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• The Institutional Research Regime (IRR) literature increasingly focuses on actors and activities. • A natural resource focus predominates across time in the IRR literature. • We identify three groups of IRR studies: complex, bottom-up and top-down IRR studies. This article presents a review of over 100 studies relying on the Institutional Research Regime (IRR) framework. We base the review on a set of 13 elements that cover categories concerning research focus, research design and results. The article first presents the development of IRR studies over time by showing and discussing how different elements of the IRR framework are more or less popular in the literature, and how this popularity develops over time. For example, we have identified a trend in the IRR literature to focus more on actors and activities, while the resource focus predominates across time. Second, the article analyses how different elements of the IRR framework are combined in the existing literature and thus identifies three groups of IRR studies, that is, complex IRR studies, bottom-up IRR studies, and top-down IRR studies. [ABSTRACT FROM AUTHOR]

This article presents a reflection and a methodological proposal on the use of the typological paragon in mixed sequential designs. The paragon is defined as the average or individual profile of a particular cluster that is obtained through multivariate techniques (correspondences analysis and cluster analysis). Here we propose four applications of the typological paragon: first, as a descriptive element, which aims to account for the content; second, as an element of sampling; third, as a link or connecting element between databases with the aim of facilitating matching; and finally, as an element that allows the convergence of results. Two studies provide the empirical basis of this innovative procedure and show the practical implementation of the four uses of paragon. The first deals with the analysis of the social stratification of households in Argentina (Fachelli, 2009). The second focuses on the study and identification of employment trajectories and their mutual influence with social capital in Spain and Catalonia (Alcaide, 2013). The four uses of the paragon that derive from both investigations allow us to reflect on the potentialities and limitations of the proposed model, the typological paragon. [ABSTRACT FROM AUTHOR]

The objective of this paper is to evaluate the impact of information demand and supply on stock market trading volume. Few studies have demonstrated the role of Google search data in analyzing trading volume activity. In this study, we employ a proxy for information demand which is derived from weekly internet search volume. The latest is from Google Trends database, for 25 of the largest stocks traded on CAC40 index, between April 2007 and March 2014. We use news headlines as a proxy for information supply. We use Garch model to analyze and predict trading volume. The empirical results present new evidences. First, information supply has an impact on trading volume but information demand's impact is much more important. Secondly, by applying MCA to results found, it could be concluded that the impact of public information on transaction volume is conditioned by two elements: the firm and market news disclosure and the second element relates to the characteristics of the market participants, more precisely their news interpretations and their risk aversion. Thirdly, we used Chow structural break test to verify the stability of our model. We found that for securities with structural changes, information demand is the responsible variable of the change in our model. Finally, we found that information variables have a predictive power on transaction volume. This paper contributes to existing literature by incorporating open source internet-based data into the analysis and prediction of transaction volume. Using internet information about the stock market, which has appeared recently as an interesting research for financial empiricists, computer scientists and practitioners, will have a very important utility because quantifying demand and supply of information becomes possible. [ABSTRACT FROM AUTHOR]

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Abstract: Some of the main research questions in the assessment ecosystem services include how to integrate ecological and social information into the analysis and how to make it spatially explicit. We mapped six ecosystem services delivered by forests in the Sierra Nevada Mountains (south-east Spain) from the supply- to the demand-sides, taking into account the influence of protected areas on the capacity of supply services. Semi-structured interviews and geographical information system sources were used to map the supply-side, whereas 205 face-to-face questionnaires were distributed to assess and map the demand-side. Our results show the existence of consistent ecosystem service bundles in terms of both the supply- and demand-sides, particularly between erosion control–recreational hunting and between mushroom harvesting–nature tourism. We found a spatial scale mismatch for the erosion control, with its supply at the local scale and its demand at the regional–national scales, with implications at the institutional scale at which it should be managed. Consequently, mapping both the supply- and demand-sides is essential for environmental decision making because it can indicate where management interventions should be focused, either by defining high-priority areas for protection or defining the institutional scale at which these services should be managed. [Copyright &y& Elsevier]

This study aims to offer a basis for developing appropriate response strategies through understanding tourists' natural perceptions related to their rural tourism experiences and the characteristics of the rural tourism market segments. To achieve this purpose, the netnography methodology, which is an online participatory observation technique based on user-generated online content that enables market researchers to observe natural consumer behaviors, was conducted on 1002 tourists' opinions in three online communities. Qualitative information derived from netnography combined with quantitative tools, multiple correspondence analysis and cluster analysis, allows for the segmentation of the rural tourism market. It has been confirmed that three market segments exist in rural tourism in Spain and three corresponding themes have also been derived from the results; comfort-driven visitor, rural ambiance spender, and active leisure seeker. This study is significant in that it began with the qualitative collection of user-generated online content from netnography and finished with the quantitative segmentation of the market based on tourist experiences. • Qualitative data combined with quantitative tools was used to describe the market. • Rural tourism experiences comprise a mix of cognitive, affective, and sensory parts. • Three primary segments in the rural tourism market in Spain were identified. • Defined as comfort-driven visitor, rural ambiance spender, and active leisure seeker. • These three segments differ in terms of trip types, price levels and region of origin. [ABSTRACT FROM AUTHOR]

• Decision support system for urbans square design and social monitoring. • Statistical approach for the social-economic sustainability estimation. • Image processing oriented on citizen behavior analysis and on urban square livability. In this paper we proposed a new platform based on data processing suitable for urban square design optimization. The re-design of square in cities is important to make the urban squares livable especially for cities with high population density. Innovative solutions about green space design, computer vision and environmental sensing have been implemented, observing experimentally the influence of the public space on people's behaviour. Furthermore, the platform processes data about weather condition, objects detected by image processing algorithms, environmental pollution and occupancy patterns to perform correlation analysis. A Convolutional Neural Network (CNN) has been used to predict people occupancy over time, basing on a weather dataset. The platform implements a Decision Support System (DSS) aimed to read the data, and provide guidelines for urban square design optimization. The DSS is constructed by data modeling, data mining and multiple correspondence analysis (MCA) tools tailored on the specific case of study. Experimental observations provide insights for a self-adaptive urban square design. Multiple correspondence analysis best fits qualitative and quantitative data obtained from sensors, and highlights people's behaviour in the urban square in correlation with different variables. After a complete overview of factors which can influence the livability of a square and economical/social models are presented different results about citizen behaviour interpretation in function of weather data and attitudes. The proposed framework can be potentially applied for a generic urban square. [ABSTRACT FROM AUTHOR]