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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.

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. 

Modern retail businesses in Indonesia are growing rapidly thanks to open market opportunities, manufacturing businesses that support product supply, and government support for economic growth through retail development. The purpose of this study is to determine the intrinsic value of shares of companies engaged in the food and staples retail sub-sector which in this case is represented by PT. Sumber Alfaria Trijaya, Tbk. (AMRT), PT. Diamond Food Indonesia, Tbk. (DMND), PT.  Midi Utama Indonesia, Tbk. (MIDI), PT. Enseval Putera Megatrading, Tbk. (EPMT), and PT. Millenium Pharmacon Internatioanl, Tbk. (SDPC) listed on the Indonesia Stock Exchange (IDX). This research uses historical data for 2018-2022 which will be used as the basis for projections for 2023-2027. The results showed that by using the Discounted Cash Flow (DCF) method, specifically the Free Cash Flow to Firm (FCFF) approach, under an optimistic scenario, the intrinsic values of AMRT, SDPC, and DMND were undervalued. EPMT and MIDI stocks are overvalued. In the moderate scenario, the intrinsic values of AMRT, MIDI, EPMT, and DMND are overvalued, while SDPC is undervalued. Calculations on a pessimistic scenario, all companies studied are overvalued. In this study, the valuation of stocks was also conducted using the Relative Valuation (RV) method with the Price to Earnings Ratio (PER) and Price to Book Value (PBV) approaches. In an optimistic scenario, the stocks of AMRT, MIDI, and SDPC are considered expensive, while EPMT and DMND are seen cheap. In a moderat scenario, the stocks AMRT, EPMT, and SDPC are considered undervalued, while MIDI and DMND are seen expensive. In a pessimistic scenario, it is observed that AMRT, EPMT, and SDPC are undervalued, whereas MIDI and DMND are considered overvalued. This research aims to provide an objective assessment of the company, which investors can use to consider investments and by companies to enhance corporate value.

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.

Stock returns reflect the investment returns investors receive from owning company shares, encompassing both dividends and stock price increases. High stock returns indicate strong company performance and are attractive to investors, often reflecting market expectations of the company's future profitability and contributing to increased company value. Conversely, low stock returns can reduce market confidence and negatively impact company value. This study examines factors influencing stock returns in cyclical consumer sector companies listed on the Indonesia Stock Exchange from 2019 to 2023, with a sample of 63 companies out of 124. The research utilizes principal component analysis (PCA) with regression mediation, analyzing variables such as liquidity, solvency, activity, profitability ratios, financial performance, good corporate governance, and macroeconomic factors. SPSS 25 is used for data analysis. Key findings reveal that Market Value Added (MVA) significantly impacts both stock returns and firm value, while exchange rates and GDP do not significantly affect stock returns or firm value in this sector. The money supply (M2) significantly influences stock returns but not firm value. The Sobel mediation test shows that stock returns significantly mediate the relationship between MVA and M2 with firm value, whereas they do not mediate the effects of exchange rates and GDP on firm value. These results offer insights into the economic and financial factors impacting stock performance and firm value in Indonesia's cyclical consumer sector.