1,490 research outputs found

    Digital transformation in food supply chains: an implementation framework

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    Purpose Digital transformation using Industry 4.0 technologies can address various challenges in food supply chains (FSCs). However, the integration of emerging technologies to achieve digital transformation in FSCs is unclear. This study aims to establish how the digital transformation of FSCs can be achieved by adopting key technologies such as the Internet of Things (IoTs), cloud computing (CC) and big data analytics (BDA). Design/methodology/approach A systematic literature review (SLR) resulted in 57 articles from 2008 to 2022. Following descriptive and thematic analysis, a conceptual framework based on the diffusion of innovation (DOI) theory and the context-intervention-mechanism-outcome (CIMO) logic is established, along with avenues for future research. Findings The combination of DOI theory and CIMO logic provides the theoretical foundation for linking the general innovation process to the digital transformation process. A novel conceptual framework for achieving digital transformation in FSCs is developed from the initiation to implementation phases. Objectives and principles for digitally transforming FSCs are identified for the initiation phase. A four-layer technology implementation architecture is developed for the implementation phase, facilitating multiple applications for FSC digital transformation. Originality/value The study contributes to the development of theory on digital transformation in FSCs and offers managerial guidelines for accelerating the growth of the food industry using key Industry 4.0 emerging technologies. The proposed framework brings clarity into the “neglected” intermediate stage of data management between data collection and analysis. The study highlights the need for a balanced integration of IoT, CC and BDA as key Industry 4.0 technologies to achieve digital transformation successfully

    Organizing sustainable development

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    The role and meaning of sustainable development have been recognized in the scientific literature for decades. However, there has recently been a dynamic increase in interest in the subject, which results in numerous, in-depth scientific research and publications with an interdisciplinary dimension. This edited volume is a compendium of theoretical knowledge on sustainable development. The context analysed in the publication includes a multi-level and multi-aspect analysis starting from the historical and legal conditions, through elements of the macro level and the micro level, inside the organization. Organizing Sustainable Development offers a systematic and comprehensive theoretical analysis of sustainable development supplemented with practical examples, which will allow obtaining comprehensive knowledge about the meaning and its multi-context application in practice. It shows the latest state of knowledge on the topic and will be of interest to students at an advanced level, academics and reflective practitioners in the fields of sustainable development, management studies, organizational studies and corporate social responsibility

    A person‐centred view of citizen participation in civic crowdfunding platforms: A mixed‐methods study of civic backers

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    AbstractCrowdfunding platforms have emerged as a promising contemporary means for mobilising collective civic actions to address local or social issues, improve community cohesion and develop the public good. This empirical study taps into the understudied civic crowdfunding platforms (CCP) developed to facilitate such actions, proposing, supporting and funding public‐interest projects through crowdsourcing and microfinancing. Previous studies have shown that individuals' characteristics affect their level of civic engagement with social issues. Considering the diversity of contributor motivations, we aim to shed light on the dynamics of emergent subpopulations of citizens who participate in CCPs. To this end, we use a sequential mixed‐methods approach to integrate our fuzzy set Qualitative Comparative Analysis (fsQCA) findings with the results of an in‐depth qualitative study, to gain rich and robust inferences and meta‐inferences. In Study 1 (n = 316), we used fsQCA to explore five distinctive configural profiles that display the heterogeneity of civic backers' motivations, including civic champions, prosocial advocates, normative supporters, reward seekers and regret‐averse contributors. In Study 2, we corroborated and complemented our fsQCA inferences through an extreme‐case study and identified four boundary conditions. Taken together, our inferences and meta‐inferences address the heterogeneity of motivations for participating in CCPs, by understanding and theorising about diverse profiles of citizen backers. Finally, we offer practical implications for successful civic crowdfunding initiatives.</jats:p

    Digital transformation: A multidisciplinary perspective and future research agenda

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    Digital transformation has had an unprecedented influence on all sectors of business over the last decade. We are now entering an era characterized by the extensive digital transformation of businesses, society, and consumers. Therefore, digital transformation has become a pivotal focus for organizations across various sectors in recent years. Despite differing scholarly perspectives on the concept and elements of digital transformation, a consensus exists that it significantly impacts consumer decisions and necessitates organizational adaptation. Recent challenges such as the COVID‐19 pandemic have further accelerated the need for digital transformation and its effects on consumers. This necessitates an editorial perspective on this most important topic to establish future research agenda encompassing the various dimensions of digital transformation. The purpose of this editorial perspective is to review research on digital transformation from a multidisciplinary viewpoint and provide insights into several key domains—Internet‐of‐Things, social media, mobile apps, artificial intelligence, augmented and virtual reality, the metaverse, and corporate digital responsibility—that are poised to fuel the pace of digital transformation. Each domain is analyzed through a lens of introduction, role, importance, multifaceted impact, and conclusions. Future research directions are suggested

    Driving venture capital funding efficiencies through data driven models. Why is this important and what are its implications for the startup ecosystem?

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    This thesis aims to test whether data models can fit the venture capital funding process better, and if they do fit, can they help improve the venture capital funding efficiency? Based on the reported results, venture capitalists can only see returns in 20% of their investments. The thesis argues that it is essential to help venture capital investment as it can help drive economic growth through investments in innovation. The thesis considers four startup scenarios and the related investment factors. The scenarios are a funded artificial intelligence startup seeking follow-on funding, a new startup seeking first funding, the survivability of a sustainability-focused startup, and the importance of patents for exit. Patents are a proxy for innovation in this thesis. Through quantitative analysis using generalized linear models, logit regressions, and t-tests, the thesis can establish that data models can identify the relative significance of funding factors. Once the factor significance is established, it can be deployed in a model. Building the machine learning model has been considered outside the scope of this thesis. A mix of academic and real-world research has been used for the data analysis of this thesis. Accelerators and venture capitalists also used some of the results to improve their own processes. Many of the models have shifted from a prediction to factor significance. This thesis implies that it could help venture capitalists plan for a 10% efficiency improvement. From an academic perspective, this study focuses on the entire life of a startup, from the first funding stage to the exit. It also links the startup ecosystem with economic development. Two additional factors from the study are the regional perspective of funding differences between Asia, Europe, and the US and that this study would include the recent economic sentiment. The impact of the funding slowdown has been measured through a focus on first funding and longitudinal validations of the data decision before the slowdown. Based on the results of the thesis, data models are a credible alternative and show significant correlations between returns and factors. It is advisable for a venture capitalist to consider these

    Kapitalismus und Nachhaltigkeit

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    Sind Kapitalismus und Nachhaltigkeit miteinander vereinbar? Falls ja: Mit welchen Folgen und Nebenfolgen gelingt es kapitalistischen Ökonomien, eine Antwort auf die multiplen ökologischen Krisen zu finden? Ist es ausgeschlossen, dass die "schöpferische Zerstörung" (Schumpeter) als Modus kapitalistischer Dynamik nicht auch den fossilen Kapitalismus "schöpferisch" zerstört und neue "Kombinationen von Dingen und KrĂ€ften" ersinnt, die den Kapitalismus tatsĂ€chlich ergrĂŒnen lassen? Oder gibt es materiale Schranken dafĂŒr, die in den stofflichen Eigenschaften von Öko-Systemen liegen? Scheitert der Kapitalismus letztlich an der ökologischen Frage

    Machine learning-based characterisation of urban morphology with the street pattern

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    Streets are a crucial part of the built environment, and their layouts, the street patterns, are widely researched and contribute to a quantitative understanding of urban morphology. However, traditional street pattern analysis only considers a few broadly defined characteristics. It uses administrative boundaries and grids as units of analysis that fail to encompass the diversity and complexity of street networks. To address these challenges, this research proposes a machine learning-based approach to automatically recognise street patterns that employs an adaptive analysis unit based on street-based local areas (SLAs). SLAs use a network partitioning technique that can adapt to distinct street networks, making it particularly suitable for different urban contexts. By calculating several streets’ network metrics and performing a hierarchical clustering method, streets with similar characters are grouped under the same street pattern. A case study is carried out in six cities worldwide. The results show that street pattern types are rather diverse and hierarchical, and categorising them into clearly demarcated taxonomy is challenging. The study derives a set of new morphometrics-based street patterns with four major types that resemble conventional street patterns and eleven sub-types to significantly increase their diversity for broader coverage of urban morphology. The new patterns capture urban structural differences across cities, such as the urban-suburban division and the number of urban centres present. In conclusion, the proposed machine learning-based morphometric street pattern to characterise urban morphology has an enhanced ability to encompass more information from the built environment while maintaining the intuitiveness of using patterns

    Arsenic exposure and developmental neurotoxicity: An evaluation of biomarkers of effect.

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    Revised version. Spelling and formatting errors corrected. Two figures added.Masteroppgave i biomedisinBMED395MAMD-MEDB

    Artificial Intelligence and Digital Work: The Sociotechnical Reversal

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    A well-designed information system (IS) in the classical view comprises two interrelated yet different subsystems; one that represents the technological dimension of work; and one that represents the social dimension. When these subsystems are heralded as equally important, they constitute a sociotechnical whole, producing economic outcomes such as profit and efficiency, plus humanistic outcomes, such as engagement and well-being. We see, increasingly, this classical view becoming obliviated. In this conceptual paper, we reflect upon the role of humans and technology in these changing work environments. While technical aspects from Artificial Intelligence and digital technologies are dominating the social side of work, we suggest a sociotechnical reversal to happen. Whereas this technosocial reality might be well motivated by advances in efficiency and productivity, the effects on well-being and engagement are less well understood. Consequently, we provide a set of theoretically derived principles to guide these changes in the digital workplace
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