In the ever-evolving digital era, the importance of cyber law in safeguarding user privacy cannot be overstated. As a distinct legal discipline, cyber law examines regulations that oversee activities in cyberspace, offering legal protection regarding personal data and user privacy. Referring to various credible sources, this article evaluates the role of cyber law in protecting user privacy in the Indonesian context. The article discusses regulations such as the Electronic Information and Transactions Law (UU ITE), the Government Regulation on Electronic Systems, and the Personal Data Protection Bill. The main elements analyzed include supervision, regulation, resolution, and protection of user rights when facing digital security challenges. The examination shows that cyber law plays a significant role in protecting individuals' personal information, thereby encouraging safer and more trustworthy digital services. Furthermore, the article points out the various challenges faced in enforcing cyber law, such as administrative constraints, inadequate digital literacy, and limited access to justice, especially for those without legal knowledge or support. Consequently, to improve the effectiveness of cyber law, continued efforts are needed to strengthen the digital regulatory framework and provide more comprehensive user privacy protection. This perspective seeks to enhance understanding of the significance of cyber law in safeguarding privacy and proposes recommendations for governments and relevant organizations in addressing privacy issues in the digital age.

Purpose Grounded in source credibility and the elaboration likelihood model (ELM), the current study examines the impact of influencers' credibility and consumer involvement on consumer attitudes and purchase intention, exploring potential changes in consumer behaviour before and after the COVID-19 pandemic. In addition, the mediating role of consumer involvement was examined. Design/methodology/approach A repeated cross-sectional research design was applied to compare consumer behaviour at two different time points. Two sets of data (pre-pandemic, consisting of 297 participants and post-pandemic, consisting of 307 participants) were collected through an online survey among female consumers of beauty products. Findings The findings confirm the positive effect of influencers' credibility on attitudes and consumer involvement. Moreover, the findings highlight the direct impact of consumer involvement on consumer attitudes and purchase intention. In addition, the mediating role of consumer involvement is supported. The comparison between pre- and post-pandemic periods revealed that influencers' credibility demonstrated a weaker effect on attitudes towards influencer-endorsed products after the pandemic. Conversely, consumer involvement had a strong influence on attitudes toward influencer-endorsed products after the pandemic. Practical implications The findings, along with the comparison of the two data sets, provide theoretical and practical implications regarding the relationship between influencers' credibility and consumer involvement. Originality/value This study provides empirical evidence on consumer behaviour before and after the COVID-19 pandemic, using a repeated cross-sectional design to identify and compare changes in habits and attitudes across two distinct time periods.

Science translation strategies to the public during health emergencies: A systematic review of RCTs.

Complimentary subscriptions to UpToDate, a decision support tool, were provided to community health workers (CHWs) in rural and remote primary care sites as part of a government-funded health system research program. A feasibility evaluation conducted after the first year of implementation showed that UpToDate was acceptable among CHWs despite infrastructural barriers. This follow-up study evaluated the longitudinal adoption of UpToDate among CHWs and examined how sociocultural, political, and environmental factors influenced its use. Drawing on the nonadoption, abandonment, scale-up, spread, and sustainability framework, this study aimed to understand not only use patterns but also broader challenges to scale-up, spread, and sustainability in a complex health system. An explanatory mixed methods design was used combining analysis of use and program activity logs; program reports; and focus groups with CHWs, health care professionals, and program implementers. Quantitative analysis of use logs (March2021toSeptember2023) compared adoption over time by using descriptive statistics, CIs, and chi-square tests. Qualitative data came from the reanalysis of previous focus group transcripts and program reports and from a new focus group with program implementers. Reflexive thematic analysis was used to interpret how CHWs and implementers perceived and used the tool, and findings were integrated to explain quantitative trends. Use of UpToDate was modest and declined over time. Monthly active use among CHWs and midwives fell substantially from 3.57% (97/2720 person-months) in2021 to 1.07% (37/3456) in2022 and remained low at 1.50% (39/2592) up to 2023, with markedly higher engagement in the rural site than in the remote site. Peaks in use coincided with program activities, whereas prolonged troughs followed typhoons, power outages, and other disruptions. Log data showed that users primarily consulted patient education articles rather than clinician-oriented decision tools. Qualitative analyses revealed that CHWs appropriated UpToDate as a learning aid and source of professional credibility. Structural shocks, heavy workloads, language barriers, and limited device access constrained individual use, and communal practices (shared devices and learning activities) meant that meaningful engagement often went unrecorded in vendor metrics. Our findings show that acceptability does not guarantee sustained use and that adoption cannot be captured fully by log-in counts. UpToDate's value for CHWs lay in how it was domesticated as a tool for building capacity and professional credibility, not in its intended function as a decision aid used at the point of care. Therefore, evaluations of digital health tools should incorporate indicators of learning and social capital alongside use metrics. Policymakers should recognize that infrastructural fragility and communal adaptation shape digital health uptake. Embedding tools into ongoing training and peer learning structures, providing offline and multilingual support, and investing in resilience planning will be crucial for meaningful scale-up and sustainability.

ABSTRACT The presumed importance of source credibility has left a mark on communication theory and practice. However, increased polarization and distrust in scientific authority raise questions surrounding the contemporary usefulness of source credibility. The present study (N = 248) examines how scientific trust influences perceptions of source expertise and trustworthiness. Results demonstrate a significant moderating effect of trust in science on the dimensions of perceived source expertise and trustworthiness. These findings highlight the consequences of widespread structural erosion of trust in science, potentially pointing to the conditions required to enhance persuasive scientific appeals using source credibility.

The rapid advancement of information technology has significantly increased the use of computer systems in daily activities. Alongside these developments, cyber threats have also grown, one of the most prevalent being the Trojan Horse virus. Trojan Horse is a type of malware that disguises itself as a legitimate program in order to deceive users and gain unauthorized access to computer systems. This study aims to analyze the patterns of Trojan Horse virus propagation, examine its impacts on computer system security, and identify effective prevention and mitigation strategies. The research method used is a literature review by analyzing books, scientific journals, and credible online sources related to Trojan Horse malware and information system security. The results show that Trojan Horse viruses commonly spread through email attachments, malicious downloads, fake software, and social engineering techniques. The impacts include data theft, system performance degradation, unauthorized remote access, and disruption of organizational operations. Therefore, comprehensive security measures combining technical controls and user awareness are essential to minimize Trojan Horse attacks.

Purpose This study aims to investigate the key factors influencing the adoption of generative artificial intelligence (GenAI) tools by cross-border e-commerce (CBEC) operators. Using the elaboration likelihood model (ELM), this research explores how argument quality and source credibility affect CBEC operators’ subscription intention and willingness to pay more for GenAI services. Design/methodology/approach Data were collected through a questionnaire survey of 369 CBEC operators, primarily from Taiwan. Partial least squares structural equation modeling was used to analyze the data and test the hypothesized relationships based on the ELM framework. Findings The findings indicate that both argument quality and source credibility significantly and positively impact CBEC operators’ subscription intention and willingness to pay more for GenAI services. The study also reveals that outcome quality is a significant antecedent of argument quality, internet celebrity endorsement and user endorsement are key drivers of source credibility, add-on GenAI service satisfaction has no influence on argument quality and argument quality and source credibility positively influence both subscription intention and willingness to pay more for GenAI services. Originality/value This study extends and refines the ELM for the context of modern digital technology adoption. The authors challenge the conventional view of parallel persuasive routes by theorizing and demonstrating a recursive interplay, where argument quality (the central route) dynamically enhances source credibility (the peripheral route). While argument quality is the primary driver of subscription intention, source credibility becomes the dominant factor influencing users’ willingness to pay more. These refinements provide a more nuanced understanding of persuasion in staged, digitally mediated adoption decisions. Beyond theoretical contributions, the study offers actionable guidance for GenAI providers and CBEC operators.

Green influencers' source credibility in driving customers’ green behaviors: Role of trust, engagement, and knowledge in a cross-country study

ERP Correlates of Semantic Inconsistencies in Deepfakes.

Background: Instagram influencer marketing is increasingly becoming a determining factor in the decision-making of Millennials and Gen Z regarding visits to tourism destinations. Although source credibility (attractiveness, trustworthiness, and expertise) has been widely studied, little is known about its direct influence on behavioral intention when mediated by affective mechanisms. Purpose: This study examines how parasocial interaction (PSI) mediates the relationship between source credibility and travel intention, i.e., the tendency to visit Yogyakarta. In this context, PSI refers to the emotional connection that allows followers to feel attached to influencers. Method: Data were collected via an online survey distributed via Google Forms to Indonesian Instagram users between January and March 2025. Purposive sampling was employed to obtain 213 responses from users who follow travel influencers. The data collected were analyzed using PLS-SEM. Findings: The results show that attractiveness, trustworthiness, and expertise have no direct impact on travel intention. However, all these credibility dimensions show a strong positive correlation with PSI, which predicts travel intention. Therefore, PSI is identified as a complete mediator in the model. Conclusions: Travel intention is mainly driven by emotional connection rather than by influencer traits alone. The appraisal mechanism mediates the process from perception to action. Research Implication: It is recommended that tourism marketers adopt PSI-based strategies by recognizing actors who can build substantive, affective relations with the audience.

The widespread dissemination of misinformation and fake news on social media platforms poses a significant challenge to the integrity of public discourse, democratic stability, and societal trust. Although misinformation detection has been widely studied, one critical but underexplored dimension is the misquotation of legitimate news articles on social media. Misquotations—deliberate or accidental distortions of original news content—can mislead audiences and diminish trust in credible news sources, thereby amplifying the reach of misinformation. Addressing this issue requires a robust dataset that captures the original news content and its associated social media representations. To this end, we construct a comprehensive dataset by performing multi-source triangulation across four established datasets: FAKENEWSNET, NELA-GT, TruthSeekers, and Twitter15/16. This process yields approximately 158,400 aligned pairs of news stories and related social media posts. Building on this dataset, we propose a multimodal binary classification framework designed to detect misquotations by jointly modeling textual, visual, and contextual features. The model integrates engineered features representing social context and event semantics within a shared latent space, enabling a nuanced understanding of content distortion. Furthermore, we analyze the relative contribution of each modality—textual, visual, and contextual—using ablation studies and performance metrics to assess their impact on detection accuracy. This study introduces a novel approach to misquotation detection, contributing to the combat of misinformation through multimodal analysis.

This study, entitled Enhancing the Visibility of Polyvinyl Chloride (PVC) Survey Markers Using Fluorescent Paint, aimed to determine whether applying fluorescent paint can improve the visibility and field usability of PVC markers commonly used in land surveying. The research focused on how marker visibility changes with distance and viewing angle, and on comparing the performance of fluorescent-painted markers with unpainted ones using information gathered from existing studies. A descriptive, literature-based research design was used, where journals, technical reports, and industry references were reviewed to understand how fluorescent and high-visibility coatings affect detection and recognition in outdoor environments. To ensure accuracy and reliability, the study synthesized findings from multiple credible sources discussing color perception, luminance, reflectivity, and marker detectability in varying field conditions. Findings from the reviewed literature showed that fluorescent coatings generally increase brightness, make markers easier to distinguish from surrounding terrain, and improve visibility under different lighting conditions. These improvements were linked to shorter search time, fewer errors in identifying survey points, and more efficient field operations, especially in cluttered or low-visibility environments. The reviewed studies also highlighted practical benefits such as improved workflow, reduced surveying delays, and better point identification during fieldwork. However, the literature also noted that there is limited local testing on how these coatings perform under Philippine field conditions, particularly regarding durability under heat and rainfall, long-term color retention, and cost-effectiveness in actual surveying projects. Because of this, the study recommends conducting future field-based experiments to measure actual performance, durability, and practicality in real surveying situations, and to develop standardized guidelines for the effective use of fluorescent-painted PVC markers in geodetic engineering applications

This research critically analyzes the role of TikTok's algorithm in forming echo chambers, strengthening political polarization, and driving the virality of political content during Indonesia's 2024 Presidential Election. As a social media platform with over 112 million active users in Indonesia, predominantly aged 18-24, TikTok has become a strategic arena for digital political discourse competition. Using an interdisciplinary approach combining media studies, political science, and digital technology, this research reveals how TikTok's machine learning-based algorithm compiles individual user timelines based on engagement patterns rather than source credibility or chronology, thereby creating echo chambers that limit the diversity of political information. Findings indicate that political content with emotional elements, dramatization, and entertainment (politainment) has a higher probability of virality compared to factual content, posing serious implications for the quality of public deliberation and digital democracy in Indonesia. Based on the analysis of virality dynamics and digital polarization, this research proposes an Algorithm Democratization Model that combines aspects of transparency, regulation, literacy, platform architecture, and research to create a healthier digital ecosystem that supports democratic discourse.Keywords: TikTok algorithm, echo chamber, political polarization, virality, digital democracy

Contemporary church mission faces complex challenges due to rapid social, cultural, and technological changes, including digital media dominance, globalization, secularism, and increasing pluralism. Most studies on Paul's mission have not deeply connected his flexibility principles with specific challenges faced by contemporary churches in the digital era. This research aims to examine the missionary principles of the Apostle Paul based on 1 Corinthians 9:19-23 and demonstrate their application in contemporary mission contexts. A qualitative method with literature study approach was used to analyze biblical texts and their relevance to contemporary contexts through various credible literary sources. The research findings show that Paul applied two main principles in his mission: the principle of sacrifice, where he voluntarily relinquished his rights and freedom to become a "servant to all people"; and the principle of openness, evident in his ability to "become all thing...

Native advertising refers to a paid advertisement that mimics editorial content, aiming to prompt a profitable action. Authors of native ads employ various tactics to attract attention and raise awareness of a brand, product, or service, ultimately seeking to drive the reader toward conversion. These tactics may include selecting headlines that guide the reader’s interpretation of the message, choosing credible sources of information, and incorporating (graphic) design elements. This paperinvestigates how peripheral cues – specifically clickbait headlines, sources of information, and visual components – affect the reach of native advertisements, framed within the Elaboration Likelihood Model (ELM). According to the ELM, when audiences lack sufficient motivation or cognitive resources for in-depth analysis, they predominantly rely on the peripheral route of information processing. Consequently, the use of attention-grabbing yet often superficial elements is essential for capturing initial attention and maximizing overall reach. A study based on a sample of 543 native ads from the most widely read Croatianinternet portals found that headlines emphasizing uncertainty or using vague pronouns consistently achieved the highest reach among various clickbait techniques. Additionally, uncertainty emerged as the most prevalent strategy used. Even though such headlines can spark curiosity, non-clickbait headlines often match or outperform them, especially when they rely on ambiguous language, uncertainty cues, or numeric indicators. Moreover, the findings indicate that ads featuring influencers as sources of information achieve significantly greater reach than those relying on other types of sources. These results underscore the crucialrole of peripheral cues in the creation of effective native advertisements and open avenues for future research that might explore the depth of processing and attitude change among consumers.

The limited availability of structured and credible sources that interpret Drake’s song lyrics makes it difficult for listeners to fully grasp the meaning and emotional depth within his music. This study aims to develop a web-based lyric meaning generator capable of automatically interpreting Drake’s lyrics using the Transformer architecture. The system employs the LLaMA 3 model, which is fine-tuned through Low-Rank Adaptation (LoRA) to improve training efficiency while maintaining high semantic accuracy. The curated dataset consists of Drake’s song lyrics, their corresponding interpretations, and metadata such as album and reference sources. Data preprocessing and supervised fine-tuning were conducted using the Hugging Face framework in Google Colab. A gradio-based web application was implemented with a Retrieval-Augmented Generation (RAG) mechanism to enhance contextual relevance during inference. Evaluation metrics, including Semantic Similarity and ROUGE-L, were applied to measure the model’s ability to produce coherent and contextually aligned interpretations. The results demonstrate that the fine-tuned LLaMA 3 model effectively generates accurate lyric meanings while reducing computational cost. Overall, this study highlights the potential of Transformer-based models to bridge the gap between music and natural language understanding, particularly in analyzing metaphorical and emotion-rich song lyrics.

The development of technology in the digital era has brought significant changes to global education systems, including in Indonesia. Digital transformation encourages learning models that are more flexible, interactive, and focused on 21st-century competencies. However, this progress also presents various challenges, particularly related to moral degradation among youth, unequal access to technology, and low digital literacy among teachers and students. This study aims to analyze character education models from a global perspective and evaluate the challenges and strategies for their implementation in Indonesia in the digital era. Using a qualitative research method with a library research approach, data were obtained from credible sources such as scientific journals, books, official reports, and national and international educational documents. The findings indicate that developed countries have successfully integrated technology into their curricula, teacher training, and educational policies, resulting in well- established digital learning ecosystems. In Indonesia, digitalization in education has grown rapidly, especially since the pandemic, but still faces obstacles such as limited infrastructure, varied levels of digital competence among teachers, and a lack of integration of digital pedagogy. In addition, student readiness and access inequality in frontier, outermost, and disadvantaged regions (3T) present further challenges in applying global education models

Abstract Polarisation is a widespread societal issue, dividing populations into opposing groups whose beliefs may span multiple, seemingly unrelated, topics. This phenomenon creates distinct cultural groups with internally consistent yet mutually opposing beliefs, strengthening group identities and deepening societal divides. We develop a mathematical model to investigate how polarisation and emergent associations between unrelated beliefs occur. Unlike prior models assuming irrationality, informational segregation, or similarity biases, our model employs an epistemic distinction between two filtering mechanisms: content and source filtering. In content filtering, agents evaluate beliefs based on intrinsic coherence, rejecting information opposing their current beliefs. Source filtering, in contrast, involves adopting beliefs based on the perceived credibility of information sources, where credibility corresponds to mutual consistency between individuals’ belief systems. We further incorporate external signals through varying innovation and loss rates to reflect empirical biases from real-world data. Simulations show that source filtering rapidly clusters beliefs into two dominant, highly polarised groups. Within each group, beliefs become tightly correlated, creating strong opposition between the groups across all cultural traits. Thus, new associations emerge spontaneously between previously unrelated beliefs. Arbitrary and initially independent beliefs transform into ideological bundles serving as clear group signals. External signals further reinforce these associations, allowing advantages of particular beliefs (e.g. empirical evidence) to transfer to associated beliefs. Content filtering can also produce polarised groups, but beliefs remain aligned with preexisting logical or factual structures. Source filtering mirrors algorithmic biases such as collaborative filtering prevalent on digital platforms, where content exposure depends heavily on shared user preferences rather than intrinsic coherence. Our findings illustrate how these biases amplify polarisation by reinforcing homogeneous belief packages, intensifying ideological divides within cultural systems.

Performance management in Kenya’s Technical and Vocational Education and Training (TVET) institutions has been anchored on performance contracts and individual performance appraisals to enhance accountability, resource utilization, and service delivery. This study sought to: examine the performance management process in Kenyan TVET institutions, assess the impact of performance contracting on resource utilization and institutional results, evaluate the influence of performance appraisal on institutional performance and service quality, identify challenges and gaps in implementation and provide recommendations for strengthening performance management practices. The study adopted a desktop research design that relied exclusively on secondary data obtained from academic journals, dissertations, and government publications by the Ministry of Education, TVETA, and the Public Service Commission. Official performance contracting and appraisal guidelines, institutional evaluation reports, and credible online sources were also reviewed. Searches were conducted through databases such as Google Scholar and ERIC using combinations of key terms related to performance management and TVET outcomes. Materials published from 2009 onwards were prioritized to reflect sustained implementation periods. Data were thematically analyzed, and narrative synthesis with simple vote counting was used to identify convergence across findings. The results indicate that performance contracting has moderately improved the utilization of teaching and learning resources, while structured performance appraisals are linked to enhanced service delivery and accountability. However, these gains are uneven due to inadequate staff training, limited resources, inconsistent target setting, and perceptions of the process as a bureaucratic formality. The study concludes that while performance management frameworks foster a culture of efficiency and results, their success depends on effective implementation, continuous training, staff participation, and adequate resource support. Strengthening ICT infrastructure, standardizing practices, and enhancing feedback mechanisms are essential to realizing sustainable institutional improvements across Kenya’s TVET sector.

This study aims to map the influence of digital marketing through social media on the travel decision-making processes of university students in Jayapura. Using a phenomenological qualitative approach, data were collected through open-ended surveys administered to 23 students from three universities, selected via purposive sampling. Thematic analysis revealed that social media influences travel decisions through three sequential phases: inspiration through aesthetic visual content on Instagram Reels and TikTok, validation via practical information and user reviews, and the final decision shaped by contextual factors such as distance and accessibility. The findings also identified three pillars of persuasive content: authentic visuals, practical information, and personal experience narratives. Furthermore, a hierarchy of source credibility was mapped, indicating that friends and travel bloggers are the most trusted sources, while celebrities and anonymous accounts are viewed with skepticism regarding their objectivity. This study provides theoretical contributions by enriching digital tourism decision-making models with an Eastern Indonesian contextual perspective, alongside practical implications for tourism stakeholders in developing authentic and trust-based content strategies.