drivers and emerging innovations in knowledge based destinations towards a research agenda

Abstract Research on innovation in tourism is fragmented and confined to traditional paradigms. This critical review paper, which cross-fertilises and discusses the relevant literature in tourism and other theoretical domains, proposes an integrative theoretical framework of innovation in destinations. The paper identifies four emerging innovations – experience co-creation, smart destinations, e-participative governance and social innovation – as evolutionary, knowledge-driven phenomena that are generated by the interaction among four destination actors and facilitated by information and communication technologies (ICTs) and social capital. The discussion and conclusion present some theoretical advances as follows: local contexts matter in destination innovation when assuming a repository role of spatial and cross-sectorial knowledge; social capital and ICT infrastructures facilitate innovativeness and stakeholder engagement; and emerging innovations are pervasive and the holistic results of the collective knowledge of four destination actors and are facilitated by ICT and social capital. The paper offers avenues for future research and challenges that should be explored by academics, policy makers and destination managers

Deactivation Pattern of a "Model" Ni/MgO Catalyst in the Pre-Reforming of n-Hexane †

The deactivation pattern of a "model" Ni/MgO catalyst in the pre-reforming of n-hexane with steam (T, 450 °C; P, 5–15 bar) is reviewed. The influence of the steam-to-carbon ratio (S/C, 1.5–3.5) on the rate of catalyst fouling by coking is ascertained. Catalyst fouling leads to an exponential decay in activity, denoting 1st-order dependence of the coking process on active sites availability. Hydrogen hinders the coking process, though slight activity decay is due to sintering of the active Ni phase. Deactivation by thiophene causes a sharp, almost linear, drop to nearly zero activity within only 6 h; this deactivation is likely due to dissociative adsorption of thiophene with subsequent strong, irreversible chemical adsorption of S-atoms on active Ni sites, i.e., irreversible poisoning. Modeling of activity decay curves (α, at/a0) by proper kinetic equations allows assessing the effects of temperature, pressure, S/C, H2 and thiophene feed on the deactivation pattern of the model Ni/MgO catalyst by coking, sintering, and poisoning phenomena

A Workflow-oriented Language for Scalable Data Analytics

Proceedings of: First International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2014). Porto (Portugal), August 27-28, 2014.Data in digital repositories are everyday more and more massive and distributed. Therefore analyzing them requires efficient data analysis techniques and scalable storage and computing platforms. Cloud computing infrastructures offer an effective support for addressing both the computational and data storage needs of big data mining and parallel knowledge discovery applications. In fact, complex data mining tasks involve data- and compute-intensive algorithms that require large and efficient storage facilities together with high performance processors to get results in acceptable times. In this paper we describe a Data Mining Cloud Framework (DMCF) designed for developing and executing distributed data analytics applications as workflows of services. We describe also a workflow-oriented language, called JS4Cloud, to support the design and execution of script-based data analysis workflows on DMCF. We finally present a data analysis application developed with JS4Cloud, and the scalability achieved executing it on DMCF.The work presented in this paper has been partially supported by EU under the COST programme Action IC1305, ’Network for Sustainable Ultrascale Computing (NESUS)’

Beyond the authenticity–standardisation paradox in international gastronomy retailing: Twisting the hosting city brand with the place of origin

PurposeThis study aims to frame the authenticity–standardisation relationship in international gastronomy retailing and explores how and to what extent the food place of origin and the urban context in which the gastronomy stores are located shape customers' in-store experience.Design/methodology/approachThis paper analyses the case of Eataly, which combines specialty grocery stores and restaurants disseminating the Italian eating style, quality food and regional traditions internationally. Facebook reviews (1,018) of four Eataly stores – New York City, Rome, Munich and Istanbul were analysed, adopting a web content mining approach.FindingsPlace of origin, quality and hosting city categories frame the gastronomic in-store experience. Standardisation elements (shared across the four analysed stores) and authenticity elements (specific to a single store) are identified towards defining three archetypical authenticity–standardisation relationships, namely originated authenticity, standardised authenticity and localised authenticity.Originality/valueThis study proposes original modelling that disentangles the authenticity–standardisation paradox in international gastronomy retailing. It provides evidence of the intertwining of the place of origin and the city brand in customers' in-store experience

Multi-Agent Based Modelling of an Endogenous-Money Economy

We present an agent-based model of a simple endogenous-money economy. The model simulates agents representing individual persons who can work, consume, invent new products and related production technologies, apply for a loan from the bank and start up a business. Through the interaction of persons with the firms, we simulate the production of goods, consumption and labour market. In order to achieve a significant level of realism of the simulations, the firms are modelled as adaptive agents using an effective reinforcement learning approach in continuous space. This setting allows us to explore how an endogenous-money economy can be built up from scratch, as an emergent property of actions and interactions among heterogeneous agents once money is injected into a non-monetary self-production (or barter) economy. In the paper, we first empirically investigate the learning capability of the firm agents. Then, we discuss the results of some computational experiments under different significant scenarios

Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

The proximal fracture of the femur and hip is the most common reason for hospitalization in orthopedic departments. In Italy, 115,989 hip-replacement surgeries were performed in 2019, showing the economic relevance of studying this type of procedure. This study analyzed the data relating to patients who underwent hip-replacement surgery in the years 2010-2020 at the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno. The multiple linear regression (MLR) model and regression and classification algorithms were implemented in order to predict the total length of stay (LOS). Lastly, using a statistical analysis, the impact of COVID-19 was evaluated. The results obtained from the regression analysis showed that the best model was MLR, with an R2 value of 0.616, compared with XGBoost, Gradient-Boosted Tree, and Random Forest, with R2 values of 0.552, 0.543, and 0.448, respectively. The t-test showed that the variables that most influenced the LOS, with the exception of pre-operative LOS, were gender, age, anemia, fracture/dislocation, and urinary disorders. Among the classification algorithms, the best result was obtained with Random Forest, with a sensitivity of the longest LOS of over 89%. In terms of the overall accuracy, Random Forest and Gradient-Boosted Tree achieved a value of 71.76% and an error of 28.24%, followed by Decision Tree, with an accuracy of 71.13% and an error of 28.87%, and, finally, Support Vector Machine, with an accuracy of 65.06% and an error of 34.94%. A significant difference in cardiovascular disease, fracture/dislocation, and post-operative LOS variables was shown by the chi-squared test and Mann-Whitney test in the comparison between 2019 (before COVID-19) and 2020 (in full pandemic emergency conditions)

A Multi‑modelling Approach for Assessing Sustainable Tourism

Academics, institutions and policymakers advocate systematic assessments to design sustainable development and implement proper environmental management; however, practical measurements in tourism research based on composite indicators are still in progress. This paper aims to build and validate a composite indicator of sustainable tourism (Sus-Tour-Index), which recognises the economic, environmental and social dimensions as the three main interrelated facets of tourism sustainability. The SusTour-Index is composed of 75 elementary indicators, adequately structured in pillars and sub-pillars within each economic (34), environmental (21) and social dimension (20). A multi-modelling approach tests the hierarchical structure of the SusTour-Index by combining different weighting and aggregation methods within each sustainability dimension to choose the most appropriate model once the uncertainty analysis has been performed. The structure of the SusTour-Index is validated in all 21 Italian regions by performing 23 different models of the same composite indicator. The paper presents theoretical and methodological contributions for future research and advances in practical assessments, supporting policymakers and institutions in planning and managing sustainable tourism development