Influencer Marketing: How Message Value and Credibility Affect Consumer Trust of Branded Content on Social Media
TL;DR: In this paper, preliminary research involves preliminary research to understand the mechanism by which influencer marketing affects the effectiveness of influencer campaigns, and the results show that the effect of influencers' marketing on the performance of online advertising has been studied.
Abstract: In 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...
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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.
274 citations
TL;DR: In this article, the authors studied the factors that drive success of online brand engagement at different stages of the consumer purchase funnel and found that influencer marketing is prevalent in firm strategies.
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...
255 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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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.
200 citations
TL;DR: Zhang et al. as discussed by the authors found that social media followers' emotional attachment to influencers is an important precedent that affects the followers' behavioral inclination to accept the SMIs' endorsements.
Abstract: While the literature related to this topic has predominantly focused on investigating the influence mechanism that social media influencers (SMIs) impose over their followers, less is known about their attachment mechanism. Given that social media platforms were originally designed to facilitate personal bonding and not product or brand recommendations, we posited that social media followers' emotional attachment to SMIs is an important precedent that affects the followers' behavioral inclination to accept the SMIs' endorsements. We thus drew new attention to the relationship between SMIs and their followers by focusing on their attachment development mechanism and its casual factors and effects. In doing so, Study 1 inductively analyzed the key causal factors, both with respect to SMI persona- and content-driven attributes, that make followers feel attached to SMIs. By integrating the findings of Study 1 with the human brand theory, Study 2 provided empirical evidence after analyzing 325 U.S. consumers' responses about how SMIs' personas (i.e., inspiration, enjoyability, and similarity) and content curation abilities (i.e., informativeness) affected followers to perceive the SMIs as human brands who fulfill their needs for ideality, relatedness, and competence—all of which resulted in an intense attachment to SMIs. It was this positive emotion shaped with SMIs that transferred to SMIs’ endorsements and positively influenced the followers to acquire the products/brands that the SMIs recommended.
185 citations
References
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01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
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.
37,124 citations
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
23,353 citations
TL;DR: Relationship marketing, established, developing, and maintaining successful relational exchanges, constitutes a major shift in marketing theory and practice as mentioned in this paper, after conceptualizing relationship relationships as a set of relationships.
Abstract: Relationship marketing—establishing, developing, and maintaining successful relational exchanges—constitutes a major shift in marketing theory and practice. After conceptualizing relationship marke...
19,920 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.
13,621 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.
7,536 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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