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      Promoting healthy foods in the new digital era on Instagram: an experimental study on the effect of a popular real versus fictitious fit influencer on brand attitude and purchase intentions

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          Abstract

          Background

          Most studies on social influencer marketing techniques have focused on the promotion of unhealthy foods whereas little is known about the promotion of healthier foods. The present experimental study investigated whether a popular real versus fictitious fit influencer is more successful in promoting healthy food products. In addition, we examined the role of parasocial interaction as an underlying mechanism of healthy food product endorsement.

          Methods

          We used a randomized between-subject design with 154 participants (mean age: 24.0 years). Viewers’ product attitude and purchase intention were tested after exposure to an Instagram post by a popular real fit influencer ( n = 77) or fictitious fit influencer ( n = 77).

          Results

          Results showed that parasocial interaction mediated the relation between the type of influencer and product attitude as well as purchase intention. Parasocial interaction was higher for participants exposed to the popular real fit influencer compared to the fictitious fit influencer, leading to higher healthy food brand attitude and purchase intention.

          Discussion

          The findings showed that it is crucial for social influencers to establish a warm personal relationship and connection with the their followers when promoting a healthy product successfully. We suggest that the promotion of healthy foods could be more successful in public health when using popular fit influencers.

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          Most cited references46

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          G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

          G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and chi2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.
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            Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

            G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
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              Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods.

              The most commonly used method to test an indirect effect is to divide the estimate of the indirect effect by its standard error and compare the resulting z statistic with a critical value from the standard normal distribution. Confidence limits for the indirect effect are also typically based on critical values from the standard normal distribution. This article uses a simulation study to demonstrate that confidence limits are imbalanced because the distribution of the indirect effect is normal only in special cases. Two alternatives for improving the performance of confidence limits for the indirect effect are evaluated: (a) a method based on the distribution of the product of two normal random variables, and (b) resampling methods. In Study 1, confidence limits based on the distribution of the product are more accurate than methods based on an assumed normal distribution but confidence limits are still imbalanced. Study 2 demonstrates that more accurate confidence limits are obtained using resampling methods, with the bias-corrected bootstrap the best method overall.
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                Author and article information

                Contributors
                fransfolkvord@gmail.com
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                10 November 2020
                10 November 2020
                2020
                : 20
                : 1677
                Affiliations
                [1 ]GRID grid.474035.1, Open Evidence Research, ; Barcelona, Spain
                [2 ]GRID grid.12295.3d, ISNI 0000 0001 0943 3265, Tilburg School of Humanities and Digital Sciences, , Tilburg University, ; Tilburg, the Netherlands
                [3 ]GRID grid.10417.33, ISNI 0000 0004 0444 9382, Radboud Institute for Health Sciences, , Radboud University and Medical Centre, ; Nijmegen, the Netherlands
                [4 ]GRID grid.5590.9, ISNI 0000000122931605, Behavioural Science Institute, , Radboud University Nijmegen, ; Nijmegen, the Netherlands
                Author information
                http://orcid.org/0000-0001-7602-3792
                Article
                9779
                10.1186/s12889-020-09779-y
                7654141
                33167953
                0d624a63-de37-4ea4-88e0-4e00fbeb31fc
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 30 July 2020
                : 26 October 2020
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Public health
                social influencers,marketing,healthy foods,public health,attitude,intention
                Public health
                social influencers, marketing, healthy foods, public health, attitude, intention

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