Particulate matter (PM) exposure, amongst others caused by emissions and industrial processes, is an important source of respiratory and cardiovascular diseases. There are situations in which blue-collar workers in roadwork companies are at risk. This study investigated perceptions of risk and mitigation of employees in roadwork (construction and maintenance) companies concerning PM, as well as their views on methods to empower safety behavior, by means of a mental models approach. We held semi-structured interviews with twenty-two employees (three safety specialists, seven site managers and twelve blue-collar workers) in three different roadwork companies. We found that most workers are aware of the existence of PM and reduction methods, but that their knowledge about PM itself appears to be fragmented and incomplete. Moreover, road workers do not protect themselves consistently against PM. To improve safety instructions, we recommend focusing on health effects, reduction methods and the rationale behind them, and keeping workers’ mental models into account. We also recommend a healthy dialogue about work-related risk within the company hierarchy, to alleviate both information-related and motivation-related safety issues. https://doi.org/10.1016/j.ssci.2019.06.043 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
Organizing entrepreneurial collaboration in small, self-directed teams is gaining popularity. The underlying co-creation processes of developing a shared team vision were analyzed with a core focus on three underlying processes that originate from the shared mental models framework. These processes are: 1) the emergence of individual visions and vision integration, 2) conflict solving, and 3) redesigning the emerging knowledge structure. Key in the analysis is the impact of these three processes on two outcome variables: 1)the perceived strength of the co-creation process, 2) the final team vision. The influence of business expertise and the relationship between personality traits and intellectual synergy was also studied. The impact of the three quality shared mental model (SMM) variables proves to be significant and strong, but indirect. To be effective, individual visions need to be debated during a second conflict phase. Subsequently, redesigning the shared knowledge structure resulting from the conflict solving phase is a key process in a third elaboration phase. This sequence positively influences the experienced strength of the co-creation process, the latter directly enhancing the quality of the final team vision. The indirect effect reveals that in order to be effective, the three SMM processes need to be combined, and that the influence follows a specific path. Furthermore, higher averages as well as a diversity of business expertise enhance the quality of the final team vision. Significant relationships between personality and an intellectual synergy were found. The results offer applicable insights for team learning and group dynamics in developing an entrepreneurial team vision. LinkedIn: https://www.linkedin.com/in/rainer-hensel-phd-8ba44a43/ https://www.linkedin.com/in/ronald-visser-4591034/
The role and ethics of professionals in business and economics have been questioned, especially after the financial crisis of 2008. Some suggest a reorientation using concepts such as craftsmanship. In this article, I will explore professional practices within the context of behavioural theory and business ethics. I suggest that scholars of behavioural theory need a strategy to deal with normative questions to meet their ambition of practical relevance. Evidence-based management (EBMgt), a recent behavioural approach, may assist business ethics scholars in understanding how professionals infer ‘evidence’ to make decisions. For a professional, ethical issues are an integral part of decision-making at critical moments. As reflective practitioners, they develop insights related to ethical concerns when collecting and assessing evidence within decision-making processes.
The transition towards an economy of wellbeing is complex, systemic, dynamic and uncertain. Individuals and organizations struggle to connect with and embrace their changing context. They need to create a mindset for the emergence of a culture of economic well-being. This requires a paradigm shift in the way reality is constructed. This emergence begins with the mindset of each individual, starting bottom-up. A mindset of economic well-being is built using agency, freedom, and responsibility to understand personal values, the multi-identity self, the mental models, and the individual context. A culture is created by waving individual mindsets together and allowing shared values, and new stories for their joint context to emerge. It is from this place of connection with the self and the other, that individuals' intrinsic motivation to act is found to engage in the transitions towards an economy of well-being. This project explores this theoretical framework further. Businesses play a key role in the transition toward an economy of well-being; they are instrumental in generating multiple types of value and redefining growth. They are key in the creation of the resilient world needed to respond to the complex and uncertain of our era. Varta-Valorisatielab, De-Kleine-Aarde, and Het Groene Brein are frontrunner organizations that understand their impact and influence. They are making bold strategic choices to lead their organizations towards an economy of well-being. Unfortunately, they often experience resistance from stakeholders. To address this resistance, the consortium in the proposal seeks to answer the research question: How can individuals who connect with their multi-identity-self, (via personal values, mental models, and personal context) develop a mindset of well-being that enables them to better connect with their stakeholders (the other) and together address the transitional needs of their collective context for the emergence of a culture of the economy of wellbeing?
PBL is the initiator of the Work Programme Monitoring and Management Circular Economy 2019-2023, a collaboration between CBS, CML, CPB, RIVM, TNO, UU. Holidays and mobility are part of the consumption domains that PBL researches, and this project aims to calculate the environmental gains per person per year of the various circular behavioural options for both holiday behaviour and daily mobility. For both behaviours, a range of typical (default) trips are defined and for each several circular option explored for CO2 emissions, Global warming potential and land use. The holiday part is supplied by the Centre for Sustainability, Tourism and Transport (CSTT) of the BUas Academy of Tourism (AfT). The mobility part is carried out by the Urban Intelligence professorship of the Academy for Built Environment and Logistics (ABEL).The research question is “what is the environmental impact of various circular (behavioural) options around 1) holidays and 2) passenger mobility?” The consumer perspective is demarcated as follows:For holidays, transportation and accommodation are included, but not food, attractions visited and holiday activitiesFor mobility, it concerns only the circular options of passenger transport and private means of transport (i.e. freight transport, business travel and commuting are excluded). Not only some typical trips will be evaluated, but also the possession of a car and its alternatives.For the calculations, we make use of public databases, our own models and the EAP (Environmental Analysis Program) model developed by the University of Groningen. BUAs projectmembers: Centre for Sustainability, Tourism and Transport (AT), Urban Intelligence (ABEL).
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).
