Research article Special Issues

Multi-criteria decision making in evaluation of open government data indicators: An application in G20 countries

  • Received: 13 February 2023 Revised: 21 May 2023 Accepted: 22 May 2023 Published: 30 May 2023
  • MSC : 62C86, 91B06, 90B50

  • Open data has a large means of identifying commonly reachable information on different platforms. One of the open data sources is open government data. The goals of open governments are about building transparency, accountability and participation to strengthen governance and inform citizens. The aim of this study is twofold: (ⅰ) to propose a reliable decision-making tool for dealing with real-life problems and (ⅱ) to demonstrate the practicality of the proposed model through a case study of its ranking with an open government data indicator for G20 countries. This study proposes a multi-criteria methodology that evaluates open data management systems used in e-government development. First, a set of evaluation criteria is established that cover the indicators used in the Global Open Data Index. Second, weights from the Logarithm Methodology of Additive Weights (LMAW) and Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) methods were combined with the Bayesian approach to determine the weights of these criteria. Finally, the Weighted Aggregated Sum Product Assessment (WASPAS) method was used to obtain the ranking results. The novelties of the study lie in the combination of objective and subjective weighting methods, both in determining the ranking of G20 countries with open government data indicators and in deciding the importance levels of the criteria used. The "air quality" and "procurement" criteria are the top two criteria, with weights of 0, 1378 and 0, 1254 respectively. The findings also show that Australia is the best performer, while the United Kingdom is the second best performing. Comprehensive sensitivity analysis verifies the validity, robustness and effectiveness of the proposed framework. According to research findings and analysis, the methodology applied has the potential to assist policymakers and decision-makers in the process of modernization of existing public services in terms of open data and the opportunities it presents.

    Citation: Gülay Demir, Muhammad Riaz, Yahya Almalki. Multi-criteria decision making in evaluation of open government data indicators: An application in G20 countries[J]. AIMS Mathematics, 2023, 8(8): 18408-18434. doi: 10.3934/math.2023936

    Related Papers:

  • Open data has a large means of identifying commonly reachable information on different platforms. One of the open data sources is open government data. The goals of open governments are about building transparency, accountability and participation to strengthen governance and inform citizens. The aim of this study is twofold: (ⅰ) to propose a reliable decision-making tool for dealing with real-life problems and (ⅱ) to demonstrate the practicality of the proposed model through a case study of its ranking with an open government data indicator for G20 countries. This study proposes a multi-criteria methodology that evaluates open data management systems used in e-government development. First, a set of evaluation criteria is established that cover the indicators used in the Global Open Data Index. Second, weights from the Logarithm Methodology of Additive Weights (LMAW) and Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) methods were combined with the Bayesian approach to determine the weights of these criteria. Finally, the Weighted Aggregated Sum Product Assessment (WASPAS) method was used to obtain the ranking results. The novelties of the study lie in the combination of objective and subjective weighting methods, both in determining the ranking of G20 countries with open government data indicators and in deciding the importance levels of the criteria used. The "air quality" and "procurement" criteria are the top two criteria, with weights of 0, 1378 and 0, 1254 respectively. The findings also show that Australia is the best performer, while the United Kingdom is the second best performing. Comprehensive sensitivity analysis verifies the validity, robustness and effectiveness of the proposed framework. According to research findings and analysis, the methodology applied has the potential to assist policymakers and decision-makers in the process of modernization of existing public services in terms of open data and the opportunities it presents.



    加载中


    [1] The world bank, Open data essentials, 2023. Available from: http://opendatatoolkit.worldbank.org/en/essentials.html.
    [2] R. P. Lourenço, An analysis of open government portals: A perspective of transparency for accountability, Gov. Inform. Q., 32 (2015), 323–332. https://doi.org/10.1016/j.giq.2015.05.006 doi: 10.1016/j.giq.2015.05.006
    [3] A. Zuiderwijk, M. Janssen, Open data policies, their implementation and impact: A framework for comparison, Gov. Inform. Q., 31 (2013). https://doi.org/10.1016/j.giq.2013.04.003 doi: 10.1016/j.giq.2013.04.003
    [4] Z. S. Alzamil, M. A. Vasarhelyi, A new model for effective and efficient open government data, Int. J. Discl. Gov., 16 (2019), 174–187. https://doi.org/10.1057/s41310-019-00066-w doi: 10.1057/s41310-019-00066-w
    [5] Open data charter, Principles, 2023. Available from: https://opendatacharter.net/.
    [6] About Open government partnership, 2023. Available from: https://opengovpartnership.org.
    [7] Open knowledge foundation, The open definition, 2023. Available from: https://opendefinition.org/.
    [8] Global open data index, Place overview, 2023. Available from: http://index.okfn.org/place.html.
    [9] The annotated 8 principles of open government data, 2023. Available from: https://opengovdata.org/.
    [10] B. Giles-Corti, A. V. Moudon, M. Lowe, D. Adlakha, E. Cerin, G. Boeing, et al., Creating healthy and sustainable cities: What gets measured, gets done, Lancet Glob. Health, 10 (2022), 782–785. https://doi.org/10.1016/S2214-109X(22)00070-5 doi: 10.1016/S2214-109X(22)00070-5
    [11] Y. Amara-Ouali, Y. Goude, P. Massart, J. M. Poggi, H. Yan, A review of electric vehicle load open data and models, Energies, 14 (2021), 2233. https://doi.org/10.3390/en14082233 doi: 10.3390/en14082233
    [12] European commission, The official portal for European data, 2023. Available from: https://data.europa.eu/en/publications/datastories/practical-guide-building-future-proof-open-data-portals.
    [13] P. Huston, V. L. Edge, E. Bernier, Reaping the benefits of open data in public health, Can. Commun. Dis. Rep., 45 (2019), 252–256. https://doi.org/10.14745/ccdr.v45i10a01 doi: 10.14745/ccdr.v45i10a01
    [14] C. Arderne, C. Zorn, C. Nicolas, E. E. Koks, Predictive mapping of the global power system using open data, Sci. Data, 7 (2020), 19. https://doi.org/10.1038/s41597-019-0347-4 doi: 10.1038/s41597-019-0347-4
    [15] P. Yochum, L. Chang, T. Gu, M. Zhu, Linked open data in location-based recommendation system on tourism domain: A survey, IEEE Access, 8 (2020), 16409–16439. https://doi.org/10.1109/ACCESS.2020.2967120 doi: 10.1109/ACCESS.2020.2967120
    [16] M. Z. Hanif, N. Yaqoob, M. Riaz, M. Aslam, Linear Diophantine fuzzy graphs with new decision-making approach, AIMS Math., 7 (2022), 14532–14556. https://doi.org/10.3934/math.2022801 doi: 10.3934/math.2022801
    [17] A. Habib, Z. A. Khan, N. Jamil, M. Riaz, A decision-making strategy to combat CO2 emissions using sine trigonometric aggregation operators with cubic bipolar fuzzy input, AIMS Math., 8 (2023), 15092–15128. https://doi.org/10.3934/math.2023771 doi: 10.3934/math.2023771
    [18] M. Riaz, H. M. A. Farid, J. Antucheviciene, G. Demir, Efficient decision making for sustainable energy using single-valued neutrosophic prioritized ınteractive aggregation operators, Mathematics, 11 (2023), 2186. https://doi.org/10.3390/math11092186 doi: 10.3390/math11092186
    [19] F. Feng, C. Li, B. Davvaz, M. I. Ali, Soft sets combined with fuzzy sets and rough sets: A tentative approach, Soft Comput., 14 (2010), 899–911. https://doi.org/10.1007/s00500-009-0465-6 doi: 10.1007/s00500-009-0465-6
    [20] M. Akram, A. Khan, J. C. R. Alcantud, G. Santos‐García, A hybrid decision‐making framework under complex spherical fuzzy prioritized weighted aggregation operators, Exp. Syst., 38 (2021), 1–24. https://doi.org/10.1111/exsy.12712 doi: 10.1111/exsy.12712
    [21] A. Özdağoğlu, M. K. Keleş, F. Y. Eren, Evaluation of macroelisa equipment alternatives in a univercity hospital with WASPAS and SWARA, Suleyman Demirel Univ. J. Fac. Econ. Adm. Sci., 24 (2019), 319–331. Available from: https://dergipark.org.tr/tr/pub/sduiibfd/issue/53004/704322.
    [22] L. B. Ayre, J. Craner, Open data: What it is and why you should care, Public Libr. Q., 36 (2017), 173–184. https://doi.org/10.1080/01616846.2017.1313045 doi: 10.1080/01616846.2017.1313045
    [23] A. Luthfi, Z. Rehena, M. Janssen, J. Crompvoets, A fuzzy multi-criteria decision-making approach for analyzing the risks and benefits of opening data, Lecture Notes in Computer Science, Springer, Cham, 2018. https://doi.org/10.1007/978-3-030-02131-3_36
    [24] R. Máchová, M. Lněnička, A multi-criteria decision-making model for the selection of open data management systems, Electron. Gov. Int. J., 15 (2019), 372–391. https://doi.org/10.1504/EG.2019.102579 doi: 10.1504/EG.2019.102579
    [25] D. Boulbazine, A. Kebiche, Measuring transit-oriented development in Algerian light rail transit lines by using hybrid multi-criteria decision making and open data sources, Case Stud. Transp. Pol., 10 (2022), 331–340. https://doi.org/10.1016/j.cstp.2021.12.013 doi: 10.1016/j.cstp.2021.12.013
    [26] S. Kubler, J. Robert, T. Y. Le, J. Umbrich, S. Neumaier, Open data portal quality comparison using AHP, In: 17th International digital government research conference on digital government research, Shanghai, China, 2016.
    [27] M. B. Grace, J. R. Gil-Garcia, Understanding the actual use of open data: Levels of engagement and how they are related, Telemat. Inform., 63 (2021), 101673. https://doi.org/10.1016/j.tele.2021.101673 doi: 10.1016/j.tele.2021.101673
    [28] A. S. Towse, D. A. Ellis, J. N. Towse, Making data meaningful: Guidelines for good quality open data, J. Soc. Psychol., 161 (2021), 395–402. https://doi.org/10.1080/00224545.2021.1938811 doi: 10.1080/00224545.2021.1938811
    [29] D. Pamucar, M. Žižović, S. Biswas, D. Božanić, A new logarithm methodology of additive weights (LMAW) for multi-criteria decision-making: Application in logistics, Facta Univ. Ser.- Mech. Eng., 19 (2021), 361–380. https://doi.org/10.22190/FUME210214031P doi: 10.22190/FUME210214031P
    [30] D. Božanić, D. Pamučar, A. Milić, D. Marinković, N. Komazec, Modification of the logarithm methodology of additive weights (LMAW) by a triangular fuzzy number and its application in multi-criteria decision making, Axioms, 11 (2022), 89, https://doi.org/10.3390/axioms11030089 doi: 10.3390/axioms11030089
    [31] Ö. F. Görçün, Ö. H. Küçükönder, Evaluation of the transition potential to cyber-physical production system of heavy industries in Turkey with a novel decision-making approach based on Bonferroni function, Verimlilik Dergisi, 2022, 1–16. https://doi.org/10.51551/verimlilik.983133 doi: 10.51551/verimlilik.983133
    [32] A. Puška, D. Božanić, M. Nedeljković, M. Janošević, Green supplier selection in an uncertain environment in agriculture using a hybrid MCDM model: Z-numbers-fuzzy LMAW-fuzzy CRADIS model, Axioms, 11 (2022), 427. https://doi.org/10.3390/axioms11090427 doi: 10.3390/axioms11090427
    [33] D. Tešić, D. Božanić, A. Puška, A. Milić, D. Marinković, Development of the MCDM fuzzy LMAW-grey MARCOS model for the selection of a dump truck, Rep. Mech. Eng., 4 (2023), 1–17. https://doi.org/10.31181/rme20008012023t doi: 10.31181/rme20008012023t
    [34] Ç. Sıcakyüz, Analyzing healthcare and wellness products' quality embedded in online customer reviews: Assessment with a hybrid fuzzy LMAW and Fermatean fuzzy WASPAS method, Sustainability, 15 (2023), 3428. https://doi.org/10.3390/su15043428 doi: 10.3390/su15043428
    [35] M. Asadi, S. H. Zolfani, D. Pamucar, J. Salimi, S. Saberi, The appropriation of blockchain implementation in the supply chain of SMES based on fuzzy LMAW, Eng. Appl. Artif. Intel., 123 (2021), 106169. https://doi.org/10.1016/j.engappai.2023.106169 doi: 10.1016/j.engappai.2023.106169
    [36] M. Subotić, V. Radičević, Z. Pavlović, G. Ćirović, Development of a new risk assessment methodology for light goods vehicles on two-lane road sections, Symmetry, 13 (2021), 1271. http://dx.doi.org/10.3390/sym13071271 doi: 10.3390/sym13071271
    [37] M. Deveci, I. Gokasar, D. Pamucar, Y. Chen, D. M. Coffman, Sustainable e-scooter parking operation in urban areas using fuzzy Dombi-based RAFSI model, Sustain. Cities Soc., 91 (2023), 104426. https://doi.org/10.1016/j.scs.2023.104426 doi: 10.1016/j.scs.2023.104426
    [38] F. Ecer, D. Pamucar, A novel LOPCOW-DOBI multi-criteria sustainability performance assessment methodology: An application in developing country banking sector, Omega, 2022. https://doi.org/10.1016/j.omega.2022.102690 doi: 10.1016/j.omega.2022.102690
    [39] F. Ecer, İ. Y. Ögel, R. Krishankumar, E. B. Tirkolaee, The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era, Artif. Intell. Rev., 2023. https://doi.org/10.1007/s10462-023-10476-6 doi: 10.1007/s10462-023-10476-6
    [40] F. Ecer, H. Küçükönder, S. K. Kaya, Ö. F. Görçün, Sustainability performance analysis of micro-mobility solutions in urban transportation with a novel IVFNN-Delphi-LOPCOW-CoCoSo framework, Transport. Res. A-P., 172 (2023), 103667. https://doi.org/10.1016/j.tra.2023.103667 doi: 10.1016/j.tra.2023.103667
    [41] W. Niu, Y. Rong, L. Yu, L. Huang, A novel hybrid group decision-making approach based on EDAS and regret theory under a Fermatean cubic fuzzy environment, Mathematics, 10 (2022), 3116. http://dx.doi.org/10.3390/math10173116 doi: 10.3390/math10173116
    [42] S. Biswas, N. Joshi, A performance-based ranking of initial public offerings (IPOs) in India, J. Decis. Anal. Intell. Comput., 3 (2023), 15–32. https://doi.org/10.31181/10023022023b doi: 10.31181/10023022023b
    [43] S. Biswas, G. Bandyopadhyay, J. N. Mukhopadhyaya, A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian FMCG and consumer durable sector, Decis. Mak. Appl. Manag. Eng., 5 (2022), 140–175. https://doi.org/10.31181/dmame0306102022b doi: 10.31181/dmame0306102022b
    [44] S. Biswas, G. Bandyopadhyay, D. Pamucar, N. Joshi, A Multi-criteria based stock selection framework in emerging market, Oper. Res. Eng. Sci., 5 (2022). https://doi.org/10.31181/oresta161122121b doi: 10.31181/oresta161122121b
    [45] N. V. Thanh, N. T. K. Lan, Solar energy deployment for the sustainable future of Vietnam: hybrid SWOC-FAHP-WASPAS analysis, Energies, 15 (2022), 2798. https://doi.org/10.3390/en15082798 doi: 10.3390/en15082798
    [46] M. Eghbali-Zarch, R. Tavakkoli-Moghaddam, K. Dehghan-Sanej, A. Kaboli, Prioritizing the effective strategies for construction and demolition waste management using fuzzy IDOCRIW and WASPAS methods, Eng. Constr. Architect. Ma., 29 (2022), 1109–1138. https://doi.org/10.1108/ECAM-08-2020-0617 doi: 10.1108/ECAM-08-2020-0617
    [47] S. K. Vaid, G. Vaid, S. Kaur, R. Kumar, M. S. Sidhu, Application of multi-criteria decision-making theory with VIKOR-WASPAS-Entropy methods: A case study of silent Genset, Mat. Today Proc., 50 (2022), 2416–2423. https://doi.org/10.1016/j.matpr.2021.10.259 doi: 10.1016/j.matpr.2021.10.259
    [48] P. Liu, A. Saha, A. R. Mishra, P. Rani, D. Dutta, J. Baidya, A BCF-CRITIC-WASPAS method for green supplier selection with cross-entropy and Archimedean aggregation operators, J. Amb. Intell. Hum. Comput. 2022, 1–25. https://doi.org/10.1007/s12652-022-03745-9 doi: 10.1007/s12652-022-03745-9
    [49] S. Salimian, S. M. Mousavi, J. Antuchevičienė, Evaluation of infrastructure projects by a decision model based on RPR, MABAC, and WASPAS methods with interval-valued intuitionistic fuzzy sets, Int. J. Strateg. Prop. M., 26 (2022), 106–118. https://doi.org/10.3846/ijspm.2022.16476. doi: 10.3846/ijspm.2022.16476
    [50] A. P. Darko, D. Liang, Probabilistic linguistic WASPAS method for patients' prioritization by developing prioritized Maclaurin symmetric mean aggregation operators, Appl. Intell., 52 (2022), 9537–9555. https://doi.org/10.1007/s10489-021-02807-3 doi: 10.1007/s10489-021-02807-3
    [51] V. Dede, K. Zorlu, Geoheritage assessment with entropy-based WASPAS approach: An analysis on Karçal Mountains (Turkey), Geoheritage, 15 (2023), 5. https://doi.org/10.1007/s12371-022-00777-7 doi: 10.1007/s12371-022-00777-7
    [52] Ö. F. Görçün, D. Pamucar, R. Krishankumar, H. Küçükönder, The selection of appropriate Ro-Ro Vessel in the second-hand market using the WASPAS' Bonferroni approach in type 2 neutrosophic fuzzy environment, Eng. Appl. Artif. Intell., 117 (2023), 105531. https://doi.org/10.1016/j.engappai.2022.105531 doi: 10.1016/j.engappai.2022.105531
    [53] M. B. Kar, R. Krishankumar, D. Pamucar, S. Kar, A decision framework with nonlinear preferences and unknown weight information for cloud vendor selection. Exp. Syst. Appl., 213 (2023), 118982. https://doi.org/10.1016/j.eswa.2022.118982 doi: 10.1016/j.eswa.2022.118982
    [54] D. Pamučar, M. Žižović, S. Biswas, D. Božanić, A new logarithm methodology of additive weights (LMAW) for multi-criteria decision-making: Application in logistics, Facta Univ.- Ser. Mech., 19 (2021), 361–380. https://doi.org/10.22190/FUME210214031P doi: 10.22190/FUME210214031P
    [55] F. Ecer, D. Pamučar, A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector, Omega, 112 (2022), 102690. https://doi.org/10.1016/j.omega.2022.102690 doi: 10.1016/j.omega.2022.102690
    [56] I. Vinogradova, V. Podvezko, E. K. Zavadskas, The recalculation of the weights of criteria in MCDM methods using the Bayes approach, Symmetry, 10 (2018), 1–18. https://doi.org/10.3390/sym10060205 doi: 10.3390/sym10060205
    [57] E. K. Zavadskas, J. Antucheviciene J. Šaparauskas, Z. Turskis, Multicriteria assessment of facades' alternatives: Peculiarities of ranking methodology, Proc. Eng., 57 (2013), 107–112. https://doi.org/10.1016/j.proeng.2013.04.016 doi: 10.1016/j.proeng.2013.04.016
    [58] M. Bakır, Ş. Akan, K. Kıracı, D. Karabasevic, D. Stanujkic, G. Popovic, Multiple-criteria approach of the operational performance evaluation in the airline industry: Evidence from the emerging markets, Rom. J. Econ. Forecast., 2020,149–172.
    [59] M. Yazdani, P. Chatterjee, D. Pamucar, M. D. Abad, A risk-based integrated decision-making model for green supplier selection: A case study of a construction company in Spain, Kybernetes, 49 (2020), 1229–1252. https://doi.org/10.1108/K-09-2018-0509 doi: 10.1108/K-09-2018-0509
    [60] M. Deveci, D. Pamucar, I. Gokasar, W. Pedrycz, X. Wen, Autonomous bus operation alternatives in urban areas using fuzzy Dombi-Bonferroni operator based decision making model, IEEE T. Intel. Transp. Syst., 2022. https://doi.org/10.1109/TITS.2022.3202111 doi: 10.1109/TITS.2022.3202111
    [61] M. N. Jafar, M. Saeed, M. Saqlain, M. S. Yang, Trigonometric similarity measures for neutrosophic hypersoft sets with application to renewable energy source selection, IEEE Access, 9 (2021), 129178–129187. https://doi.org/10.1109/ACCESS.2021.3112721 doi: 10.1109/ACCESS.2021.3112721
    [62] T. Mahmood, K. Ullah, Q. Khan, N. Jan, An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets, Neural Comput. Appl., 31 (2019), 7041–7053. https://doi.org/10.1007/s00521-018-3521-2 doi: 10.1007/s00521-018-3521-2
    [63] Q. Muhammad, S. Abdullah, M. Naeem, N. Khan, S. Okyere, T. Botmart, Decision support system based on complex q-rung orthopair fuzzy rough Hamacher aggregation operator through modified EDAS method, J. Funct. Space., 2022, 5437373. https://doi.org/10.1155/2022/5437373 doi: 10.1155/2022/5437373
    [64] S. Ashraf, S. Abdullah, T. Mahmood, F. Ghani, T. Mahmood, Spherical fuzzy sets and their applications in multi-attribute decision making problems, J. Intell. Fuzzy Syst., 36 (2019), 2829–2844. https://doi.org/10.3233/JIFS-172009 doi: 10.3233/JIFS-172009
    [65] R. Kausar, H. M. A. Farid, M. Riaz, D. Božanić, Cancer therapy assessment accounting for heterogeneity using q-rung picture fuzzy dynamic aggregation approach, Symmetry, 14 (2022), 2538. https://doi.org/10.3390/sym14122538 doi: 10.3390/sym14122538
    [66] S. Enginoglu, S. Memis, F. Karaaslan, A new approach to group decision-making method based on TOPSIS under fuzzy soft environment, J. New Results Sci., 8 (2019), 42–52. https://dergipark.org.tr/en/pub/jnrs/issue/51087/656500
    [67] C. W. Churchman, R. L. Ackoff, An approximate measure of value, J. Oper. Res. Soc. Am., 2 (1954), 172–187. https://www.jstor.org/stable/166603
    [68] W. K. M. Brauers, E. K. Zavadskas, Project management by MULTIMOORA as an instrument for transition economies, Technol. Econ. Dev. Eco., 16 (2010), 5–24. https://doi.org/10.3846/tede.2010.01 doi: 10.3846/tede.2010.01
    [69] C. L. Hwang, K. Yoon, Multiple attribute decision making methods and application, A State-of-The-Art Survey, Berlin, Heidelberg, New York, 1981.
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1316) PDF downloads(120) Cited by(0)

Article outline

Figures and Tables

Figures(8)  /  Tables(11)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog