Abstract
Considering the beauty industry's potential for further expansion and the mismatch between the attitudes of consumers and their buying behavior, brands should comprehend the factors that influence consumers' intention to purchase environmentally friendly cosmetics. As such, the present study examined what encourages consumers of environmentally friendly cosmetics to choose these products. To answer the main objective of the work, the elaborated literature review aimed at identifying the factors that influence the buying of environmentally friendly cosmetics. Thus, the following were found: environmental consciousness, certification labels, brand trust, quality expectation, lifestyle, advertising, willingness to pay the price, ethical concerns and social and financial equity, physical health considerations, and knowledge of the product. The study was conducted using exploratory research with a qualitative approach. Data was collected from eight interviews, and it was identified that factors such as environmental consciousness, lifestyle, willingness to pay the price, quality expectations, ethical concerns and social and financial equity, as well as physical health considerations and knowledge of the product are the most significant determinants in the intention to buying environmentally friendly cosmetics. One of the aims of the investigation was to distinguish between the notions of green, traditional, organic, and natural cosmetics. As a result, it was found that there is a lack of clarification of the green cosmetic concept in literature, as well as a lack of standardization of criteria used by multiple systems to define different cosmetics.

Abstract
In today's world, the idea of sustainable fashion is gaining traction. Finding a link between pricing and the purchase of sustainable clothes is the aim of this study. Regression models and t-tests of two independent samples (two-tailed tests) were applied by means of the application of a questionnaire. The study found that consumers' willingness to pay for price increases is related with non-linear (quadratic or exponential) product pricing. The results of this study suggest that consumers are willing to pay higher prices for sustainable clothing. Through an understanding of the relationship between price and consumer behavior, businesses can more effectively align their pricing strategies with the demands of environmentally conscious consumers. © 2024, IGI Global. All rights reserved.

Abstract
Shopping on the Internet is now an everyday activity for consumers. An understanding of which constructs are relevant in this activity is of crucial importance for online stores to adapt their strategies. The existence of a holistic model with these relevant constructs, however, is lacking in the literature. This research is exploratory in nature. The study aimed to identify the constructs that are closely and consistently related to the customer experience in online stores. In the literature review, 15 constructs were identified. They are web content, customer service, service quality, terms and conditions, digital channels, security and privacy, brand, perceived price, perceived risk, word of mouth, perceived value, trust, satisfaction, and loyalty. The review of the literature also revealed the imperative of building or revising the measurement scales of those constructs that were identified to allow for their operationalization. For this reason, a questionnaire with scales that have been adapted from several authors has also been proposed. This questionnaire has a feasible number of questions to be answered.

Abstract
The aquaculture industry faces significant challenges related to sustainability, productivity, and fish welfare. Key issues include managing environmental conditions, disease, pests, and data integration from various sensors and monitoring systems. The BigFish project aims to address these challenges through advanced analytics and machine learning, focusing on three case studies in Atlantic salmon farms: predicting oxygen levels, reducing sea lice infestations, and improving data interaction and visualization. Predictive models for oxygen levels and sea lice infestation, as well as natural language interfaces for data visualization, demonstrate the potential for improved decision-making and management practices in aquaculture. Early results indicate the effectiveness of these approaches, highlighting the importance of data-driven solutions in enhancing industry sustainability and productivity. © 2024 IEEE.

Abstract
IntroductionHypoxia is defined as a critically low-oxygen condition of water, which, if prolonged, can be harmful to fish and many other aquatic species. In the context of ocean salmon fish farming, early detection of hypoxia events is critical for farm managers to mitigate these events to reduce fish stress, however in complex natural systems accurate forecasting tools are limited. The goal of this research is to use a machine learning approach to forecast oxygen concentration and predict hypoxia events in marine net-pen salmon farms.MethodsThe developed model is based on gradient boosting and works in two stages. First, we apply auto-regression to build a forecasting model that predicts oxygen concentration levels within a cage. We take a global forecasting approach by building a model using the historical data provided by sensors at several marine fish farms located in eastern Canada. Then, the forecasts are transformed into binary probabilities that indicate the likelihood of a low-oxygen event. We leverage the cumulative distribution function to compute these probabilities.Results and discussionWe tested our model in a case study that included several cages across 14 fish farms. The experiments suggest that the model can detect future hypoxic events with a commercially acceptable false alarm rate. The resulting probabilistic predictions and oxygen concentration forecasts can help salmon farmers to prioritize resources, and reduce harm to crops.

Abstract