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This study aims to analyze the effect of servicescape on revisit intention at fine dining restaurants in Malang City, by reviewing the mediating role of customer satisfaction and restaurant reputation. The restaurant industry, especially fine dining, faces major challenges due to tight competition triggered by the increasing number of restaurants in Indonesia. This phenomenon requires restaurants to offer more than just food and drinks, namely an exclusive dining experience, involving servicescape elements such as ambiance, functionality, and signs that affect customer satisfaction and restaurant reputation. This study found that servicescape has a positive and significant effect on revisit intention (?=0.379; p<0.001), customer satisfaction (?=0.156; p<0.001), and restaurant reputation (?=0.745; p<0.001). Elements such as interior design, lighting, aroma, cleanliness, and service create a memorable experience for customers, strengthen the restaurant's reputation, and increase customer loyalty. In addition, mediation by customer satisfaction and restaurant reputation showed significant results (p<0.001 and p=0.020), strengthening the relationship between servicescape and revisit intention. These results confirm that a good restaurant reputation not only increases consumer trust but also strengthens their perception of the quality of the experience offered. In the context of fine dining, servicescape is key to creating a high-end experience that meets customer expectations, thereby increasing return intentions and building loyalty. This study provides important insights for fine dining restaurant managers on how servicescape elements and restaurant reputation can be optimized to create a competitive advantage amidst increasingly fierce industry competition.

This study analyzes the effect of sustainable investment awareness and risk perception on students' interest in green finance instruments, with financial literacy as a mediating variable. A quantitative approach was used, involving 180 student respondents in the Special Region of Yogyakarta, who were selected through a purposive sampling technique using an online questionnaire via Google Forms. Data analysis was conducted using the SEM-PLS method through SmartPLS version 4.1.1.2. The results showed that sustainable investment awareness, risk perception, and financial literacy positively and significantly affect student investment interest in green finance. In addition, financial literacy acts as a significant mediator in the relationship between sustainable investment awareness and risk perception on interest in investing in green finance. This study contributes to developing literature on sustainable investment behavior among the younger generation, especially university students, by integrating financial literacy variables as a mediating mechanism. The findings expand the understanding of how psychological factors and financial knowledge influence investment decisions in sustainability.  

The objective of this study was to explore the factors influencing the intention to use Islamic digital banking in Indonesia, focusing on the roles of performance expectancy, effort expectancy, social influence, facilitating conditions, habit, hedonic motivation, price value, and religiosity. Data were collected from a sample of 434 respondents in January 2024 through an online survey. The study utilized a research model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The data were analyzed using structural equation modeling (SEM). The results indicate that effort expectancy, social influence, habit, price value, and religiosity significantly affect the behavioral intention to use Islamic digital banking, while performance expectancy, facilitating conditions, and hedonic motivation did not show significant effects. These findings highlight the importance of religious and ethical considerations, ease of use, and social factors in shaping the adoption of Islamic digital banking services. The study offers valuable insights for practitioners and policymakers, emphasizing the need for tailored strategies that promote the use of Islamic digital banking in Indonesia.

This study looks at the effect of accounting flexibility on earnings quality in public companies listed on the Indonesia Stock Exchange during the period 2016 to 2019. This study uses two models to measure earnings quality, namely ERC and earnings persistence. The results showed that accounting flexibility, namely the limitations of companies to carry out accrual earnings management, can reduce earnings quality, as shown by the abnormal accrual model which shows a significant positive coefficient direction, and for the ERC model and earnings persistence which shows a negative and significant coefficient direction. The higher the level of accrual earnings management up to a certain limit point, the more incentive company managers will have to increase accounting flexibility and switch to real earnings management (Xu and Yang, 2013; Masri, 2018). Theoretical contributions show that the higher the level of accrual earnings management up to a certain limit point, the more incentive company managers will have to increase accounting flexibility and switch to real earnings management (Xu and Yang, 2013; Masri, 2018; Purba, Surya, Joshi & Tiagy, 2023). The Implications of this research show that accounting flexibility is carried out when abnormal accruals reach the highest limit point reflected in accounting choices in net operating assets in the balance sheet, so in this case it results in a low level of earnings informativeness and earnings persistence. 

This study investigates the application of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models for predicting Bitcoin prices, with the objective of enhancing investment approaches. Given Bitcoin's significant volatility and inherent risk, accurate price forecasting is vital for making strategic decisions. The research evaluates model performance by analyzing Bitcoin price data spanning May 25, 2020, to May 25, 2024, employing quantitative methodologies like experimental design and predictive analysis. Results indicate the LSTM model achieves superior predictive accuracy over the RNN model, demonstrated by reduced Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) figures. Additionally, the research delves into how these models affect the Dollar Cost Averaging (DCA) investment strategy, offering perspectives on the advantages and drawbacks of using machine learning to optimize cryptocurrency investments. 

This study aims to empirically analyze the three key pillars of university financial sustainability—revenue-generating activities, good financial governance, and IT capabilities—with performance, age, and size as control variables. Using a survey method targeting private universities in Indonesia, data were collected from financial leadership representatives across 14 provinces, yielding 627 valid responses. The data were analyzed using structural equation modeling (SEM) to examine the relationships among variables. The results reveal significant positive effects of good financial governance on financial sustainability, IT capabilities on financial sustainability and revenue-generating activities on financial sustainability. Furthermore, revenue-generating activities have a strong positive impact on both good financial governance and IT capabilities. The analysis also identifies IT capability as a mediating variable between revenue-generating activities and financial sustainability, as well as between good financial governance and financial sustainability. These findings underscore the strategic role of technology-based governance in enhancing the financial resilience of higher education institutions. Accordingly, the study recommends strengthening the three pillars of financial sustainability—revenue diversification, sound governance practices, and robust IT capabilities—to ensure long-term institutional sustainability.

This research investigates the connection between governmental assistance and business outcomes, incorporating the intermediary functions of business networking and digital marketing. Data were gathered through a questionnaire, which was distributed to 152 participants who represent MSMEs in the culinary industry in Surabaya City. The examination of this data was performed utilizing the SEM-PLS method, facilitated by SmartPLS 3.0 software. Findings from the study reveal that governmental support significantly enhances both business networking and digital marketing. Additionally, business networking and digital marketing significantly improve business performance. The research additionally illustrates that both business networking and digital marketing play a significant role in mediating the connection between government support and the success of culinary MSMEs in Surabaya City.

This study examines the impact of Foreign Direct Investment (FDI) on economic growth in South Africa over the period 2000 to 2023. An Ordinary Least Squares (OLS) regression model was employed to assess the relationship between FDI inflows and Gross Domestic Product (GDP), while controlling for key macroeconomic variables. The findings reveal that FDI exerts a significant positive influence on economic performance, with a 1% increase in FDI inflows associated with a 1.93% rise in GDP. However, high inflation exerts a detrimental effect, with a coefficient of -1.99, highlighting the importance of macroeconomic stability. The analysis also indicates that trade openness contributes positively to economic growth, while political risk remains a substantial deterrent to investment, as shown by a coefficient of -3.11. These findings underline the importance of maintaining a stable political environment, managing inflation effectively, and enhancing trade liberalisation to maximise the benefits of FDI. Policy recommendations include strengthening regulatory frameworks and fostering trade openness.

Although halal tourism has attracted growing scholarly attention, existing studies predominantly emphasize market potential, tourist perceptions, and promotional strategies. To date, no research has rigorously examined the probability of Muslim tourists’ visit decisions by integrating halal attributes, religiosity, and attitudes within Indonesia’s five Super Priority Destinations (SPDs). Addressing this gap, the present study applies a quantitative design using a probabilistic logit model based on survey data from Muslim tourists. The findings reveal that the completeness of halal attributes substantially increases the probability of destination choice, while religiosity and positive tourist attitudes further strengthen the likelihood of visits. These results demonstrate that halal tourism decisions can be modelled as a probabilistic function of destination attributes rather than solely as cultural or religious expressions. The study highlights the critical role of Destination Management Organizations (DMOs) in institutionalizing halal standards, thereby enhancing visit probabilities, boosting competitiveness, and ensuring sustainable tourism development.

The rapid growth of the digital information ecosystem, characterized by cross-channel conversations, the spread of misinformation, and the increasing vulnerability of corporate reputation, highlights the urgency of developing strategic communication systems powered by data and artificial intelligence for early detection and crisis mitigation. This study therefore focuses on examining the role and effectiveness of the Digital Engagement Center (DEC) at PT United Tractors Tbk (UT), while also exploring the experiences, practices, and perceptions of communication and management practitioners regarding its use in strategic decision-making. Although the literature on media monitoring, AI-driven communication, and early warning systems is extensive, research on their application in the form of an integrated system such as DEC remains limited, particularly in developing countries like Indonesia. This makes the study relevant in addressing this gap and providing both theoretical and practical contributions. In the employing qualitative intrinsic case study design, data were collected through interviews, observations, and document analysis, and analyzed using thematic analysis. The findings indicate that DEC is an effective AI-enabled early warning system for real-time analysis, classification, and prediction of issues while requiring validation by a human for shifting communication toward a proactive rather than reactive model. Effectiveness is further evidenced by early detection of issues such as the internal PPE case, whereby supported automated notifications, monthly sentiment reports, and the AMEC model underscore the significance of timely imports, interpretation, and support within the organization. Moreover, DEC serves as a strategic instrument for leaders and communication practitioners to design narratives, strategies, and data-driven policies through its interactive dashboard and escalation SOPs, thereby strengthening public trust and fostering a more adaptive and collaborative organizational communication culture.