Multi-criteria Evaluation + Positional Ranking Approach for Candidate Selection in E-voting

  • Farhad Yusifov Institute of Information Technology, Baku, Azerbaijan
  • Rasim Alguliyev Institute of Information Technology, Baku, Azerbaijan
  • Ramiz Aliguliyev Institute of Information Technology, Baku, Azerbaijan
Keywords: E-government, E-democracy, E-voting, MCDM, Candidate Selection, Election, E-Government Maturity Model, Governance

Abstract

E-voting is one of the most important components of e-democracy and includes interesting research topics, such as the mechanisms of participation in elections, technological solutions to e-voting and the efficient application of those in e-voting. Currently, there are numerous voting systems adopted in many countries of the world and each of those has specific advantages and problems. The paper explores the e-voting system as one of the main tools of e-democracy and analyzes its advantages and drawbacks. Voting results always lead to a broad debate in terms of candidate selection and of whether the candidate elected to a position is suitable for that position. At present, the selection of qualified personnel and their appointment to responsible positions in public administration is one of the topical issues. In the paper, multi-criteria decision-making (MCDM) is proposed for the selection of candidates in e-voting. The criteria for candidate selection are determined and the relationship of each candidate with the other candidates is assessed by using a binary matrix. The candidate rank is calculated according to all the criteria. In a numerical experiment, candidate evaluation is enabled based on the selected criteria and ranked by using a positional ranking approach. The proposed model allows for the selection of a candidate with the competencies based on the criteria set out in the e-voting process and the making of more effective decisions as well.

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References

Abu-Shanab, E., Knight, M., & Refai H. (2010). E-voting systems: a tool for e-democracy. Management Research and Practice, 2(3), 264-274.

Afshari, A.R., Nikolić, M., & Akbari Z. (2017). Personnel selection using group fuzzy AHP and SAW methods. Journal of Engineering Management and Competitiveness, 7(1), 3-10. DOI: https://doi.org/10.5937/jemc1701003A

Alguliyev, R., Aliguliyev, R. & Yusifov, F. (2019). MCDM for Candidate Selection in E-Voting, International Journal of Public Administration in the Digital Age, 6(2), 35-48.

Aliguliyev, R. (2009). Performance evaluation of density-based clustering methods. Information Sciences, 179, 3583-3602. DOI: https://doi.org/10.1016/j.ins.2009.06.012

Andersen, K. V., & Henriksen, H. Z. (2006). E-government maturity models: Extension of the Layne and Lee model. Government Information Quarterly, 23(2), 236–248. DOI: https://doi.org/10.1016/j.giq.2005.11.008

Anttiroiko, A.V. (2003). Building Strong E-Democracy - The Role of Technology in Developing Democracy for the Information Age. Communications of the ACM, 46(9), 121-128. DOI: https://doi.org/10.1145/903893.903926

Braun, N., & Brändli, D. (2006). Swiss E-Voting Pilot Projects: Evaluation, Situation Analysis and How to Proceedings, In: Electronic Voting 2006. 2nd International Workshop Co-organized by Council of Europe, IFIPWG8.5 and E-Voting.

Carrizales, T. (2008). Critical Factors in an Electronic Democracy: a study of municipal managers. The Electronic Journal of e-Government, 6(1), 23-30.

Chang, Y.H., Yeh, C.H., & Chang, Y. W. (2013). A new method selection approach for fuzzy group multicriteria decision making. Applied Soft Computing, 13(4), 2179-2187. DOI: https://doi.org/10.1016/j.asoc.2012.12.009

Chondros, N., Delis, A., Gavatha, D., Kiayias, A., Koutalakis & et al. (2014) In: Sideridis A., Kardasiadou Z., Yialouris C., Zorkadis V. (eds) E-Democracy, Security, Privacy and Trust in a Digital World. e-Democracy 2013. Communications in Computer and Information Science, 441. Springer, 441, 113-122.

Concha, G., Astudillo, H., Porrúa, M., & Pimenta, C. (2012). E-Government procurement observatory. maturity model and early measurements. Government Information Quarterly, 29, 43-50. DOI: https://doi.org/10.1016/j.giq.2011.08.005

Drechsler, W., & Madise, U. (2004). Electronic Voting in Estonia. In: Kersting, N., Baldersheim, H. (eds.) Electronic Voting and Democracy. A Comparative Analysis, Palgrave Macmillan, Basingstoke, 97–108. DOI: https://doi.org/10.1057/9780230523531_6

Dursun, M., & Karsak, E.E. (2010). A fuzzy MCDM approach for personnel selection. Expert Systems with Applications, 37, 4324-4330. DOI: https://doi.org/10.1016/j.eswa.2009.11.067

Fath-Allah, A., Cheikhi, L., Al-Qutaish, R. E., & Idri, A. (2014). E-Government maturity models: a comparative study International. Journal of Software Engineering & Applications (IJSEA), 5(3), 71-91.

Kabak, M., Burrnaoglu, S., & Kazancoglu, Y. (2012). A fuzzy hybrid MCDM approach for professional selection. Expert Systems with Applications, 39, 3516-3525. DOI: https://doi.org/10.1016/j.eswa.2011.09.042

Kazana, H., Özçelik, S., & Hobikoğlu, E.H. (2015). Election of deputy candidates for nomination with AHP-Promethee methods. Procedia - Social and Behavioral Sciences, 195, 603-613. DOI: https://doi.org/10.1016/j.sbspro.2015.06.141

Khorami, M., & Ehsani, R. (2015). Application of Multi Criteria Decision Making approaches for personnel selection problem: A survey. International journal of engineering research and applications, 5(5), 14-29.

Layne, K., & Lee, J. (2001). Developing fully functional E-government: A four stage model. Government Information Quarterly, 18(2), 122–136. DOI: https://doi.org/10.1016/S0740-624X(01)00066-1

Lee, J. (2010). 10 Year Retrospect on Stage Models of e-Government: A Qualitative Meta-Synthesis. Government Information Quarterly, 27, 220-230. DOI: https://doi.org/10.1016/j.giq.2009.12.009

Lin, H. T. (2010). Personnel selection using analytic network process and fuzzy data envelopment analysis approaches. Computers & Industrial Engineering, 59(4), 937–944. DOI: https://doi.org/10.1016/j.cie.2010.09.004

Mardani, A., Jusoh, A., MD Nor K., Khalifah, Z., Zakwan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014. Economic Research – Ekonomska Istrazivanja, 28(1), 516-571. DOI: https://doi.org/10.1080/1331677X.2015.1075139

McCormack, C.B. (2016). Democracy Rebooted: The Future of Technology in Elections, The Atlantic Council of the United States. http://publications.atlanticcouncil.org/election-tech/assets/report.pdf Accessed 10 October 2018.

Meier, A. (2012). eDemocracy & eGovernment. Springer-Verlag: Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-24494-0

Meserve, S.A., Palani, S., & Pemstein, D. (2017). Measuring candidate selection mechanisms in European elections: comparing formal party rules to candidate survey responses. www.danpemstein.com Accessed 10 October 2018. DOI: https://doi.org/10.1177/1465116517729539

Mona, F.M. Mursi, Ghazy, M.R. Assassa, Abdelhafez, A., Kareem, & M. Abo Samra (2013). On the Development of Electronic Voting: A Survey. International Journal of Computer Applications, 61(16), 1-13. DOI: https://doi.org/10.5120/10009-4872

Mukhametzyanov, I., & Pamučar, D. (2018). A Sensitivity analysis in MCDM problems: a statistical approach. Decision Making: Applications in Management and Engineering, 1(2), 51-80

Musia-Karg, M. (2014). The use of e-voting as a new tool of e-participation in modern democracies. http://pressto.amu.edu.pl/index.php/pp/article/viewFile/2101/2091 Accessed 10 October 2018.

Royes, G.F., & Bastos, R.C. (2001). Fuzzy MCDM in election prediction. IEEE International Conference on Systems, Man, and Cybernetics, Tucson, 7-10 October, 3258-3263.

Sakthivel, G., & Ilangkumaran, M. (2015). A hybrid multi-criteria decision making approach of ANP and TOPSIS to evaluate the optimum fuel blend in IC engine. International Journal of Decision Support Systems, 1(3), 268–293. DOI: https://doi.org/10.1504/IJDSS.2015.070170

Shahkooh K. A., Saghafi F., & Abdollahi A. (2008). A proposed model for e-Government maturity. In Information and Communication Technologies: 3rd International Conference on From Theory to Applications (ICTTA’2008), Damascus, 7-11 April, 1-5. DOI: https://doi.org/10.1109/ICTTA.2008.4529948

Siau, K., & Long, Y. (2005). Synthesizing e-government stage models–a meta-synthesis based on metaethnography approach. Industrial Management & Data Systems, 105(4), 443-458. DOI: https://doi.org/10.1108/02635570510592352

Stanujkic, D., Djordjevic, B., & Djordjevic, M. (2013). Comparative analysis of some prominent MCDM methods: A case of ranking Serbian banks. Serbian journal of management, 8(2), 213-241. DOI: https://doi.org/10.5937/sjm8-3774

Strielkowski, W., Gryshova, I., & Kalyugina, S. (2017). Modern Technologies in Public Administration Management: A Comparison of Estonia, India and United Kingdom. Administratie si Management Public, 28, 174-185.

Taghavifard, M.T., Fadaei, R., & Ebrahimi, S. (2014). E-democracy adoption factors by e-government citizens. International Research Journal of Applied and Basic Sciences, Science Explorer Publications, 8(8), 1114-1125.

Trechsel, A. H. (2007). E-voting and Electoral Participation, in: Dynamics of Referendum Campaigns. In: de Vreese (ed.) An International Perspective. Palgrave Macmillan, London, 159-182. DOI: https://doi.org/10.1057/9780230591189_8

Published
2019-10-15
How to Cite
Yusifov, F., Alguliyev, R., & Aliguliyev, R. (2019). Multi-criteria Evaluation + Positional Ranking Approach for Candidate Selection in E-voting. Decision Making: Applications in Management and Engineering, 2(2), 65-80. https://doi.org/10.31181/dmame1902119a