Switching intention and switching behavior of adults in the non-life insurance sector: Mediating role of brand love

  • Received December 1, 2022;
    Accepted January 19, 2023;
    Published February 20, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ins.14(1).2023.01
  • Article Info
    Volume 14 2023, Issue #1, pp. 1-7
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This work is licensed under a Creative Commons Attribution 4.0 International License

In this digital era, customers in the insurance sector always look for better insurance products and services at an affordable price. When customers are unsure about service, they switch over to a better service provider. This behavior is more relevant to non-life insurance. However, the switching behavior of customers is hampered by certain switchover barriers such as “brand consciousness”, “brand pride”, “brand loyalty”, etc. This study focuses on exploring switching intentions and switching behaviors of adults in India keeping “brand love” as a mediator. A structured questionnaire was employed to collect the primary data from adults having non-life insurance products to analyze switching intentions and switching behaviors. The collected data were analyzed employing SPSS software and Hayes Process Model and appropriate statistical tools. The study results show that the switching intentions of adults vary based on their age, annual income, and education. Mean scores reveal that the lesser the age, the higher the intention to switch over. Further, based on annual income, adults who earn up to Rs 2 lakhs annually have more switching-over intentions (Mean score: 3.9719) followed by adults who earn Rs more than 2 lakhs to 5 lakhs annually (Mean score: 3.7590). Mean scores of education levels regarding switching intentions are higher among more educated adults and less among those who are qualified up to the school level.

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    • Table 1. Changes in switching intentions
    • Table 2. Changes in switching behavior
    • Table 3. Changes in brand love
    • Table 4. Correlation matrix
    • Table 5. Model summary
    • Table 6. Tested model
    • Table 7. Model outline
    • Table 8. Model coefficients
    • Table 9. Direct effect
    • Table 10. Indirect effect
    • Conceptualization
      Arun Kumar N., Suresha B.
    • Data curation
      Arun Kumar N., Girish S., Mahesh E.
    • Formal Analysis
      Arun Kumar N., Suresha B., Mahesh E.
    • Investigation
      Arun Kumar N., Girish S., Suresha B.
    • Methodology
      Arun Kumar N., Girish S., Suresha B., Mahesh E.
    • Project administration
      Arun Kumar N., Girish S.
    • Software
      Arun Kumar N., Girish S.
    • Supervision
      Arun Kumar N., Girish S., Suresha B.
    • Validation
      Arun Kumar N.
    • Writing – original draft
      Girish S., Suresha B., Mahesh E.
    • Visualization
      Mahesh E.