Reference:

Tian, Z., Dew, R., & Iyengar, R. (2024). Mega or Micro? Influencer Selection Using Follower Elasticity. Journal of Marketing Research (JMR), 61(3), 472–495. https://doi-org.devinci.idm.oclc.org/10.1177/00222437231210267

Keywords: Influencer marketing ,Streaming video & television, Causal inference, Deep learning Tags (Metadata) Online social networks

Summary: this article adresses the criteria for selecting influencers to partner .

While some firms collaborate with“mega” influencers with millions of followers, other firms partner with “micro” influencers with only several thousand followers,
but who also cost less to sponsor. To quantify this trade-off between popularity and cost, the authors develop a framework for
estimating the follower elasticity of impressions (FEI), which measures a video’s percentage gain in impressions (i.e., views) corresponding
to a percentage increase in the number of followers of its creator. Computing FEI involves estimating the causal effect
of an influencer’s popularity on the view counts of their videos, which is achieved through a combination of (1) a unique data set
collected from TikTok,

(2) a representation learning model for quantifying video content, and (3) a machine learning–based causal
inference method. The authors find that FEI is always positive, averaging .10, but often nonlinearly related to follower size.

They examine the factors that predict variation in these FEI curves and show how firms can use these results to better determine
influencer partnerships.

Development: 

Concepts :

Social network and influencer Marketing : how the structure of social networks can
impact downstream micro- and market-level outcomes.

• Mega-influencers are useful for awareness.
• Micro-influencers are perceived as more authentic and relatabl

Advertising:  the authors position influencer marketing as a form of social media advertising.

They study  the relationship between advertising investment and performance outcomes like sales or engagement.

The main research objective here is to know How does an influencer’s popularity (number of followers) causally affect the impressions/views of their videos?

in order to answer this  question the authors adobt a   causal inference and machine learning framework :

• Representation Learning using Variational Autoencoder (VAE) to analyze video content.
• Deep Instrumental Variables (Deep IV) to estimate the causal relationship between followers and impressions with content type and appeal type as moderators.

The data was collected from the Discover page of TikTok over six months (October 2020 – April 2021).

• 216 hashtags.
• 30 sponsored hashtags.
• More than 500,000 videos.

Key findings:

FEI is positive but non linear meaning that the following elasticity of impressions is positive on average so more followers generally increase impressions but the relationship is not linear.

Midtier influencers generate the most marginal returns compared to mega or micro influencers.

the effectiveness of the influencer depends on the content type (category) and the engagement goal (informative, entertaining, socializing).

Limitations: 

-Tiktok specific content : the data was only collected through the tiktok platform and no other social media platforms so the results cannot be generalized all across platforms.

-The focus was only on impressions / views and didnt focus on sales or purchase behavior which is the ultimate goal.

Reference:

Sun, Y., Wang, Y., Zhong, Y., Zhang, Z., & Zhu, M. (2024). How technical features of virtual live shopping platforms affect purchase intention: Based on the theory of interactive media effects. Decision Support Systems, 180. https://doi-org.devinci.idm.oclc.org/10.1016/j.dss.2024.114189

Keywords: Virtual live shopping platforms, anthropomorphism,media richness,psychological distance,customer engagement, purchase intention.

Summary:

This study addresses the research gaps by developing a theoretical
model based on the theory of interactive media effects (TIME) to investigate the influence of VLSPs’ technical features on customers’ purchase intentions.

results indicate that psychological distance plays a mediating role between anthropomorphism (full mediation) and customer engagement, as well as between media richness (partial mediation) and customer engagement in a survey of 299
VLSP users.

Development :

Virtual live shopping platforms (VLSPs) are an innovative form of intelligent shopping DSS that offer brands novel opportunities to interact with customers.

VLSPs represent the application system of artificial intelligence, 3D modeling, deep learning, and speech synthesis technologies in the live e-commerce field.

personified virtual streamer makes it easier to establish relationships with users, thereby promoting familiarity with their characteristics.

2 key features of VLSP:

• Media richness: The level to which a medium can supply communication capabilities to media users. /the medium’s ability to produce and
  transmit a diversity of sensory stimuli, as well as the ability of numerous
  cues to decide the channel’s ability to convey rich information (Huang and li su)

elements of Media richness : immediate feedback,
linguistic diversity, personal attention, and multiple cues

• Anthropomorphism :

  making virtual streamers appear more human-like in behavior, appearance, or interaction style.

Theoritical Framework : Theory of Interactive Media Effects (TIME)

Explains how media technology features influence consumers through psychological mechanisms.

Variables :

1-Independant variables : Media Richness, Anthropomorphism

2-Dependant variable: Purchase Intention

3-Pshychological (mediators)

• Pshychological distance: The extent to which consumers perceive something as close, tangible, and present
• Customer engagement: A psychological state of continuous attention while using VLSPs.

H1. Psychological distance mediates the relationship between
anthropomorphism and customer engagement.
H2. Psychological distance mediates the relationship between media
richness and customer engagement.

H4. Psychological distance and customer engagement play a role in
mediating the chain between anthropomorphism and purchase
intention.
H5. Psychological distance and customer engagement play a role in

Methodology : Quantitative research with data collected through a survey with 402 questionnaires ( 299 valid responses).

Key findings:

-Anthropomorphism has no significant direct effect on customer engagement or purchase intention. but has an indirect effect through psychological distance → customer engagement

-Media richness has a significant direct effect on customer engagement. It also has a significant indirect effect via psychological distance.

-Customer engagement is confirmed as a critical mediator leading to purchase intention.

-Pshychological distance has a strong indirect effect via customer engagement.

Limitations :

Data collected only from Taobao VLSP users.

Reference :

Zhang, M., Liu, Y., Wang, Y., & Zhao, L. (2022). How to retain customers: Understanding the role of trust in live streaming commerce with a socio-technical perspective. Computers in Human Behavior, 127. https://doi-org.devinci.idm.oclc.org/10.1016/j.chb.2021.107052

Keywords: Live streaming commerce,Trust,Continuance intention,Social interactivity,IT affordance.

Summary : this article is about what role does trust play in customer’s buying decision in the context of live streaming shopping. The author explains how both social factors (interaction) and technical factors (platform features) can build trust in live streaming commerce.

Development: 

The author distinguishes two types of trust in this article :

trust in the streamer

Trust in the product .

Method : Online questionnaire survey with users of TAOBAO (chinese live streaming commerce platform).

Valid responses : 446

Statistical method: Structural Equation Modeling (SEM)

Conceptual model : based on  the Socio-Technical Systems Theory.

Social enablers :

• Active control
• Two-way communication
• Synchronicity

Technical enablers:

• Visibility affordance
• Personalization affordance

Influence : trust in streamers / product

Moderating effect :of live streaming genre

Key findings: 

Trust is one of the strongest drivers of continuance intention in live streaming commerce.

Trust is multidimensional : a customer can trust both the streamer and the product  and trust in the streamer can transfer to the trust in the product.

Real-time interaction and visibility are especially important in reducing uncertainty.

Reference

Tian, Z., Dew, R., & Iyengar, R. (2024). Mega or Micro? Influencer Selection Using Follower Elasticity. Journal of Marketing Research (JMR), 61(3), 472–495. https://doi-org.devinci.idm.oclc.org/10.1177/00222437231210267

Keywords: influencer marketing, causal inference, deep learning, representation learning, heterogeneous treatment effects, video data
Online supplement.

Summary: This article focuses on one of the main criteria to choose an influencer to partner with based on their effect on the consumer’s buying decision which is the influencer’s popularity .

Mega-influencers have millions of followers and large reach, but they are expensive. Micro-influencers have smaller audiences but are cheaper and often seen as more authentic. The authors wanted to measure the real causal impact of follower size on video performance.

the authors develop a framework estimating the follower’s elasticity of impression (FEI) and the calculates the causal effect between an influencer’s populairty on the view counts of their videos with data collected from Tiktok.

Development: 

1- follower elasticity of impressions (FEI) : measures the percentage increase in video impressions generated by a 1% increase in followers.

2-Representation Learning Framework (SMVAE):

AI model extracts and compresses information from:

• text,
• images,
• audio,
• editing styles/effects

In order to create its representation

3- Deep IV (Deep Instrumental Variables) :

estimates the causal effect of followers on impressions while controlling for:

• nonlinear relationships,
• heterogeneity,
• and unobserved confounders.

Independent Variable

• Influencer follower size

Dependent Variable

• Video impressions/views after 2 weeks

Moderators

The relationship changes depending on:

• content type (food, gaming, beauty, etc.)
• engagement style:
  • entertaining,
  • informative,
  • socializing/emotional.

Key findings: 

• More followers generally increase video impressions, but the relationship is nonlinear.
• The average Follower Elasticity of Impressions (FEI) is about 0.10, meaning a 1% increase in followers leads to a 0.10% increase in views.
• The FEI curve is inverted U-shaped: mid-tier influencers generate the highest marginal gains in impressions compared to micro or mega influencers.
• After controlling for content and hidden confounders, mega-influencers are not always the most effective choice.
• The effectiveness of influencer size depends on the type of content and the campaign objective (informative, entertaining, or socializing).
• Some campaigns benefit more from mega-influencers, while others perform better with smaller or mid-tier creators.
• The study shows that brands should select influencers strategically rather than assuming that bigger influencers always produce better results.

Reference:

Teresa Borges-Tiago, M., Santiago, J., & Tiago, F. (2023). Mega or macro social media influencers: Who endorses brands better? Journal of Business Research, 157. https://doi-org.devinci.idm.oclc.org/10.1016/j.jbusres.2022.113606

Keywords:  Celebrity endorsement, Brand equity, customer-brand management, Brand credibility,customer-endorser envolvement.

Summary: 

This study conducted an online survey to determine the antecedents and consequences of endorsers’ participation in marketing and communication strategies. The results of path analysis showed that brand and endorser credibility played a significant role in determining customer brand engagement and brand equity. Endorser credibility impacted brand equity only in the case of mega-influencers. Smaller influencers exhibited higher prowess than celebrities to engage customers, thus suggesting that “less is more.”

Development: 

The research mode was l built on the Associative-Network Memory Theory

The study employed a two-phase exploratory research design:

Phase 1 : Influencer pool selection

mega-influencers: christiano ronaldo endorsing nike

macro-influencers: couple Ana Guiomar and Diogo Valsassina endorsing Vodafone

Phase 2 : Data collection

online survey with 270 valid responses from portugese consumers  between 18-25 years old

. 61.1% evaluated the mega-influencer scenario and 38.9% evaluated the macro-influencer scenario.

Key findings: 

Brand credibility has a massive direct impact on brand equity (0.697) and customer brand engagement (0.318).

A customer’s emotional/cognitive involvement with an influencer strongly and positively affects both the influencer’s perceived credibility and customer-brand engagement.

the less is more theory was rejected (H12) type of influencer does matter significantly.

Mega-influencers effectively boost brand credibility and brand equity, but requires a re-existing congruence between the brand and the celebrity

Macro-influencers (smaller networks) display a higher prowess for generating direct consumer engagement

Limitation:

The data collection was limited exclusively to Portuguese consumers so it cant be generalizable to other countries or cultural contexts.