This study investigated the crucial factors for measuring the success of the information system used in the e-learning process, considering the transformations in the work environment. This study was motivated by the changes caused by COVID-19 witnessed after the shift to fully online learning environments supported by e-learning systems, i.e., learning emphasized with information systems. Empirical research was conducted on a sample comprising teaching staff from two European universities: the University of Novi Sad, Faculty of Technical Sciences in Serbia and the Polytechnic Institute of Castelo Branco in Portugal. By synthesizing knowledge from review of the prior literature, supported by the findings of this study, the authors propose an Extended Information System Success Measurement Model—EISSMM. EISSMM underlines the importance of workforce agility, which includes the factors of proactivity, adaptability, and resistance to change, in the information system performance measurement model. The results of our research provide more extensive evidence and findings for scholars and practitioners that could support measuring information system success primarily in e-learning and other various contextual settings, highlighting the importance of people’s responses to work environment changes.

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Currently, food waste is a global concern, a problem that arises mainly at the consumption level and generates environmental, economic, and social impacts. One way to reduce the food waste problem is to use the food we already have at home. However, this causes another concern, which is what to cook with certain foods. Sometimes we do not know what recipes can be made. Knowing which ingredients can be mixed and how to mix them can be a difficult task for a beginner cook, so selecting the right ingredients for a recipe is essential. Therefore, it is proposed to develop a recipe recommendation system through image recognition of food ingredients. Presently, the system is a web application that recognizes an image given by the user and recommends recipes containing the recognized ingredient. For this, a convolutional neural network model, the ResNet-50, was built to perform image recognition and trained with a dataset that contains about 36 classes of vegetables and fruits. Through this training, the model reached 96% accuracy in classifying the dataset images. The recommendation system uses the label of the recognized ingredient to obtain the recipes, which are searched through the Edamam API.