Performance evaluation using data envelopment analysis - stepwise modeling approach: A case study of construction industries in Indonesia

Erni Puspanantasari Putri; Ivan A. Parinov; Almaceley S. Plando

doi:10.30656/jsmi.v8i2.8936

Authors

Erni Puspanantasari Putri Universitas 17 Agustus 1945 Surabaya, Indonesia https://orcid.org/0000-0001-6350-1732
Ivan A. Parinov Southern Federal University, Russian Federation https://orcid.org/0000-0003-3833-0331
Almaceley S. Plando Eastern Visayas State University, Philippines https://orcid.org/0009-0003-9290-2166

DOI:

https://doi.org/10.30656/jsmi.v8i2.8936

Keywords:

Performance evaluation, DEA, Stepwise approach, Variable selection

Abstract

The construction industries are inextricably linked to employment, investment, the quantity of infrastructure building projects, and other economic sectors in Indonesia. They serve as catalysts for the expansion of goods and service production. Apart from having a strategic role in the national economic, construction companies also experience various obstacles to developing their businesses. These obstacles include weakening the IDR exchange rate against the US dollar, regulatory and legal frameworks, labor and skills shortages, economic and financial instability, and environmental and sustainability concerns. In order for the construction industry to survive, develop, and remain competitive in the face of international competition, it is crucial to evaluate its performance constantly. This research aims to evaluate the construction industry's performance in Indonesia. There are 151,183 construction companies included in this study. Hence, these companies will continue to survive, grow, and compete in the face of global competition. The methods applied in this research are an input-oriented DEA envelopment model and a stepwise modeling approach. The research results indicated that 3% of the Indonesian construction industry is made up of efficient DMUs, and the remaining 97% are inefficient DMUs. DMUs are classified according to the distribution of efficiency scores. It is considered that for the classification of inefficient DMU, there exist four ranges, Rs: R1 (ES = 0.16-0.99), R2 (ES = 0.050-0.15), R3 (ES = 0.015-0.049), and R4 (ES = 0.000-0.014). The criteria for each classification, in terms of the level of effectiveness, are as follows: i) R0 Range (ES = 1]): Effective; ii) R1 Range (ES = 0.16-0.99): Relatively Low Ineffectiveness; iii) R2 Range (ES = 0.050-0.15): Moderate Ineffectiveness; iv) R3 Range (ES = 0.015-0.049): Significant Ineffectiveness; and v) R4 Range (ES = 0.000-0.014): Very High Ineffectiveness. The percentage of each classification is as follows: inefficient DMU-R1 0%, inefficient DMU-R2 30%, inefficient DMU-R3 37%, inefficient DMU-R4 30%.

References

F. Handayani and W. Yuniastuti, ‘Konstruksi dalam angka 2020’, Badan Pusat Statistik – BPS RI, 2020. [Online]. Available: https://www.bps.go.id/id/publication/2021/06/11/13b1dd33aebe9366db474c83/konstruksi-dalam-angka-2020.html.

I. I. Praditya, ‘Sederet tantangan sektor jasa konstruksi, Apa solusinya?’, 2024. [Online]. Available: https://www.liputan6.com/bisnis/read/5623675/sederet-tantangan-sektor-jasa-konstruksi-apa-solusinya?page=2.

Redaksi, ‘Tantangan dan peluang dalam sektor konstruksi di Indonesia’, 2024. [Online]. Available: https://www.dailyklik.id/2024/07/13/tantangan-dan-peluang-dalam-sektor-konstruksi-di-indonesia/.

E. P. Putri, Z. Arief, and I. Yuwono, ‘Performance evaluation using input-oriented envelopment DEA method: A case study of micro and small industry in Indonesia’, In Physics and Mechanics of New Materials and Their Applications, 2021 – 2022, Nova Science Publishers, Inc., New York, pp. 289-304, February 2023, doi: https://doi.org/10.52305/QLWW2709.

A. Emrouznejad and G. L. Yang, ‘A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016’, Socio-Economic Planning Sciences, vol. 61, pp. 4-8, February 2018, doi: https://doi.org/10.1016/j.seps.2017.11.002.

K. Wang, W. Meng, and J. Zhang, ‘A study of performance appraisal using DEA and its extensions in the energy sector’, Journal of Cleaner Production, vol. 151, pp. 123-133, April 2017, doi: https://doi.org/10.1016/j.jclepro.2017.03.028.

E. P. Putri and C. Kusoncum, ‘Performance evaluation using PCA-CRS input-oriented DEA method. A case study of East Java exports in Indonesia to ASEAN countries’, Proceedings of the 2019 International Conference on “Physics, Mechanics of New Materials and Their Applications”, Nova Science Publishers, Inc., New York, pp. 445-452, October 2020. [Online]. Available: https://www.researchgate.net/publication/339675364.

H. Aguinis and C. A. Pierce, ‘Enhancing the relevance of performance appraisal research’, Journal of Management, vol. 45, no. 1, pp. 59-94, January 2019, doi: https://doi.org/10.1177/0149206318801934.

J. H. 1. Dyer and T. Reeves, ‘Human resource strategies and firm performance: What do we know?’, Annual Review of Organizational Psychology and Organizational Behavior, vol. 4, pp. 375-398, January 2017, doi: https://doi.org/10.1146/annurev-orgpsych-032516-11304.

M. J. Farrell, ‘The measurement of productive efficiency’, Journal of the Royal Statistical Society. Series A (General), vol. 120, no. 3, pp. 253-290, March 1957, doi: https://doi.org/10.2307/2343100.

A. S. Oliveira, C. F. S. Gomes, C. T. Clarkson, A. M. Sanseverino, M. R. S. Barcelos, I. P. A. Costa, and M. Santos, ‘Multiple criteria decision making and prospective scenarios model for selection of companies to be incubated’, Algorithms, vol. 14, no. 4, doi: https://doi.org/10.3390/a14040111.

M. A. L. Moreira, C. F. S. Gomes, M. dos Santos, M. do Carmo Silva, and J. V. G. A. Araujo, ‘Promethee-sapevo-m1 a hybrid modeling ˆ proposal: Multicriteria evaluation of drones for use in naval warfare’, Industrial Engineering and Operations Management, Springer International Publishing, Cham, 2020, pp. 381–393, doi: https://doi.org/10.1007/978-3-030-56920-4_31.

M. L. Moreira, I. P. de Araujo Costa, M. T. Pereira, M. dos Santos, C. F. S. Gomes, and F. M. Muradas, ‘Promethee-sapevo-m1 a hybrid ´ approach based on ordinal and cardinal inputs: multi-criteria evaluation of helicopters to support Brazilian navy operations’, Algorithms, vol. 14, no. 5, pp. 1-26, April 2021, doi: https://doi.org/10.3390/a14050140.

C. F. S. A. Gomes, M. d. Santos, L. F. H. A. d. S. d. B. Teixeira, A. M. Sanseverino, and M. R. d. S. Barcelos, ‘SAPEVO-M: A group multicriteria ordinal ranking method’, Pesquisa Operacional, vol. 40, no. 1, pp. 1-20, August 2020, doi: https://doi.org/10.1590/0101-7438.2020.040.00226524.

A. L. S. Rodrigues, M. dos Santos, and C. de S. R. Junior, “Application of DEA and Group Analysis using K-means; compliance in the context of the performance evaluation of school networks,” Procedia Computer Science, vol. 199, pp. 687–696, 2022, doi: https://doi.org/10.1016/j.procs.2022.01.085.

A. Charnes, W.W. Cooper, and E. Rhodes, ‘Measuring the efficiency of decision-making units’, European Journal of Operational Research, vol. 2, issue 6, pp. 429-444, Nov. 1978, doi: https://doi.org/10.1016/0377-2217(78)90138-8.

K. Sekitani and Y. Zhao, ‘Least-distance approach for efficiency analysis: A framework for nonlinear DEA models’, European Journal of Operational Research, vol. 306, issue 3, pp. 1296-1310, May 2023, doi: https://doi.org/10.1016/j.ejor.2022.09.001.

E. P. Putri, ‘Performance evaluation using the DEA-stepwise modeling approach method: Case study of the export-import sector in Indonesia’, Jurnal Serambi Engineering, vol. 9, no. 1, pp. 7758–7767, Dec. 2023, doi: https://doi.org/10.32672/jse.v9i1.740.

W. D. Cook and L. M. Seiford, ‘Data envelopment analysis (DEA) – Thirty years on’, European Journal of Operational Research, vol. 192, no. 1, pp. 1-17, Januari 2014, doi: https://doi.org/10.1016/j.ejor.2010.09.003.

A. Emrouznejad and G. L. Yang, ‘A comprehensive survey and analysis of the first 40 years of DEA’, Socio-Economic Planning Sciences, vol. 61, pp. 4-8, March 2018, doi: https://doi.org/10.1016/j.seps.2017.01.008.

J. M. Wagner and D. G. Shimshak, ‘Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives’, European Journal of Operational Research, vol. 180, issue 1, pp. 57–67, July 2007, doi: https://doi.org/10.1016/j.ejor.2006.02.048.

J. M. Wagner and D. G. Shimshak, ‘Stepwise selection of variables in data envelopment analysis: Procedures and managerial perspectives’, European Journal of Operational Research, vol. 180, no. 1, pp. 57–67, July 2007, doi: https://doi.org/10.1016/j.ejor.2006.02.048.

W. W. Cooper, L. M. Seiford, and K. Tone, ‘Introduction to data envelopment analysis and its uses’, Springer New York, NY, vol. 1, pp. 1-354, 2006, doi: https://doi.org/10.1007/0-387-29122-9.

. M.-L. Bougnol, J. H. Dula, “Validating DEA as a Ranking Tool: An Application of DEA to Assess Performance in Higher Education,” Annals of Operations Research, Springer, vol. 145 (1), pp. 339-365, 2006, doi: https://doi.org/10.1007/s10479-006-0039-2.

E. P. Putri, S. Aduldaecha, B. R. S. P. Putra, A. H. A. Puteri, ‘Performance evaluation of Indonesia's large and medium-sized industries using data envelopment analysis method’, OPSI, vol. 17, no. 1, pp. 118–134, June 2024, doi: https://doi.org/10.31315/opsi.v17i1.11785.

A. Peyrache, C. Rose, G. Sicilia, ‘Variable selection in Data Envelopment Analysis’, European Journal of Operational Research, vol. 282, no. 2, pp. 644-659, April 2020, doi: https://doi.org/10.1016/j.ejor.2019.09.028.

W.-P. Wong, ‘A Global Search Method for Inputs and Outputs in Data Envelopment Analysis: Procedures and Managerial Perspectives’, Symmetry, vol. 13, no. 1155, pp. 1-15, April 2020, doi: https://doi.org/10.3390/ sym13071155.

W. Abdelfattah, ‘Variables Selection Procedure for the DEA Overall Efficiency Assessment Based Plithogenic Sets and Mathematical Programming’, International Journal of Scientific Research and Management, vol. 10, no. 5, pp. 397-409, 2021, doi: https://doi.org/10.18535/ijsrm/v10i5.m01.

B. Golany and Y. Roll, ‘An application procedure for DEA’, Omega, vol. 17, no. 3, pp. 237-250, 1989, doi: https://doi.org/10.1016/0305-0483(89)90029-7.

W. F. Bowlin, ‘Measuring performance: an introduction to data envelopment analysis (DEA)’, The Journal of Cost Analysis, vol. 15, no. 2, pp. 3–27, Dec 2011, doi: https://doi.org/10.1080/08823871.1998.10462318.

R.G. Dyson, R. Allen, A.S. Camanho, V.V. Podinovski, C.S. Sarrico, and E.A. Shale, ‘Pitfalls and protocols in DEA’, European Journal of Operational Research, vol. 132, no. 2, pp. 245-259, July 2001, doi: https://doi.org/10.1016/S0377-2217(00)00149-1.

W. W. Cooper, L. M. Seiford, and K. Tone, ‘Introduction to data envelopment analysis and its uses’, Springer New York, NY, vol. 1, pp. 1-354, 2006, doi: https://doi.org/10.1007/0-387-29122-9.

R. D. Banker, ‘Hypothesis tests using data envelopment analysis’, Journal of Productivity Analysis, vol. 7, pp. 139–159, July 1996, doi: https://doi.org/10.1007/BF00157038.

J. T. Pastor, J. L. Ruiz, and I. Sirvent, ‘A statistical test for nested radial DEA models’, Operations Research, vol. 50, no. 4, pp. 728-735, July – August 2002. doi: https://doi.org/10.1287/opre.50.4.728.2866.

J. Ruggiero, ‘Impact assessment of input omission on DEA’, International Journal of Information Technology & Decision Making, vol. 4, no. 3, pp. 359-368, 2005, doi: https://doi.org/10.1142/S021962200500160X.

T. Ueda and Y. Hoshiai, ‘Application of principal component analysis for parsimonious summarization of DEA inputs and/or outputs’, Journal of the Operations Research Society of Japan, vol. 40, no. 4, pp. 466-478, 1997, doi: https://doi.org/10.15807/jorsj.40.466.

N. Adler and B. Golany, ‘Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to western Europe’, European Journal of Operational Research, vol. 132, no. 2, pp. 260-273, July 2001, doi: https://doi.org/10.1016/S0377-2217(00)00150-8.

M. Norman and B. Stoker, ‘Data envelopment analysis: The assessment of performance’, John Wiley and Sons Chichester, England, vol. 1, August 1991. [Online]. Available: https://dl.acm.org/doi/abs/10.5555/574174.

V. Valdmanis, ‘Variable selection in data envelopment analysis’, Journal of Public Economics, vol. 48, no. 2, pp. 185-205, July 1992, doi: https://doi.org/10.1016/0047-2727(92)90026-C.

M. Sigala, D. Airey, P. Jones, and A. Lockwood, ‘ICT paradox lost? A Stepwise DEA methodology to evaluate technology investments in tourism settings’, Journal of Travel Research, vol. 43, no. 2, pp. 180-192, November 2004, doi: https://doi.org/10.1177/0047287504268247.

N. R. Nataraja and A. L. Johnson, ‘Guidelines for using variable selection techniques in data envelopment analysis’, European Journal of Operational Research, vol. 215, no. 3, pp. 662-669, December 2011, doi: https://doi.org/10.1016/j.ejor.2011.06.045.

Y. Li, X. Shi, M. Yang, and L. Liang, ‘Variable selection in data envelopment analysis via Akaike’s information criteria’, Annals of Operations Research, vol. 253, pp. 453–476, 2017, doi: https://doi.org/10.1007/s10479-016-2382-2.

E. P. Putri, ‘Performance measurement using DEA-multipliers method: A case study of clean water companies in Indonesia’, Proceedings of International Exchange and Innovation Conference on Engineering & Sciences (IEICES) 8, Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Japan, pp. 114-121, October 2022, doi: https://doi.org/10.5109/5909075.

E. P. Putri, D. Chetchotsak, M. A. Jani, and R. Hastijanti, ‘Performance evaluation using PCA and DEA: A case study of the micro and small manufacturing industries in Indonesia’, ASR: CMU Journal of Social Sciences and Humanities, vol. 4, no. 1, pp. 37-56, Dec. 2017, doi : https://doi.org/10.12982/CMUJASR.2017.0003.

E. P. Putri, D. Chetchotsak, P. Ruangchoenghum, M. A. Jani, and R. Hastijanti, ‘Performance evaluation of large and medium scale manufacturing industry clusters in East Java Province, Indonesia’, International Journal of Technology, vol. 7, no. 7, pp. 1269-1279, Dec. 2016, doi: https://doi.org/10.14716/ijtech.v7i7.5229.

O. B. Olesen and N. C. Petersen, ‘Stochastic data envelopment analysis – a review’, European J. Oper. Res., vol. 251, issue 1, pp. 2-21, May 2016, doi: https://doi.org/10.1016/j.ejor.2015.07.058.

M. Afsharian, ‘Metafrontier efficiency analysis with convex and non-convex metatechnologies by stochastic nonparametric envelopment of data’, Econom. Lett., vol. 160, pp. 1-3, Nov. 2017, doi: https://doi.org/10.1016/j.econlet.2017.08.006.

E. Thanassoulis, ‘Introduction to the Theory and Application of Data Envelopment Analysis’, Springer, New York, 2001, doi: https://doi.org/10.1007/978-1-4615-1407-7.

W. W. Cooper, L. M. Seiford, and K. Tone, ‘Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software’, 2nd ed., Springer Science Business Media, New York, 2007, doi: https://doi.org/10.1007/978-0-387-45283-8.

M. Afsharian, H. Ahn, and S. Kamali, ‘Performance analytics in incentive regulation: A literature review of DEA publications’, Decision Analytics Journal, vol. 4, pp. 1-9, Sep. 2022, doi: https://doi.org/10.1016/j.dajour.2022.100079.

W.W. Cooper, L.M. Seiford, and J. Zhu, ‘Handbook on data envelopment analysis,” eds, Boston: Kluwer Academic’, 2004, doi: https://doi.org/10.1007/978-1-4419-6151-8.

P. Shewell and S. Migiro, ‘Data envelopment analysis in performance measurement: a critical analysis of the literature’, Problems and Perspectives in Management, vol. 14, pp. 705-713, Nov. 2016, doi: https://doi.org/10.21511/ppm.14(3-3).2016.14.

Y. Chen and J. Zhu, “Measuring information technology’s indirect impact on firm performance,” Information Technology and Management, vol. 5, issue 1-2, pp. 9-22, Jan. 2004, doi: https://doi.org/10.1023/B:ITEM.0000008075.43543.97.

E. P. Putri and D. Chetchotsak, ‘Variable selection in data envelopment analysis using stepwise modeling approach: A case study of tourism sector in Indonesia’, Proceedings of the 2018 International Conference on “Physics, Mechanics of New Materials and Their Applications”, Nova Science Publishers, Inc., New York, pp. 387-396, October 2019. [Online]. Available: https://www.researchgate.net/publication/339229782.

W. D. Cook and J. Zhu, ‘Data envelopment analysis: modeling operational processes and measuring productivity’, 2008. [Online]. Available: https://books.google.co.id/books?id=4riRQQAACAAJ.

R.D. Banker, A. Charnes, & W.W. Cooper, ‘Some models for estimating technical and scale inefficiencies in data envelopment analysis’, Management Science, vol. 30, no. 9, pp. 1078-1092, 1984, doi: https://doi.org/10.1287/mnsc.30.9.1078.

T. Sueyoshi, ‘DEA non-parametric ranking test and index measurement: Slack adjustment approach applied to Japanese banking industry in 1980s.’, European Journal of Operational Research, vol. 115, no. 3, pp. 564-582, 1999, doi: https://doi.org/10.1016/S0377-2217(98)00216-7.

M. R. Kazemi & H. Azizi, ‘Efficiency evaluation of construction companies using DEA approach: The case study of Iran’, International Journal of Civil Engineering, vol. 15, no. 1, pp. 33-44, 2017, doi: https://doi.org/10.1007/s40999-016-0049-0.

R. Madhanagopal and R. Chandrasekaran, ‘Selecting appropriate variables for DEA using genetic algorithm (GA) search procedure’, International Journal of Data Envelopment Analysis and Operations Research, vol. 1, no. 2, pp. 28-33, July 2014, doi: https://doi.org/10.12691/ijdeaor-1-2-3.

A. Charnes, W.W. Cooper, and E. Rhodes, ‘Data Envelopment Analysis: A handbook of empirical studies and applications’, Socio-Economic Planning Sciences, vol. 61, issue 6, pp. 429-444, Nov. 1978, doi: https://doi.org/10.1016/0377-2217(78)90138-8.

A. Emrouznejad and G-L. Yang, ‘A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016’, Socio-Economic Planning Sciences, vol. 61, pp. 4-8, March 2018, doi: https://doi.org/10.1016/j.seps.2017.01.008.

J. Zhu, ‘Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets and DEA Solver Software’, 3rd ed., Springer, New York, Sep. 2014, doi: https://doi.org/10.1007/978-0-387-85982-8.

A. Emrouznejad and G. L. Yang, ‘A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016’, Socio-Economic Planning Sciences, vol. 41, pp. 4-8, March 2018, doi: https://doi.org/10.1016/j.seps.2017.01.008.

E. Thanassoulis, R. G. Dyson and J. Foster, ‘Evaluation of the efficiency of organizations: The application of Data Envelopment Analysis’, European Journal of Operational Research, vol. 93, no. 2, pp. 280-295, 1996, doi: https://doi.org/10.1016/0377-2217(95)00085-6.

S. Huang and X. Hu, ‘Efficiency evaluation of the Chinese banking system: A data envelopment analysis approach’, Journal of Banking & Finance, vol. 26, no. 1, pp. 217-229, 2002, doi: https://doi.org/10.1016/S0378-4266(01)00135-1.

K. Tone, ‘A slacks-based measure of efficiency’, European Journal of Operational Research, vol. 130, no.3, pp. 498-509, May 2001, doi: https://doi.org/10.1016/S0377-2217(00)00223-2.

H. R. HassabElnaby and R. W. Ingram, ‘The effect of ownership structure on efficiency: Evidence from the Egyptian banking sector’, Journal of Banking & Finance, vol. 34, no. 3, pp. 522-529, 2010, doi: https://doi.org/10.1016/j.jbankfin.2009.08.012.

C. J. Dahlman, ‘The role of the state in the economy: The case of the Latin American experience’, Journal of Economic Literature, vol. 41, no. 4, pp. 1147-1172, 2003, doi: https://doi.org/10.1257/002205103771946072.

T. R. Sexton, A. J. Silk, and M. Torkkeli, ‘The use of data envelopment analysis in assessing relative efficiency in a public sector’, Omega, vol. 32, no. 6, pp. 599-613, 2004, doi: https://doi.org/10.1016/j.omega.2004.01.001.

C. D. Ittner and D. F. Larcker, ‘Assessing empirical research in managerial accounting: A value-based management perspective’, Journal of Accounting and Economics, vol. 32, no. 1, pp. 92-126, 2001, doi: https://doi.org/10.1016/S0165-4101(01)00024-5.

J. C. Paradi and H. Zhu, ‘Benchmarking the efficiency of the Canadian banking sector’, International Journal of Banking, Accounting and Finance, vol. 3, no. 1, pp. 44-62, 2011, doi: 10.1504/IJBAAF.2011.037042.