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Nutritional advice has mainly focused on population-level recommendations. Recent developments in nutrition, communication, and marketing sciences have enabled potential deviations from this dominant business model in the direction of personalisation of nutrition advice. Such personalisation efforts can take on many forms, but these have in common that they can only be effective if they are supported by a viable business model. The present paper takes an inventory of approaches to personalised nutrition currently available in the market place as its starting point to arrive at an identification of their underlying business models. This analysis is presented as a unifying framework against which the potential of nutrigenomics-based personalised advice can be assessed. It has uncovered nine archetypical approaches to personalised nutrition advice in terms of their dominant underlying business models. Differentiating features among such business models are the type of information that is used as a basis for personalisation, the definition of the target group, the communication channels that are being adopted, and the partnerships that are built as a part of the business model. Future research should explore the consumer responses to the diversity of ''archetypical'' business models for personalised nutrition advice as a source of market information on which the delivery of nutrigenomics-based personalised nutrition advice may further build.
Through a Privacy Calculus (i.e. risk–benefit trade-off) lens, this study identifies factors that contribute to consumers’ adoption of personalised nutrition services. We argue that consumers’ intention to adopt personalised nutrition services is determined by perceptions of Privacy Risk, Personalisation Benefit, Information Control, Information Intrusiveness, Service Effectiveness, and the Benevolence, Integrity, and Ability of a service provider. Data were collected in eight European countries using an online survey. Results confirmed a robust and Europe-wide applicable cognitive model, showing that consumers’ intention to adopt personalised nutrition services depends more on Perceived Personalisation Benefit than on Perceived Privacy Risk. Perceived Privacy Risk was mainly determined by perceptions of Information Control, whereas Perceived Personalisation Benefit primarily depended on Perceived Service Effectiveness. Services that required increasingly intimate personal information, and in particular DNA, raised consumers’ Privacy Risk perceptions, but failed to increase perceptions of Personalisation Benefit. Accordingly, to successfully exploit personalised nutrition, service providers should convey a clear message regarding the benefits and effectiveness of personalised nutrition services. Furthermore, service providers may reduce Privacy Risk by increasing consumer perceptions of Information Control. To enhance perceptions of both Information Control and Service Effectiveness, service providers should make sure that consumers perceive them as competent and reliable.
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.
There is a potential market for PN compared to non-PN advice, particularly among men on higher incomes. These findings raise questions to what extent personalized nutrition can be left to the market or should be incorporated into public health programs.
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