Abstract
In the last decade, gamification has been a widely used mechanism to influence behavioral habits in users so that they are more positively involved in learning and business processes. There are many articles or applications that materialize this adoption by showing success stories. However, few are those who mention the dangers of its use. This article shares the main precautions to be taken when creating a gamified strategy, promoting the use of gamification design and evaluation frameworks, in order to create a balanced approach that meets the profiles of its users.

Abstract
Organic food purchase behavior is attracting increasing attention from researchers and managers. However, there is a need to further explore differences among groups of consumers, namely with regards to cultural dimensions. To help fill this gap, this article aims to examine the impact of collectivism on the determinants of organic food purchase intention and behavior. Building on the theory of planned behavior, this article suggests its extension by considering an additional set of explanatory variables that are shown to be relevant to explain consumer behavior. It includes a quantitative study conducted with young consumers from two European countries, Norway (n = 468) and Portugal (n = 448). Structural equation modelling allowed to conclude that collectivism positively impacts attitude, subjective norm, perceived price, and environmental concern towards organic food. The expected positive impacts of collectivism on product availability and health concerns were not supported by the study. Furthermore, the positive impact of attitude, subjective norm, perceived price, health consciousness, and environmental concerns on intention to purchase organic food were also confirmed, even though availability had an insignificant impact on intentions. Finally, and aligned with extant literature, this study also found a positive impact of intention to purchase behavior on organic food. Interestingly, the positive relationship between Collectivism and Availability, and between Availability and Purchase Intention, was only significant for Portugal. The study confirms the relevance of considering cultural dimensions, particularly collectivism/individualism, to further understand consumer behaviors toward organic food. Based on the findings, implications for both managers and researchers are highlighted.

Abstract
Objective. In this work, an analytical framework for the multiscale analysis of multivariate Gaussian processes is presented, whereby the computation of Partial Information Decomposition measures is achieved accounting for the simultaneous presence of short-term dynamics and long-range correlations. Approach. We consider physiological time series mapping the activity of the cardiac, vascular and respiratory systems in the field of Network Physiology. In this context, the multiscale representation of transfer entropy within the network of interactions among Systolic arterial pressure (S), respiration (R) and heart period (H), as well as the decomposition into unique, redundant and synergistic contributions, is obtained using a Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This novel approach allows to quantify the directed information flow accounting for the simultaneous presence of short-term dynamics and long-range correlations among the analyzed processes. Additionally, it provides analytical expressions for the computation of the information measures, by exploiting the theory of state space models. The approach is first illustrated in simulated VARFI processes and then applied to H, S and R time series measured in healthy subjects monitored at rest and during mental and postural stress. Main Results. We demonstrate the ability of the VARFI modeling approach to account for the coexistence of short-term and long-range correlations in the study of multivariate processes. Physiologically, we show that postural stress induces larger redundant and synergistic effects from S and R to H at short time scales, while mental stress induces larger information transfer from S to H at longer time scales, thus evidencing the different nature of the two stressors. Significance. The proposed methodology allows to extract useful information about the dependence of the information transfer on the balance between short-term and long-range correlations in coupled dynamical systems, which cannot be observed using standard methods that do not consider long-range correlations.

Abstract
This paper explores which variables are more significant in municipal executive recommendation by citizens. We estimated the influence of public dimensions, such as municipe loyalty, municipe satisfaction, and municipe perceived value in municipal executive recommendation by citizens. Then, we tried to understand if the citizen's opinions influenced the evaluation of the municipal executive recommendation. The parishes of the municipality of Valongo were selected and analyzed, namely the parishes of Alfena, Campo e Sobrado, Valongo, and Ermesinde, and a total of 998 questionnaires were collected. Data were collected in November 2020 in the different parishes under study. It was concluded that all studied dimensions were statistically significant in the final structural estimated model. The structural results point to municipe loyalty and municipe satisfaction dimensions having a direct, positive, and statistically significant influence on municipal executive recommendation. On the other side, the municipe perceived value dimension has a direct positive but not statistically significant influence on municipal executive recommendation. This study showed that a loyal and satisfied citizen recommends the continuity of the municipal executive in the city's political leadership in which he or she lives. Therefore, for the municipal executive administration, it is fundamental to know which dimensions the society considers most important in order to be able to remain in the management of the shared destinies of a city. In this sense, political decisions throughout the mandates can be directed, on the one hand, to the satisfaction and loyalty of the citizens and, on the other hand, to the balanced management of the destinies of this type of public entity.

Abstract
Gamification has been growing at a meteoric rate. Such growth is because this is a valid mechanism to add desirable behaviors in an organizational process or even in an educational process. In fact, these are the two major contexts where gamification has been applied. In organizations, gamification is applied in various business contexts from recruitment, through onboarding, training, talent retention, customer service, sales, and marketing. In education, gamification is used essentially in contexts of promoting student involvement in existing training offers. This chapter presents a gamification design framework called GAFRHE aimed to be used in all domains, but with extensive facets for the healthcare domain. Unlike other frameworks, it is a concrete framework that not only presents the basic concepts that must be followed by the gamified strategy, but also presents a system architecture that must be instantiated, and a set of components already coded and that can be used. The framework is mostly based on existing standards promoting reusability and interoperability.

Abstract
Demand response (DR) is an important technique to explore the demand-side flexibility. The wide deployment of smart meters makes it possible to quantify the baseline load. As an intermediate agent, demand response aggregator needs to obtain the aggregated baseline load (ABL) for the DR event. Previous studies about the household level estimation focus on the estimation method. However, for ABL estimation, customer division is an important issue. A major limitation is the mismatch between the objectives of segmentation and estimation. Therefore, this paper proposes a new closed-loop method for estimating the ABL, which utilizes the contextual bandit with policy gradient to link the segmentation with the estimation. As such, the ABL estimation accuracy can guide the segmentation to divide the customers. The segmentation and estimation optimize collaboratively to improve the ABL estimation accuracy. An ensemble method for combining network's weights during the training process is proposed. Moreover, a pre- and post-event adjustment method is developed to further improve the estimation accuracy. Comprehensive comparisons demonstrate the proposed method can achieve the best estimation performance with regard to the MAPE and RMSE. It improves the estimation accuracy by 7% in terms of MAPE, and 11% in terms of RMSE.