Technological developments go fast and are interrelated and multi-interpretable. As consumer needs change, the technological possibilities to meet those needs are constantly evolving and new technology providers introduce new disruptive business models. This makes it difficult to predict what the world of tomorrow will look like for an organization and that makes the risks for organizations substantial. In this context, it is difficult for organizations to determine what constitutes a good strategy to adopt digital developments. This paper describes a first step of a study with the objective to design a method for organizations to formulate a future-proof strategy in a rapidly changing, complex and ambiguous context. More specifically, this paper describes the results of a sequence of three focus groups that were held with a group of eight experts, with extensive experience as members of the decision making unit in organizations. The objectives of these sessions were to determine possible solutions for the outlined challenge in order to provide direction for continuation and scoping of the following research phases.
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Most existing models in supply chain management literature proving the potential of a vertical logistics collaboration decision see individual decision makers as fully rational agents. Nevertheless, literature review makes clear individuals are usually reluctant to change and in consequence they do not always respond to relative differences in a rational manner. The conducted Stated Preference experiment confirms this statement and shows that shippers leave beneficial collaboration opportunities unexploited because they have a certain level of resistance to intensify collaboration with their LSP. This inertia level is measured in terms of costs.
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This overview paper examines three areas crucial to understanding why, despite clear scientific evidence for the growing environmental impacts of tourism transport, there is large-scale inertia in structural transitions and a lack of political will to enact meaningful sustainable mobility policies. These include the importance of addressing socio-technical factors, barriers posed by “technology myths” and the need to overcome “transport taboos” in policy-making. The paper seeks pathways to sustainable mobility by bridging the science–policy gap between academic research and researchers, and policy-makers and practitioners. It introduces key papers presented at the Freiburg 2014 workshop, covering the case for researcher engagement using advocacy and participatory approaches, the role of universities in creating their own social mobility policies, the power of social mechanisms encouraging long-haul travel, issues in consumer responsibility development, industry self-regulation and the operation of realpolitik decision-making and implementation inside formal and informal destination-based mobility partnerships. Overall, the paper argues that governments and the tourism and transport industries must take a more cautious approach to the technological optimism that fosters policy inertia, and that policy-makers must take a more open approach to implementing sustainable transport policies. A research agenda for desirable transport futures is suggested.
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The dynamic inflow effect describes the unsteady aerodynamic response to fast changes in rotor loading due to the inertia of the wake. Fast changes in turbine loading due to pitch actuation or rotor speed transients lead to load overshoots. The phenomenon is suspected to be also relevant for gust situations; however, this was never shown, and thus the actual load response is also unknown. The paper’s objectives are to prove and explain the dynamic inflow effect due to gusts, and compare and subsequently improve a typical dynamic inflow engineering model to the measurements. An active grid is used to impress a 1.8m diameter model turbine with rotor uniform gusts of the wind tunnel flow. The influence attributed to the dynamic inflow effect is isolated from the comparison of two experimental cases. Firstly, dynamic measurements of loads and radially resolved axial velocities in the rotor plane during a gust situation are performed. Secondly, corresponding quantities are linearly interpolated for the gust wind speed from lookup tables with steady operational points. Furthermore,simulations with a typical blade element momentum code and a higher-fidelity free-vortex wake model are performed. Both the experiment and higher-fidelity model show a dynamic inflow effect due to gusts in the loads and axial velocities. An amplification of induced velocities causes reduced load amplitudes. Consequently, fatigue loading would be lower. This amplification originates from wake inertia. It is influenced by the coherent gust pushed through the rotor like a turbulent box. The wake is superimposed on that coherent gust box, and thus the inertia of the wake and consequently also the flow in the rotor plane is affected. Contemporary dynamic inflow models inherently assume a constant wind velocity. They filter the induced velocity and thus cannot predict the observed amplification of the induced velocity. The commonly used Øye engineering model predicts increased gust load amplitudes and thus higher fatigue loads. With an extra filter term on the quasi-steady wind velocity, the qualitative behaviour observed experimentally and numerically can be caught. In conclusion, these new experimental findings on dynamic inflow due to gusts and improvements to the Øye model enable improvements in wind turbine design by less conservative fatigue loads.
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De Resistance to Meat Reduction Scale (RMRS) is een vragenlijst die ontwikkeld is om verschillende vormen van psychologische weerstand tegen vleesvermindering te meten. Kennis over deze weerstand biedt aanknopingspunten voor effectieve interventies om vleesreductie te stimuleren. De RMRS onderscheidt vijf typen subschalen die elk een bepaald type weerstand meet: Reactance, Skepticism, Inertia, Social Validation en Psychological Distance. Binnen het MAP-project is de RMRS toegepast onder 124 mbo-studenten om hun gevoelens van weerstand ten opzichte van minder vlees eten te onderzoeken. Juist mbo-studenten vormen een relevante doelgroep voor toekomstig duurzaam eetgedrag. De resultaten laten zien dat mbo-studenten vooral hoge scores vertonen op Inertia, een passieve vorm van weerstand die samenhangt met gewoontegedrag en lage motivatie om te veranderen. Ook Psychological Distance scoorde hoog, wat betekent dat veel studenten klimaatproblematiek als abstract en niet-urgent ervaren. Mannen bleken significant meer weerstand te tonen dan vrouwen op bijna alle sub-schalen, behalve op de sub-schaal Social Validation. Er werden geen verschillen gevonden tussen studenten uit dorp, stad of middelgrote gemeenten. Deze studie laat zien dat gedragsverandering bij mbo-studenten op het gebied van vleesconsumptie vooral wordt belemmerd door gewoonte en een lage urgentiebeleving. Op basis van deze resultaten wordt aanbevolen om kleine, haalbare stappen te stimuleren, de psychologische afstand te verkleinen met persoonlijke verhalen, positieve mannelijke rolmodellen te benutten en groepsgerichte interventies op scholen te ontwikkelen die keuzevrijheid benadrukken. Dr. Patricia Bulsing is senior onderzoeker bij het Lectoraat Data-driven Marketing bij De Haagse Hogeschool in Den Haag. Dr. Esther Veen is lector Stedelijke Voedselvraagstukken bij Aeres Hogeschool in Almere. Dr.ir. Antien Zuidberg is lector Design Methoden in Food bij HAS green academy in ‘s-Hertogenbosch. Dit rapport is een onderdeel van het subsidieproject Mbo-student in Actie voor Plantaardige voeding (MAP), SIA dossiernummer PVG.DZ23.07.002.
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Insider ethnographic analysis is used to analyze change processes in an engineering department. Distributed leadership theory is used as conceptual framework.
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In the high-tech mechatronics world, aluminum and steel are well known materials, while carbon fiber is often neglected. In the RAAK project 'Composites in Mechatronics', the use of carbon fiber composites in mechatronics is investigated.
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The revolutionary dimension of technological developments is all too often placed in the foreground, painting a picture of a future we can hardly imagine, let alone perceive. This book emphasises the evolutionary dimension of developments, and how we take small steps. How certain underlying, invariable aspects survive hype after hype, and have their own rhythm or longue durée. It is the search for where change meets inertia. Progression is a balance between the familiar and the unfamiliar. The essays bundled in this book have as a leitmotiv this attention to how progression is a matter of locating the small steps we can take and the invariables that lie beneath developments that stimulate or hinder certain big steps forward. It concentrates on the zone of proximal development, to use the term of Vygotsky. This collection of essays represents a personal view of the area where psychology, media, technology, and culture meet in the context of technological and social developments. These crossroads are addressed through topics such as digital identities, interactive media, the museum visitor in a digital world, and growing up in a digital society.
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Purpose: Intellectual capital theory and practice predominantly focus on measuring and managing intangible assets. However, if we want to balance the intellectual capital books (Harvey and Lusch, 1999), we should recognize both intellectual assets and intellectual liabilities (Caddy, 2000). Therefore, the purpose of this article is to present a theoretical framework for measuring intellectual liabilities. Design: Identifying intangible liabilities is identifying the risk of decline and fall of organizations. One of the first extensive studies related to causes of decline and fall is Gibbon‟s Decline and Fall of the Roman Empire (Gibbon, 2003 [original publication 1776]). It seems as if the main lessons that were drawn from this study are also applicable to today‟s business environment. Therefore, the framework that is developed in this article is not only based on intellectual capital literature, but also on Gibbon‟s study into the causes of decline and fall of the Roman Empire. Findings: The findings are combined in a framework for measuring intellectual liabilities. The main distinction within the proposed framework is the distinction between internal and external liabilities. Internal liabilities refer to the causes of deterioration that arise from the sources of value creation within the organization. External liabilities refer to the causes of deterioration that come from outside and are beyond control of the organization. Originality: This article explores a relatively new topic (intellectual liabilities) from a perspective (historical sciences) that is hardly used in management science.
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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