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This paper aims to present a mechanism that explains how followers become loyal to social media influencers. It suggests influencer interactivity as a unique feature of influencer brands and investigates how it affects influencer authenticity, emotional attachment, and thus brand loyalty.

A cross-sectional survey of 282 social media users was conducted to estimate the conceptual model. Participants were asked to respond to the survey questions based on their perceptions of a specific influencer they follow on social media. The relationships among variables were tested via structural equation modeling.

The findings show that influencer interactivity is positively related to influencer authenticity and emotional attachment. Influencer authenticity mediates the effect of interactivity on emotional attachment and directly affects brand trust. Furthermore, the results demonstrate that followers’ emotional attachment to influencer brands increases brand loyalty through brand trust.

This study is the first to approach influencers as a brand, rather than a brand endorser. Specifically, this study conceptualizes the influencer brand’s interactivity and finds a link between influencer interactivity and the followers’ perception of influencer authenticity. It discusses why influencer interactivity and authenticity play key roles in shaping influencer brand equity. This study contributes to both the human brands and the influencer marketing literature by proposing a model to understand how the brand equity of the influencer brand is formed.

Given its growing economic potential and social impact, this study aims to understand the motivations and concerns regarding metaverse usage. It identifies user needs and risks around the metaverse grounded on uses and gratifications theory and perceived risk theory.

The authors analyzed user reviews and rating data from Roblox, a representative modern metaverse platform. They applied BERTopic modeling to extract topics from reviews, identifying key motivations and risk aspects related to metaverse usage. They further constructed an explanatory model to assess how those affect user satisfaction and changes in these effects over time.

This study discovered that gratifications like entertainment, escapism, social interaction and avatar-based self-expression significantly influence user satisfaction in the metaverse. It also highlighted that users find satisfaction in self-expression and self-actualization through creating virtual spaces, items and video content. However, factors such as identity theft, fraud and child safety were identified as potential detriments to satisfaction. These influences fluctuated over time, indicating the dynamic nature of user needs and risk perceptions.

The novelty of this study lies in its dual application of the uses and gratifications theory and perceived risk theory to the metaverse. It provides a novel perspective on user motivations and concerns, shedding light on the distinct elements driving user satisfaction within the metaverse. This study unravels the metaverse’s unique capacity to assimilate features from established digital media while offering a distinctive user-generated experience. This research offers valuable insights for academics and practitioners in digital media and marketing.

This research pioneers the application of both uses and gratifications and perceived risk theories to understand factors influencing metaverse satisfaction. By establishing a comprehensive framework, it explores the metaverse’s unique value as a user-content creation platform, while encompassing existing digital platform characteristics. This study enriches the academic literature on the metaverse and offers invaluable insights for both metaverse platforms and brand marketers.

This research aims to investigate the role of brand types (value brand vs premium brand) in determining review content. Specifically, this research focuses on attribute-based reviews for an innovative product and suggests that consumers of value brands tend to discuss more attributes in their product reviews than those of premium brands. Also, this research suggests that review valence and time have moderating effects on the relationship between brand types and the number of attributes discussed in a review.

This research employed a data set of online consumer reviews (N = 106,980) for wireless earbuds from Amazon.com. It extracted products' attributes from review text using Bigram analysis and measured the number of attributes discussed in a review. It then analyzed the effect of brand types (value brand vs premium brand) on the number of attributes and the moderating effect of review valence and time.

The estimation results of Poisson models reveal that reviews for value brands tend to contain more product attributes than reviews for premium brands. Interestingly, the tendency is stronger among positive (vs negative) reviews, and it decreases over time as more reviews are accumulated.

While existing studies focused on the outcomes of the review content, there was not enough investigation into the determinants of the review content. This study focuses on the number of attributes discussed in a review, a key content characteristic, and provides the first empirical evidence that review content differs by brand types of an innovative product.

The evaluation of perceived attribute performance reflected in online consumer reviews (OCRs) is critical in gaining timely marketing insights. This study proposed a text mining approach to measure consumer sentiments at the feature level and their asymmetric impacts on overall product ratings.

This study employed 49,130 OCRs generated for 14 wireless earbud products on Amazon.com. Word combinations of the major quality dimensions and related sentiment words were identified using bigram natural language processing (NLP) analysis. This study combined sentiment dictionaries and feature-related bigrams and measured feature level sentiment scores in a review. Furthermore, the authors examined the effect of feature level sentiment on product ratings.

The results indicate that customer sentiment for product features measured from text reviews significantly and asymmetrically affects the overall rating. Building upon the three-factor theory of customer satisfaction, the key quality dimensions of wireless earbuds are categorized into basic, excitement and performance factors.

This study provides a novel approach to assess customer feature level evaluation of a product and its impact on customer satisfaction based on big data analytics. By applying the suggested methodology, marketing managers can gain in-depth insights into consumer needs and reflect this knowledge in their future product or service improvement.

In the context of influencer marketing, this study examines the effects of explicit advertising disclosure on consumers' inference regarding influencers' sincere recommendation intent, and its consequences for consumers' purchase intentions.

In Study 1, participants were randomly assigned to the conditions of explicit advertising disclosure (e.g. #AD, #Sponsored) and subtle advertising disclosure (e.g. #ThankYou), and indicated their inference regarding influencers' recommendation motives and their own purchase intentions. One-way ANOVA analysis reveals the effect of advertising disclosure on consumers' purchase intentions and the PROCESS model indicates the mediating role of consumers' inferences regarding influencers' sincere recommendation intent. In Study 2, we implemented a 2 (explicit vs. subtle advertising disclosure) × 2 (moderate vs. large size of followers) between-subjects design, using a two-way ANOVA analysis. We analyzed the moderated mediation effect via the PROCESS model.

Consumers are less inclined to purchase products when influencers express explicit advertising disclosures, compared to when the advertising disclosures are subtle (Study 1). These negative effects of advertising disclosure can be caused by consumers' inferences regarding influencers' sincere recommendation intent in terms of posting content (Studies 1 and 2). Additionally, the negative effect is particularly prominent when an influencer has a large (vs. moderate) number of followers (Study 2).

By examining (1) the effects of explicit advertising disclosure on consumers' inference regarding influencers' persuasion motives and (2) the role of the influencer's number of followers, this research provides both theoretical and practical insights for understanding how best to implement influencer marketing.

The purpose of this paper is to examine the influence of source credibility, message credibility and tie strength, on acceptance and subsequent forwarding of electronic word of mouth (EWOM). Forwarding EWOM (FEWOM) also depends on personality traits, which this work investigates in form of moderation effect of individual regulatory focus.

Hypotheses are developed using social exchange theory and the elaboration likelihood model and tested using structural equation modelling. Data were collected online, using a random sample of 324 respondents in India.

Findings indicate that source credibility and tie strength are instrumental in influencing acceptance of EWOM. A strong mediation role of acceptance of EWOM, confirms that people tend to forward information online only if they accept it.

This study represents a unique effort to focus on the combined effects of message credibility, source credibility and tie strength on acceptance and subsequent forwarding of EWOM.

This study provides original insights about antecedents of FEWOM as well as the role of individual regulatory focus as a moderator in the process.