Variations of Greetings in Gender-Based Scincare Live Commerce on TikTok

Abstract

Variations in greetings can change and shift. This study aims to describe the form, factors, and functions of greetings based on gender perspectives in skin care product promotion content on TikTok. This study uses a qualitative descriptive approach and corpus linguistics. The data in this study is the speech of skincare product sellers sourced from the TikTok accounts of KAHF and L'Oreal Paris Indonesia within one month. Specifically, the amount of male speaker speech data sourced from the KAHF TikTok account amounted to 5 live content, and male speaker speech data sourced from the L'Oreal Paris Indonesia TikTok account amounted to 5 live content. The data is processed using the AntConc 4.2.4 software. There are three main findings in this study. First, this study shows that men use more greetings in the form of pronouns. The difference is that men from the KAHF TikTok account tend to use gender-based variations of greetings, such as bro. Meanwhile, men from the L'Oreal Paris Indonesia TikTok account tend to use unisex greetings, such as friends. Second, the variety of greetings is influenced by various factors, such as: (1) the type of skincare product, (2) the limited identification of speech partners, (3) politeness and familiarity efforts, and (4) technological advances and language contact. Finally, the function of using greetings is to: (1) familiarize or build emotional relationships, (2) respect, and (3) identification of speech partners. Practical implications of this study suggest that the findings can serve as a guide for marketers in selecting greeting variations (gender-specific or neutral) that align with product identity and target audiences in TikTok live commerce, in order to build emotional connections and enhance promotional effectiveness. Academically, this study enriches the fields of sociolinguistics and language and gender by uncovering new forms of address in digital contexts, while demonstrating the relevance of the corpus linguistics approach for analyzing language variation on social media platforms.