Kao_Liu_2003: Data: Kao and Liu (2003).

In deaR: Conventional and Fuzzy Data Envelopment Analysis

Data: Kao and Liu (2003).

Description

Data of 24 university libraries in Taiwan with one input and five outputs.

Usage

data("Kao_Liu_2003")

Format

Data frame with 24 rows and 11 columns. Definition of fuzzy inputs (X) and fuzzy outputs (Y):

x1 = Patronage

It is a weighted sum of the standardized scores of faculty, graduate students, undergraduate students, and extension students in the range of 0 and 1.

y1 = Collections

Books, serials, microforms, audiovisual works, and database.

y2 = Personnel

Classified staff, unclassified staff, and student assistants.

y3 = Expenditures

Capital expenditure, operating expenditure, and special expenditure.

y4 = Buildings

Area and seats

y5 = Services

Operating hours, attendance, circulation, communication channels, range of services, amount of services, etc.

beta3_l

lower spread vector Expenditures

beta3_u

upper spread vector Expenditures

beta5_l

lower spread vector Services

beta5_u

upper spread vector Services

Note

There are three observations that are missing: expenditures of Library 24 and services of Library 22 and Library 23. Kao and Liu (2000b) represent the expenditures of Library 24 by the triangular fuzzy number Y = (0.11; 0.41; 1.0). The services of Library 22 and Library 23 are expressed by a same triangular fuzzy number Y = (0.41; 0.69; 1.0).

Author(s)

Vicente Coll-Serrano ([e-mail]). Quantitative Methods for Measuring Culture (MC2). Applied Economics.

Vicente Bolos ([e-mail]). Department of Business Mathematics

Rafael Benitez ([e-mail]). Department of Business Mathematics

University of Valencia (Spain)

Source

Kao, C., Liu, S.T. (2003). “A mathematical programming approach to fuzzy efficiency ranking”, International Journal of Production Economics, 85. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0925-5273(03)00026-4")}

See Also

make_deadata_fuzzy, model_basic

Examples

# Example. Replication of results in Kao and Liu (2003, p.152)
data_example <- make_deadata_fuzzy(Kao_Liu_2003,
                                   dmus = 1,
                                   inputs.mL = 2,
                                   outputs.mL = 3:7,
                                   outputs.dL = c(NA, NA, 8, NA, 10),
                                   outputs.dR = c(NA, NA, 9, NA, 11))
result <- modelfuzzy_kaoliu(data_example,
                            kaoliu_modelname = "basic",
                            orientation = "oo",
                            rts = "vrs",
                            alpha = 0)
eff <- efficiencies(result)
eff

deaR documentation built on May 2, 2023, 5:13 p.m.