J For Res. 20:18–23.
- Ancarani A, Di Mauro C, Giammanco M. 2009. The impact of managerial and organizational aspects on hospital wards’ efficiency: evidence from a case study. Eur J Oper Res. 194:280–293.
- Aristovnik A, Seljak J, Mencinger J. 2014. Performance measurement of police forces at the local level: A non-parametric mathematical programming approach. Expert Syst Appl. 41:1647–1653.
- Banker RD, Charnes A, Cooper WW. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci. 30:1078–1092.
- Bayne KM, Parker RJ. 2012. The introduction of robotics for New Zealand forestry operations: forest sector employee perceptions and implications. Technol Soc. 34:138–148.
- Benito B, Solana J, Moreno MR. 2014. Explaining efficiency in municipal services providers. J Prod Anal. 42:225–239.
- Blomberg J, Henriksson E, Lundmark R. 2012. Energy efficiency and policy in Swedish pulp and paper mills: a data envelopment analysis approach. Energy Pol. 42:569–579.
- Charnes A, Cooper WW, Rhodes E. 1978. Measuring the efficiency of decision making units. Eur J Oper Res. 2:429–444.
- Coelli T. 1996. A guide to DEAP version 2.1: a data envelopment analysis (computer) program. Centre for Efficiency and Productivity Analysis, University of New England, Australia; [ accessed 2016 Aug 16]. http://www.uq.edu.au/economics/cepa/deap.htm.
- Coelli TJ, Rao DSP, O’Donnell CJ, Battese GE. 2005. An introduction to efficiency and productivity analysis. 2nd ed. New York: Springer Science & Business Media.
- Cooper WW, Seiford LM, Tone K. 2007. Data envelopment analysis, a comprehensive text with models, applications, references and DEA-solver software. 2nd ed. New York: Springer Science & Business Media.
- Cordero JM, Alonso-Morán E, Nuño-Solinis R, Orueta JF, Arce RS. 2015. Efficiency assessment of primary care providers: a conditional nonparametric approach. Eur J Oper Res. 240:235–244.
- Da Cruz NF, Marques RC. 2014. Revisiting the determinants of local government performance. Omega. 44:91–103.
- Enache A, Kühmaier M, Visser R, Stampfer K. 2016. Forestry operations in the European mountains: a study of current practices and efficiency gaps. Scand J Forest Res. 31:412–427.
- Eriksson M, LeBel L, Lindroos O. 2015. Management of outsourced forest harvesting operations for better customer-contractor alignment. Forest Policy Econ. 53:45–55.
- Farrell MJ. 1957. The measurement of productive efficiency. J R Stat Soc. 120:253–290.
- Fried HO, Lovell CK, Schmidt SS, Yaisawarng S. 2002. Accounting for environmental effects and statistical noise in data envelopment analysis. J Prod Anal. 17:157–174.
- Ghaffariyan M, Sessions J, Brown M. 2012. Machine productivity and residual harvesting residues associated with a cut-to-length harvest system in southern Tasmania. South Forests. 74:229–235.
- Golany B, Roll Y. 1989. An application procedure for DEA. Omega. 17:237–250.
- Hailu A, Veeman TS. 2003. Comparative analysis of efficiency and productivity growth in Canadian regional boreal logging industries. Can J Forest Res. 33:1653–1660.
- He H, Weng Q. 2012. Ownership, autonomy, incentives and efficiency: evidence from the forest product processing industry in China. J For Econ. 18:177–193.
- Hoff A. 2007. Second stage DEA: comparison of approaches for modelling the DEA score. Eur J Oper Res. 181:425–435.
- Hwang SN, Chang TY. 2003. Using data envelopment analysis to measure hotel managerial efficiency change in Taiwan. Tour Manage. 24:357–369.
- Kant S, Nautiyal J. 1997. Production structure, factor substitution, technical change, and total factor productivity in the Canadian logging industry. Can J Forest Res. 27:701–710.
- Kao C, Chang PL, Hwang S. 1993. Data envelopment analysis in measuring the efficiency of forest management. J Environ Manage. 38:73–83.
- LeBel L, Stuart W. 1998. Technical efficiency evaluation of logging contractors using a nonparametric model. J For Eng. 9:15–24.
- Leibenstein H, Maital S. 1992. Empirical estimation and partitioning of X-inefficiency: a data-envelopment approach. Am Econ Rev. 82:428–433.
- Lien G, Størdal S, Baardsen S. 2007. Technical efficiency in timber production and effects of other income sources. Small Scale For. 6:65–78.
- Liu JS, Lu LY, Lu WM. 2016. Research fronts in data envelopment analysis. Omega. 58:33–45.
- Mäkinen P. 1997. Success factors for forest machine entrepreneurs. J For Eng. 8:27–35.
- Mason E. 2012. Designing silvicultural regimes with a structural log index. N Z J For. 57:13–18.
- Nakagawa M, Hayashi N, Narushima T. 2010. Effect of tree size on time of each work element and processing productivity using an excavator-based single-grip harvester or processor at a landing. J For Res. 15:226–233.
- Nanang DM, Ghebremichael A. 2006. Inter-regional comparisons of production technology in Canada’s timber harvesting industries. Forest Policy Econ. 8:797–810.
- Obi OF, Visser R. 2017. Operational efficiency analysis of New Zealand timber harvesting contractors using data envelopment analysis. Int J For Eng. 28:85–93. doi:10.1080/14942119.2017.1313489
- Orfila-Sintes F, Mattsson J. 2009. Innovation behavior in the hotel industry. Omega. 37:380–394.
- 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. 204:613–620.
- Puttock D, Spinelli R, Hartsough BR. 2005. Operational trials of cut-to-length harvesting of poplar in a mixed wood stand. Int J For Eng. 16:39–49.
- Ruggiero J. 1996. On the measurement of technical efficiency in the public sector. Eur J Oper Res. 90:553–565.
- Salehirad N, Sowlati T. 2005. Performance analysis of primary wood producers in British Columbia using data envelopment analysis. Can J For Res. 35:285–294.
- Seifert S, Nieswand M. 2014. What drives intermediate local governments’ spending efficiency: the case of French Départements. Local Gov Stud. 40:766–790.
- Siry JP, Newman DH. 2001. A stochastic production frontier analysis of Polish state forests. Forest Sci. 47:526–533.
- Spinelli R, Hartsough BR, Magagnotti N. 2010. Productivity standards for harvesters and processors in Italy. Forest Prod J. 60:226–235.
- Šporčić M, Martinić I, Landekić M, Lovrić M. 2009. Measuring efficiency of organizational units in forestry by nonparametric model. Croat J For Eng. 30:1–13.
- Strandgard M, Alam M, Mitchell R. 2014. Impact of slope on productivity of a self-levelling processor. Croat J For Eng. 35:193–200.
- Sueyoshi T, Aoki S. 2001. A use of a nonparametric statistic for DEA frontier shift: the Kruskal and Wallis rank test. Omega. 29:1–18.
- Tolan A, Visser R. 2015. The effect of the number of log sorts on mechanized log processing productivity and value recovery. Int J For Eng. 26:36–47.
- Upadhyay TP, Shahi C, Leitch M, Pulkki R. 2012. An application of data envelopment analysis to investigate the efficiency of lumber industry in northwestern Ontario, Canada. J For Res. 23:675–684.
- Visser R. 2009. Benchmarking harvesting cost and productivity. Future Forests Res Harvest Tech Note. 2:1–8.
- Visser R. 2011. 2010 Benchmarking of harvesting cost and productivity. Future Forests Res Harvest Tech Note. 3:1–6.
- Visser R. 2016. Trends in harvesting cost and productivity benchmarking. Future Forests Res Harvest Tech Note. 9:1–5.
- Visser R, Spinelli R. 2012. Determining the shape of the productivity function for mechanized felling and felling-processing. J For Res. 17:397–402.
- Yang H, Pollitt M. 2009. Incorporating both undesirable outputs and uncontrollable variables into DEA: the performance of Chinese coal-fired power plants. Eur J Oper Res. 197:1095–1105.
- Ye J, Que YX, Li YR, Xu LP. 2016. Evaluating sugarcane productivity in China over different periods using data envelopment analysis and the Malmquist index. Sugar Tech. 18:478–487.
- Yu C. 1998. The effects of exogenous variables in efficiency measurement—a Monte Carlo study. Eur J Oper Res. 105:569–580.