The development of the gig economy increases the role of digital platform drivers in maintaining the quality of food delivery services. Driver performance is an important factor influenced by work flexibility, incentive systems, and work stress levels. This study aims to analyze the effect of work flexibility, incentives, and work stress on the performance of ShopeeFood Drivers in Semarang City. The study used a quantitative approach with the Partial Least Square–Structural Equation Modeling (PLS-SEM) method through SmartPLS 4.1.1.8 software. The research sample consisted of 150 respondents selected using a purposive sampling technique, with the criteria of domiciled in Semarang City, having an active ShopeeFood Driver account, and having been a driver for at least 1 year. The results showed that work flexibility had a negative and significant effect on driver performance, incentives had a positive and significant effect on driver performance, and work stress had a positive and significant effect on driver performance. These findings indicate that providing appropriate incentives and optimal work stress levels can improve driver performance, while excessive work flexibility does not necessarily have a positive impact on performance. This research provides implications that incentive system management, work flexibility control, and stress management need to be considered in improving ShopeeFood driver performance.
Within the context of PT Bhumi Varta Technology, where employee turnover increased by 9.5 percentage points during 2025, this study examines the effects of work-life balance and work flexibility on employee loyalty, considering both direct and indirect pathways via work engagement. The trend is hypothesized to be associated with the adoption of a hybrid work model, which may intensify work-life integration issues, increase stress, and contribute to employee burnout. A quantitative approach was used, employing survey-based data collection. The population consisted of 150 employees, and a purposive sampling method selected 110 respondents who had worked for at least one year. Data were measured using a 1–5 Likert scale. Analysis was conducted using PLS-SEM with SmartPLS 4. Results show that work-life balance has a significant positive effect on work engagement (H1 accepted) and employee loyalty (H4 accepted). Work flexibility significantly improves work engagement (H2 accepted), but no significant direct effect on employee loyalty was found (H5 rejected). Work engagement is the strongest predictor of loyalty (H3 accepted) and significantly mediates the effects of both work-life balance (H6 accepted) and work flexibility (H7 accepted), including full mediation for the latter. The model shows moderate predictive power (R-square = 0.636).
This study aims to examine the effects of Social Media Marketing on Brand Awareness, Brand Trust, and Purchase Intention, as well as the effects of Brand Awareness and Brand Trust on Purchase Intention among Generation Z TikTok users interested in purchasing Skintific facial moisturizer in Semarang City. This study is motivated by the need to understand the factors influencing consumers' purchase intention amid the increasingly competitive skincare industry and the rapid growth of social media marketing. A quantitative approach was employed, with data collected through questionnaires from 150 respondents. The data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS version 4.1.1.8. The findings reveal that Social Media Marketing has a positive and significant effect on Brand Awareness, Brand Trust, and Purchase Intention. In addition, Brand Trust has a positive and significant effect on Purchase Intention, whereas Brand Awareness does not have a significant effect on Purchase Intention. The study concludes that Social Media Marketing and Brand Trust are key determinants of consumers' Purchase Intention toward Skintific facial moisturizer. These findings provide practical implications for companies by emphasizing the importance of optimizing social media marketing strategies through informative, interactive, and credible content to strengthen consumer trust, which in turn can enhance consumers' intention to purchase the product.
Rapidly increasing global food-waste generation poses major environmental, economic, and waste-management challenges due to its high organic load and improper disposal practices. Addressing this problem requires sustainable valorization strategies, including bioethanol production, which can simultaneously reduce waste burdens and contribute to renewable-energy generation. This review synthesizes current knowledge on the physical and chemical characteristics of food waste, the rationale behind pretreatment methods, and their role in improving downstream bioconversion efficiency. Pretreatments-physical, chemical, physicochemical, and biological-are examined with emphasis on how they enhance hydrolysis and improve fermentable-sugar release. Fermentation is the critical biochemical step in this pathway, as it converts the hydrolyzed sugars into bioethanol through the metabolic activity of yeast and bacteria. Enzymatic hydrolysis and microbial fermentation, the core steps that convert complex biomass into ethanol, are critically evaluated alongside bioprocessing strategies such as SHF, SSF, SSCF, and consolidated bioprocessing. The review identifies that physical and chemical pretreatments improve fermentable-sugar release but may involve higher energy or chemical inputs, whereas enzymatic and biological methods offer more sustainable alternatives with lower inhibitory by-product formation. Among bioprocessing strategies, SSF and SSCF consistently demonstrate higher bioethanol yields and reduced processing time compared with SHF. Consolidated bioprocessing shows strong potential for future development due to its reduced operational steps and lower overall costs. Collectively, these findings highlight the importance of integrating efficient pretreatment with optimized fermentation strategies to maximize bioethanol production while enhancing the sustainability of food-waste management.
Retail transaction data can be utilized to identify consumer purchasing patterns and product relationships. This study evaluates and compares the performance of the Apriori and ECLAT algorithms in generating association rules from a retail dataset comprising 30,000 transactions collected between January and April 2025. A grid search method was employed to determine the optimal minimum support and minimum confidence thresholds. Using a minimum support of 0.005 and a minimum confidence of 0.3, both algorithms generated 1,736 frequent itemsets and 78 association rules. The resulting rules were assessed using support, confidence, and lift measures, and all rules obtained lift values greater than 1, indicating meaningful positive dependencies among products. The strongest rule revealed that customers who purchased Bread and Milk were highly likely to also purchase Cereal, with a confidence of 60.5% and a lift value of 2.70. Although both algorithms produced comparable rule quality, ECLAT demonstrated superior computational efficiency due to its vertical data representation approach. These findings provide practical insights for retail decision-making, particularly in cross-selling strategies, product bundling, and shelf arrangement optimization.
