Abstract
In the last decade, the use of smartphones has grown steadily. The way consumers interact with brands has changed owing to the accessibility of interne connection on smartphones, and ubiquitous mobility. It is crucial to understand the factors that motivate consumers to interact with smartphone advertisements and therefore what stimulates their decision to purchase. To achieve this goal, we proposed a conceptual model that combines Ducoffe's web advertising model and flow experience theory. Based on the data collected from 303 Portuguese respondents we empirically tested the conceptual model using a partial least squares (PIS) estimation. The results showed that advertising value, flow experience, web design quality, and brand awareness explain purchase intention. The study provides results that allow marketers and advertisers to understand how smartphone advertisements contribute to consumer purchase intention.

Abstract
Business ideas competitions have gained increasing importance in stimulating entrepreneurial activity mainly among highly qualified graduates. However, the operating model of these competitions is quite heterogeneous, complex and often confusing, since the perception of the merit of each project is assessed differently by each jury member. Therefore, it is important to propose a decision support system that simplifies the evaluation process of competing projects and ensures all the opinions of the jury members are considered and have the same importance. The developed application uses C# and Windows Forms technologies and the AHP method to serialize competing projects according to the individual evaluation of each jury member. The results of the study allowed testing the application considering four scenarios in which the relative importance of each criterion and the performance of each project according to these criteria are changed and evaluated.

Abstract

Abstract
Social media are privileged vehicles to generate rich data created with unprecedented multi-faceted insights to drive faster ideation and commercialisation of client-centric innovations. The essence of data generated through social media is rooted in the connections and relationships it enables between firms and their stakeholders, and represents one of the greatest assets for data-driven innovation. As most of the firms are still experiencing and trailblazing in this matter, the current challenge is therefore to learn how to benefit from social media's potential for innovation purposes. In the last decade, research interest has increased towards understanding social media – innovation interactions. The reliance on the wisdom of the crowd in driving major business decisions and shaping society's way of life is now well acknowledged in academic and business literature. Social media is increasingly used as a tool to manage knowledge flows within and across organisation boundaries in the process of innovation. Yet, conceptualisation of social media and innovation interaction and a systematic review of how far the field has come remains providential. Therefore, through a systematic literature review we aim to identify research trends and gaps in the field, conceptualise current paradigmatic views and therein provide clear propositions to guide future research. Based on a systematic review, 111 articles published in peer-reviewed journals and found in EBSCO Host® and Scopus® databases are descriptively analysed, with results synthesized across current research trends. Findings suggest social media is seen as enabler and driver of innovation, with behavioural and resource based perspectives being the most popular theoretical lens used by researchers. The originality of the paper is rooted in the comprehensive search and systematic review of studies in the discourse, which have not been unified to date. Implications for advancement of knowledge are embedded in the purposefully proposed theoretical, contextual and methodological perspectives, providing future research directions for exploring social media capability in innovation management.

Abstract
Introduction: Foreign languages are of vital importance in tourism today, particularly in rural tourism. If we consider that more than a third of the demand for TER in the national territory is made up of foreigners (INE, 2017), we understand the importance of foreign language skills. Moreover, the Portuguese quality standard of the TER states that promoters of the activity must have at least knowledge of a foreign language. If this is unquestionable, it is also true that in the universe of existing enterprises, several indications are that foreign language competence presents itself as a fragility for many enterprises. Objetives: Taking as a reference a rural tourism enterprise, the objective of this study is to explore the importance of foreign languages in the success of tourism activity in rural areas. Methods: In order to allow a deeper analysis, a case study methodology is used. In addition to a semi-structured interview, we use documentary information and the observation of the languages used in the online comments (in TRIPadvisor and booking.com). Results: In the interview the importance of foreign languages was highlighted. Many of the comments online are made in a foreign language. Conclusion: Competence in foreign languages is vital to the success of rural tourism. © 2019, Polytechnic Institute of Viseu. All rights reserved.

Abstract
Accurate solar photovoltaic power forecasting can help mitigate the potential risk caused by the uncertainty of photovoltaic out power in systems with high penetration levels of solar photovoltaic generation. Weather classification based photovoltaic power forecasting modeling is an effective method to enhance its forecasting precision because photovoltaic output power strongly depends on the specific weather statuses in a given time period. However, the most intractable problems in weather classification models are the insufficiency of training dataset (especially for the extreme weather types) and the selection of applied classifiers. Given the above considerations, a generative adversarial networks and convolutional neural networks-based weather classification model is proposed in this paper. First, 33 meteorological weather types are reclassified into 10 weather types by putting several single weather types together to constitute a new weather type. Then a data-driven generative model named generative adversarial networks is employed to augment the training dataset for each weather types. Finally, the convolutional neural networks-based weather classification model was trained by the augmented dataset that consists of both original and generated solar irradiance data. In the case study, we evaluated the quality of generative adversarial networks-generated data, compared the performance of convolutional neural networks classification models with traditional machine learning classification models such as support vector machine, multilayer perceptron, and k-nearest neighbors algorithm, investigated the precision improvement of different classification models achieved by generative adversarial networks, and applied the weather classification models in solar irradiance forecasting. The simulation results illustrate that generative adversarial networks can generate new samples with high quality that capture the intrinsic features of the original data, but not to simply memorize the training data. Furthermore, convolutional neural networks classification models show better classification performance than traditional machine learning models. And the performance of all these classification models is indeed improved to the different extent via the generative adversarial networks-based data augment. In addition, weather classification model plays a significant role in determining the most suitable and precise day-ahead photovoltaic power forecasting model with high efficiency.