Abstract
The technology readiness (TR) index aims to better understand people’s propensity to embrace and use cutting-edge technologies. The initial TR construct considers four dimensions—innovativeness, optimism, insecurity, and discomfort—that collectively explain technology usage. The present meta-analysis advances understanding of TR by reexamining its dimensionality, and investigating mediating mechanisms and moderating influences in the TR–technology usage relationship. Using data from 193 independent samples extracted from 163 articles reported by 69,263 individuals, we find that TR is best conceptualized as a two-dimensional construct differentiating between motivators (innovativeness, optimism) and inhibitors (insecurity, discomfort). We observe strong indirect effects of these dimensions on technology usage through mediators proposed by the quality–value–satisfaction chain and technology acceptance model. The results suggest stronger relationships for motivators than for inhibitors, but also that these TR dimensions exert influence through different mediators. Further, the moderator results suggest that the strength of TR–technology usage relationships depends on the technology type (hedonic/utilitarian), examined firm characteristics (voluntary/mandatory use; firm support), and country context (gross domestic product; human development). Finally, customer age, education, and experience are related to TR. These findings enhance managers’ understanding of how TR influences technology usage.
Original language | English |
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Pages (from-to) | 649-669 |
Number of pages | 21 |
Journal | Journal of the Academy of Marketing Science |
Volume | 48 |
Issue number | 4 |
DOIs | |
Publication status | Published - Jul 2020 |
Externally published | Yes |
Bibliographical note
Copyright The Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- Meta-analysis
- Technology readiness
- Technology acceptance
- Quality–value–satisfaction chain
- Structural equation modeling
- Hierarchical linear meta-analysis