This essay presents the concept of sustainability intelligence as a possible response to the current unsustainable course of society. We expound on the three components shaping this concept – naive intelligence, native intelligence, and narrative intelligence – and argue why they could thus serve as inspiration and key reference points for rising to our collective sustainability challenge. The essay ends with a brief exploration of the wider practical, policy and political implications of the concept.
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Expectations are high for digital technologies to address sustainability related challenges. While research into such applications and the twin transformation is growing rapidly, insights in the actual daily practices of digital sustainability within organizations is lacking. This is problematic as the contributions of digital tools to sustainability goals gain shape in organizational practices. To bridge this gap, we develop a theoretical perspective on digital sustainability practices based on practice theory, with an emphasis on the concept of sociomateriality. We argue that connecting meanings related to sustainability with digital technologies is essential to establish beneficial practices. Next, we contend that the meaning of sustainability is contextspecific, which calls for a local meaning making process. Based on our theoretical exploration we develop an empirical research agenda.
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The concept of business sustainability has been investigated, reviewed, and criticized by a plethora of scholars. What constitutes the essence of business sustainability—and its relationship to the actual state of our planet—is still an integral part of the discourse on business-society relations. Recently, Dyllick and Muff (Organization & Environment, 29:156–174, 2016) have reviewed literature in order to uncover what constitutes ‘true’ business sustainability, explaining the apparent absence of a coupling between corporate sustainability initiatives and the state of the planet and explore how this coupling can be strengthened. As such, the authors provide many relevant pointers for answering the question: when is business truly sustainable? This paper aims to respond both critically and constructively to Dyllick and Muff’s article by addressing three points: the somewhat confusing conception of what actually comprises ‘true’ business sustainability, the authors’ choice not to address the underlying economic model and the model of consumer behavior, and the types of sustainability intelligence that, in our view, business needs to develop to truly become a force for spurring sustainable development. We use the Sustainable Development Goals (SDGs) as a case in point to illustrate our argument. In doing so, this paper aims to add to a firm-centered conceptualization of the business-society interface in a constructive way to stimulate further discourse on the concept, and to make a theoretical contribution with respect to coupling mechanisms in the realm of business sustainability.
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Artificial Intelligence (AI) biedt kansen. Het biedt mogelijkheden voor vooruitgang in gezondheidszorg, communicatie, bestuur en productie. Het biedt mogelijkheden voor het creëren van tekst, beeld, geluid en kunst. Het helpt om de effecten van de klimaatcrisis op te vangen door intelligente energienetten te ontwikkelen, door infrastructuren te ontwikkelen die geen of nauwelijks CO2 emissie hebben en door klimaatvoorspellingen te modelleren.Niet alles is positief. AI speelt een groeiende rol in de verspreiding van ‘fake news’, ‘deep fakes’ en andere vormen van misinformatie waardoor onze democratische samenleving wordt bedreigd door populisme en polarisatie.Een onduidelijker effect van AI is het ecologisch effect dat het heeft. Daar is de afgelopen paar jaar veel over gepubliceerd, maar het duurt lang voordat berichten daarover in het maatschappelijke bewustzijn indalen.
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De meest gebruikte opbouw in business intelligence, predictive analitics en analytics modellen is de moeilijkheidsgraad: 1) descriptive, 2) diagnostic, 3) predictive en 4) prescriptive. Deze schaal vertelt iets over de volwassenheid van het gebruik van data door de organisatie. Een model dat niet op zichzelf staat en een achterliggende methode kent is de data driehoek van EDM (Figuur 1), welke in dit artikel zal worden toegelicht.
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Editorial on the Research Topic "Leveraging artificial intelligence and open science for toxicological risk assessment"
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Why cities need economic intelligenceThe economies of Europe’s cities are changingfast, and it is not easy to predict which segmentsof the local economy will grow and which oneswill decline. Yet, cities must make decisions as towhere to invest, and face a number of questionsthat are difficultto answer:Where dowe putour bets? Should we go for biotech, ICT, or anyother sector that may have growth potential?Do we want to attract large foreign companies,or rather support our local indigenous smallerfirms, ormustwe promotethestart-up scene?Or is it better not to go for any particularindustry but just improve the quality of lifein the city, hoping that this will help to retainskilled people and attract high tech firms?
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With artificial intelligence (AI) systems entering our working and leisure environments with increasing adaptation and learning capabilities, new opportunities arise for developing hybrid (human-AI) intelligence (HI) systems, comprising new ways of collaboration. However, there is not yet a structured way of specifying design solutions of collaboration for hybrid intelligence (HI) systems and there is a lack of best practices shared across application domains. We address this gap by investigating the generalization of specific design solutions into design patterns that can be shared and applied in different contexts. We present a human-centered bottom-up approach for the specification of design solutions and their abstraction into team design patterns. We apply the proposed approach for 4 concrete HI use cases and show the successful extraction of team design patterns that are generalizable, providing re-usable design components across various domains. This work advances previous research on team design patterns and designing applications of HI systems.
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Our world is dealing with several pressing sustainability problems. Corporate social responsibility (CSR) initiatives seem to have failed: despite the actions firms have taken over the years to contribute to a better world in an ecological and social sense through directing their resources and competencies towards this goal, the world has been degrading on many important sustainability-related indicators. By implication, firms need to resort to other ways of integrating societal goals into their strategies, organizational architecture, and decision-making processes. Sustainability-oriented business models (SOBMs) may present a way to turn the tides. Adding to the developing discourse on this topic, this chapter identifies three generations of SOBMs and their limitations in realizing sustainable development as well as by presenting an interpretation of fourth generation SBOMs. In doing so, it integrates insights from evolutionary psychology and identifies three types of ‘sustainability intelligence’ firms need to develop in order to be successful in developing SOBMs.
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Education for sustainability scholarship argues that sustainability competence is more than cognitive domain learning that is traditionally (over) focused on reason, knowledge application and testing. Affective domain is missing from the education curricula in general (Sowel, 2005, Dernikos et al, 2020), and in Higher Education in Sustainability (HES) (Shepard, 2008). Yet, “it is possible to construct an argument that the essence of education for sustainability is a quest for affective outcomes” (Shepard, 2008). For example, there is a link between personal values and sustainability performance (Potocan 2021), and emotional intelligence has been seen to be “the foundation of a more cooperative and compassionate [sustainable] society” (Estrada, Rodriguez, Moliner, 2021).
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