ORIGINAL ARTICLE
Exploring knowledge benchmarking using time-series directional distance functions and bibliometrics
Thyago Celso C. Nepomuceno,
Victor Diogho H. de Carvalho,
Késsia Thais C. Nepomuceno,
Ana Paula C. S. Costa,
Corresponding Author
Thyago Celso C. Nepomuceno
Núcleo de Tecnologia, Federal University of Pernambuco, Recife, Pernambuco, Brazil
Dipartimento di Ingegneria Informatica Automatica e Gestionale Antonio Ruberti, Sapienza University of Rome, Rome, Italy
Dipartimento di Economia Aziendale, University of Verona, Verona, Italy
Correspondence
Thyago Celso C. Nepomuceno, Federal University of Pernambuco, Recife, Pernambuco, Brazil.
Email: [email protected]
Search for more papers by this authorVictor Diogho H. de Carvalho
Campus do Sertão, Federal University of Alagoas, Delmiro Gouveia, Alagoas, Brazil
Search for more papers by this authorKéssia Thais C. Nepomuceno
Centro de Informática, Federal University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorAna Paula C. S. Costa
Departamento de Engenharia de Produção, Federal University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorThyago Celso C. Nepomuceno,
Victor Diogho H. de Carvalho,
Késsia Thais C. Nepomuceno,
Ana Paula C. S. Costa,
Corresponding Author
Thyago Celso C. Nepomuceno
Núcleo de Tecnologia, Federal University of Pernambuco, Recife, Pernambuco, Brazil
Dipartimento di Ingegneria Informatica Automatica e Gestionale Antonio Ruberti, Sapienza University of Rome, Rome, Italy
Dipartimento di Economia Aziendale, University of Verona, Verona, Italy
Correspondence
Thyago Celso C. Nepomuceno, Federal University of Pernambuco, Recife, Pernambuco, Brazil.
Email: [email protected]
Search for more papers by this authorVictor Diogho H. de Carvalho
Campus do Sertão, Federal University of Alagoas, Delmiro Gouveia, Alagoas, Brazil
Search for more papers by this authorKéssia Thais C. Nepomuceno
Centro de Informática, Federal University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorAna Paula C. S. Costa
Departamento de Engenharia de Produção, Federal University of Pernambuco, Recife, Pernambuco, Brazil
Search for more papers by this authorFunding information: Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Abstract
For strategic reasons, benchmarking best practices from efficient competitors is not usual in many data envelopment analysis (DEA) applications. Even for industries composed of multiple branches, providing information about efficient practices for their peers can jeopardize results for those branches if they compete for market, resources or recognition by the central administration. In this work, a time-series adaptation for the DEA directional model is proposed as an alternative for coping with this problem. The methodological approach has three stages for this benchmarking to occur: Data, Information and Knowledge Extraction. In the first stage, we compare the same unit in different moments to identify efficient periods instead of efficient competitors. As a result, successful performance strategies are investigated using the bibliometric coupling of employees' relevant statements in the second and third stages. The application in a branch of the Brazilian Federal Savings Bank allowed an internal benchmarking of efficient periods when specific performance incentives, innovative processes, competitive strategies, and human resource changes were adopted for improving the unit's performance.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
REFERENCES
- An, Q., Tao, X., & Xiong, B. (2021). Benchmarking with data envelopment analysis: An agency perspective. Omega, 101, 102235. https://doi.org/10.1016/j.omega.2020.102235
- Banker, R. D. (1984). Estimating most productive scale size using data envelopment analysis. European journal of operational research, 17(1), 35–44. https://doi.org/10.1016/0377-2217(84)90006-7
- Bradbury, M. E., & Rouse, P. (2002). An application of data envelopment analysis to the evaluation of audit risk. Abacus, 38(2), 263–279.
10.1111/1467-6281.00108 Google Scholar
- Calvo-Mora, A., Navarro-García, A., Rey-Moreno, M., & Periañez-Cristobal, R. (2016). Excellence management practices, knowledge management and key business results in large organisations and SMEs: A multi-group analysis. European Management Journal, 34, 661–673. https://doi.org/10.1016/j.emj.2016.06.005
- Cepeda-Carrion, I., Martelo-Landroguez, S., Leal-Rodríguez, A. L., & Leal-Millán, A. (2016). Critical processes of knowledge management: An approach toward the creation of customer value. European Research on Management and Business Economics, 23, 1–7. https://doi.org/10.1016/j.iedeen.2016.03.001
- Chambers, R. G., Chung, Y., & Färe, R. (1998). Profit, directional distance functions, and Nerlovian efficiency. Journal of Optimization Theory and Applications, 98(2), 351–364.
- Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
- Chang, C. L., & Lin, T.-C. (2015). The role of organizational culture in the knowledge management process. Journal of Knowledge Management, 19, 433–455. https://doi.org/10.1108/JKM-08-2014-0353
- Cook, W. D., Seiford, L. M., & Zhu, J. (2004). Models for performance benchmarking: Measuring the effect of e-business activities on banking performance. Omega, 32(4), 313–322. https://doi.org/10.1016/j.omega.2004.01.001
- Daraio, C., Kerstens, K., Nepomuceno, T. C. C., & Sickles, R. C. (2020). Empirical surveys of frontier applications: A meta-review. International Transactions in Operational Research, 27, 709–738. https://doi.org/10.1111/itor.12649
- Daraio, C., Kerstens, K. H., Nepomuceno, T. C. C., & Sickles, R. (2019). Productivity and efficiency analysis software: An exploratory bibliographical survey of the options. Journal of Economic Surveys, 33(1), 85–100.
- Daraio, C., & Simar, L. (2014). Directional distances and their robust versions: Computational and testing issues. European Journal of Operational Research, 237(1), 358–369.
- de Carvalho, V. D. H., Poleto, T., & Costa, A. P. C. S. (2016). The main critical success factors of contractual and relational governances in outsourcing relationships. In Á. Rocha, A. M. Correia, H. Adeli, L. P. Reis, & M. M. Teixeira (Eds.), New advances in information systems and technologies. WorldCIST 2016. Advances in intelligent systems and computing (Vol. 1159, 444). Springer. https://doi.org/10.1007/978-3-319-31232-3_1
10.1007/978-3-319-31232-3_1 Google Scholar
- de Carvalho, V., Heuer, D., Poleto, T., Nepomuceno, T. C. C., & Costa, A. P. C. S. (2021). A study on relational factors in information technology outsourcing: Analyzing judgments of small and medium-sized supplying and contracting ‘companies’ managers. Journal of Business & Industrial Marketing, 37(4), 893–917. https://doi.org/10.1108/jbim-10-2020-0475
- de Carvalho, V., Heuer, D., Poleto, T., & Seixas, A. P. C. (2018). Information technology outsourcing relationship integration: A critical success factors study based on ranking problems (P.γ) and correlation analysis. Expert Systems, 35(1), e12198. https://doi.org/10.1111/exsy.12198
- Deville, A. (2009). Branch banking network assessment using DEA: A benchmarking analysis—A note. Management Accounting Research, 20(4), 252–261. https://doi.org/10.1016/j.mar.2009.08.001
- Du, J., Liang, L., Yang, F., Bi, G.-B., & Yu, X.-B. (2010). A new DEA-based method for fully ranking all decision-making units. Expert Systems, 27(5), 363–373. https://doi.org/10.1111/j.1468-0394.2010.00553.x
- Färe, R., & Grosskopf, S. (2006). New directions: Efficiency and productivity (Vol. 3). Springer Science & Business Media.
- Gorecki, S., Possik, J., Zacharewicz, G., Ducq, Y., & Perry, N. (2020). A multicomponent distributed framework for smart production system modeling and simulation. Sustainability, 12(17), 6969.
- Hooff, B., & Huysman, M. (2009). Managing knowledge sharing: Emergent and engineering approaches. Information Management, 46, 1–8. https://doi.org/10.1016/j.im.2008.09.002
- Hu, Z.-H., Zhou, J.-X., Zhang, M.-J., & Zhao, Y. (2015). Methods for ranking college sports coaches based on data envelopment analysis and PageRank. Expert Systems, 32(6), 652–673. https://doi.org/10.1111/exsy.12108
- Hwang, G.-J. (1994). Knowledge elicitation and integration from multiple experts. Journal of Information Science and Engineering, 10(1), 99–109.
- Kao, C. (2021). Closest targets in the slacks-based measure of efficiency for production units with multi-period data. European Journal of Operational Research, 297, 1042–1054. https://doi.org/10.1016/j.ejor.2021.05.050
- Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25.
- Kumar, M., & Vincent, C. (2011). Benchmarking Indian banks using DEA in post-reform period: A progressive time-weighted mean approach. The Service Industries Journal, 31(14), 2455–2485. https://doi.org/10.1080/02642069.2010.504818
- Kyrö, P. (2003). Revising the concept and forms of benchmarking. Benchmarking: An International Journal, 10(3), 210–225. https://doi.org/10.1108/14635770310477753
10.1108/14635770310477753 Google Scholar
- Leu, J. D., Tsai, W. H., Fan, M. N., & Chuang, S. (2020). Benchmarking sustainable manufacturing: A DEA-based method and application. Energies, 13(22), 5962.
- Liu, S. T. (2010). Measuring and categorizing technical efficiency and productivity change of commercial banks in Taiwan. Expert Systems with Applications, 37(4), 2783–2789.
- Liu, J. S., Lu, L. Y., Lu, W. M., & Lin, B. J. (2013). Data envelopment analysis 1978–2010: A citation-based literature survey. Omega, 41(1), 3–15. https://doi.org/10.1016/j.omega.2010.12.006
- Liu, X., & Huang, J. (2019). China's high-tech industry efficiency measurement with virtual frontier data envelopment analysis and Malmquist productivity index. Expert Systems, 39(2), e12450. https://doi.org/10.1111/exsy.12450
- Lou, S. W., Yang, Y., & Tseng, C. W. (2021). Data envelopment analysis based assessment of human resource management strategy in the banking industry: A case study of a well-known Taiwanese Bank. Managerial and Decision Economics, 42(5), 1172–1182.
- Machado, L. M. O. (2021). Ontologies in knowledge organization. Encyclopedia, 1(1), 144–151. https://doi.org/10.3390/encyclopedia1010015
10.3390/encyclopedia1010015 Google Scholar
- Mazumder, S., Kabir, G., Hasin, M., & Ali, S. M. (2018). Productivity benchmarking using analytic network process (ANP) and data envelopment analysis (DEA). Big Data and Cognitive Computing, 2(3), 27.
10.3390/bdcc2030027 Google Scholar
- Nepomuceno, K., Nepomuceno, T., & Sadok, D. (2020). Measuring the internet technical efficiency: A ranking for the world wide web pages. IEEE Latin America Transactions, 18(06), 1119–1125.
- Nepomuceno, T. C., Daraio, C., & Costa, A. P. (2021a). Multicriteria ranking for the efficient and effective assessment of police departments. Sustainability, 13(8), 4251. https://doi.org/10.3390/su13084251
- Nepomuceno, T. C. C., & Costa, A. P. C. (2019). Resource allocation with time series DEA applied to Brazilian Federal Saving banks. Economics Bulletin, 39(2), 1384–1392.
- Nepomuceno, T. C. C., Daraio, C., & Costa, A. P. C. S. (2020). Combining multicriteria and directional distances to decompose non-compensatory measures of sustainable banking efficiency. Applied Economics Letters, 27(4), 329–334.
- Nepomuceno, T. C. C., Daraio, C., & Costa, A. P. C. S. (2021b). Theoretical and empirical advances in the assessment of productive efficiency since the introduction of DEA: A Bibliometric analysis. Forthcoming in Int. Journal of Operational Research., 1, 1. https://doi.org/10.1504/IJOR.2020.10035180
10.1504/IJOR.2020.10035180 Google Scholar
- Nepomuceno, T. C. C., de Carvalho, V. D. H., & Costa, A. P. C. S. (2020). Time-series directional efficiency for knowledge benchmarking in service organizations. In Á. Rocha, H. Adeli, L. Reis, S. Costanzo, I. Orovic, & F. Moreira (Eds.), Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing (Vol. 1159). Springer. https://doi.org/10.1007/978-3-030-45688-7_34
10.1007/978-3-030-45688-7_34 Google Scholar
- Nepomuceno, T. C. C., Santiago, K. T. M., Daraio, C., & Costa, A. P. C. S. (2020). Exogenous crimes and the assessment of public safety efficiency and effectiveness. Annals of Operations Research, 1–34. https://doi.org/10.1007/s10479-020-03767-6
- Nonaka, I., & Konno, N. (1998). The concept of “Ba”: Building a Foundation for Knowledge Creation. California Management Review, 40, 40–54. https://doi.org/10.2307/41165942
- O'Dell, C., Wiig, K., & Odem, P. (1999). Benchmarking unveils emerging knowledge management strategies. Benchmarking: An International Journal, 6(3), 202–211.
10.1108/14635779910288550 Google Scholar
- Park, J., & Sung, S. I. (2016). Integrated approach to construction of benchmarking network in DEA-based stepwise benchmark target selection. Sustainability, 8(7), 600.
- Pereira, D. d. S., & Soares de Mello, J. C. C. B. (2021). Efficiency evaluation of Brazilian airlines operations considering the Covid-19 outbreak. Journal of Air Transport Management, 91(2020), 101976. https://doi.org/10.1016/j.jairtraman.2020.101976
- Perianes-Rodriguez, A., Waltman, L., & Van Eck, N. J. (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178–1195.
- Peykani, P., Mohammadi, E., Saen, R. F., Sadjadi, S. J., & Rostamy-Malkhalifeh, M. (2020). Data envelopment analysis and robust optimization: A review. Expert Systems, 37(4), e12534.
- Rijal, S., Huang, Y. H., & Lin, H. Y. (2021). An integrated approach to municipal solid waste recycling performance evaluation by incorporating local demographic features. Sustainability, 13(18), 10446.
- Rostamzadeh, R., Akbarian, O., Banaitis, A., & Soltani, Z. (2021). Application of DEA in benchmarking: A systematic literature review from 2003–2020. Technological and Economic Development of Economy, 27(1), 175–222. https://doi.org/10.3846/tede.2021.13406
- Ruiz, J. L., & Sirvent, I. (2021). Identifying suitable benchmarks in the way toward achieving targets using data envelopment analysis. International Transactions in Operational Research, 29, 1749–1768. https://doi.org/10.1111/itor.13029
- Sahoo, B. (2007). Scale, scope and capacity utilization in DEA. International Journal of Applied Economics and Econometrics, 15(4), 298–328.
- Sherman, H. D., & Zhu, J. (2006). Benchmarking with quality-adjusted DEA (Q-DEA) to seek lower-cost high-quality service: Evidence from a U.S. bank application. Annals of Operations Research, 145(1), 301–319. https://doi.org/10.1007/s10479-006-0037-4
- Stefanović, M., Tadic, D., Arsovski, S., Pravdic, P., Abadić, N., & Stefanović, N. (2015). Determination of the effectiveness of the realization of enterprise business objectives and improvement strategies in an uncertain environment. Expert Systems, 32(4), 494–506.
- Sufian, F. (2011). Benchmarking the efficiency of the Korean banking sector: A DEA approach. Benchmarking: An International Journal, 18(1), 107–127. https://doi.org/10.1108/14635771111109841
10.1108/14635771111109841 Google Scholar
- Sutopo, W., Astuti, R. W., & Suryandari, R. T. (2019). Accelerating a technology commercialization; with a discussion on the relation between technology transfer efficiency and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 5(4), 95.
10.3390/joitmc5040095 Google Scholar
- Tavassoli, M., Farzipoor Saen, R., & Faramarzi, G. R. (2015). Developing network data envelopment analysis model for supply chain performance measurement in the presence of zero data. Expert Systems, 32(3), 381–391. https://doi.org/10.1111/exsy.12097
- Thimm, H. (2012). Cloud-based collaborative decision making: Design considerations and architecture of the GRUPO-MOD system. International Journal of Decision Support System Technology (IJDSST), 4(4), 39–59.
10.4018/jdsst.2012100103 Google Scholar
- van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.
- van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact: Methods and practice (pp. 285–320). Springer.
- Wang, S., & Noe, R. A. (2010). Knowledge sharing: A review and directions for future research. Human Resource Management Review, 20, 115–131. https://doi.org/10.1016/j.hrmr.2009.10.001
- Zhu, N., Shah, W. U. H., Kamal, M. A., & Yasmeen, R. (2021). Efficiency and productivity analysis of Pakistan's banking industry: A DEA approach. International Journal of Finance & Economics, 26(4), 6362–6374.
- Zhu, Q., Aparicio, J., Li, F., Wu, J., & Kou, G. (2021b). Determining closest targets on the extended facet production possibility set in data envelopment analysis: Modeling and computational aspects. European Journal of Operational Research, 296, 927–939. https://doi.org/10.1016/j.ejor.2021.04.019
- Zhu, Q., Aparicio, J., Li, F., Wu, J., & Kou, G. (2021a). Determining closest targets on the extended facet production possibility set in data envelopment analysis: Modeling and computational aspects. European Journal of Operational Research, 296, 927–939. https://doi.org/10.1016/j.ejor.2021.04.019
