Search results

Localization, glocalization, and standardization advertising strategies have scarcely been examined in the context of internationally acknowledged heritage products aimed at young domestic consumers in emerging markets. This study investigated two essential advertising cues: endorser nationality (local vs Western) and language (local vs English). National pride and gender effects were also analyzed.

Eight brochure types were constructed to represent localized, glocalized, and standardized print advertisements and examine their effects on brand image and purchase intention. MANOVA, MANCOVA, and moderated mediation analysis were employed to test the model.

The localization presenting same-sex endorsement is the best fit for promoting an internationally acknowledged heritage product to young, educated domestic consumers who have a low-to-moderate level of national pride (NP).

This study provides theoretical implications in localization, NP, and gender effect in ad strategy.

This study fills a literature gap regarding the effects of localization, glocalization, and standardization advertising strategies on culturally bound heritage products aimed at young consumers in emerging markets. The moderating effect of NP adds to the novelty of this study.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.