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Consumer research comics: Multi-representational knowledge and regenerative translations of consumer behaviour

Stephen R. O’Sullivan

Marketing Theory2026https://doi.org/10.1177/14705931261429324article
AJG 3ABDC A
Weight
0.50

Abstract

This paper expands non-representational theory’s emphasis on translation by exploring the potential of comic-based research (CBR) to illuminate consumer behaviour and intensify knowledge translation processes. CBR uses art-full mechanisms of knowledge translation to foster increased imaginative engagements and enhanced ontological author-ity resulting in the co-production of more personalised and applicable knowledge for audiences. Research comics are multi-representations – interactive interfaces – combinations of theory, data, and imagination capable of generating unlimited story-worlds and vivid knowledge translations that evolve and regenerate uniquely for each reader. CBR can be used as an effective translation device in marketing to generate unbound knowledge connections with diverse non-specialised audiences and establish a more imaginative, collaborative, and impactful culture of knowledge translation, and in doing so, realise the full potential of art-science hybrids. The illustrated consumer research comic 10 Business Days exploring the sensitive COVID-19 context is offered to support arguments.

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https://doi.org/https://doi.org/10.1177/14705931261429324

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@article{stephen2026,
  title        = {{Consumer research comics: Multi-representational knowledge and regenerative translations of consumer behaviour}},
  author       = {Stephen R. O’Sullivan},
  journal      = {Marketing Theory},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/14705931261429324},
}

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Consumer research comics: Multi-representational knowledge and regenerative translations of consumer behaviour

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.