The digital age has brought significant changes in the way organizations operate and compete. As a result of rapid technology development, many organizations are undergoing a digital transformation to stay relevant and competitive in the marketplace. This literature review aims to find future research topics by providing an overview of the current state of research on organizations in digital transformation (ODT), especially on malleable organization design and HRM aspects. The article begins by defining digital transformation (DT), and then examines how organizations change during DT, before delving into the perspectives of malleable organization design and HRM. Finally, it concludes by identifying gaps in the literature and suggesting a research agenda for future. Overall, organizational factors that need more investigation are highlighted to tackle complexities of ODT for further research.
This investigation explores relations between 1) a theory of human cognition, called Embodied Cognition, 2) the design of interactive systems and 3) the practice of ‘creative group meetings’ (of which the so-called ‘brainstorm’ is perhaps the best-known example). The investigation is one of Research-through-Design (Overbeeke et al., 2006). This means that, together with students and external stakeholders, I designed two interactive prototypes. Both systems contain a ‘mix’ of both physical and digital forms. Both are designed to be tools in creative meeting sessions, or brainstorms. The tools are meant to form a natural, element in the physical meeting space. The function of these devices is to support the formation of shared insight: that is, the tools should support the process by which participants together, during the activity, get a better grip on the design challenge that they are faced with. Over a series of iterations I reflected on the design process and outcome, and investigated how users interacted with the prototypes.
Coastal nourishments, where sand from offshore is placed near or at the beach, are nowadays a key coastal protection method for narrow beaches and hinterlands worldwide. Recent sea level rise projections and the increasing involvement of multiple stakeholders in adaptation strategies have resulted in a desire for nourishment solutions that fit a larger geographical scale (O 10 km) and a longer time horizon (O decades). Dutch frontrunner pilot experiments such as the Sandmotor and Ameland inlet nourishment, as well as the Hondsbossche Dunes coastal reinforcement project have all been implemented from this perspective, with the specific aim to encompass solutions that fit in a renewed climate-resilient coastal protection strategy. By capitalizing on recent large-scale nourishments, the proposed Coastal landSCAPE project C-SCAPE will employ and advance the newly developed Dynamic Adaptive Policy Pathways (DAPP) approach to construct a sustainable long-term nourishment strategy in the face of an uncertain future, linking climate and landscape scales to benefits for nature and society. Novel long-term sandy solutions will be examined using this pathways method, identifying tipping points that may exist if distinct strategies are being continued. Crucial elements for the construction of adaptive pathways are 1) a clear view on the long-term feasibility of different nourishment alternatives, and 2) solid, science-based quantification methods for integral evaluation of the social, economic, morphological and ecological outcomes of various pathways. As currently both elements are lacking, we propose to erect a Living Lab for Climate Adaptation within the C-SCAPE project. In this Living Lab, specific attention is paid to the socio-economic implications of the nourished landscape, as we examine how morphological and ecological development of the large-scale nourishment strategies and their design choices (e.g. concentrated vs alongshore uniform, subaqueous vs subaerial, geomorphological features like artificial lagoons) translate to social acceptance.
The impacts of tourism on destinations and the perceptions of local communities have been a major concern both for the industry and research in the past decades. However, tourism planning has been mainly focused on traditions that promote the increase of tourism without taking under consideration the wellbeing of both residents and visitors. To develop a more sustainable tourism model, the inclusion of local residents in tourism decision-making is vital. However, this is not always possible due to structural, economic and socio-cultural restrictions that residents face resulting to their disempowerment. This study aims to explore and interpret the formal processes around tourism decision-making and community empowerment in urban settings. The research proposes a comparative study of three urban destinations in Europe (The Hague in the Netherlands, San Sebastian in Spain and, Ioannina in Greece) that experience similar degree of tourism growth. The proposed study will use a design-based approach in order to understand tourism decision-making and what empowers or disempowers community participation within the destinations. Based on the findings of primary and secondary data, a community empowerment model will be applied in one the destinations as a pilot for resident engagement in tourism planning. The evaluation of the pilot will allow for an optimized model to be created with implications for tourism planning at a local level that can contribute to sustainable destinations that safeguard the interests of local residents and tourists.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).