Purpose: This study examines how consumer participation in co-creation influences the brand image of Teh Botol Sosro within a participatory marketing context. Recent research has increasingly shifted from firm-centered branding toward consumer involvement in co-creating brand value and meaning through digital and social media interactions. However, prior studies have often focused on consumers with established brand relationships, leaving limited understanding of how non-loyal consumers experience open co-creation initiatives. This study addresses this gap by examining how both loyal and non-loyal consumers construct brand meaning through participation in the 2025 Teh Botol Sosro Packaging Design Competition. Methods: A descriptive qualitative approach with a phenomenological method was employed. Data were collected through in-depth interviews with six purposively selected participants, comprising loyal and non-loyal consumers as well as winners and non-winners. Data were analyzed through data reduction, data display, and conclusion drawing. Findings: Consumer participation functioned as an experiential mechanism that strengthened brand image through cognitive, emotional, and creative engagement. Access and risk-benefit considerations were more prominent than dialogue and transparency. Participation generated stronger, more favorable, and distinctive brand associations, while non-loyal consumers also contributed to brand meaning through creative participation. Originality: This study extends co-creation research by positioning loyal and non-loyal consumers as active meaning-makers in participatory brand image formation, offering empirical insight into how open creative participation can reshape brand perceptions beyond pre-existing brand loyalty.
Purpose: This study aimed to map the intellectual structure of global research on data journalism and fact-checking and analyzed its specific adoption patterns in Indonesia to understand how global trends translated into the Global South context. Methods: Adopting a quantitative bibliometric approach, data were retrieved from the Scopus database covering the last decade. The study utilized Biblioshiny software, selected for its integrated statistical workflow and superior capability in comparative thematic mapping, to visualize and compare co-occurrence and co-citation networks between the global dataset and the Indonesian adoption dataset. Findings: Global results revealed an exponential growth trajectory driven by the interdisciplinary convergence of journalism studies and computer science, with Natural Language Processing and Pandemic emerging as dominant motor themes. Conversely, the Indonesian dataset indicated a unique, reactive adoption pattern with a sharp acceleration in publications during 2016–2025. Unlike the global trend toward automated detection, Indonesian research prioritized Social Media as the central problem context and Human collaboration as the primary solution, reflecting a sociotechnical adaptation to local resource constraints. Originality: This study provided new theoretical insights into journalism technology adoption in the Global South by conceptualizing the methodological divide between global computational trends and local human-centric practices. It offered a non-Western perspective on digital media evolution and suggested future empirical research on human-machine collaboration.
Purpose: This study aimed to develop and validate the Heart Communication Digital Literacy instrument, an integrative model combining the Digital Literacy of Tular Nalar (Contagious Reasoning) framework and the Heart Communication Theory. The model was designed to foster critical thinking, empathy, and communication ethics in digital spaces. It consisted of six aspects of heart communication linked to three levels of digital literacy from Tular Nalar. Methods: An exploratory sequential mixed-method approach was used. Qualitative development involved instrument specification and item construction, validated through expert Focus Group Discussions. A 4-point Likert-scale questionnaire was administered to 362 students in Yogyakarta quantitatively. Data were analyzed using JASP via Confirmatory Factor Analysis to evaluate construct validity, and Cronbach's alpha was used to check the reliability. Findings: Results yielded 32 valid items with good item-total correlations (0.43–0.75) and excellent reliability (Cronbach’s Alpha=0.963). CFA indicated that the three-level digital literacy model fit better than the original six-aspect heart communication construct, which proved unidimensional. Thus, this instrument is valid and reliable, and it resulted in the refinement of the Heart Communication Theory dimensions from six to four: Thought Cultivation, Feeling Cultivation, Empathy, and Peace of Heart. Originality: The study's originality lies in developing a Heart Communication-Based Digital Literacy Instrument that integrates heart communication with the Tular Nalar framework to promote empathetic and constructive digital communication. The novelty of this research is found in the four dimensions of Heart Communication, which refine the definition of Heart Communication Theory.
Purpose: This study investigates the role of pharmacist-influencer Rinaldi Nur Ibrahim (@rinaldi_ni) in promoting medication literacy among Indonesian society through digital communication on Instagram. The increasing prevalence of self-medication and difficulties in interpreting medication-use instructions highlight the need for accessible and credible medication information. Methods: This study employed a qualitative approach using a descriptive case study method. Data were collected through in-depth interviews with Rinaldi, the communicator and account owner, and non-participant observation of his Instagram activities and content. Findings: The findings indicate that Rinaldi applies the 4C concepts of social media communication: Context, Communication, Collaboration, and Connection. His content addresses current health issues using accessible language and communication styles that resonate with Generation Z. In contrast, collaborations with health institutions and other influencers strengthen credibility and audience reach. Although audience interaction remains largely one-way, particularly in comment sections, direct messages and broadcast channels support closer relationships with followers. Originality: This study offers a novel communication perspective by examining pharmacist-influencer communication for medication literacy and safe self-medication through Instagram using the 4C framework. Unlike previous studies focusing on consumer behavior, beauty products, general health literacy, or disease-specific campaigns, this study connects professional pharmaceutical expertise with digital influencer communication in the Indonesian context. It demonstrates how the 4C framework supports accessible, credible, and engaging medication information for younger audiences.
Purpose: Automated fake news detection can support verification, but predictions are less useful when textual and contextual evidence cannot be inspected. The study examined whether combining claim text with speaker metadata could improve veracity classification while retaining explanations for journalistic review. Methods: The LIAR train, validation, and test partitions were used, with six veracity labels remapped into Fake and Real classes. A BERT-base text classifier and Extreme Gradient Boosting metadata classifier produced class probabilities combined by an L1-regularized logistic regression meta-learner with disagreement and interaction features. SHapley Additive exPlanations, Local Interpretable Model-agnostic Explanations, and Integrated Gradients inspected metadata, ensemble, and token-level behavior. Findings: The stacking model reached 0.7514 accuracy, 0.7483 macro-F1, 0.8288 area under the receiver operating characteristic curve, and 0.4970 Matthews correlation coefficient. It exceeded the BERT text branch (macro-F1=0.6203) and metadata branch (macro-F1=0.7066). Metadata provided the stronger signal, with credibility score and speaker-history variables receiving the largest SHAP importance. High text-metadata disagreement occurred in 30.4% of test cases, and the ensemble reached 0.8234 accuracy within this subset. Originality: The study extends explainable fake-news detection by conceptualizing disagreement between evidence sources as a dimension of interpretability, separating semantic claim evidence, contextual speaker-history evidence, and their interaction at the fusion stage.
