This paper examines how generational cohorts influence households’ choices regarding housing tenure and considers the diverse preferences and socio-economic factors that shape decisions—using survey data on 425 families in Ho Chi Minh City, Vietnam. The data is analyzed using a multinomial logit model. The results indicate that generation significantly positively affects housing tenure choice, such that, unlike older cohorts, younger generations are more inclined to rent houses as their preferred housing option. Furthermore, permanent income plays a significant role in shaping housing tenure choices. On the other hand, social-economic variables, namely education, gender of references, family structures, and area of residence, were significant in influencing housing tenure decisions. This finding highlights the importance of housing policies prioritizing affordable and accessible rental options in large cities.

Several models have been developed to predict financial difficulties and corporate bankruptcy. In this research various models were employed, including the Altman model (referred to as the Z-Score), the Springate model (known as the S-Score), the Zmijewski model (designated as the X-Score), and the Grover model (referred to as the G-Score). These techniques serve the purpose of evaluating the likelihood of encountering financial difficulties, which in turn determines the probability of PT Garuda Indonesia (Persero) Tbk going bankrupt. The study utilized secondary data sourced from financial statements spanning the years from 2020 to 2022. The application of the Altman model for bankruptcy prediction revealed that PT Garuda Indonesia (Persero), Tbk experienced financial distress throughout the period from 2020 to 2022. According to the Springate model, the company was in a state of distress and declared bankruptcy in 2020 and 2022, while 2021 fell into a grey area. The Zmijewski model indicated that the company was on the brink of bankruptcy, with financial difficulties and a potential risk of bankruptcy within the next three years. Grover's model predicted bankruptcy for the company in 2020 and 2022, but indicated safety in 2021. Notably, the Taffler model emerged as the most accurate in forecasting bankruptcy, boasting a 100% accuracy rate with no errors. Meanwhile, the Zmijewski model achieved an 81.25% accuracy rate with an error rate of 18.75%, and the Springate model exhibited the lowest accuracy in bankruptcy prediction, scoring only 12.50% accuracy with an error rate of 87.50%.

This study applies the Motivation - Opportunity – Ability model on social networking platforms to study tourists’ selection of green hotels for their participation in sports events. The study analyzed the intention to choose green hotels of 324 Vietnamese tourists staying at 4- and 5-star hotels with green labels in Vietnam. The research results show that the Motivations, Opportunity, and Ability factors in using social networks have a positive impact on the belief in green hotels, thereby promoting the intention to choose green hotels when tourists participate in sports events in Vietnam. This study also provides some practical implications for green hotels in using social networks to enhance the trust and intention to choose a hotel of tourists participating in sports events. At the same time, the study also proposes some suggestions for directing human resources training towards the organization of tourism-integrated sports events to create responsible tourism products.

Condition-based maintenance (CBM) of industrial machines depends on the continuous, real-time monitoring of the machine’s operational condition via smart sensors attached to different components on the machine. The problem of regularly spaced missing data, which can occur due to a variety of hardware or software issues, is one that is often overlooked in the literature surrounding CBM in industrial machines. Such missing data can cause issues in interpreting the true operational state of the machine, which can reduce the effectiveness of CBM processes. In this paper, we examine the capabilities of five data imputation techniques for handling this regular missing data and examine the impact these techniques have on machine learning (ML) classification algorithms for machine fault diagnosis. We examine the following techniques: simple mean imputation, mean imputation with outliers removed, best and worst-case imputation, and previous day imputation. Each of these methods is configured with the specific parameters that they will only consider data from the previous 24 hours, to ensure that the data is recent, and adequately represents the current status of the machine. The efficacy of each method at accurately reconstructing the missing data and the impact they have on ML classification is recorded in the results. The models are evaluated on a real-world dataset and are evaluated on a variety of common performance metrics.

Sukawati Art Market is one of the distinguished art markets in Bali for decades, which is also close to the Sukawati Public Market. The government is making efforts to revitalize the Sukawati Public Market to become the Sukawati Art Market, while the Sukawati Public Market is being relocated to a new location far from settlements. So that during the two years of the market revitalization and reallocation program it was quiet and triggered the emergence of traders around the old market area which disturbed public order. The purpose of this research is to analyze the polemic of stakeholders objectives regarding the Sukawati Market revitalization and relocation program, using MACTOR analysis. The results of the analysis show that local government, village government, and custom village are regulatory actors, while traditional markets' sellers are the main target of the project and serve as mediation to other actors. Actors still have polemic interests, especially traders outside the market. Therefore the local government needs to control, educate, and make innovation to facilitate the sellers around outside the traditional market. The revitalization program and market relocation are successful, the traditional markets’ sellers return to selling, and the market will be busy again.

This paper proposes an original view to determine the effect of P2P loans on MSME and non-MSME bank loans after the COVID-19 pandemic as a whole and then focuses on the island of Java (more developed areas) and outside Java (areas which are still undeveloped). The approach used in this study uses panel data regression from 33 provinces in Indonesia during Jan-Dec 2022 after the COVID-19 pandemic. The results of this study confirm that P2P lending is not a disrupter for bank credit, the details of the results are: (1) P2P lending has a significant positive effect on overall MSME banking credit, but has no significant effect on overall non-MSME banking credit; (2) P2P lending has no significant effect on MSME banking credit in Java, but has a significant positive effect on non-MSME banking credit in Java after the COVID-19 pandemic; (3) P2P lending has a significant positive effect on MSME banking credit outside Java after the COVID-19 pandemic, but has no significant effect on non-MSME banking credit in Java post the COVID-19 pandemic.

In the contemporary business landscape, effective interpretation of customer sentiment, accurate demand forecasting, and precise price prediction are pivotal in making strategic decisions and efficiently allocating resources. Harnessing the vast array of data available from social media and online platforms, this paper presents an integrative approach employing machine learning, deep learning, and probabilistic models. Our methodology leverages the BERT transformer model for customer sentiment analysis, the Gated Recurrent Unit (GRU) model for demand forecasting, and the Bayesian Network for price prediction. These state-of-the-art techniques are adept at managing large-scale, high-dimensional data and uncovering hidden patterns, surpassing traditional statistical methods in performance. By bridging these diverse models, we aim to furnish businesses with a comprehensive understanding of their customer base and market dynamics, thus equipping them with insights to make informed decisions, optimize pricing strategies, and manage supply chain uncertainties effectively. The results demonstrate the strengths and areas for improvement of each model, ultimately presenting a robust and holistic approach to tackling the complex challenges of modern supply chain management.

This research aims to gain an overview of the evaluation results and the many challenges to implementing machining competency test certification (CTC) in Vocational High Schools (VHS). The research approach to evaluating this program is a qualitative method using the analysis of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The program evaluation design in this study uses the Assessment Evaluability and Performance Monitoring (AEPM) model, which has four evaluation components: Context, Inputs, Activities, and Performance Monitoring. The subjects involved in data collection through the distribution of questionnaires of five VHS in the Special Capital Region of Jakarta. The technique of determining all subjects using the Purposive Sampling technique. The results showed the level of effectiveness of the implementation of the machining CTC program. Some dimensions need to be strengthened, especially for the “less and “very lacking” category. Finally, the approach presented in this research using the AEPM model is a step forward in the analysis of the CTC program. This approach can easily be replicated in other countries with similar aims as this research.