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In this article, we calculate the economic impact of pilgrimage to Santiago de Compostela in the NUTS 2 region Galicia (Spain) in 2010. This economic impact is relevant to policymakers and other stakeholders dealing with religious tourism in Galicia. The analysis is based on the Input-Output model. Location Quotient formulas are used to derive the regional Input-Output table from the national Input-Output table of Spain. Both the Simple Location Quotient formula and Flegg's Location Quotient formula are applied. Furthermore, a sensitivity analysis is carried out. We found that pilgrimage expenditures in 2010 created between 59.750 million and 99.575 million in Gross Value Added and between 1, 362 and 2, 162 jobs. Most of the impact is generated within the 'Retail and Travel Services' industry, but also the 'Industry and Manufacturing', 'Services' and 'Financial and Real Estate Services' industries benefit from pilgrimage expenditures. This research indicates that in even in the most conservative scenario, the impact of pilgrimage is significant on the local economy of Galicia.
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This paper presents a new Value Adding Management (VAM) model that aims to support decision makers in identifying appropriate interventions in buildings, other facilities and services that add value to the organisation, to manage its implementation, and to measure the output and outcomes. The paper builds on value adding management theories and models that use the triplet input-throughput-output, a distinction between output, outcome and added value, and concepts, theories and data on the impact of interventions in corporate real estate and facility services, change management and performance measurement. Furthermore, input has been used from a cross-chapter analysis of a new book in which 23 authors from five different European countries present a state of the art of theory and research on 12 value parameters: satisfaction, image, culture, health and safety, productivity, adaptability, innovation, risk, cost, value of assets, sustainability and Corporate Social Responsibility. The new VAM model follows the steps from the well-known Plan-Do-Check-Act cycle, which are supported by various tools that were found in the literature or came to the fore in the state-of-the-art sections. In order to be able to select appropriate interventions in the Plan-phase, this paper includes a typology of typical interventions in corporate real estate and facility services that may add value to the organisation. The Check-phase is supported by an overview of ways to measure the 12 value parameters and related Key Performance Indicators. The new Value Adding Management model connects Corporate Real Estate Management (CREM) and Facilities Management (FM) with general business management in order to align CREM/FM interventions to the organizational context and organizational objectives. The VAM model opens the black box of input-throughput-output-outcome and is action oriented due to the connection to various management and measurement tools.
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Purpose: To present a new Value Adding Management model in order to support decision makers in identifying appropriate interventions to add value to the organisation, to manage its implementation, and to measure the output and outcomes.Theory: The paper builds on value adding management theories and models including the triplet input-throughput-output, a distinction between output, outcome and added value, the Plan-Do-Act-Check cycle, change management and performance measurement.Design/methodology/approach: Literature review and a cross-chapter analysis of a forthcoming book, where authors from different European countries present a state of the art of theory and research on 12 value parameters, how to manage and measure each value, and to discuss the costs and benefits of typical FM and CREM interventions to enhance satisfaction, image, culture, health and safety, productivity, adaptability, innovation, risk, cost, value of assets, sustainability and Corporate Social Responsibility.Findings: The new Value Adding Management model follows the steps from the well-known Plan-Do-Check-Act cycle. The four steps are supported by various tools that were found in the literature or came to the fore in the state-of-the-art sections of the 12 value parameters. Furthermore an overview is presented of ways to measure the 12 value parameters and related Key Performance Indicators.Originality/value: Much has been written about adding value by FM and CREM. This paper presents a new Value Adding Management model that opens the black box of input-throughput-output-outcome and which is supported by various management and measurement tools.
In this paper we investigate the expression of emotions through mediated touch. Participants used the Tactile Sleeve for Social Touch (TaSST), a wearable sleeve that consists of a pressure sensitive input layer, and a vibration motor output layer, to record a number of expressions of discrete emotions. The aim was to investigate if participants could make meaningful distinctions in the tactile expression of the emotions.
In this paper we present the concept and initial design stages of the TaSST (Tactile Sleeve for Social Touch). The TaSST consists of a soft pressure-sensitive input layer, and an output layer containing vibration motors. A touch to ones own sleeve is felt as a vibration on the sleeve of another person. The idea behind the TaSST is to enable two people to communicate dierent types of touch at a distance. We will outline the design process of the TaSST, describe some initial results from a user study, and discuss possible applications of the TaSST.
We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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