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Social media marketers are keen to understand how viewers perceive their brands on a platform and how the learning experiences from content can impact their attitudes toward a brand. This study aims to focus on examining the effect of firm-generated content (FGC) on X (formerly known as Twitter), using Kolb’s experiential learning theory to analyze the viewers’ learning process. In addition, the study investigates how the length of time a viewer follows a brand and the type of brand can influence their attitudes toward it.

This study involved three qualitative studies on X to investigate how content learning affects consumer attitudes toward two brands, namely, Nike and Subway. The study also examined the impact of the duration of following the brands, with participants following the brands for 4, 8 and 12 weeks, respectively, to assess changes in their attitudes.

The results demonstrate that content learning significantly impacts consumer attitudes. By following brands and engaging with their FGC over time, viewers can transition from being occasional or intermittent followers to becoming devoted brand enthusiasts. Through the four-stage experiential learning process, followers undergo cognitive, emotional and behavioral transformations that collectively shape their brand attitudes. The impact of content learning varies according to the brand type, and the duration of following has a positive effect on brand attitudes.

The study’s findings have significant marketing implications for social media marketers, suggesting that they should restructure their social media platforms as learning platforms to effectively engage followers. Companies should adjust their content marketing strategies from a learner’s perspective, providing followers with content that resonates with them, enhances their learning outcomes and helps shift their beliefs and brand attitudes, ultimately converting them into loyal consumers.

To the best of the author’s knowledge, this qualitative research is the first of its kind to apply experiential learning theories to investigate how users learn from FGC by following brands on social media and how this learning ultimately changes their brand attitude. The study provides a unique perspective on social media marketing, enriching the understanding of content marketing and consumer experiences on social media platforms.

The uncertainty of consumers' perceived value makes online education enterprises face great challenge in developing the pricing strategy. So the purpose of this paper is to research the pricing strategies of online education products by considering knowledge consumers' characteristics.

Considering consumer matching degree and price comparison, this study establishes the utility functions of consumers in normal sales period and discount selling period. On this basis, the research builds pricing models of the online education enterprise under the strategy of price undertaking and intertemporal pricing strategy. It further discusses the impact of consumer matching degree, consumer price sensitivity and different types of consumers on the product price and profit of online education enterprises, and reveals the optimal pricing strategy of the enterprise.

Consumer matching degree and price sensitivity coefficient have positive effects on product price and enterprise profit, but they have different effects on product demand; there are differences in the perceived value of the three types of consumers, and matching consumers are the optimal consumer group; the intertemporal pricing strategy is better than the strategy of price undertaking only when the price sensitivity coefficient is greater than a critical value.

This study enriches the literature on the pricing model of online education products and owns a practical significance to guide the online enterprise to make marketing strategies to increase profit.

Urging people to avoid stockpiling was a common declaration made by governments during the COVID-19 pandemic outbreak, yet empty supermarket shelves and supply shortages of basic products were observed worldwide. This study aims to (a) identify the factors that activate consumer personal norms towards socially responsible behaviours, specifically resisting stockpiling, and (b) examine how fear moderates the link between personal norms and consumer engagement in stockpiling during public crises.

The study recruited a sample of US consumers who were responsible for household grocery shopping during the COVID-19 pandemic. A total of 593 individuals participated in the study, and the collected data were analysed using structural equation modelling.

The results show that awareness of the negative consequences of stockpiling and a sense of personal responsibility for those consequences activate personal norms towards responsible shopping during public crises. However, perceived fear has the opposite effect, encouraging stockpiling. In addition, fear weakens the negative relationship between personal norms and stockpiling.

This study extends the norm activation model and indicates that personal norms may not always promote responsible behaviours when fear is high. It is unique in that it sheds light on non-mainstream responsible consumption behaviours (e.g. resisting stockpiling), and the interaction between consumption and social responsibility.

This study aims to explain the factors associated with receiving a specific brand of COVID-19 vaccine within the framework of the theory of reasoned action (TRA). This study extends the TRA with the country-of-origin image (COI), brand image and electronic word of mouth variables.

This study is based on a cross-sectional survey conducted among 460 people who received the Sinofarm vaccine. Participants were selected using an online convenience sampling method. The structural equation modeling technique tests the proposed hypotheses.

The results showed that the essential factor associated with the intent to get the Chinese Sinopharm vaccine is the attitude toward the Signopharm vaccine. Also, word of mouth, subjective norms and brand image are the most critical factors that play a role in forming a favorable attitude toward the Sinopharm vaccine. Finally, the country-of-origin image does not affect attitudes toward the Sinopharm vaccine.

The area of vaccine marketing has been given limited attention in academic literature. This study addresses this area with little research and is greatly attractive to many brands targeting the consumer market. The study results can form a foundation for creating the branding strategy for this product category and assessing its demand in various markets.

This study examines the research that has been conducted on user-generated advertising content in the social marketing strategies of commercial brands to understand the phenomenon, explore academic interest in the topic and identify areas of limited thematic coverage.

A systematic review of existing scientific literature in the Web of Science (WoS) and Scopus was carried out using the PRISMA protocol. A co-occurrence matrix was used to review emerging topics on user-generated content (UGC) and influencer marketing, allowing the identification of articles (n = 59) related to the objective of this research.

Most research has analyzed UGC in images or text, but only very few have addressed videos and other digital formats (such as reels, image carousels or podcasts), although there is sufficient work focused on Twitter, Facebook and YouTube. There was no evidence of work exploring the effects, repercussions and possible dangers of uncontrolled brand exposure through Unofficial Brand Ambassadors.

The literature review has allowed finding important areas of future research that the scientific community has not sufficiently addressed. Likewise, this work shows structurally several classifications of UGC, which will facilitate future research to deepen and broaden these categories.

Social commerce has brought about a significant transformation in consumer experience due to diverse factors. As a result, users often find themselves prone to impulsive buying behaviour when exposed to such an environment. Prior research was limited to demonstrating the expanding influence of celebrities on social media and the linkage between social engagement and impulse buying context. Furthermore, the impulse buying tendency of consumers on social media in the context of celebrity posts has yet to be validated. This paper aims to assess the influence of consumer awareness, consumer trust and observational learning on the latent state-trait (LST) theory regarding celebrity posts on impulse buying tendencies.

The empirical research builds on a sample survey involving 750 students from the “Big Four” economics universities in Hanoi. The proposed model was analysed using a partial least squares structural equation modelling technique.

The authors find that consumer trust and observational learning from celebrity’ posts positively affect impulse buying tendency. Yet celebrity influence awareness directly impacts trust in celebrity’ posts rather than directly impacting impulse buying tendency. Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

Perceiving the importance of interactive and authentic posts by a celebrity in influencing consumers’ purchase behaviour on social media, this research offers valuable insights for stakeholders in the digital celebrity sphere of communication and marketing.

From a theoretical perspective, this expands the applicability of the LST theory in social commerce to promote impulse buying tendencies. Second, this contributes to the literature on the emerging phenomenon of social media celebrities, as existing literature does not clarify their influence on impulse buying behaviour. Third, this research applies the concept of observational learning in online shopping through key features of social media platforms, namely, likes, shares and comments, to investigate their influence on the impulse buying tendency of consumers. Concerning managerial implications, the authors propose practical recommendations for practitioners, particularly those involved or interested in the commercial services industry and social media marketing (namely, celebrities and partner companies).

In the context of virtual brand communities (VBCs), based on self-determination theory and organizational binary learning (OBL) theory, this study investigates the direct and indirect effects of various dimensions of consumer participation (CP) on firm innovation performance (FIP) and the mediating role of OBL. By introducing the perspective of knowledge absorptive capacity (KAC), this study examines the moderating effect of KAC on the relationship between OBL and TIP.

In this study, 751 samples are collected from Chinese firms, and stratified adjusted regression analysis is used to conduct empirical tests.

Information provision or co-creation directly affects FIP; the latter is more significant than the former, while social interaction does not directly affect FIP. Exploitative or exploratory learning plays a mediating role in the relationship between information provision or co-creation and FIP. In contrast, exploratory learning plays a mediating role in the relationship between social interaction and FIP. KAC has a positive moderating effect on the relationship between exploitative learning or exploratory learning and FIP. When the firm has a low KAC, the improvement of FIP through exploratory learning is slight.

In VBCs, this study tries to divide the dimensions of CP. It also clarifies the mediating effect of OBL on CP and FIP. In addition, it explores the moderating effect of KAC on OBL and FIP. The conclusions of this study provide theoretical support and practical inspiration for firms to use VBCs to improve FIP.

Online brand communities (OBCs) are important platforms to obtain consumers' ideas. The purpose of this study is to examine how peer influence and consumer contribution behavior simulate innovative behaviors in OBCs to increase idea quality.

Using a firm-hosted popular online brand community – Xiaomi Community (MIUI), the authors collected a set of data from 6567 consumers and then used structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to empirically test the impact of peer influence and consumer contribution behaviors on idea quality in OBCs.

The results of this study show that both peer influence breadth and depth have a positive effect on idea adoption and peer recognition, wherein proactive contribution behavior positively mediates these relationships, and responsive contribution behavior negatively mediates the impact of peer influence breadth and peer influence depth on peer recognition. A more detailed analysis using the fsQCA method further identifies four types of antecedent configurations for better idea quality.

Based on the attention-based view and the theory of learning by feedback, this study explores the factors that affect idea quality in the context of social networks and extends the research of peer influence in the digital age. The paper helps improve our understanding of how to promote customer idea quality in OBCs.

Anthropomorphized brands have been widely used as marketing communication tools to engage consumers on social media, especially on Twitter. Guided by the social exchange theory (SET) and the dialogic theory, this study aims to investigate how anthropomorphized brands leverage different communication strategies on Twitter and how these strategies are related to consumer engagement.

Supervised machine learning was used to identify the communication strategies (i.e. message types and dialogic principles) of 125,887 tweets from 21 brand characters. Some statistical analyses (e.g. frequency analysis, Chi-square analysis and Poisson regression analysis) were performed to explore the relationships between communication strategies and consumer engagement (i.e. retweets and replies).

The majority of anthropomorphized brands’ tweets belonged to the socioemotional category and the most adopted dialogic principles were generation of return visits and conservation of visitors. Consumers engaged more with socioemotional tweets as well as the tweets that adopted the principles of dialogic loop and conservation of visitors. There were clear relationships between message types and dialogic principles in anthropomorphized brands’ tweets, and certain dialogic principles were found to effectively improve consumer engagement with certain message types.

To the best of the authors’ knowledge, this study is the first to investigate the communication strategies of anthropomorphized brand characters on Twitter using computational research methods. It not only provides brand managers a systematic review of how current anthropomorphized brands communicate with consumers on Twitter and what strategies work more effectively to trigger consumer engagement but also contributes to theory building in brand management by integrating the SET and the dialogic theory in brand anthropomorphism research.

We propose a machine learning based methodology to deal with data collected from a mobile application asking users their opinion regarding fashion products. Based on different machine learning techniques, the proposed approach relies on the data value chain principle to enrich data into knowledge, insights and learning experience.

Online interaction and the usage of social media have dramatically altered both consumers’ behaviors and business practices. Companies invest in social media platforms and digital marketing in order to increase their brand awareness and boost their sales. Especially for fashion retailers, understanding consumers’ behavior before launching a new collection is crucial to reduce overstock situations. In this study, we aim at providing retailers better understand consumers’ different assessments of newly introduced products.

By creating new product-related and user-related attributes, the proposed prediction model attends an average of 70.15% accuracy when evaluating the potential success of new future products during the design process of the collection. Results showed that by harnessing artificial intelligence techniques, along with social media data and mobile apps, new ways of interacting with clients and understanding their preferences are established.

From a practical point of view, the proposed approach helps businesses better target their marketing campaigns, localize their potential clients and adjust manufactured quantities.

The originality of the proposed approach lies in (1) the implementation of the data value chain principle to enhance the information of raw data collected from mobile apps and improve the prediction model performances, and (2) the combination consumer and product attributes to provide an accurate prediction of new fashion, products.