Using technology, such as human-machine interfaces, can enhance operational performance processes and increase the flexibility of the supply chain. Human-machine interfaces can produce operational control systems quickly and accurately. The research aims to explore the impact of human-machine interface on operational performance through collaborative decision making and supply chain agility. The sample criteria are the manufacturing companies with over 20 employees in Indonesia. The questionnaires were distributed offline (76 respondents) and online through Google Forms (427 respondents), so 503 questionnaires were valid—data processing using SmartPLS software version 4.0. The study results showed that the human-machine interface technology positively affects collaborative decision-making, supply chain flexibility, and operational performance with coefficients of 0,559, 0,490, and 0,340, respectively. Collaborative decision-making involving customer partners in planning decisions and communicating decisions with external partners influences supply chain flexibility by a coefficient of 0.375 and operational performance by 0.149. Moreover, supply chain flexibility with flexible planning and production processes and flexible labor placement influences operational performance by a coefficient of 0.381. The practical contribution of research enlightens company managers to build integrated systems and automation. It encourages top management and owners to think about investing in machines with high automation in the economy. Besides, these findings enrich the theoretical background in supply chain management and the resource-based view.

The shift in learning communication patterns in higher education emerged along with the end of the pandemic, accelerating the transformation of digital communication. This study aims to explain the digital communication variable of the CMC model, the digital technology variable of the technology and determinism model, the SIKA LMS variable of the technology acceptance model (TAM), the discipline variable of the Attitude and Behavior theory model, and the graduation variable of the Media Dependence model as part of the communication using the computer. Quantitative research method with SEM PLS analysis. Furthermore, the data collection technique was used with a proportionate stratified random sampling population of 3416 students and a sample of 302 active students working on the final project. The analysis in this study uses Structural Equation Modeling, with 5 variables, namely digital communication (X1), digital technology (X2), SIKA LMS (X3), student discipline (X4) and timely graduation (X5), conducting outer model tests, goodness of fit models, and model testing inner. Results show that the discipline variable obtained the highest average of 4.22. In contrast, the digital communication transformation obtained a very significant direct influence on the shift in learning patterns for timely graduation.

In recent years, fintech has received much attention due to the introduction of new technologies in banking and electronic payment. For financial service providers to compete in the industries, they should apply the business model as a conceptual framework to improve performance. The current research is exploratory and tries to identify the factors influencing fintech design in electronic payment using the Osterwalder business model. This study aims to integrate three methods named DEMATEL, ISM, and ANP from MCDM techniques. To analyze the identified factors affecting the design of fintech in electronic payment, the indicators were examined in terms of influence and effectiveness by the DEMATEL method, then the levels of influence and effectiveness of the factors were investigated using the interpretive structural modeling method. Finally, the network analysis method was used to prioritize the factors. The findings showed that recognizing and identifying electronic payment customers is the most effective among the factors, and determining the type of relationship with customers is the most impressionable factor. In addition, after ranking the factors, the type of relationship with customers was the first rank, and the criteria of the company's cost structure and revenue streams were determined as the second and third, respectively.