Purpose: This study examined how family communication transmitted environmental values among the Baduy (Indonesia), linking their unique traditional ecological knowledge, cultural practices, and identity-based frameworks to sustainability outcomes to inform broader conservation efforts.
Methods: This qualitative study conducted in-depth interviews with four purposefully selected Baduy participants (Banten, Indonesia) to capture diverse perspectives. Informants included a ‘Tokoh Adat’ (customary leader), the ‘Kepala Desa Kanekes’ (village head), and two ‘Warga Baduy’ (general community members), representing traditional authority, administrative leadership, and community viewpoints.
Findings: Findings illuminate the Baduy people's profound integration of traditional agriculture and environmental preservation. This commitment manifests in their strict chemical prohibition, reliance on natural fertilizers, deep reverence for protected forests, and adherence to eco-friendly farming methods. These practices are embedded within a robust framework of customary law (‘adat’) and significantly informed by ‘Karma’ belief, reflecting a sophisticated understanding of long-term ecological consequences. Furthermore, these ingrained traditions fundamentally shape intergenerational communication, positioning the family as the primary conduit for transmitting vital environmental values and ecological knowledge. This dynamic process actively cultivates environmental responsibility and perpetuates a harmonious coexistence with nature within the community.
Originality: Providing novel insights, this qualitative study uniquely examined family communication's role in embedding environmental values across Baduy generations (Indonesia). Findings illuminate how traditional ecological knowledge and cultural practices synergize, driving sustainable agriculture/waste management and reinforcing community resilience through ancestral wisdom, informing culturally grounded conservation.

Purpose: This research aims to explore how endorsements by celebrities on Instagram play a role in the formation and development of social identities among users. It will investigate the psychological processes, including relationships and the desire for social connection, that influence the link between celebrity impact and identity development.
Methods: The method used was a qualitative descriptive method, data collection techniques by observations, interviews and documentations. Researchers choose influencers with high engagement rates. Key Informants that being choosen are three celebgram from Palembang, South Sumatera, Indonesia.
Findings: A celebrity's social identity is shaped by their interactions with their fans, as seen by the quantity of views, comments, direct messages, re-posts, and mentions. Along with participation, interpersonal interactions, communication style, social relationships, lifestyle relevance, and the presence of online communities. The visual appearance of celebrities is a major factor in the success of product endorsements because visuals are the first element that attracts the audience's attention.
Originality: The originality of this study stems from identifying a distinctive concept known as ‘Local Empathetic Communication’ as an essential element in forming social identity among celebgrams in Palembang. In contrast to prior research, this study combines the Social Construction of Reality Theory with the VisCap Model to illustrate that followers engage more deeply with influencers who embody local culture and values through empathetic and relatable dialogue. This emphasis on a localized emotional connection has not been previously addressed in earlier works, making it a new and significant contribution to the discipline.

The convergence of artificial intelligence—especially artificial superintelligence (ASI)—and cyberterrorism presents serious threats to peace, order, and justice. These threats require strong criminal law enforcement and robust national security strategies. However, Indonesian criminal law policy has yet to adequately address the risks posed by ASI. Criminal law traditionally protects the interests of the state, society, and individuals—an approach also relevant to national security. Ignoring ASI’s development could lead to increased cyberterrorism, turning overlooked risks into real threats. An interdisciplinary perspective reveals that the growing intersection of AI and cyberterrorism introduces urgent new challenges for Indonesia’s national security and criminal law enforcement.

Integrating Artificial Intelligence (AI) into digital recruitment platforms has introduced significant enhancements in efficiency and decision-making, alongside complex ethical challenges regarding fairness, transparency, and accountability in candidate evaluation. This study investigates how leading AI-driven recruitment platforms articulate and operationalize ethical principles and whether these commitments are effectively translated into practice. Employing a qualitative exploratory design, the research analyzes official white papers, privacy policies, and AI ethics statements from LinkedIn, HireVue, Pymetrics, and ModernHire. Data was examined using AI-assisted text mining and thematic content analysis to identify ethical discourse patterns and assess the depth of implementation. The findings indicate that moral terms such as “fairness” and “bias” are cited frequently, with LinkedIn referencing them 27 times and HireVue 19 times. A comparative transparency assessment yielded scores of 8.5 out of 10 for LinkedIn, 7.2 for HireVue, 6.8 for Pymetrics, and 4.3 for ModernHire, while formal mechanisms for candidate appeals were absent on most platforms. This study contributes to the field by revealing a persistent gap between stated ethical ideals and operational practices in AI recruitment and by recommending the adoption of explainable AI, transparent auditing frameworks, and international regulatory standards. Such measures are essential to foster more accountable, equitable, and humane AI-based hiring processes.

This study addresses the growing imperative for environmentally responsible supply chain management in Southeast Asia and the challenges of assessing corporate sustainability disclosures. Although companies increasingly produce sustainability reports, the extent to which these documents reflect genuine green practices remains unclear. This research systematically evaluates how five major Southeast Asian firms, including Unilever SEA, Nestlé Indonesia, Indofood, Danone, and ThaiBev, articulate green supply chain initiatives in reports published between 2022 and 2023. Employing a qualitative exploratory design, the study integrates document analysis with text mining and thematic coding; approximately 33,000 words from the five reports were processed, yielding 1,300 occurrences of green supply chain terms categorized into three themes: eco-packaging, green logistics, and carbon tracking. The results reveal a pronounced imbalance: eco-packaging comprised 54 percent of keywords (n = 702), green logistics 29 percent (n = 377), and carbon tracking 17 percent (n = 221). Unilever’s 9,300-word report contained 350 mentions of eco-packaging, while Danone’s 5,900-word report featured 310; carbon tracking averaged under 45 references per report. The study introduces a replicable text mining framework for ESG disclosure analysis and underscores the need for more balanced reporting, including Scope 3 emissions data. Future mixed-method approaches that combine computational analysis with qualitative validation are advocated. The findings provide evidence for policymakers and investors to refine ESG guidelines and highlight the potential of computational tools to enhance corporate accountability in sustainability reporting

Financial statement fraud threatens investor trust at a substantial level in the present market conditions. AI technology, through data pattern analysis, helps financial auditing reach better results when detecting rumors along with anomalies and suspicious trends. This research evaluates artificial intelligence's effectiveness in yeast-free detection systems through several investigative methods. An evaluation of AI systems by professionals indicates their ability to detect financial statement fraud accurately. A quantitative analysis of historical data through AI enables fraud pattern detection according to this study method. The researchers who utilize the qualitative method meet with forensic accountants for their research work. The research delivers both forensic accountants and financial auditors definitive information about the challenges they face and their perspectives toward AI system implementation in audit procedures. The results show that AI is very successful when recognising fraud trends, particularly when using machine learning and deep learning approaches. However, the quality of the data and the settings of the algorithms still have an impact on how reliable AI is. Furthermore, despite ongoing worries about result interpretation and accountability of AI models, qualitative data suggests that auditors generally embrace AI as a tool that speeds up the audit process. According to the study's findings, artificial intelligence (AI) can effectively assist financial audits; however, to improve the validity of fraud detection, it should be used in addition to the analysis of qualified examiners. To increase the accuracy of fraud detection in the future, this study suggests creating more transparent AI models and integrating AI with blockchain technology.

The coffee industry has grown increasingly competitive, making it essential to understand the key drivers of consumer loyalty. This study investigates the influence of brand image, pricing strategies, and product quality on consumer loyalty within the coffee sector. Specifically, it examines how these factors interact and assesses their relative impact on customer retention. A quantitative research design was adopted, employing a cross-sectional survey of 400 coffee consumers in Indonesia. Data were collected through a structured questionnaire and analyzed using Structural Equation Modeling (SEM) in SmartPLS 4.0. The findings indicate that brand image significantly positively affects consumer loyalty (? = 0.35, t = 3.64, p < 0.05), suggesting that a well-established and consistent brand identity enhances customer retention. Pricing strategies also demonstrate a notable influence (? = 0.42, t = 6.63, p < 0.05), highlighting that fair and transparent pricing fosters brand trust. Furthermore, product quality emerges as the strongest predictor of consumer loyalty (? = 0.66, t = 5.35, p < 0.05), emphasizing the importance of sensory attributes such as taste and aroma in driving repeat purchases. Additionally, product quality is found to mediate the relationship between brand image, pricing strategies, and consumer loyalty, reinforcing the necessity of maintaining high product standards. This study contributes to the existing literature on consumer behavior by integrating these three key factors into a unified framework, addressing gaps in previous research. The findings offer practical implications for coffee brands, underscoring the importance of a balanced approach that combines strong branding, strategic pricing, and superior product quality to enhance customer loyalty.

The rapid development of digital technology has transformed marketing strategies, enabling companies to leverage big data analytics to enhance personalized marketing approaches. With the increasing volume of customer interaction data collected from various digital platforms, businesses can now gain deeper insights into consumer preferences and behaviors. This study aims to analyze the impact of big data analytics on personalized digital marketing and evaluate the role of data visualization in improving decision-making processes. The research employs an exploratory approach by analyzing secondary data from multiple digital sources, including e-commerce platforms, social media, and company websites. The study applies data-driven segmentation models and machine learning-based predictive analytics to assess customer engagement and conversion rates. The findings reveal that implementing big data analytics leads to a 48.57% increase in customer engagement and a 132% improvement in conversion rates compared to traditional marketing methods. Furthermore, the integration of data visualization techniques enables marketers to interpret complex consumer patterns effectively, contributing to a 46.67% rise in average transaction value per customer. These results indicate that data-driven personalization significantly enhances marketing effectiveness and customer loyalty. This research contributes to the field by providing empirical evidence on the advantages of utilizing big data analytics in digital marketing and highlighting the importance of interactive dashboards for real-time customer trend analysis. Future research is encouraged to explore the automation of personalized marketing through machine learning algorithms and the optimization of real-time data-driven strategies.

The implementation of e-government has become a key strategy for enhancing public trust in government institutions, particularly in ASEAN countries with varying levels of digital transformation. However, the extent to which e-government adoption influences public trust remains unclear due to disparities in digital infrastructure, public engagement, and policy frameworks. This study examines the relationship between e-government implementation and public trust in ASEAN, identifying key influencing factors. This research employs a quantitative approach using panel data analysis of e-government indices and public trust levels from 2015 to 2023. The dataset includes ten ASEAN countries, covering variables such as the E-Government Development Index (EGDI), digital participation rates, and GDP per capita. The results indicate a significant positive correlation (r = 0.78, p < 0.01) between e-government adoption and public trust, with countries having higher digital participation rates (above 60%) experiencing greater trust improvements. Furthermore, transparency and service reliability (? = 0.64) are more influential than economic factors such as GDP per capita (? = 0.32) in shaping public trust. These findings highlight the crucial role of digital service quality and citizen engagement in fostering public trust beyond economic growth. This study contributes to the literature by emphasizing the need to strengthen digital infrastructure and public participation in e-government initiatives. Future research should explore the socio-political aspects of e-government adoption to provide a deeper understanding of its long-term impact on governance.

As a key enabler of enhancement in financial performance and sustainability, Data-Driven Decision-Making (DDDM) within the digital transformation discourse has been helpful for MSMEs. It is unfortunate that in many developing economies, MSMEs become hindered by informal practices due to limited resources, low digital literacy, and complicated perceptions of analytics tools. In this study, we will investigate the practical application of free digital platforms- Microsoft Excel, Google Data Studio, and Canva Analytics support financial planning and operational efficiency in MSMEs. The research applies a descriptive-applicative approach to create realistic financial data representing the fictitious operations of an MSME-from daily sales, operational costs, to promotional expenses over 30 days. Results show that simple dashboards can lead to some critical insights, for instance, weekly net cash flow that peaked at IDR 2,150,000 in Week 3 and IDR 1,980,000 in Week 5, which means greater operational efficiency. A simulated digital promotion campaign saw a Return On Investment (ROI) of 220%, thereby reinforcing the importance of sales and marketing analytics. Furthermore, the operational expense accounted for about 65% of the total expenses, thus showing room for cost optimization. The findings substantiate the fact that with a little training, MSMEs can now take their financial decisions away from intuition and into data-driven decisions using tools that are freely available online. This study presents a framework that is replicable and scalable in the same resource-constrained environments, with enough practical insights for policymakers and MSME development programs who wish to promote digital financial literacy and performance monitoring.