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Journal ArticleDOI

Influencer Marketing: How Message Value and Credibility Affect Consumer Trust of Branded Content on Social Media

12 Feb 2019-Journal of Interactive Advertising (Routledge)-Vol. 19, Iss: 1, pp 58-73

AbstractIn the past few years, expenditure on influencer marketing has grown exponentially. The present study involves preliminary research to understand the mechanism by which influencer marketing affects...

Topics: Influencer marketing (73%), Branded content (59%), Source credibility (51%), Credibility (50%)

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Citations
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Journal ArticleDOI
TL;DR: A mechanism for measuring the influencer index across popular social media platforms including Facebook, Twitter, and Instagram is proposed and findings indicate that engagement, outreach, sentiment, and growth play a key role in determining the influencers.
Abstract: The growth of social media has completely revamped the way people interact, communicate and engage. These platforms play a key role in facilitating greater outreach and influence. This study proposes a mechanism for measuring the influencer index across popular social media platforms including Facebook, Twitter, and Instagram. A set of features that determine the impact on the consumers are modelled using a regression approach. The underlying machine learning algorithms including Ordinary Least Squares (OLS), K-NN Regression (KNN), Support Vector Regression (SVR), and Lasso Regression models are adapted to compute a cumulative score in terms of influencer index. Findings indicate that engagement, outreach, sentiment, and growth play a key role in determining the influencers. Further, the ensemble of the four models resulted in the highest accuracy of 93.7% followed by the KNN regression with 93.6%. The study has implications across various domains of e-commerce, viral marketing, social media marketing and brand management wherein identification of key information propagators is essential. These influencer indices may further be utilized by e-commerce portals and brands for the purpose of social media promotion and engagement for larger outreach.

135 citations


Journal ArticleDOI
Abstract: Influencer marketing is prevalent in firm strategies, yet little is known about the factors that drive success of online brand engagement at different stages of the consumer purchase funnel. The fi...

113 citations


Cites background from "Influencer Marketing: How Message V..."

  • ...Lou and Yuan (2019) demonstrate the importance of message content, source credibility, and homophily in influencer marketing....

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Journal ArticleDOI
TL;DR: Results from a sample of 280 followers show that the perceived influential power of digital influencers not only helps to generate engagement but also increases expected value and behavioral intention regarding the recommended brands.
Abstract: Despite the growing interest in digital influencers as a brand communication tool in recent years, much remains to be explored to understand how they can build a bond with their followers that shapes their perceptions and behaviors towards the endorsed brands. This study aims to determine how effective digital influencers are in recommending brands via electronic word-of-mouth by examining whether the potential influence they have on their followers may affect brand engagement in self-concept, brand expected value and intention to purchase recommended brands. The results from a sample of 280 followers show that the perceived influential power of digital influencers not only helps to generate engagement but also increases expected value and behavioral intention regarding the recommended brands. Moreover, brand engagement in self-concept raises brand expected value and both variables also affect the intention to purchase recommended brands. The study contributes to a deeper understanding of the persuasive power of digital influencers, which is still limited. It can be also useful for companies when developing their own social media communication strategy.

73 citations



Journal ArticleDOI
Abstract: Web has a ubiquitous presence in our lives today. Incessant use of mobile technology and social media has affected our daily routine, life style, and decision-making processes. Millions of people a...

52 citations


References
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Journal ArticleDOI
01 Jan 1973
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. Table of Contents 1. Introduction. I. PREPARING FOR A MULTIVARIATE ANALYSIS. 2. Examining Your Data. 3. Factor Analysis. II. DEPENDENCE TECHNIQUES. 4. Multiple Regression. 5. Multiple Discriminant Analysis and Logistic Regression. 6. Multivariate Analysis of Variance. 7. Conjoint Analysis. 8. Canonical Correlation Analysis. III. INTERDEPENDENCE TECHNIQUES. 9. Cluster Analysis. 10. Multidimensional Scaling. IV. ADVANCED AND EMERGING TECHNIQUES. 11. Structural Equation Modeling. 12. Emerging Techniques in Multivariate Analysis. Appendix A: Applications of Multivariate Data Analysis. Index.

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TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
Abstract: I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and Correspondence Analysis V Moving Beyond the Basic Techniques 10 Structural Equation Modeling: Overview 10a Appendix -- SEM 11 CFA: Confirmatory Factor Analysis 11a Appendix -- CFA 12 SEM: Testing A Structural Model 12a Appendix -- SEM APPENDIX A Basic Stats

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Abstract: Relationship marketing—establishing, developing, and maintaining successful relational exchanges—constitutes a major shift in marketing theory and practice. After conceptualizing relationship marke...

18,980 citations


Book
01 Jan 2014
TL;DR: The Second Edition of this practical guide to partial least squares structural equation modeling is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.
Abstract: With applications using SmartPLS (www.smartpls.com)—the primary software used in partial least squares structural equation modeling (PLS-SEM)—this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.

10,268 citations


"Influencer Marketing: How Message V..." refers background or methods in this paper

  • ...A collinearity assessment showed no significant levels of collinearity between any sets of predicting variables (with variance inflation factor [VIF] falling between tolerance range .20 and 5.0) (Hair et al. 2014)....

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  • ...Conversely, PLS-SEM estimates model parameters in a way that maximizes the variance explained in endogenous variables and is preferred for research aimed at theory development and prediction (Hair et al. 2014, p. 14)....

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  • ...CB-SEM uses a maximum likelihood estimation (MLE) procedure to estimate model coefficients “so that the discrepancy between the estimated and sample covariance matrices is minimized” (Hair et al. 2014, p. 27)....

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  • ...The latent variables in the current model all have reflective measurements: indicators which predict one particular construct and which are highly correlated to one another and represent the effects of the latent construct (Hair et al. 2014, p. 43)....

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Posted Content
TL;DR: An evaluation of double-blind reviewed journals through important academic publishing databases revealed that more than 30 academic articles in the domain of international marketing (in a broad sense) used PLS path modeling as means of statistical analysis.
Abstract: Purpose: This paper discusses partial least squares path modeling (PLS), a powerful structural equation modeling technique for research on international marketing. While a significant body of research provides guidance for the use of covariance-based structural equation modeling (CBSEM) in international marketing, there are no subject-specific guidelines for the use of PLS so far.Methodology/approach: A literature review of the use of PLS in international marketing reveals the increasing application of this methodology.Findings: This paper reveals the strengths and weaknesses of PLS in the context of research on international marketing, and provides guidance for multi-group analysis.Originality/value of paper: The paper assists researchers in making well-grounded decisions regarding the application of PLS in certain research situations and provides specific implications for an appropriate application of the methodology.

6,292 citations


"Influencer Marketing: How Message V..." refers methods in this paper

  • ...PLS path modeling is also recommended over CBSEM for testing complex models with many latent variables (Henseler, Ringle, and Sinkovics 2009)....

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  • ...Then we performed a second bootstrapping analysis, specifying 5,000 subsamples and a 95% significance level, to obtain each path coefficient’s standard error and p value (Henseler, Ringle, and Sinkovics 2009) (Table 3)....

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