Abstract
Managing company knowledge and using it effectively is more than ever a strong competitive advantage in the business world. The scientific area of knowledge management and knowledge management systems have been intensively studied in the last years; however, we still see the unstructured implementation of knowledge management systems in organizations, the misalignment of knowledge management systems from the business model and the frustration non-use, lack of systems integration and/or non-return on investment made either in technology or spent on heavy implementation processes. The state-of-the-art conducted during this study, showed that most knowledge management systems alignment models in the business context have a strong focus on the organizational dimension, e.g., culture, organizational processes, organizational structure, and leadership, having been identified only three models that also cover, simultaneous, the technological and strategic dimension. Our final objective in this study is, following the research survey methodology, to develop a proposed framework for the strategic alignment of knowledge management systems that can support company managers in their decision-making, and to contribute to the development of scientific knowledge in this area.

Abstract
In the digital age, the training in companies can be facilitated through a proper system to the company's demand. A learning platform personalized to the profile of employees can facilitate the selection of training that tailored to their roles. This research aims to investigate the existence of adaptation and personalization of learning management systems (LMS) in enterprise context, that facilitate the selection of learning's content suited for employees' roles. This study focuses on literature reviewto understand the importance of a personalizedLMSin company, especially in selection of content that adequate to role of each employee.

Abstract
Serious Games have been used in professional training to increase employee engagement and improve the results of training initiatives. This work intends to investigate the influence of game elements, in adaptable Serious Games, according to the users' interactions, on the increase of engagement in the game itself and, as the main goal, on the learning results and the transfer of the acquired knowledge and practised skills to the daily work activities. Using the Design Science Research - DSR methodology, this study aims to develop a framework for the development and evaluation of Serious Games to improve the user experience, the learning outcomes, the transfer of knowledge to work situations, and the application of the skills practised in the game in real professional scenarios. This paper presents an initial Framework for Adaptive Serious Games derived from a systematic literature review. The next steps in this investigation are pointed out following the DSR methodology.

Abstract
Sectorization problems have significant challenges arising from the many objectives that must be optimised simultaneously. Several methods exist to deal with these many-objective optimisation problems, but each has its limitations. This paper analyses an application of Preference Inspired Co-Evolutionary Algorithms, with goal vectors (PICEA-g) to sectorization problems. The method is tested on instances of different size difficulty levels and various configurations for mutation rate and population number. The main purpose is to find the best configuration for PICEA-g to solve sectorization problems. Performance metrics are used to evaluate these configurations regarding the solutions’ spread, convergence, and diversity in the solution space. Several test trials showed that big and medium-sized instances perform better with low mutation rates and large population sizes. The opposite is valid for the small size instances. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Despite the wide spread of gamification as a means of influencing behavior, we do not yet fully understand its effectiveness in promoting sustainable behaviors among young people. This question becomes all the more relevant when it comes to influencing their mobility habits, considering the negative impact of motorized transportation on urban livability. As a consequence, the promotion of soft mobility has been on the policy agenda in many countries. In this study, we explore the potential of gamification and the use of rewards as a way to incentivize young citizens to adopt soft mobility over motorized transports. Our goal was to understand how a gamified app with a built-in reward system can influence the promotion of soft mobility among young people in cities, focusing particularly on walking and cycling. To achieve this, we adopted a quantitative research methodology, carrying out a structured survey in three schools enrolled in the Sharing Lisboa project. We used statistical tools based on partial least squares structural equation modeling (PLS-SEM) to analyze the data. We found that an app influences the users' perception of its usefulness, leading to a positive attitude towards its use. Contrary to what was initially assumed, the reward system only influences the perceived usefulness, suggesting that it is important to convince potential users to try the system but that it does not influence their attitude. Moreover, the instrumental attitude, which is related to the benefits and functions of an app, together with the subjective (injunctive/descriptive) norms and perceived behavioral control, have a positive influence on walking/cycling travel intention. Therefore, social pressure, especially from family and friends, is important for building the intention to travel by bicycle/on foot.

Abstract
Food waste reduction represents a potential opportunity to enhance environmental sustainability. This is especially important in fresh products such as fresh seafood, where waste levels are substantially higher than those of other food products. In this particular case, reducing waste is also vital to meet demand while conserving fisheries. This paper aims to promote more sustainable supply chains by proposing daily fresh fish demand forecasting models that can be used by grocery retailers to align supply and demand, and hence prevent the production of food waste. To accomplish this goal, we explored the potential of different machine learning models, namely Long Short-Term Memory networks, Feedforward neural networks, Support Vector Regression, and Random Forests, as well as a Holt-Winters statistical model. Demand censorship was considered to capture real demand. To validate the proposed methodology, we estimated the demand for fresh fish in a representative store of a large European retailing company used as a case study. The results revealed that the machine learning models provided accurate forecasts in comparison to the baseline models and the statistical model, with the Long Short-Term Memory networks model yielding, in general, the best results in terms of root mean squared error (27.82), mean absolute error (20.63) and mean positive error (17.86). Thus, the implementation of these types of models can thus have a positive impact on the sustainability of fresh fish species and customer satisfaction.