Price, location, and physical evidence against decision of budget traveler stay at Oyo Rajawali Homestay Palembang

Tourism is closely related to the service and hospitality business. The hospitality business began to shift towards technology in 2015. This led to the emergence of various Virtual Hotel Operators, such as OYO. One of OYO's budget hotels in Palembang which is famous for its strategic location, affordable prices, and adequate physical evidence is Rajawali Homestay. This study aims to determine whether the price, location, and physical evidence affect the budget traveler's decision to stay at the hotel. The data used in this study are primary data by distributing questionnaires to all budget travelers who stay at OYO Rajawali Homestay. The number of samples used was 100 respondents. The analysis technique used is multiple linear regression analysis that was previously tested for validity and reliability. The results obtained are the price, location and physical evidence have a positive and significant impact on budget traveler's decision to stay at OYO Rajawali Homestay. The percentage of influence between the three variables on purchasing decisions is 77% of which 33% is influenced by other variables not examined. This research is expected to be a reference to optimize the variables that influence budget travelers' decisions in choosing hotels

The open banking era:An optimal model for the emergency fund

The COVID-19 outbreak has negatively impacted the income of many bank users. Many users without emergency funds had difficulty coping with this unexpected event and had to use credit or apply to the government for bailout funds. Therefore, it is necessary to develop spending plans and deposit plans based on transaction data of users to assist them in saving sufficient emergency funds to cope with unexpected events. In this paper, an emergency fund model is proposed, and two optimization algorithms are applied to solve the optimal solution of the model. Secondly, an early warning mechanism is proposed, i.e. an unexpected prevention index and a consumption index are proposed to measure the ability of users to cope with unexpected events and the reasonableness of their expenditure respectively, which provides early warning to users. Finally, the model is experimented with real bank users and the performance of the model is analysed. The experiments show that compared to the no-planning scenario, the model helps users to save more emergency funds to cope with unexpected events, furthermore, the proposed model is real-time and sensitive.</p

4.0 technologies in city logistics: an empirical investigation of contextual factors

Industry 4.0 technologies, originally developed in the manufacturing sector, can be purposefully implemented to improve City Logistics (CL) processes by automatizing some of their operational tasks and enabling real-time exchange of information, with the ultimate goal of providing better interconnection among the actors involved. This work aims to identify the main social and economic contextual drivers for investing in the application of Industry 4.0 technologies to urban logistics. To this end, a dataset based on the primary collection of 105 CL projects exploiting the main 4.0 technologies has been built. After that, a regression model has been completed including potential economic, strategic, and demographic determinants of investments in CL 4.0. According to the obtained outcomes, Gross Domestic Product, Foreign Direct Investments, R&D Expenditure, Employment Rate, and Number of Inhabitants are significant contextual factors for the adoption of Industry 4.0 technologies in last mile logistics. The study might support academicians to investigate novel application fields of Industry 4.0 technologies. Also, it can serve as a roadmap for orienting the investments of private organizations and public entities to promote CL innovation and digitalization. Moreover, Industry 4.0 technology providers might find this study interesting to uncover prospective business sectors and markets. Future research efforts will analyse the impacts of internal business factors on CL 4.0 and the satisfaction levels of urban logistics stakeholders

Simulation Modeling for Robust and Just Public Policy Decision-Making

Public policy decision-making is challenging for several reasons. First, the outcomes of pulling a public policy lever are often deeply uncertain because of the complexity of the social and physical systems involved. Second, even if outcomes can be predicted, there are multiple points of view to consider, and the same outcome can be viewed anywhere from very positively to very negatively by different stakeholders. Because of this, public policy decisions should be both robust and just. Robustness helps with the uncertainty in outcomes and justice helps with differences in worldview. In this dissertation, I employ system dynamics and agent-based simulation modeling techniques to assist decision-making in two public policy contexts: COVID-19 non-pharmaceutical interventions and police funding. I also develop a framework in which both robustness and justice can be handled simultaneously in complex public policy problems