Skip to main content
SpringerLink
Log in

Chatbot for Technical Support, Analysis of Critical Success Factors Using Fuzzy Cognitive Maps

  • Conference paper
  • First Online:
Applied Technologies (ICAT 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1388))

Included in the following conference series:

  • 922 Accesses

Abstract

Remote assistance needs to be automated for better coverage in time, quality, and quantity of clients. The problem is that by increasing the number of clients with the same amount of attention personnel or none, the waiting time of clients increases, and the quality of attention decreases. The objective of this research is to perform an analysis of the general critical success factors by simulating fuzzy cognitive maps applied to a chatbot for technical support. The methodology applied is exploratory, qualitative, descriptive research and de-duction to analyze the references on chatbots, technical support, critical success factors and fuzzy cognitive maps. This research resulted in a Definition of general critical success factors for a technical support chatbot, a Simulation of critical factors in a fuzzy cognitive map, an Analysis of critical success factors, and a general architecture prototype for the technical support chatbot. It was concluded that among the main critical factors for a project are important elements the knowledge of experts, expertise, and human resources; the application and analysis of CSF through FCM helps in the improvement and optimization of the factors/tasks of the chatbot project for technical support.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Adikari, A., De Silva, D., Alahakoon, D., Yu, X.: A cognitive model for emotion awareness in industrial chatbots. In: 2019 IEEE International Conference on Industrial Informatics, pp. 183–186 (2019). https://doi.org/10.1109/INDIN41052.2019.8972196

  2. Parthornratt, T., Kitsawat, D., Putthapipat, P., Koronjaruwat, P.: A smart home automation via Facebook chatbot and raspberry Pi. In: 2018 2nd International Conference on Engineering Innovation (ICEI), pp. 52–56 (2018). https://doi.org/10.1109/ICEI18.2018.8448761

  3. Ranoliya, B.R., Raghuwanshi, N., Singh, S.: Chatbot for university related FAQs. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1525–1530 (2017). https://doi.org/10.1109/ICACCI.2017.8126057

  4. Kohli, B., Choudhury, T., Sharma, S., Kumar, P.: A platform for human-chatbot interaction using python. In: Proceedings of the 2nd International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 439–444 (2018). https://doi.org/10.1109/ICGCIoT.2018.8753031

  5. Naveen Kumar, M., Linga Chandar, P.C., Venkatesh Prasad, A., Sumangali, K.: Android based educational Chatbot for visually impaired people. In: 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 16–19 (2017). https://doi.org/10.1109/ICCIC.2016.7919664

  6. Srivastava, P., Singh, N.: Automatized medical chatbot (Medibot), pp. 351–354 (2020). https://doi.org/10.1109/parc49193.2020.236624

  7. Singh, R., Paste, M., Shinde, N., Patel, H., Mishra, N.: Chatbot using tensorFlow for small businesses. In: Proceedings of the International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1614–1619 (2018). https://doi.org/10.1109/ICICCT.2018.8472998

  8. Rosruen, N., Samanchuen, T.: Chatbot utilization for medical consultant system. In: 2018 3rd Technology Innovation Management and Engineering Science International Conference (TIMES-iCON) (2019). https://doi.org/10.1109/TIMES-iCON.2018.8621678

  9. Bozzon, A.: Enterprise crowd computing for human aided chatbots. In: Proceedings of the International Conference on Software Engineering, pp. 29–30 (2018). https://doi.org/10.1145/3195555.3195566

  10. Kao, C.H., Chen, C.C., Tsai, Y.T.: Model of multi-turn dialogue in emotional chatbot. In: Proceedings of the 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 5–9 (2019). https://doi.org/10.1109/TAAI48200.2019.8959855

  11. Ribeiro, M.B., Duarte, V.D., Salgado, E.G., Castro, C.V.: Prioritization of critical success factors in the process of software development. IEEE Lat. Am. Trans. 15, 137–144 (2017). https://doi.org/10.1109/TLA.2017.7827917

    Article  Google Scholar 

  12. Priambodo, A., Handayani, P.W., Pinem, A.A.: Success factor for IT project implementation in banking industry: a case study. In: 2019 3rd International Conference on Informatics and Computational Sciences (ICICOS). Accelerators Informatics Computational Research Smarter Society Era Industry 4.0, Proceedings (2019). https://doi.org/10.1109/ICICoS48119.2019.8982404

  13. Henly-Shepard, S., Gray, S.A., Cox, L.J.: The use of participatory modeling to promote social learning and facilitate community disaster planning. Environ. Sci. Policy. 45, 109–122 (2015). https://doi.org/10.1016/j.envsci.2014.10.004

    Article  Google Scholar 

  14. Lamb, H.: News briefing - healthcare doctors slam claims that chatbot is on par with human doctors. Eng. Technol. 13, 12 (2018). https://doi.org/10.1049/et.2018.0713

    Article  Google Scholar 

  15. Murad, D.F., Fernando, E., Irsan, M., Murad, S.A., Akhirianto, P.M., Wijaya, M.H.: Learning support system using chatbot in “Kejar C Package” homeschooling program. In: 2019 International Conference on Information and Communications Technology (ICOIACT), pp. 32–37 (2019). https://doi.org/10.1109/ICOIACT46704.2019.8938479

  16. Yadav, A.A., Garg, I., Mathur, P.: PACT - programming assistant chatbot. In: 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT), pp. 131–136 (2019). https://doi.org/10.1109/ICCT46177.2019.8969070

  17. Edwita, A., Sensuse, D.I., Noprisson, H.: Critical success factors of information system development projects. In: Proceedings of the 2017 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 285–290 (2017)

    Google Scholar 

  18. de Oliveira, E.T., Rodello, I.A.: Fatores críticos de sucesso para implantações de sistemas ERP no formato cloud computing deployment. In: 2018 13th Iberian Conference on Information Systems and Technologies, pp. 1–6 (2018)

    Google Scholar 

  19. Saleem, N.: Empirical analysis of critical success factors for project management in global software development. In: Proceedings of the 2019 ACM/IEEE 14th International Conference on Global Software Engineering (ICGSE), pp. 68–71 (2019). https://doi.org/10.1109/ICGSE.2019.00025

  20. Octavianus, R., Mursanto, P.: The analysis of critical success factor ranking for software development and implementation project using AHP. In: 2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 313–318 (2019). https://doi.org/10.1109/ICACSIS.2018.8618147

  21. Soni Fajar Surya, G., Amalia, A.: The critical success factors model for e-Government implementation in Indonesia. In: 2017 5th International Conference on Information and Communication Technology (ICoIC7) (2017). https://doi.org/10.1109/ICoICT.2017.8074711

  22. University of Massachusetts: Mental Modeler Software. https://dev.mentalmodeler.com

  23. Gray, S.R.J., et al.: Are coastal managers detecting the problem? Assessing stakeholder perception of climate vulnerability using Fuzzy Cognitive Mapping. Ocean Coast. Manag. 94, 74–89 (2014). https://doi.org/10.1016/j.ocecoaman.2013.11.008

    Article  Google Scholar 

Download references

Acknowledgments

This work has been supported by the GIIAR research group and the Salesian Polytechnic University of Guayaquil.

Author information

Authors and Affiliations

  1. Department of Computer Science, Universidad Politécnica Salesiana Sede Guayaquil, Ecuador, Chamber 227 y 5 de junio, USA

    Miguel Angel Quiroz Martinez, Segundo Estuardo Mayorga Plua, Monica Daniela Gomez Rios, Maikel Yelandi Leyva Vázquez & Daniel Humberto Plua Moran

Authors
  1. Miguel Angel Quiroz Martinez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Segundo Estuardo Mayorga Plua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Monica Daniela Gomez Rios
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maikel Yelandi Leyva Vázquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel Humberto Plua Moran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maikel Yelandi Leyva Vázquez .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Miguel Botto-Tobar

  2. Universidad de las Fuerzas Armadas (ESPE), Quito, Ecuador

    Sergio Montes León

  3. Escuela Politécnica Nacional, Quito, Ecuador

    Oscar Camacho

  4. Escuela Politécnica Nacional, Quito, Ecuador

    Danilo Chávez

  5. Universidad Técnica Particular de Loja, Loja, Ecuador

    Pablo Torres-Carrión

  6. Universidad Técnica del Norte, Ibarra, Ecuador

    Marcelo Zambrano Vizuete

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Quiroz Martinez, M.A., Mayorga Plua, S.E., Gomez Rios, M.D., Leyva Vázquez, M.Y., Plua Moran, D.H. (2021). Chatbot for Technical Support, Analysis of Critical Success Factors Using Fuzzy Cognitive Maps. In: Botto-Tobar, M., Montes León, S., Camacho, O., Chávez, D., Torres-Carrión, P., Zambrano Vizuete, M. (eds) Applied Technologies. ICAT 2020. Communications in Computer and Information Science, vol 1388. Springer, Cham. https://doi.org/10.1007/978-3-030-71503-8_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-71503-8_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71502-1

  • Online ISBN: 978-3-030-71503-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation