Understanding consumer evaluations of personalised nutrition services in terms of the privacy calculus: A qualitative study

Aleksandra Berezowska, Arnout R. H. Fischer, Amber Ronteltap, Sharron Kuznesof, Anna Macready, Rosalind Fallaize, Hans C.M. Van Trijp

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Background: Personalised nutrition (PN) may provide major health benefits to consumers. A potential barrier to the uptake of PN is consumers' reluctance to disclose sensitive information upon which PN is based. This study adopts the privacy calculus to explore how PN service attributes contribute to consumers' privacy risk and personalisation benefit perceptions. Methods: Sixteen focus groups (n = 124) were held in 8 EU countries and discussed 9 PN services that differed in terms of personal information, communication channel, service provider, advice justification, scope, frequency, and customer lock-in. Transcripts were content analysed. Results: The personal information that underpinned PN contributed to both privacy risk perception and personalisation benefit perception. Disclosing information face-to-face mitigated the perception of privacy risk and amplified the perception of personalisation benefit. PN provided by a qualified expert and justified by scientific evidence increased participants' value perception. Enhancing convenience, offering regular face-to face support, and employing customer lock-in strategies were perceived as beneficial. Conclusion: This study suggests that to encourage consumer adoption, PN has to account for face-to-face communication, expert advice providers, support, a lifestyle-change focus, and customised offers. The results provide an initial insight into service attributes that influence consumer adoption of PN.
Original languageEnglish
Pages (from-to)127-140
Number of pages14
JournalPublic Health Genomics
Issue number3
Publication statusPublished - Jun 2014


  • Adoption
  • Business models
  • Consumers
  • Focus groups
  • Food4Me
  • Personalised nutrition
  • Privacy calculus


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