IDEAS home Printed from https://ideas.repec.org/a/rse/wpaper/v23y2022i1p28-39.html
   My bibliography  Save this article

Measuring Client’s Feelings on Mobile Banking

Author

Listed:
  • Orkida Ilollari

    (Mediterranean University of Albania)

  • Petraq Papajorgji

    (European University of Tirana, Albania)

  • Adrian Civici

    (Mediterranean University of Albania)

  • Howard Moskowitz

    (White Plains, New York, USA)

Abstract

Mobile banking is relatively a new service offered by banks around the world. Banks are obliged to keep investing in new technologies as otherwise, they would lose competitiveness and market share. It is, although interesting, to know how clients react to these innovation efforts. This study aims to understand the client’s responses to bank innovation, especially to mobile banking technology in Albania. An online experiment is conceived based on Experimental Design Principles. Participants evaluate combinations of messages (elements) about mobile banking and rate each combination. The collected data are used to create individual models and later a general model to calculate the statistical relevance of each of the messages. The models use ordinary least squares regression and advanced data mining techniques (k—means clustering) to analyze the data and classify participants accordingly. At the end of the analyses, a set of two or three mindsets are depicted to show what pushes participants in the study in their decision-making process. These mindsets help banks understand clients’ reactions and allow banks to address different issues to serve their clients better.

Suggested Citation

  • Orkida Ilollari & Petraq Papajorgji & Adrian Civici & Howard Moskowitz, 2022. "Measuring Client’s Feelings on Mobile Banking," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 23(1), pages 28-39, June.
  • Handle: RePEc:rse:wpaper:v:23:y:2022:i:1:p:28-39
    as

    Download full text from publisher

    File URL: http://reaser.eu/RePec/rse/wpaper/REASER23_03_Orkida_P28-39.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Data Mining in Agriculture," Springer Optimization and Its Applications, Springer, number 978-0-387-88615-2, June.
    2. Shahzada Nayyar Jehan & Zaid Ahmad Ansari, 2018. "Internet Banking Adoption in Saudi Arabia: An Empirical Study," International Journal of Marketing Studies, Canadian Center of Science and Education, vol. 10(3), pages 1-57, August.
    3. Orkida Ilollari (Findiku) & Gentiana Gjino, 2013. "Which forces drive the banks to new investments? Innovation mechanisms banks use to succeed challenges," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 6(2), pages 121-130, December.
    4. Gentiana Gjino & Orkida Ilollari (Findiku), 2014. "Mobile banking: near future of banking," Review of Applied Socio-Economic Research, Pro Global Science Association, vol. 7(1), pages 43-51, June.
    5. Butler, Patrick & Peppard, Joe, 1998. "Consumer purchasing on the Internet:: Processes and prospects," European Management Journal, Elsevier, vol. 16(5), pages 600-610, October.
    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. Chetan Badgujar & Sanjoy Das & Dania Martinez Figueroa & Daniel Flippo, 2023. "Application of Computational Intelligence Methods in Agricultural Soil–Machine Interaction: A Review," Agriculture, MDPI, vol. 13(2), pages 1-39, January.
    2. Hui Zou & Zhihong Zou & Xiaojing Wang, 2015. "An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China," IJERPH, MDPI, vol. 12(11), pages 1-14, November.
    3. Grewal, Dhruv & Iyer, Gopalkrishnan R. & Krishnan, R. & Sharma, Arun, 2003. "The Internet and the price-value-loyalty chain," Journal of Business Research, Elsevier, vol. 56(5), pages 391-398, May.
    4. Sarv Devaraj & Ming Fan & Rajiv Kohli, 2002. "Antecedents of B2C Channel Satisfaction and Preference: Validating e-Commerce Metrics," Information Systems Research, INFORMS, vol. 13(3), pages 316-333, September.
    5. Odile Carisse & Mamadou Lamine Fall, 2021. "Decision Trees to Forecast Risks of Strawberry Powdery Mildew Caused by Podosphaera aphanis," Agriculture, MDPI, vol. 11(1), pages 1-16, January.
    6. Hananiel M. Gunawan & Oliandes Sondakh, 2021. "The Effect of Company’s Culture and Dynamic Capability toward Company’s Innovation and Performance on F&B SME in Surabaya," International Journal of Science and Business, IJSAB International, vol. 5(11), pages 65-78.
    7. Waymond Rodgers & Tam Nguyen, 2022. "Advertising Benefits from Ethical Artificial Intelligence Algorithmic Purchase Decision Pathways," Journal of Business Ethics, Springer, vol. 178(4), pages 1043-1061, July.
    8. Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018. "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series 7259, CESifo.
    9. Arif Jamal Siddiqui & Sadaf Jahan & Maqsood Ahmed Siddiqui & Andleeb Khan & Mohammed Merae Alshahrani & Riadh Badraoui & Mohd Adnan, 2023. "Targeting Monoamine Oxidase B for the Treatment of Alzheimer’s and Parkinson’s Diseases Using Novel Inhibitors Identified Using an Integrated Approach of Machine Learning and Computer-Aided Drug Desig," Mathematics, MDPI, vol. 11(6), pages 1-17, March.
    10. Junlong Zhang & Youbin He & Yuan Zhang & Weifeng Li & Junjie Zhang, 2022. "Well-Logging-Based Lithology Classification Using Machine Learning Methods for High-Quality Reservoir Identification: A Case Study of Baikouquan Formation in Mahu Area of Junggar Basin, NW China," Energies, MDPI, vol. 15(10), pages 1-15, May.
    11. Muhammad Islam & Muhammad Usman & Azhar Mahmood & Aaqif Afzaal Abbasi & Oh-Young Song, 2020. "Predictive analytics framework for accurate estimation of child mortality rates for Internet of Things enabled smart healthcare systems," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
    12. Danijel Jevtic & Romain Deleze & Joerg Osterrieder, 2022. "AI for trading strategies," Papers 2208.07168, arXiv.org.
    13. Suleyman Ozarslan & P. Erhan Eren, 2018. "MobileCDP: A mobile framework for the consumer decision process," Information Systems Frontiers, Springer, vol. 20(4), pages 803-824, August.
    14. Bohumil Kába, 2011. "Exploratory analysis of selected indicators of the Czech Republic regional labour markets," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 59(4), pages 123-128.
    15. Sarah Bayer & Henner Gimpel & Daniel Rau, 2021. "IoT-commerce - opportunities for customers through an affordance lens," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 27-50, March.
    16. Yotsaphat Kittichotsatsawat & Varattaya Jangkrajarng & Korrakot Yaibuathet Tippayawong, 2021. "Enhancing Coffee Supply Chain towards Sustainable Growth with Big Data and Modern Agricultural Technologies," Sustainability, MDPI, vol. 13(8), pages 1-20, April.
    17. Md. Mehedi Hasan, 2021. "The Popularity of Online Shopping is increasing during COVID-19 Pandemic: An Online Study in Khulna City of Bangladesh," International Journal of Science and Business, IJSAB International, vol. 5(5), pages 88-100.
    18. Peppard, Joe & Rylander, Anna, 2005. "Products and services in cyberspace," International Journal of Information Management, Elsevier, vol. 25(4), pages 335-345.
    19. Jui-Lung Chen & Siriwat Prommetta, 2022. "A Discussion on University Students’ Online Shopping Behaviors Amid the COVID-19 Pandemic," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(3), pages 1-1.
    20. Suleyman Ozarslan & P. Erhan Eren, 0. "MobileCDP: A mobile framework for the consumer decision process," Information Systems Frontiers, Springer, vol. 0, pages 1-22.

    More about this item

    Keywords

    Bank; Clients; Mind Genomics Technology; Mobile Banking; Statistical Models;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services

    Statistics

    Access and download statistics

    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:rse:wpaper:v:23:y:2022:i:1:p:28-39. 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: Manuela Epure (email available below). General contact details of provider: https://edirc.repec.org/data/pgsaaea.html .

    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.