The future of the internet is moving toward decentralization, with decentralized networks and blockchain technology playing essential roles in different sectors. Decentralized networks offer equality, accessibility, and security at a societal level, while blockchain technology guarantees security, authentication, and openness. Integrating blockchain technology with decentralized characteristics has become increasingly significant in finance; we call this “decentralized finance” (DeFi). As of January 2023, the DeFi crypto market capitalized USD 46.21 billion and served over 6.6 million users. As DeFi continues to outperform traditional finance (TradFi), it provides reduced fees, increased inclusivity, faster transactions, enhanced security, and improved accessibility, transparency, and programmability; it also eliminates intermediaries. For end users, DeFi presents asset custody options, peer-to-peer transactions, programmable control features, and innovative financial solutions. Despite its rapid growth in recent years, there is limited comprehensive research on mapping DeFi’s benefits and risks alongside its role as an enabling technology within the financial services sector. This research addresses these gaps by developing a DeFi classification system, organizing information, and clarifying connections among its various aspects. The research goal is to improve the understanding of DeFi in both academic and industrial circles to promote comprehension of DeFi taxonomy. This well-organized DeFi taxonomy aids experts, regulators, and decision-makers in making informed and strategic decisions, thereby fostering responsible integration into TradFi for effective risk management. This study enhances DeFi security by providing users with clear guidance on existing mechanisms and risks in DeFi, reducing susceptibility to misinformation, and promoting secure participation. Additionally, it offers an overview of DeFi’s role in shaping the future of the internet.

The tourism sector plays a crucial role in the global economy, encompassing both physical infrastructure and cultural engagement. Indonesia has a wide range of attractions and has experienced remarkable growth, with Bali as a notable example of this. With the rapid advancements in technology, travelers now have the freedom to explore independently, while online travel agencies (OTAs) serve as important resources. Reviews from tourists significantly impact the service quality and perception of destinations, and text mining is a valuable tool for extracting insights from unstructured review data. This research integrates multiclass text classification and a network analysis to uncover tourists’ behavioral patterns through their perceptions and movement. This study innovates beyond conventional sentiment and cognitive image analysis to the tourists’ perceptions of cognitive dimensions and explores the sentiment correlation between different cognitive dimensions. We find that destinations generally receive positive feedback, with 80.36% positive reviews, with natural attractions being the most positive aspect while infrastructure is the least positive aspect. We highlight that qualitative experiences do not always align with quantitative cost-effectiveness evaluations. Through a network analysis, we identify patterns in tourist mobility, highlighting three clusters of attractions that cater to diverse preferences. This research underscores the need for tourism destinations to strategically adapt to tourists’ varied expectations, enhancing their appeal and aligning their services with preferences to elevate destination competitiveness and increase tourist satisfaction.

The growth of the tourism industry in Southeast Asia, particularly in Indonesia, Thailand, and Vietnam, establishes the region as a leading global tourism destination. Numerous studies have explored tourist behavior within specific regions. However, the question of whether tourists’ experience perceptions differ based on their cultural backgrounds is still insufficiently addressed. Previous articles suggest that an individual’s cultural background plays a significant role in shaping tourist values and expectations. This study investigates how tourists’ cultural backgrounds, represented by their geographical regions of origin, impact their entertainment experiences, sentiments, and mobility patterns across the three countries. We gathered 387,010 TripAdvisor reviews and analyzed them using a combination of advanced text mining techniques and network analysis to map tourist mobility patterns. Comparing sentiments and behaviors across cultural backgrounds, this study found that entertainment preferences vary by origin. The network analysis reveals distinct exploration patterns: diverse and targeted exploration. Vietnam achieves the highest satisfaction across the cultural groups through balanced development, while Thailand’s integrated entertainment creates cultural divides, and Indonesia’s generates moderate satisfaction regardless of cultural background. This study contributes to understanding tourism dynamics in Southeast Asia through a data-driven, comparative analysis of tourist behaviors. The findings provide insights for destination management, marketing strategies, and policy development, highlighting the importance of tailoring tourism offerings to meet the diverse preferences of visitors from different global regions.

In 2008, the Lehman Brothers’ bankruptcy, accumulated from the global financial crisis, proved a unique role of the highly interconnected financial entities. Shocks in a bank might trigger loss, induce spillovers, provoke a contagion shock spreading to other entities, trigger the whole banking system to collapse, and ultimately unsettle the worldwide economy. Therefore, evaluating financial stability through a system-wide network approach provides more adequate knowledge than evaluating a bank as an individual. In this approach, individual banks and their transaction activities are modeled into a transaction network, forming a network topology. Financial shocks are generally detected through various macro procedures, such as outstanding external debt and uncontrolled transaction deficits. This study proposes financial shock detection from a macro and micro perspective by exploring the effect of disruption on transaction network structure. We investigate the most changing triadic motif as a crisis predictor from a micro perspective due to the crisis period. The case study is the transaction network structural shift under the 2008 crisis in Indonesia, where the observations were performed from the pre-crisis to the post-crisis period. We discovered a motif with the significant changes as the underlying financial crisis predictor. This scenario provides support for the financial system’s stability control.

Given the rapid evolution of information technology increases the number of individual tourists who choose to enjoy free travel without depending on guidebooks and tour agency services. Information sharing activities continue without limitation facilitate most tourists to explore and independently decide their destinations based on online traveler review pages. This freedom generates an increasingly complex tourist visiting pattern. On other side, the abundance of data provides a new approach in analyzing this visiting pattern. We processed 214,228 reviews written by tourists all over the world regarding tourist destinations in Bali, Indonesia. This research analyzes the online traveler review data through association rule mining techniques to detect pairs of tourist’s destinations and mapped them through the network analysis approach. We separate our exploration by tracing the tourist visiting movement as the underlying factor to understand tourist visiting behavior. We discovered the most popular tourist destination pair in one trip is Tegalalang Rice Terrace and Sacred Monkey Forest Sanctuary, and there are differences in tourist visit preferences for each tourist origin continent but still have identical main choices. This research contributes to supporting efficient mobility in tourism management by providing tourism destination networks insight.

In the credit acceptance process, the financial institutions analyze the borrowers' creditworthiness through their demographic data based on the 5C principle; character, capacity, conditions, capital, and collateral. However, the legacy credit scoring methods have drawbacks, including not having an excellent credit reputation as it is limited to the structural nature of demographic data. We construct a credit scoring model by combining the demographic element and adding two social media elements; content and network. The content considers creditworthiness by assessing borrowers' posts, which consist of opinions and conversations on social media. In comparison, the network considers borrowers' connectivity to their social community. The paper proposes a new credit scoring model better to represent the quality of borrowers' characteristics and behavior. The data is collected from LinkedIn, which is suitable to represent the professional network. The proposed model has been verified through expert judgment, including the credit providers, and has been simulated through a machine learning approach to automate credit acceptance decisions.