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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27 Apr 2021
TL;DR: In this article, the authors investigate the process of building consumer-brand relationships with Millennial consumers through social media micro-influencers and propose a three-stage building method towards brand evangelism through micro- influencer, including selecting influencers, constructing intense emotional responses to the brand (brand engagement and brand love), and ultimately becoming a brand evangelist.
Abstract: Undoubtedly, in the modern age of digitalization, Millennials, who are considered digital natives, have become a massive target market for salespersons. Changes in the way Millennials think accompanied by an explosion of social media have led to an increased focus on social media influencer marketing in the company sector. To help establish a new marketing paradigm that accounts for these changes, this research aims to conceptualize and investigate the process of building consumer-brand relationships with Millennial consumers through social media micro-influencers. Findings based on structural equation modeling revealed that four core characteristics of social media micro-influencers (i.e., authenticity, the meaning of the influencer, specific content, and secret sharing) were a significant antecedent of brand engagement and brand love, which, in turn, mediated the pathway from social media micro-influencer characteristics to brand evangelism. Understanding what social media micro-influencers mean to Millennials offers the promise of improving brand evangelism through more precise market analysis and market strategy. In the discussion, the paper introduces a three-stage building method towards brand evangelism through social media micro- influencer, including: (1) the stage of selecting influencers; (2) the stage of constructing intense emotional responses to the brand (brand engagement and brand love); and ultimately (3) the stage of becoming a brand evangelist. Lastly, limitations and future directions were discussed.
7 citations
Cites background from "Influencer Marketing: How Message V..."
...It is worth to note that social media in!uencers are perceived as a useful source of recommendation (Hsu et al., 2013; Meng & Wei, 2015), enabling purchase intention of consumers to buy a product (Colliander & Dahlén, 2011; Lou & Yuan, 2019; Meng & Wei, 2015)....
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...$e majority of studies exist in the realm of behavioral intentions (Chatzigeorgiou, 2017; Choi & Rifon, 2012; Dân, 2018; Ge & Gretzel, 2018; Kim & Ko, 2012; Lim et al., 2017; Lou & Yuan, 2019; Loureiro & Sarmento, 2019; Meng & Wei, 2015)....
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TL;DR: This article explored how social media influencers' intimate self-disclosure (SMIs' ISD) can shape perceptions of their credibility and how followers' relatedness need fulfillment and source credibility are affected.
7 citations
TL;DR: An approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels by using deep learning to approximate the topic modeling process of the conventional approaches.
Abstract: This paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.,We deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.,The approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.,This work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.,This work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.,In this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).
7 citations
TL;DR: In this paper, a model that considers online brand trust in different non-brand-owned touchpoints as a multifactorial construct constituted by: social network influencers, bloggers, online retail platforms and brand-related user generated content was used to test the hypotheses.
Abstract: The study tests a model that considers online brand trust in different nonbrand-owned touchpoints as a multifactorial construct constituted by: social network influencers, bloggers, online retail platforms and brand-related user generated content. Furthermore, it examines the influences that offline and online brand trust exert on consumer buying intention.,A convenience sample of 3,335 total individuals participated in the survey. Structural equation modelling was used to test the hypotheses.,Online brand trust is significantly influenced by trust in all investigated nonbrand-owned touchpoints. Both offline and online brand trust positively influence buying intention.,Whereas brand trust is considered a multidimensional construct that includes both cognitive and affective aspects, in addition to individuals' personality traits, the present study only investigated the rational dimension of the brand trust paradigm. Moreover, this study examined the influence of brand trust on consumers' buying intention and not overt behavior. In addition, even though the extant literature suggests that the relation between trust and behavioral outcomes may vary across cultures, no test of the possible influences that culture exerted on brand trust and BInt was run. Finally, given the convenience sampling method used in this research, statistically significant surveys would provide a more solid basis for the investigated phenomenon, and they would enable an appropriate generalization of the findings.,To build brand trust and favour buying intention, marketers should monitor and influence the online touchpoints that are partially under or totally out of their control, and reconceive and manage physical stores.,This paper contributes to the stream of literature on online brand trust by proving that it is a multifactorial construct resulting from trust in different non-proprietary online entities and pointing out the prevalent role that physical stores play in shaping consumer buying intention. It also indicates that a trust transfer effect takes place between different online information sources and offline outlets.
7 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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