What are socially disruptive technologies?

Hopster, Jeroen

doi:10.1016/j.techsoc.2021.101750

Cited by 96 publications

(48 citation statements)

References 16 publications

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“…Breakthroughs can lead to radical innovation ( Hein and Juliette Brun, 2019 ) which is considered new and unique compared to what already exists ( Chandy and Tellis, 1998 ), impacting future technology ( Dahlin and Behrens, 2005 ; Datta and Jessup, 2013 ) but with the same targeted customers. A disruptive innovation, popularized by Christensen (1997) , modifies the way individuals, organizations, and industries operate and interact, changing their habits and destroying existing business models ( Schuelke-Leech, 2018 ), much like e-commerce ( Hopster, 2021 ). Disruptive technologies, focused on niche segments in the early development phase, are distinguished from mainstream technologies by a different performance package with low-end and low-profit portions ( Adner, 2002 ; Ray and Ray, 2011 ).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs

Baudier¹,

Kondrateva²,

Ammi³

et al. 2023

Technovation

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Section: Theoretical Backgroundmentioning

confidence: 99%

Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs

Baudier¹,

Kondrateva²,

Ammi³

et al. 2023

Technovation

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“…The extent to which a disruptive exemplar leads to scaling and widespread adoption is difficult to predict; this in itself is not a precondition for being disruptive. As such, the analysis of disruptive exemplars serves a wider purpose that goes beyond a mere retrospective exercise with limited relevance for understanding current and emerging societal dynamics (Hopster 2021). One such exemplar is the agroecological symbiosis piloted in Finland, in which the generation of on-farm bioenergy from green manure is combined with precision fertilization, resulting in a concomitant increase in the production area of cash crops for human consumption (Koppelmäki et al 2019).…”

Section: Associated Transition Pathwaysmentioning

confidence: 99%

Learning from the future: mainstreaming disruptive solutions for the transition to sustainable food systems

Valencia

Bennett

Altieri

et al. 2022

Environ. Res. Lett.

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We comment on one of the scientific paradoxes of our times in the field of global studies on food: the influential scenario studies that call for new paradigms for the global food system are themselves locked into a past solution space. We posit that the extrapolation of past solution spaces into the future runs the risk of restricting the modelled scenarios to futures that are very similar to the status quo. We propose a novel complementary approach to scenario building, which we provocatively refer to as “Learning from future successes”: identifying and studying existing, successful early exemplars of disruptively novel farming systems that have independently reconfigured, and as such ‘break the rules’ of the relationships between the agronomic, environmental, economic and entrepreneurial variables commonly modelled in global food studies. By analyzing these existing exemplars, we can reconstruct their transition pathways as the basis to examine futures for regional and global food systems that are simultaneously radically progressive, yet proven to be attainable and desirable. Using disruptive exemplars as the starting point, we may discover novel sets of drivers of the future (e.g., cultural diversity, trust, gender relations), that have thus far eluded past experiences and trends. Drawing also from imagination and creativity, this approach broadens the gamut of possible pathways and people’s agency to shape desired futures. We illustrate our approach with three international initiatives: the Global Network of Lighthouse Farms, Agroecological Lighthouses, and Seeds of Good Anthropocenes. All three networks have identified and brought together existing early exemplars of disruptive farming and food systems; together they demonstrate complementary applications of our approach, namely: 1) the redesign of future farming systems, 2) the illumination of pathways for scaling disruptive systems, and 3) the formulation of narratives and visions that are inspirational and that incentivize change.

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“…Many research studies have focused their attention on various aspects and challenges that online social networks face. The focus of these studies ranges from community detection [ 11 – 15 ], social recommender systems [ 16 , 17 ], social media analysis [ 18 – 21 ] to misbehaviour and disruptive activities [ 22 – 25 ]. In particular, the topic of troll detection in online social networks has attracted many research studies in the course of the past few years [ 26 – 28 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Detection of fickle trolls in large-scale online social networks

Shafiei

Dadlani

2022

J Big Data

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Online social networks have attracted billions of active users over the past decade. These systems play an integral role in the everyday life of many people around the world. As such, these platforms are also attractive for misinformation, hoaxes, and fake news campaigns which usually utilize social trolls and/or social bots for propagation. Detection of so-called social trolls in these platforms is challenging due to their large scale and dynamic nature where users’ data are generated and collected at the scale of multi-billion records per hour. In this paper, we focus on fickle trolls, i.e., a special type of trolling activity in which the trolls change their identity frequently to maximize their social relations. This kind of trolling activity may become irritating for the users and also may pose a serious threat to their privacy. To the best of our knowledge, this is the first work that introduces mechanisms to detect these trolls. In particular, we discuss and analyze troll detection mechanisms on different scales. We prove that the order of centralized single-machine detection algorithm is $$O(n^3)$$ O ( n 3 ) which is slow and impractical for early troll detection in large-scale social platforms comprising of billions of users. We also prove that the streaming approach where data is gradually fed to the system is not practical in many real-world scenarios. In light of such shortcomings, we then propose a massively parallel detection approach. Rigorous evaluations confirm that our proposed method is at least six times faster compared to conventional parallel approaches.

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What are socially disruptive technologies?

Cited by 96 publications

References 16 publications

Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs

Digital transformation of healthcare during the COVID-19 pandemic: Patients’ teleconsultation acceptance and trusting beliefs

Learning from the future: mainstreaming disruptive solutions for the transition to sustainable food systems

Detection of fickle trolls in large-scale online social networks

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