IDEAS home Printed from https://ideas.repec.org/a/spr/infsem/v14y2016i3d10.1007_s10257-015-0299-y.html
   My bibliography  Save this article

Understanding Internet service switching behaviour based on the stage model

Author

Listed:
  • Chang Hee Han

    (Hanyang University)

  • Swati Tyagi

    (Cubic Transportation Systems (Australia) Pty Limited)

  • Namgyu Kim

    (Kookmin University)

  • Byounggu Choi

    (Kookmin University)

Abstract

As customer switching is the major concern in the competitive Internet industry, many studies have sought to identify the determinants that cause customers to switch in order to build effective customer retention strategies. However, they were found to be insufficient for explaining the determinants and processes related to service switching. To fill this gap, this study attempts to provide a theoretical mechanism explaining customer service switching behaviours. More specifically, this study examines three hypotheses that may help ISPs develop appropriate marketing and business strategies. Survey data collected from 151 ISP customers in Australia were analysed to test the hypotheses. The results identify four stages of customers switching behaviours and suggest that motivational variables for switching behaviours differ across stages. This study provides a stepping-stone for analysing the staged model in the service-switching context and will help managers enhance their customer retention capability, and thus improve their organizational performance.

Suggested Citation

  • Chang Hee Han & Swati Tyagi & Namgyu Kim & Byounggu Choi, 2016. "Understanding Internet service switching behaviour based on the stage model," Information Systems and e-Business Management, Springer, vol. 14(3), pages 665-689, August.
  • Handle: RePEc:spr:infsem:v:14:y:2016:i:3:d:10.1007_s10257-015-0299-y
    DOI: 10.1007/s10257-015-0299-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10257-015-0299-y
    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/s10257-015-0299-y?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. Lai, Jung-Yu & Wang, Juite, 2015. "Switching attitudes of Taiwanese middle-aged and elderly patients toward cloud healthcare services: An exploratory study," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 155-167.
    2. H. Bock, 1985. "On some significance tests in cluster analysis," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 77-108, December.
    3. Wirtz, Jochen & Xiao, Ping & Chiang, Jeongwen & Malhotra, Naresh, 2014. "Contrasting the Drivers of Switching Intent and Switching Behavior in Contractual Service Settings," Journal of Retailing, Elsevier, vol. 90(4), pages 463-480.
    4. Shirley Taylor & Peter A. Todd, 1995. "Understanding Information Technology Usage: A Test of Competing Models," Information Systems Research, INFORMS, vol. 6(2), pages 144-176, June.
    5. Dahui Li & Glenn J. Browne & James C. Wetherbe, 2007. "Online Consumers' Switching Behavior: A Buyer-Seller Relationship Perspective," Journal of Electronic Commerce in Organizations (JECO), IGI Global, vol. 5(1), pages 30-42, January.
    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. Zeng, Zhongping & Li, Siqi & Lian, Jiunn-Woei & Li, Jiang & Chen, Tao & Li, Yujia, 2021. "Switching behavior in the adoption of a land information system in China: A perspective of the push–pull–mooring framework," Land Use Policy, Elsevier, vol. 109(C).
    2. MinJae Lee & JinKyu Lee, 2012. "The impact of information security failure on customer behaviors: A study on a large-scale hacking incident on the internet," Information Systems Frontiers, Springer, vol. 14(2), pages 375-393, April.
    3. Francisco Liébana-Cabanillas & Nidhi Singh & Zoran Kalinic & Elena Carvajal-Trujillo, 2021. "Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: a multi-analytical approach," Information Technology and Management, Springer, vol. 22(2), pages 133-161, June.
    4. Pan, Jing Yu & Liu, Dahai, 2022. "Mask-wearing intentions on airplanes during COVID-19 – Application of theory of planned behavior model," Transport Policy, Elsevier, vol. 119(C), pages 32-44.
    5. Hasan, Rajibul & Lowe, Ben & Petrovici, Dan, 2020. "Consumer adoption of pro-poor service innovations in subsistence marketplaces," Journal of Business Research, Elsevier, vol. 121(C), pages 461-475.
    6. Paul-Emmanuel Pichon & Denis Bories & Christian Laborde, 2023. "The determinants of the adoption of cryptocurrencies in the tourism industry : Application to the case of hotel room reservations [Les déterminants de l'adoption des cryptomonnaies : application au," Post-Print hal-04398288, HAL.
    7. Joey F George & Rui Chen & Lingyao Yuan, 2021. "Intent to purchase IoT home security devices: Fear vs privacy," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-14, September.
    8. Paul Juinn Bing Tan, 2013. "Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan," SAGE Open, , vol. 3(4), pages 21582440135, October.
    9. Chen, Shih-Chih & Hung, Chung-Wen, 2016. "Elucidating the factors influencing the acceptance of green products: An extension of theory of planned behavior," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 155-163.
    10. Chuhan Chen & Syarmila Hany Haron, 2023. "The Influence of Multistakeholder Value Cognition and Risk Attitudes on Sustainable Interior Landscape Design Decisions," Sustainability, MDPI, vol. 15(3), pages 1-22, February.
    11. Hoon S. Choi & Darrell Carpenter & Myung S. Ko, 2022. "Risk Taking Behaviors Using Public Wi-Fi™," Information Systems Frontiers, Springer, vol. 24(3), pages 965-982, June.
    12. Venkatesh, Viswanath & Maruping, Likoebe M. & Brown, Susan A., 2006. "Role of time in self-prediction of behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 100(2), pages 160-176, July.
    13. Garima Malik & A. Sajeevan Rao, 2019. "Extended expectation-confirmation model to predict continued usage of ODR/ride hailing apps: role of perceived value and self-efficacy," Information Technology & Tourism, Springer, vol. 21(4), pages 461-482, December.
    14. Borhan, Muhamad Nazri & Ibrahim, Ahmad Nazrul Hakimi & Miskeen, Manssour A. Abdulasalm, 2019. "Extending the theory of planned behaviour to predict the intention to take the new high-speed rail for intercity travel in Libya: Assessment of the influence of novelty seeking, trust and external inf," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 373-384.
    15. Javad Shahreki & Jaya Ganesan & Kavitha Raman & Audrey Lim Li Chin & Tee Suan Chin, 2019. "The effect of human resource information system application on employee satisfaction and turnover intention," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(2), pages 1462-1479, December.
    16. Pakvalit Kurkoon & Daranee Pimchangthong & Veera Boonjing, 2015. "A Conceptual Framework for Individual Green Information Technology Consumption and its Impact," Journal of Business & Management (COES&RJ-JBM), , vol. 3(3), pages 388-396, July.
    17. Fernanda Leão Ramos & Jorge Brantes Ferreira & Angilberto Sabino de Freitas & Juliana Werneck Rodrigues, 2018. "The Effect of Trust in the Intention to Use m-banking," Brazilian Business Review, Fucape Business School, vol. 15(2), pages 175-191, March.
    18. Nistor, Cristian, 2013. "A conceptual model for the use of social media in companies," MPRA Paper 44224, University Library of Munich, Germany.
    19. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    20. Cowan, Kelly R. & Daim, Tugrul U., 2011. "Review of technology acquisition and adoption research in the energy sector," Technology in Society, Elsevier, vol. 33(3), pages 183-199.

    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:infsem:v:14:y:2016:i:3:d:10.1007_s10257-015-0299-y. 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.