The Use of Code-Mixing in Instagram Captions of a B2B Logistics Company - Di Jogjana, Najla Aprilia; Yuanti, Erlin Estiana (JLA (Jurnal Lingua Applicata), 2025)
This research investigates the use of code-mixing in the Instagram captions of Brand X, a B2B logistics company, focusing on its posts from August 2023 to March 2024. The study aims to identify the types and forms of code-mixing, determine the most frequently used types, and analyze their functions within Brand X's social media strategy. Using a descriptive qualitative approach, the study employed AntConc as a corpus analysis tool analyzing the frequency of the occurrence of the code-mixing, both in word and phrase levels. An in-depth interview with Brand X's digital content strategist was also conducted to support the analysis. The research is grounded on Muysken's (2000) theory of code-mixing classification and Hoffmann's (1991) theory of code-mixing functions. The findings reveal that single-word insertion is the most dominant type  of code-mixing, accounting for 56.59% of occurrences, followed by phrase insertion at 42.93%, while alternation is the least frequent at only 0.49%. The identified functions of code-mixing align with the  content strategist’s statement, which are to address specific topics and express group identity. This study concludes that even B2B companies utilize code-mixing in copywriting and social media marketing to engage audiences and reinforce brand identity.