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Gerontographics and consumer behavior in later life: Insights from the life course paradigm

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  • George P. Moschis
  • Anil Mathur
  • Thuckavadee Sthienrapapayut

Abstract

The present article applies the life course paradigm to the study of older consumers. It is based on the general life course premise that events experienced in later life in the form of biophysical, social, and psychological changes create demand for readjustment and adaptation to new life conditions that define the multi-dimensional processes of aging and influence consumer behavior. These aging processes are collectively integrated into a model to develop stages of aging, known as “gerontographics,” that people go through in later life. To test the model’s efficacy in predicting consumption-related activities, a convenience sample (N = 383) of adults aged 45 and older is used to develop the gerontographics model and compared it to commonly used measures of aging (chronological and cognitive age) in predicting select consumer behaviors relevant to people in later life. The results reveal the value of the gerontographics model in understanding and explaining the consumer behavior of older adults. Implications of these findings and directions for further research are also discussed.

Suggested Citation

  • George P. Moschis & Anil Mathur & Thuckavadee Sthienrapapayut, 2020. "Gerontographics and consumer behavior in later life: Insights from the life course paradigm," Journal of Global Scholars of Marketing Science, Taylor & Francis Journals, vol. 30(1), pages 18-33, January.
    • Handle: RePEc:taf:jgsmks:v:30:y:2020:i:1:p:18-33
    DOI: 10.1080/21639159.2019.1613908
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    Cited by:

    1. Kamila Migdał-Najman & Krzysztof Najman & Sylwia Badowska, 2020. "The GNG neural network in analyzing consumer behaviour patterns: empirical research on a purchasing behaviour processes realized by the elderly consumers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 947-982, December.

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