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The global industry has been significantly affected by Covid 19, resulting in a reduction in organizational structure, an increase in demand for raw materials and delivery services, and a rise in prices that negatively affect the manufacturing industry. The department responsible for managing these issues is the purchasing department, which will experience a surge in workload and work pressure for its employeesImproving employee performance is a challenge for companies to maintain survival. The thing that needs to be considered is the workload of employees which affects work stress and ultimately causes a decrease in employee performance. The main objective of this research is to examine the effect of workload on performance through work stress. Research using quantitative research methods. The questionnaire has been tested for validity and reliability and distributed to 110 purchasing employees. SmartPLS 3 is used to process data from research results. The results of the study are that there is a negative effect of workload on performance, there is a positive influence of workload on work stress, there is a negative effect from work stress on employee performance, and there is a negative effect of workload on performance through work stress on purchasing employees. The results of this study can be used by companies to improve their performance, especially from factors related to workload and employee work stress.

The accelerated advancement in technology resulted to the appearance of Self Service Technology. One form of self-service technology in the banking sector is the presence of mobile banking. This study aims to examine the influence of five factors toward the reusing of Mobile Bank Central Asia (BCA) in Bandung. Those factors used in this study were the extension of Technology Acceptance Model (TAM) constructs, i.e perceived usefulness, perceived ease of use, perceived credibility, customer awareness and social influence. Data was collected through distributed questionnaire to 100 respondents who used Mobile BCA by using judgment sampling. Multiple linear regression technique was employed to investigate the influence among variables. This study empirically concluded that consumer intention to reuse BCA mobile services was positively influenced by social influence, customer awareness and perceived ease of use. On the other hand, perceived usefulness and perceived credibility did not influence the intention of reusing Mobile BCA in Bandung. Further study is suggested to use probability sampling technique to cover the real voice of mobile banking user in Bandung and to explore the lack influence of perceived usefulness and perceived credibility toward reusing of Mobile BCA.

Bandung is one of the cities with a high culinary appeal. However, there are several problems found such as a decrease in sales due to the Covid-19, not conducting the market surveys, and tight competition in the culinary industry. One of the concepts that can solve these problems is a human-centered approach called Design Thinking. Hence, it is needed to measure whether the concept is already implemented or not. The result of this measurement can define the further plan to solve the problems stated. Design thinking is the rationale in the process of transforming a creative mindset into an innovation. This study measures the implementation of the Design Thinking concept in the culinary subsector in Bandung which includes five stages, namely: empathize, define, ideation, prototype, and test. The data collection is conducted by distributing questionnaires to 92 dine-in and 92 takeaway culinary businesses. The data gathered is then processed by using the descriptive statistical analysis technique and a two-tailed t-test. The result of the study indicates that the culinary business actors in Bandung city have implemented the Design Thinking concept well, although the empathize and define stages are still included in the bad category. There is also a difference in implementing the Design Thinking concept between dine-in and takeaway culinary businesses. Further study is expected to measure the impact of the Design Thinking concept’s implementation on the culinary businesses’ sales performance.           

Currently, e-marketplace utilization grows significantly both on the buying side as well as on the selling side. However, the complete utilization rate is still low. Hence, this study investigates the factors considered by the prospective entrepreneurs to utilize the e-marketplace. The purpose of this study is also to find out the most potential e-marketplace to be employed by the prospective entrepreneurs. This study distributes questionnaires by using a convenience sampling technique to the 285 students at a business school. This study applies the Importance Performance Analysis and Correspondence Analysis methods to process the data gathered. It is found that the trust factor becomes the least important factor as well as the worst performance factor at the same time. On the other hand, the technical factor becomes the most important factor and the environmental factor is recorded as the best performance factor. To utilize e-marketplace, prospective entrepreneurs pay the highest attention to several indicators such as latency, user friendly, marketing, supply chain, price competitiveness, service quality, support/helpdesk, and bank transfer. Furthermore, Tokopedia and Shopee are rated as the best e-marketplaces. This study illuminates the prospective entrepreneurs’ consideration toward e-marketplace utilization; hence the platform owner can make a better service. The findings of the study will inspire the preparation steps for entrepreneurs to start their businesses and enrich the online consumer buying decision theory.

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 examines how Sustainable Innovation contributes to Business Sustainability in food-sector small and medium-sized enterprises (SMEs) through the mediating role of Zero Waste Operations. The study responds to the need to understand sustainability not only as a strategic objective, but also as an operational process embedded in inventory control, product return handling, near-expiry product management, packaging practices, and waste-reduction routines. A quantitative explanatory design was employed at Pasundan Snack Purwokerto, a distribution-based food SME in Indonesia. Using a census approach, data were collected through structured questionnaires from 65 employees involved in operational and distribution activities. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that Sustainable Innovation has a positive and significant effect on Business Sustainability and Zero Waste Operations. Zero Waste Operations also positively and significantly influence Business Sustainability. Mediation testing confirms that Zero Waste Operations partially mediate the relationship between Sustainable Innovation and Business Sustainability, indicating that innovation improves sustainability both directly and indirectly through waste-reduction routines. These findings suggest that sustainable innovation becomes more effective when translated into concrete operational practices, such as digital stock recording, FIFO or FEFO implementation, return classification, supplier coordination, packaging reuse, and near-expiry product handling. The study contributes to sustainability management literature by positioning Zero Waste Operations as an operational mediation mechanism in a distribution-centered food SME. Practically, the findings provide guidance for SME owners and policymakers to design feasible zero-waste initiatives that fit the resource limitations of small food distribution businesses in developing economy food supply chains.

This study examines the implementation of the vMedis ERP system at Greencare Pharmacy using Balanced Scorecard (BSC) and SWOT analysis. The research evaluates financial, customer, internal process, and learning perspectives, revealing significant improvements post-implementation. Key findings include a 15% profit margin increase, enhanced customer satisfaction, streamlined inventory management, and improved staff competencies. Statistical analysis using the Wilcoxon Signed-Rank Test confirmed that these improvements across the BSC perspectives were significant (p < 0.05). SWOT analysis highlights internal strengths, such as financial stability and advanced technology adoption, and external opportunities like market growth potential. Challenges include regulatory complexity and high competition. Overall, vMedis demonstrates its effectiveness in enhancing operational efficiency and competitiveness in the healthcare sector.

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. 

Idiosyncratic risk refers to a distinct category of risk that impacts individual firms and is shaped by multiple aspects, including carbon performance and Environmental, Social, and Governance (ESG) disclosures. This risk, particularly concerning environmental sustainability, can provide considerable hurdles for companies. This study aims to examine the influence of carbon performance and ESG disclosure on idiosyncratic risk, with the speed of adjustment serving as a moderating variable. This research examines coal production firms listed on the Indonesia Stock Exchange (IDX) from 2021 to 2023, with a sample of 26 companies selected through purposive sampling. This research utilizes quantitative methodologies, specifically multiple linear regression and a moderating variable approach. Data is derived from annual reports, financial statements, and sustainability reports of coal firms, as well as information from investing.com and the Katadata Corporate Sustainability Index. The data indicate an inverse correlation between carbon performance and idiosyncratic risk, as well as between ESG disclosure and idiosyncratic risk, suggesting that organizations with superior ratings in both domains encounter diminished idiosyncratic risk. Moreover, whereas the speed of adjustment does not reinforce the connection between carbon performance and idiosyncratic risk, it does augment the association between ESG disclosure and idiosyncratic risk.

The arrival of foreign nationals in Indonesia for various purposes, including state visits, tourism, business, and education, has become common. This study aims to analyze the factors influencing the application process for study permits for foreign nationals and map the ecosystem of this process, particularly for those studying at Indonesian universities. The research follows a five-step method to analyze the digital business ecosystem: 1) defining the ecosystem boundaries; 2) identifying actors and their roles; 3) outlining value propositions; 4) exploring actor interactions; and 5) verifying the ecosystem design. Using qualitative methods like interviews and literature review, the study finds that Indonesia's foreign student licensing ecosystem involves multiple actors and complex procedures, leading to delays and confusion. The lack of integration between agencies makes the process inefficient. The study suggests digital integration to enhance speed, transparency, and coordination, aiding the internationalization of higher education in Indonesia.