IDEAS home Printed from https://ideas.repec.org/a/spr/opmare/v17y2024i2d10.1007_s12063-023-00437-1.html
   My bibliography  Save this article

How does technological innovation impact the service time and the attraction of new customers in the financial sector? Evidence from an emerging economy

Author

Listed:
  • Lorena Reyes-Rubiano

    (Universidad de La Sabana
    RWTH Aachen University)

  • Ingrid Y. Amaya

    (Universidad de La Sabana)

  • David Medina Mayorga

    (Universidad Nacional de Colombia)

  • Andrés Muñoz-Villamizar

    (Universidad de La Sabana)

  • Elyn Solano-Charris

    (Universidad de La Sabana)

Abstract

Due to safety perceptions, Colombian banking clients prefer to visit bank branch offices instead of other channels. Thus, there are long waiting for lines at branch offices. Considering the need for more tools for strengthening and streamlining client service, the number of financial clients tends to diminish. In this context, this paper aims to measure the impact of technological innovation on the clients’ waiting time and the attraction of new customers. We propose a simulation-based methodology to analyze customer behaviors and forecast the diffusion effect on mobile app adoption. Furthermore, our study provides managerial insights and future research lines.

Suggested Citation

  • Lorena Reyes-Rubiano & Ingrid Y. Amaya & David Medina Mayorga & Andrés Muñoz-Villamizar & Elyn Solano-Charris, 2024. "How does technological innovation impact the service time and the attraction of new customers in the financial sector? Evidence from an emerging economy," Operations Management Research, Springer, vol. 17(2), pages 596-611, June.
    • Handle: RePEc:spr:opmare:v:17:y:2024:i:2:d:10.1007_s12063-023-00437-1
    DOI: 10.1007/s12063-023-00437-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12063-023-00437-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12063-023-00437-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yu, Song-min & Fan, Ying & Zhu, Lei & Eichhammer, Wolfgang, 2020. "Modeling the emission trading scheme from an agent-based perspective: System dynamics emerging from firms’ coordination among abatement options," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1113-1128.
    2. William Rand & Christian Stummer, 2021. "Agent‐based modeling of new product market diffusion: an overview of strengths and criticisms," Annals of Operations Research, Springer, vol. 305(1), pages 425-447, October.
    3. Hoyeop Lee & Jongsu Lim & Keeheon Lee & Chang Ouk Kim, 2019. "Agent simulation-based ordinal optimisation for new product design," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(3), pages 502-515, March.
    4. Rand, William & Rust, Roland T., 2011. "Agent-based modeling in marketing: Guidelines for rigor," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 181-193.
    5. C M Macal, 2016. "Everything you need to know about agent-based modelling and simulation," Journal of Simulation, Taylor & Francis Journals, vol. 10(2), pages 144-156, May.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Hu, Hai-hua & Lin, Jun & Qian, Yanjun & Sun, Jian, 2018. "Strategies for new product diffusion: Whom and how to target?," Journal of Business Research, Elsevier, vol. 83(C), pages 111-119.
    8. Petra Ahrweiler, 2017. "Agent-based simulation for science, technology, and innovation policy," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 391-415, January.
    9. Elyn Lizeth Solano-Charris & Carlos D. Paternina-Arboleda, 2013. "Simulation model of the supply chain on a naval shipyard," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 13(3), pages 280-297.
    10. Samitas, Aristeidis & Polyzos, Stathis & Siriopoulos, Costas, 2018. "Brexit and financial stability: An agent-based simulation," Economic Modelling, Elsevier, vol. 69(C), pages 181-192.
    11. Amini, Mehdi & Wakolbinger, Tina & Racer, Michael & Nejad, Mohammad G., 2012. "Alternative supply chain production–sales policies for new product diffusion: An agent-based modeling and simulation approach," European Journal of Operational Research, Elsevier, vol. 216(2), pages 301-311.
    12. Joao Aguirre, 2015. "Inteligencia estratégica: un sistema para gestionar la innovación," Estudios Gerenciales, Universidad Icesi, January.
    13. Ashkan Negahban & Jeffrey S. Smith, 2018. "A joint analysis of production and seeding strategies for new products: an agent-based simulation approach," Annals of Operations Research, Springer, vol. 268(1), pages 41-62, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiao, Yu & Liu, Liangliang, 2024. "How does information competition affect new product diffusion? Insights from computational experiments," Journal of Business Research, Elsevier, vol. 183(C).
    2. Nejad, Mohammad G. & Amini, Mehdi, 2024. "Designing profitable seeding Programs: The effects of social network properties and consumer homophily," Journal of Business Research, Elsevier, vol. 173(C).
    3. Ashkan Negahban & Jeffrey S. Smith, 2018. "A joint analysis of production and seeding strategies for new products: an agent-based simulation approach," Annals of Operations Research, Springer, vol. 268(1), pages 41-62, September.
    4. Karsten Kieckhäfer & Thomas Volling & Thomas Stefan Spengler, 2014. "A Hybrid Simulation Approach for Estimating the Market Share Evolution of Electric Vehicles," Transportation Science, INFORMS, vol. 48(4), pages 651-670, November.
    5. Nejad, Mohammad G. & Amini, Mehdi & Babakus, Emin, 2015. "Success Factors in Product Seeding: The Role of Homophily," Journal of Retailing, Elsevier, vol. 91(1), pages 68-88.
    6. Ding, Haixin & Xie, Li, 2023. "Simulating rumor spreading and rebuttal strategy with rebuttal forgetting: An agent-based modeling approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    7. Mohammad G Nejad & Sertan Kabadayi, 2016. "Optimal introductory pricing for new financial services," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 21(1), pages 34-50, March.
    8. Christian Stummer & Lars Lüpke & Markus Günther, 2021. "Beaming market simulation to the future by combining agent-based modeling with scenario analysis," Journal of Business Economics, Springer, vol. 91(9), pages 1469-1497, November.
    9. Ponta, Linda & Puliga, Gloria & Lazzarotti, Valentina & Manzini, Raffaella & Cincotti, Silvano, 2023. "To copatent or not to copatent: An agent-based model for firms facing this dilemma," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1349-1363.
    10. Delre, Sebastiano A. & Panico, Claudio & Wierenga, Berend, 2017. "Competitive strategies in the motion picture industry: An ABM to study investment decisions," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 69-99.
    11. Piotr Przybyła & Katarzyna Sznajd-Weron & Rafał Weron, 2014. "Diffusion Of Innovation Within An Agent-Based Model: Spinsons, Independence And Advertising," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-22.
    12. Abedi, Vahideh Sadat, 2019. "Compartmental diffusion modeling: Describing customer heterogeneity & communication network to support decisions for new product introductions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    13. Ding, Haixin & Xie, Li, 2024. "The applicability of positive information in negative opinion management: An attitude-laden communication perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 645(C).
    14. Rixen, Martin & Weigand, Jürgen, 2014. "Agent-based simulation of policy induced diffusion of smart meters," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 153-167.
    15. Guo, Xuezhen, 2014. "A novel Bass-type model for product life cycle quantification using aggregate market data," International Journal of Production Economics, Elsevier, vol. 158(C), pages 208-216.
    16. Martin Zsifkovits & Markus Günther, 2015. "Simulating resistances in innovation diffusion over multiple generations: an agent-based approach for fuel-cell vehicles," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(2), pages 501-522, June.
    17. Barsha Saha & Miguel Martínez-García & Sharad Nath Bhattacharya & Rohit Joshi, 2022. "Overcoming Choice Inertia through Social Interaction—An Agent-Based Study of Mobile Subscription Decision," Games, MDPI, vol. 13(3), pages 1-16, June.
    18. Negahban, Ashkan & Dehghanimohammadabadi, Mohammad, 2018. "Optimizing the supply chain configuration and production-sales policies for new products over multiple planning horizons," International Journal of Production Economics, Elsevier, vol. 196(C), pages 150-162.
    19. William Rand & Roland T. Rust & Min Kim, 2018. "Complex systems: marketing’s new frontier," AMS Review, Springer;Academy of Marketing Science, vol. 8(3), pages 111-127, December.
    20. Stephen, Andrew T. & Lehmann, Donald R., 2016. "How word-of-mouth transmission encouragement affects consumers' transmission decisions, receiver selection, and diffusion speed," International Journal of Research in Marketing, Elsevier, vol. 33(4), pages 755-766.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:opmare:v:17:y:2024:i:2:d:10.1007_s12063-023-00437-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.