Manuel Paulo Melo

Personal Details

First Name:	Manuel
Middle Name:	Paulo
Last Name:	Melo
Suffix:
RePEc Short-ID:	pme742
	Paulo Melo, Gabinete 500 Faculdade de Economia da Universidade de Coimbra Av. Dias da Silva, 165 3004-512 COIMBRA Portugal

Affiliation

(75%) Centre for Business and Economics Research (CeBER)
Faculdade de Economia
Universidade do Coimbra

Coimbra, Portugal
http://www.uc.pt/go/ceber
RePEc:edi:cebucpt (more details at EDIRC)

(25%) Faculdade de Economia
Universidade do Coimbra

Coimbra, Portugal
http://www.fe.uc.pt/
RePEc:edi:fecucpt (more details at EDIRC)

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

1. Francineide Morais Bezerra & Paulo Melo & João Paulo Costa, 2017. "Reaching Consensus with VICA-ELECTRE TRI: A Case Study," Group Decision and Negotiation, Springer, vol. 26(6), pages 1145-1171, November.

   Cited by:

   1. Khaled Belahcène & Vincent Mousseau & Wassila Ouerdane & Marc Pirlot & Olivier Sobrie, 2023. "Multiple criteria sorting models and methods—Part I: survey of the literature," 4OR, Springer, vol. 21(1), pages 1-46, March.
   2. Zhen Zhang & Zhuolin Li, 2023. "Consensus-based TOPSIS-Sort-B for multi-criteria sorting in the context of group decision-making," Annals of Operations Research, Springer, vol. 325(2), pages 911-938, June.
   3. Liang, Decui & Fu, Yuanyuan & Ishizaka, Alessio, 2023. "A consensual group ELECTRE-SORT approach considering the incomparable classes with the application of machine maintenance strategy assignment," Omega, Elsevier, vol. 118(C).
   4. Miłosz Kadziński & Lucia Rocchi & Grzegorz Miebs & David Grohmann & Maria Elena Menconi & Luisa Paolotti, 2018. "Multiple Criteria Assessment of Insulating Materials with a Group Decision Framework Incorporating Outranking Preference Model and Characteristic Class Profiles," Group Decision and Negotiation, Springer, vol. 27(1), pages 33-59, February.

2. Francineide Morais Bezerra & Paulo Melo & João Paulo Costa, 2014. "Visual and Interactive Comparative Analysis of Individual Opinions: A Group Decision Support Tool," Group Decision and Negotiation, Springer, vol. 23(1), pages 101-125, January.

   Cited by:

   1. Francineide Morais Bezerra & Paulo Melo & João Paulo Costa, 2017. "Reaching Consensus with VICA-ELECTRE TRI: A Case Study," Group Decision and Negotiation, Springer, vol. 26(6), pages 1145-1171, November.
   2. Liu, Jiapeng & Liao, Xiuwu & Yang, Jian-bo, 2015. "A group decision-making approach based on evidential reasoning for multiple criteria sorting problem with uncertainty," European Journal of Operational Research, Elsevier, vol. 246(3), pages 858-873.
   3. Hsu-Shih Shih, 2016. "A Mixed-Data Evaluation in Group TOPSIS with Differentiated Decision Power," Group Decision and Negotiation, Springer, vol. 25(3), pages 537-565, May.
   4. L. R. Figueiredo & E. A. Frej & G. L. Soares & P. Ya. Ekel, 2021. "Group Decision-Based Construction of Scenarios for Multicriteria Analysis in Conditions of Uncertainty on the Basis of Quantitative and Qualitative Information," Group Decision and Negotiation, Springer, vol. 30(3), pages 665-696, June.

3. Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.

   Cited by:

   1. Jichang Dong & Wei Dai & Ying Liu & Lean Yu & Jie Wang, 2019. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1605-1629, September.
   2. Mihaela Simionescu & Javier Cifuentes-Faura, 2022. "Forecasting National and Regional Youth Unemployment in Spain Using Google Trends," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(3), pages 1187-1216, December.
   3. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
   4. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
   5. Nymand-Andersen, Per & Pantelidis, Emmanouil, 2018. "Google econometrics: nowcasting euro area car sales and big data quality requirements," Statistics Paper Series 30, European Central Bank.
   6. Thomas Dimpfl & Tobias Langen, 2019. "How Unemployment Affects Bond Prices: A Mixed Frequency Google Nowcasting Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 551-573, August.
   7. Dimpfl, Thomas & Langen, Tobias, 2015. "A Cross-Country Analysis of Unemployment and Bonds with Long-Memory Relations," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112921, Verein für Socialpolitik / German Economic Association.
   8. Mihaela Simionescu & Dalia Streimikiene & Wadim Strielkowski, 2020. "What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?," Sustainability, MDPI, vol. 12(3), pages 1-10, January.
   9. France, Stephen L. & Shi, Yuying & Kazandjian, Brett, 2021. "Web Trends: A valuable tool for business research," Journal of Business Research, Elsevier, vol. 132(C), pages 666-679.
   10. Alessia Naccarato & Andrea Pierini & Stefano Falorsi, 2015. "Using Google Trend Data To Predict The Italian Unemployment Rate," Departmental Working Papers of Economics - University 'Roma Tre' 0203, Department of Economics - University Roma Tre.
   11. Andrius Grybauskas & Vaida Pilinkienė & Mantas Lukauskas & Alina Stundžienė & Jurgita Bruneckienė, 2023. "Nowcasting Unemployment Using Neural Networks and Multi-Dimensional Google Trends Data," Economies, MDPI, vol. 11(5), pages 1-23, April.
   12. Naccarato, Alessia & Falorsi, Stefano & Loriga, Silvia & Pierini, Andrea, 2018. "Combining official and Google Trends data to forecast the Italian youth unemployment rate," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 114-122.
   13. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
   14. Simionescu, Mihaela & Cifuentes-Faura, Javier, 2022. "Can unemployment forecasts based on Google Trends help government design better policies? An investigation based on Spain and Portugal," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 1-21.
   15. Chumnumpan, Pattarin & Shi, Xiaohui, 2019. "Understanding new products’ market performance using Google Trends," Australasian marketing journal, Elsevier, vol. 27(2), pages 91-103.
   16. Gulsah Senturk, 2022. "Can Google Search Data Improve the Unemployment Rate Forecasting Model? An Empirical Analysis for Turkey," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 229-244, July.
   17. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
   18. Michael Olumekor & Hossam Haddad & Nidal Mahmoud Al-Ramahi, 2023. "The Relationship between Search Engines and Entrepreneurship Development: A Granger-VECM Approach," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
   19. N. Nima Haghighi & Xiaoyue Cathy Liu & Ran Wei & Wenwen Li & Hu Shao, 2018. "Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service," Public Transport, Springer, vol. 10(2), pages 363-377, August.

4. Costa, Joao P. & Melo, Paulo & Godinho, Pedro & Dias, Luis C., 2003. "The AGAP system: A GDSS for project analysis and evaluation," European Journal of Operational Research, Elsevier, vol. 145(2), pages 287-303, March.

   Cited by:

   1. Alessio Ishizaka & Philippe Nemery, 2013. "A Multi-Criteria Group Decision Framework for Partner Grouping When Sharing Facilities," Group Decision and Negotiation, Springer, vol. 22(4), pages 773-799, July.
   2. Jessop, Alan, 2014. "IMP: A decision aid for multiattribute evaluation using imprecise weight estimates," Omega, Elsevier, vol. 49(C), pages 18-29.
   3. Fernandez, Eduardo & Olmedo, Rafael, 2013. "An outranking-based general approach to solving group multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 225(3), pages 497-506.
   4. Madjid Tavana & Mariya Sodenkamp & Leena Suhl, 2010. "A soft multi-criteria decision analysis model with application to the European Union enlargement," Annals of Operations Research, Springer, vol. 181(1), pages 393-421, December.
   5. Francineide Morais Bezerra & Paulo Melo & João Paulo Costa, 2014. "Visual and Interactive Comparative Analysis of Individual Opinions: A Group Decision Support Tool," Group Decision and Negotiation, Springer, vol. 23(1), pages 101-125, January.
   6. M Tavana & M A Sodenkamp, 2010. "A fuzzy multi-criteria decision analysis model for advanced technology assessment at Kennedy Space Center," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1459-1470, October.
   7. Antunes, Francisco & Melo, Paulo & Costa, Joao Paulo, 2007. "Information management in distributed collaborative systems: The case of collaboration studio," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1385-1399, March.
   8. Galo, Nadya Regina & Calache, Lucas Daniel Del Rosso & Carpinetti, Luiz Cesar Ribeiro, 2018. "A group decision approach for supplier categorization based on hesitant fuzzy and ELECTRE TRI," International Journal of Production Economics, Elsevier, vol. 202(C), pages 182-196.

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