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Food literacy as a resilience factor in response to health-related uncertainty
No full textPurpose During the Covid-19 pandemic, people were deprived of their freedom, unable to engage in physical and social activities, and worried about their health. Uncertainty, insecurity, and confinement are all factors that may induce stress, uneasiness, fear, and depression. In this context, this study aims to identify possible relationships of emotions caused by health risks and restrictions to outdoor activities with well-informed decisions about food consumption. Design/methodology/approach The theoretical framework of this research draws on the stimulus-organism-response paradigm yielding six research hypotheses. An online survey was designated to test these hypotheses. A total of 1,298 responses were gathered from Italy, Greece, and the United Kingdom. Data analyses include demographic group comparisons, moderation, and multiple regression tests. Findings The results showed that when people miss their usual activities (including freedom of movement, social contact, travelling, personal care services, leisure activities, and eating at restaurants) and worry about their health and the health of their families, they turn to safer food choices of higher quality, dedicating more of their time and resources to cooking and eating. Research limitations/implications The findings showcase how risk-based thinking is critical for management and marketing strategies. Academics and practitioners may rely on these findings to include extreme conditions within their scope, understanding food literacy as a resilience factor to cope with health risks and stimulated emotions. Originality/value This study identified food behavioural patterns under risk-laden conditions. A health risk acted as an opportunity to look at food consumption as a means of resilience.12531067109
Graph Neural Networks and Open-Government Data to Forecast Traffic Flow
No full textTraffic forecasting has been an important area of research for several decades, with significant implications for urban traffic planning, management, and control. In recent years, deep-learning models, such as graph neural networks (GNN), have shown great promise in traffic forecasting due to their ability to capture complex spatio–temporal dependencies within traffic networks. Additionally, public authorities around the world have started providing real-time traffic data as open-government data (OGD). This large volume of dynamic and high-value data can open new avenues for creating innovative algorithms, services, and applications. In this paper, we investigate the use of traffic OGD with advanced deep-learning algorithms. Specifically, we deploy two GNN models—the Temporal Graph Convolutional Network and Diffusion Convolutional Recurrent Neural Network—to predict traffic flow based on real-time traffic OGD. Our evaluation of the forecasting models shows that both GNN models outperform the two baseline models—Historical Average and Autoregressive Integrated Moving Average—in terms of prediction performance. We anticipate that the exploitation of OGD in deep-learning scenarios will contribute to the development of more robust and reliable traffic-forecasting algorithms, as well as provide innovative and efficient public services for citizens and businesses.14422
XLCNN: A Transformer Model for Malware Detection
No full textThe present research describes a Transformer-based neural network model that was developed in order to detect malicious software. We believe that the scientific community should take advantage of the contribution of Transformer models in the field of cybersecurity and go beyond the limits set by the classic natural language processing. For this purpose a new and more sophisticated algorithm was created based on the methodology used by the XLNet neural network which was proposed by the Google AI Brain Team. The proposed XLCNN model detects malicious code with a higher success rate than its predecessor. The method of detecting malware is based on the extraction and analysis of metadata contained in Windows executable files. From our carried out experiments, it was found that the size and architecture of the feed-forward neural network in combination with our proposed tokenizer, is one of the most important factors of XLCNN for classification problems. To justify the concept of XLCNN as an effective approach to detecting malware, the effectiveness and efficiency of the algorithm was measured for a finite number of epochs and compared to other Transformer models such as XLNet, BERT and Transformer-XL using exactly the same inputs. Using our proposed network has proven to be not only a reliable way for security researchers to detect malware, but also an effective and highly accurate method that offers high accuracy rate of 98.88%.21
A Comparative Analysis of Low or No-Code Authoring Tools for Location-Based Games
No full textThis article presents a comparative analysis of four low or no-code location-based game (LBG) authoring tools, namely Taleblazer, Aris, Actionbound, and Locatify. Each tool is examined in detail, with an emphasis on the functions and capabilities it provides for the development of LBGs. The article builds on the history and purpose of LBGs, their characteristics, as well as basic concepts and previous applications, placing emphasis both on the technological and pedagogical dimensions of these games. The evaluation of the tools is based on certain criteria, or metrics, recorded in the literature and empirical data collected through the development of prototype games for each tool. The tools are comparatively analyzed in terms of the LBG’s constituent features they incorporate, the fundamental and additional functionality provided to the developer, as well as the existence or absence of features that captivate players in the game experience. Moreover, feedback is provided based on the practical use of the platforms for developing LBGs in order to support prospective developers in making an informed choice of an LBG platform for implementing a specific game. The games were created by taking advantage of as many features of the tools as possible in order to have a more fair and complete evaluation. This study aims to highlight the affordances and limitations of the investigated low or no-code LBG authoring tools, enabling anyone interested in developing an LBG to choose the most appropriate tool taking into account their needs and technological background or designing their own LBG authoring tools.798
Distributed Pair Programming in Higher Education: A Systematic Literature Review
No full textThis paper presents a Systematic Literature Review (SLR) of fifty-seven studies on Distributed Pair Programming (DPP) in higher education, identifying which studies investigated factors on the effectiveness of DPP as a method for learning programming, factors related to mediating and stimulating interactions between students, the measures/instruments used for exploring these factors, as well as the tools and their features. As DPP effectiveness is very promising as regards code quality and academic performance, the findings can contribute to a better understanding of how communication, collaboration and coordination are investigated. It was found that there are very few studies concerning compatibility, pair formation, and role contribution, the majority of which did not use data derived from students’ actions. Only a small number of studies used DPP tools with logging capabilities. Even though several IDEs and Eclipse plugins have been designed for DPP and offer specialized features, there are still studies which use a combination that include video conferencing and remote sharing tools along with various IDEs and auxiliary tools. This SLR can be of interest to educators that aim to apply DPP in educational settings, researchers designing informed empirical studies, as well as designers of DPP tools.61354657
Office Madness: Investigating the impact of a game using a real life job and programming scenario on player experience and perceived short-term learning
No full textSerious games, or more specifically educational games, are considered to be suitable to motivate and engage users in learning through playing. Serious games take advantage of successful game mechanics used in entertainment games in order to engage players in learning cognitively demanding subjects, such as computer programming. Several games for computer programming have been developed and their majority utilizes a role-playing or puzzle game scenario. In this article, a new game for learning the C++ programming language targeted to young adults is presented. The distinctive feature of the proposed game, called Office Madness, is the use of a real life job and programming game scenario. Office Madness was designed taking into account both educational games for programming and entertainment games, as well as the EFM design model. The main goal of the study presented is to investigate the impact of a game using a real life job and programming scenario on player experience and short-term learning. One hundred and seventy-four final-year students of an Informatics Department play-tested the game and provided feedback through an online questionnaire based on the MEEGA+ model. The results show that the use of a real life scenario had a positive impact on player experience and short-term learning.4410052
An SLR of firm ambidexterity: organizing a future research path forward
No full textPurpose: Given that the literature in terms of business ambidexterity is continually growing, the of this paper is to identify the future research suggestions made by several authors with regard to ambidexterity and to group them into meaningful themes. Design/methodology/approach: A systematic literature review (SLR) of peer reviewed journal articles in the field of ambidexterity was conducted. A total of 128 relevant articles were selected, which were published in 58 journals over the past 2 decades (2000–2021). Findings: The plethora of the future research suggestions made by several authors with regard to ambidexterity are analytically presented. Moreover, based on these suggestions, meaningful future research themes were revealed and these were further classified into three broad categories, namely “factors influencing the adoption of ambidexterity and its success”, “the types of ambidexterity” and “the effects of ambidexterity”. Research limitations/implications: The subjectivity of grouping the future research suggestions into themes as well as not examining the interrelationships among these themes, are limitations of the present study. Based on these limitations, future literature review studies can be conducted. Practical implications: As this is a SLR focusing on developing future ambidexterity research themes, there are no direct practitioner implications. However, practitioners may benefit from future research prompted by this SLR. Originality/value: This study contributes to management literature by suggesting future research not only on organizational ambidexterity like previous studies, but also on four different approaches to ambidexterity.42318320
May human capital rescue the Empty Planet?
No full textEvidence suggests that fertility rates are already below the replacement level in many advanced countries, meaning that population is decreasing in these regions. We build an R&D-based growth model with human capital and declining population to show that the introduction of human capital can mitigate (or overcome) the stagnation in GDP per capita that may otherwise occur. Also, our model allows us simultaneously to observe sustained economic growth combined with secular productivity stagnation.23211135
Financialization Historically Contemplated: Putting Old Wine in New Barrels
No full textThis article examines the extent to which financialization is a new phase of
capital accumulation characterized by its own economic laws in which the real (production)
economy adjusts accordingly. In order to examine this hypothesis, the authors invoke
the share of the financial sector in the GDP of the US, as the best meaningful metric to
approximate the expansion of the financialization over time. The findings suggest that
the financialization phenomena of the post-1982 years are comparable to those of the
“roaring twenties.” The observed differences are quantitative, in the main, and although
they indicate the presence of regularities, they, nevertheless, do not suggest an altogether
different stage of finance-led capitalism.14332835
Investigating the Influence of Artificial Intelligence on Business Value in the Digital Era of Strategy: A Literature Review
No full textFor organizations, the development of new business models and competitive advantages through the integration of artificial intelligence (AI) in business and IT strategies holds considerable promise. The majority of businesses are finding it difficult to take advantage of the opportunities for value creation while other pioneers are successfully utilizing AI. On the basis of the research methodology of Webster and Watson (2020), 139 peer-reviewed articles were discussed. According to the literature, the performance advantages, success criteria, and difficulties of adopting AI have been emphasized in prior research. The results of this review revealed the open issues and topics that call for further research/examination in order to develop AI capabilities and integrate them into business/IT strategies in order to enhance various business value streams. Organizations will only succeed in the digital transformation alignment of the present era by precisely adopting and implementing these new, cutting-edge technologies. Despite the revolutionary potential advantages that AI capabilities may promote, the resource orchestration, along with governance in this dynamic environment, is still complex enough and in the early stages of research regarding the strategic implementation of AI in organizations, which is the issue this review aims to address and, as a result, assist present and future organizations effectively enhance various business value outcomes.1428