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      Perceived authenticity of social media influencers: scale development and validation

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      Journal of Research in Interactive Marketing
      Emerald

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          Abstract

          Purpose

          Public perceptions of the authenticity of social media influencers (SMIs) are a key driver of the latter's persuasiveness as brand endorsers. Despite its importance, no measurement scale currently exists for perceived authenticity of social media influencers (PASMIs). This prevents practitioners from effectively assessing consumers' perceptions of an influencer's authenticity prior to a potential partnership. To provide better guidance, this research develops and validates a scale of PASMI as well as examines the relationships between the underlying dimensions of the scale and key consumer behavior variables.

          Design/methodology/approach

          The current research consists of two studies: the first study constructs a scale; the second validates it. In Study 1, items were generated from existing scales as well as from qualitative responses. These items were revised based on feedback provided by an independent group of reviewers. Furthermore, an online survey was conducted to purify the items. In Study 2, the scale was validated with a new sample.

          Findings

          Results suggest that perceived SMI authenticity is a multidimensional construct consisting of sincerity, truthful endorsements, visibility, expertise and uniqueness. Each of the five dimensions has varying effects on consumers' evaluation of an SMI, willingness to follow an SMI, and intention to purchase products that SMIs recommend.

          Originality/value

          This research extends theoretical work on authenticity by developing and validating a scale as well as delving into the construct of perceived SMI authenticity. Practical implications are provided for marketers and SMIs.

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          Most cited references79

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          Sources of method bias in social science research and recommendations on how to control it.

          Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms "method" and "method bias" and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.
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            Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics

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              • Abstract: not found
              • Article: not found

              Partial least squares structural equation modeling (PLS-SEM)

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                Author and article information

                Contributors
                Journal
                Journal of Research in Interactive Marketing
                JRIM
                Emerald
                2040-7122
                June 25 2021
                October 15 2021
                June 25 2021
                October 15 2021
                : 15
                : 4
                : 822-841
                Article
                10.1108/JRIM-12-2020-0253
                0a786075-9ecc-4eb3-be36-8fe2a83aff41
                © 2021

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