Advanced SearchSearch Tips
A Tradeoff between Customer Efficiency and Firm Productivity in Service Delivery Systems
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A Tradeoff between Customer Efficiency and Firm Productivity in Service Delivery Systems
Trinh, Truong Hong; Kachitvichyanukul, Voratas; Luong, Huynh Trung;
  PDF(new window)
The paper proposes a non-parametric methodology, data envelopment analysis, for measuring efficiency and productivity in service delivery systems with capacity constraints. The methodology provides allocation approaches for studying behaviors of firm and customers in service delivery strategy. The experimental study is carried out to investigate allocation behaviors and conduct an objective tradeoff between efficiency approach and productivity approach. The experimental result indicates that the efficiency approach allocates resource via maximizing customer efficiency rather than firm productivity as in the productivity approach. Moreover, the experiment reveals that there exists an objective tradeoff between the efficiency approach and the productivity approach. These findings provide strategic options for allocation policy in service delivery systems.
DEA Method;Customer Efficiency;Firm Productivity;Malmquist TFP Index;Service Delivery System;
 Cited by
DCBA-DEA: A Monte Carlo Simulation Optimization Approach for Predicting an Accurate Technical Efficiency in Stochastic Environment,;;

Industrial Engineering and Management Systems, 2014. vol.13. 2, pp.210-220 crossref(new window)
DCBA-DEA: A Monte Carlo Simulation Optimization Approach for Predicting an Accurate Technical Efficiency in Stochastic Environment, Industrial Engineering and Management Systems, 2014, 13, 2, 210  crossref(new windwow)
Balk, B. M. (2001), Scale efficiency and productivity change, Journal of Productivity Analysis, 15(3), 159-183. crossref(new window)

Banker, R. D., Charnes, A., and Cooper, W. W. (1984), Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science, 30(9), 1078-1092. crossref(new window)

Camanho, A. and Dyson, R. (2006), Data envelopment analysis and Malmquist indices for measuring group performance, Journal of Productivity Analysis, 26(1), 35-49. crossref(new window)

Caves, D. W., Christensen, L. R., and Diewert, W. E. (1982), The economic theory of index numbers and the measurement of input, output, and productivity, Econometrica, 50(6), 1393-1414. crossref(new window)

Charnes, A., Cooper, W. W., and Rhodes, E. (1978), Measuring the efficiency of decision making units, European Journal of Operational Research, 2(6), 429-444. crossref(new window)

Chase, R. B. (1978), Where does the customer fit in a service operation? Harvard Business Review, 56(6), 137-142.

Fare, R., Grosskopf, S., Lindgren, B., and Roos, P. (1992), Productivity changes in Swedish pharamacies 1980-1989: a non-parametric Malmquist approach, Journal of Productivity Analysis, 3(1/2), 85-101. crossref(new window)

Fare, R., Grosskopf, S., Norris, M., and Zhang, Z. (1994), Productivity growth, technical progress, and efficiency change in industrialized countries, American Economic Review, 84(1), 66-83.

Farrell, M. J. (1957), The measurement of productive efficiency, Journal of the Royal Statistical Society Series A, 120(3), 253-290. crossref(new window)

Felthoven, R. G., Horrace, W. C., and Schnier, K. E. (2009), Estimating heterogeneous capacity and capacity utilization in a multi-species fishery, Journal of Productivity Analysis, 32(3), 173-189. crossref(new window)

Globerson, S. and Maggard, M. J. (1991), A conceptual model of self-service, International Journal of Operations and Production Management, 11(4), 33-43. crossref(new window)

Grifell-Tatje, E. and Lovell, C. K. (1999), A generalized Malmquist productivity index, TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 7(1), 81-101.

Haksever, C., Render, B., Russell, R. S., and Murdick, R. G. (2000), Service Management and Operations, 2nd ed., Prentice Hall, Upper Saddle River, NJ.

Nguyen, S., Ai, T. J., and Kachitvichyanukul, V. (2010), Object Library for Evolutionary Techniques ET-Lib: User's Guide, High Performance Computing Group, Asian Institute of Technology, Thailand.

Nguyen, S. and Kachitvichyanukul, V. (2010), Movement strategies for multi-objective particle swarm optimization, International Journal of Applied Metaheuristic Computing, 1(3), 59-79. crossref(new window)

Ojasalo, K. (2003), Customers' influence on service productivity, SAM Advanced Management Journal, 68(3), 14-19.

Parks, R. B., Baker, P. C., Kiser, L., Oakerson, R., Ostrom, E., Ostrom, V., Percy, S. L., Vandivort, M. B., Whitaker, G. P., and Wilson, R. (1981), consumers as coproducers of public services: some economic and institutional considerations, Policy Studies Journal, 9(7), 1001-1011. crossref(new window)

Sampson, S. E. and Froehle, C. M. (2006), Foundations and implications of a proposed unified services theory, Production and Operations Management, 15(2), 329-343.

Shephard, R. W. (1970), Theory of Cost and Production Functions, Princeton University Press, Princeton, NJ.

Wikstrom, S. (1996), The customer as co-producer, European Journal of Marketing, 30(4), 6-19. crossref(new window)

Xue, M. and Harker, P. T. (2002), Customer efficiency: concept and its impact on e-business management, Journal of Service Research, 4(4), 253-267. crossref(new window)

Xue, M., Hitt, L. M., and Harker, P. T. (2007), Customer efficiency, channel usage, and firm performance in retail banking, Manufacturing and Service Operations Management, 9(4), 535-558. crossref(new window)