This research explores the mediating role of the value perceived and received by consumers (customer-perceived value) in the context of brand experience and relational benefits, as well as their impact on consumers' willingness to pay more. Additionally, this research investigates consumers' desires to share experiences via social media (e-WOM) when consuming these products and services with their relatives. Using a purposive sampling technique, we obtained responses from 256 consumers of fast-food restaurants spread across Jakarta. The research results prove that relational benefits and brand experience are important in building consumer perceived value, ultimately influencing the emergence of positive e-WOM and the willingness of fast-food restaurant consumers to pay more.

This study aimed to address the underexplored area of the adoption of Business-to-Consumer (B2C) Commerce by Small and Medium Enterprises (SMEs). In addition, this study specifically focused on factors influencing B2C adoption by SMEs, its impact on marketing performance, and potential strategies for optimization. Recognizing the scarcity of quantitative studies on digitization's impact on SMEs, this study emphasized the need for a systematic understanding of these enterprises’ responses to e-commerce adoption. In line with the Technology-Organization-Environment (TOE) framework, the primary focus was on the continuous evaluation and optimization of e-commerce platforms, including AI integration, within core marketing strategies. Based on customer tech-savviness in the environmental dimension, adapting e-commerce strategies ensured a comprehensive approach in the evolving technological landscape. While providing valuable insights, several limitations, such as context-specific findings and potential response bias due to self-reported data were also identified. Consequently, future investigations were advised to include comparative studies between e-commerce-adopting and conventionally operating organizations, as well as explore perspectives of e-commerce users and consider industry-specific variations. This was pertinent because investigating e-commerce implementation in emerging technologies and platforms could offer insights into the dynamic landscape of digital business. In conclusion, this study contributed to the cognition of B2C Commerce adoption in SMEs, offering practical insights and strategic recommendations for leveraging technology to enhance marketing performance and overall business growth.

The aim of this research is to analyze the influence of supply chain and digital marketing in increasing the acceleration of repositioning in the Millennial Generation and its implications for the sustainability of cooperatives in the city of Palangka Raya where the study was executed. The sample in this study was 250 respondents consisting of the millennial generation (born 1981-1996) and Generation Z (born 1997-2012), using the Stratified random sampling technique. Data collected through questionnaires was then analyzed using SEM-PLS. The findings of the research and data analysis indicate that: Supply Chain and Digital Marketing directly have a positive and significant effect on the Acceleration of Repositioning in the Millennial Generation in Palangka Raya City; Apart from that, Supply Chain, Digital Marketing and Accelerated Repositioning to the Millennial Generation directly have a positive and significant impact on the Sustainability of Cooperatives in the City of Palangka Raya; Accelerating Repositioning in the Millennial Generation was able to partially mediate Supply Chain and Digital Marketing towards Cooperative Sustainability in Palangka Raya City, Central Kalimantan Province, Indonesia. So, it can be concluded that to improve Cooperative Sustainability among the Millennial Generation in Indonesia, important factors that must be improved include Supply Chain, Digital Marketing, and Accelerated Repositioning.

This paper aims to explore the role of service innovation and competitive advantage as a mediator of product innovation on marketing performance of small and medium enterprises (SMEs) manufacturing firms in Indonesia. The sample size used in the research was 180 SMEs and the sampling technique is area (cluster) sampling. Furthermore, the sample criteria or respondents, whose researchers' recommendations for filling in the research instrument are managers and can represent company owners. Findings provide empirical evidence that service innovation on marketing performance; competitive advantage on marketing performance; product innovation on service innovation; and product innovation on competitive advantage have positive and significant effects, except the influence of competitive advantage on marketing performance which was not significant.

The aim of the research is to analyze the influence of viral marketing and customer reviews on purchasing decisions with consumer trust as a mediating variable. In marketing theory, purchasing decisions can be improved by mediating consumer trust, so that products can be sustainable. The study was conducted on Indonesian iced tea products, which are typical tea leaf products from Indonesia. The type of research used in this research is quantitative and analyzed using PLS. 105 respondents in the research were customers of Indonesian iced tea products in Palangka Raya City. The research results show that viral marketing, customer reviews mediated by consumer trust variables had a significant positive effect on purchasing decisions, viral marketing had a significant positive effect on purchasing decisions, viral marketing had a significant positive effect on consumer trust, customer reviews had a significant positive effect on purchasing decisions, customer reviews had a significant positive effect on consumer trust and consumer trust had a significant positive effect on purchasing decisions for Indonesian Iced Tea products in the city of Palangkaraya.

The purpose of this study was to determine the role of subjective norms as a mediation between the relationship opportunity to donate and agreeableness personality on intention to donate. This research includes a quantitative approach with a survey method distributed to 400 participants of BPJAMSOSTEK East Java Province using a Likert scale. Data analysis using SEM using WarpPLS 6.0 software. The results of this study state that subjective norms can be a link between the opportunity to donate and in accordance with the intention to donate. This can increase the intention to donate for BPJAMSOSTEK participants. The increased intention to donate owned by participants will increase participants who donate.

Recently, the development of cross-laminated timber (CLT) products that can substitute large-dimensional wood for structures and reduce waste of wood products with small dimensions and pieces continues to be developed, and demand from industries continues to increase. Therefore, this study aims to measure and analyze the mechanical properties of cross-laminated timber (CLT) products designed with a typical arrangement of the Lampung region, Indonesia, called a screen arrangement. Measurement and analysis of mechanical properties include measuring load tests and analyzing moments and stiffness of CLT products intended for floor and beam structures. The screen-type wood arrangement used in the product consists of an inner screen arrangement, an outer screen arrangement, and a standing screen arrangement with polyvinyl acetate adhesive. Load test measurements were carried out using a universal testing machine (UTM) and moment and bending stress analysis using engineering mechanics. Generally, the results show that CLT products with standing screens have smaller mechanical properties than those intended for floors and beams. This research shows that the typical CLT arrangement of the Lampung region (Indonesia), in the form of a screen, has strong mechanical properties and characteristics that can be compared with other arrangements.

Losses experienced by the Indonesian government due to floods are predicted. It is because of the significance of population growth, closure of water catchment areas, and climate change in many regions in Indonesia. The government has tried to reduce the risk but faces insufficient funds. Therefore, new innovative funding sources are essential to overcome these limitations. One way to obtain it is through issuing Flood Catastrophe Bonds (FCB). Unfortunately, Indonesia has had no FCB price estimate until now. On the basis of this problem, this study aims to estimate the FCB price in Indonesia. The primary method used is the approximation method of the aggregate loss distribution. This method can compute the aggregate flood loss cumulative distribution function value faster. The FCB fair price estimation results are cheap because the risk of the instrument is significant. This significant risk is also proportional to the large return. Finally, further analysis shows that in Indonesia, the attachment point of the FCB has a relationship that is in line with the price, while the term of FCB does not. This research is expected to assist the Indonesian government in determining the fair price of FCB in Indonesia. This research can assist the investors in choosing FCB based on expected return, attachment point, and the term they want.

COVID-19 detection through radiological examination is favoured since it is fast and produces more accurate results than the laboratory approach. However, when it has infected many people and put a strain on the healthcare system, the need for fast, automatic COVID-19 detection in patients has become critical. This study proposes to detect COVID-19 from chest X-ray (CXR) images with a machine learning approach. The main contributions of this paper are to compare two powerful deep learning models, i.e., convolutional neural networks (CNN) and the combination of CNN and Long Short-Term Memory (LSTM). In the combination model, CNN is recommended for feature extraction, and COVID-19 is classified using the features of LSTM. The dataset used in this study amounted to 4,095 CXR images, consisting of 1,400 images of normal conditions, 1,350 images of COVID-19, and 1,345 images of pneumonia. Both CNN and CNN-LSTM were executed in a similar experimental setup and evaluated using a confusion matrix. The experiment results provide evidence that the CNN-LTSM is better than the CNN deep learning model, with an overall accuracy of about 98.78%. Furthermore, it has a precision and recall of 99% and 98%, respectively. These findings will be valuable in the fast and accurate detection of COVID-19.

Giving credit to debtors can pose a default risk. This risk arises because of an error in analyzing the credit risk rate of the debtor. Therefore, this study aims to design a framework for analyzing the credit risk rate of debtors so that the default risk can be reduced. This framework is created using the integration of factor analysis and Tsukamoto’s fuzzy logic method. This integration method can group many credit assessment variables into several decisive factors. In addition, the integration method can estimate credit risk rate firmly based on the α-predicate of each basic rule. This analytical framework is simulated on credit application data at a Rural Bank, in Indonesia. The simulation results show that there are three factors and one variable to measure the credit risk rate, namely: factor 1 represents repayment capacity, business length, working capital, and liquidity value; factor 2 represents the age and the difference between the granted and the proposed loan amount; factor 3 represents the stay length, character, and credit history; and one variable represents a dependent number. This research is expected to help credit institutions measure the credit risk rate in making credit decisions for prospective debtors.