Developing Design Principles for a Crowd-Based Business Model Validation System

Dellermann, Dominik; Lipusch, Nikolaus; Ebel, Philipp

doi:10.1007/978-3-319-59144-5_10

Cited by 12 publications

(11 citation statements)

References 58 publications

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“…Moreover, end users can provide the system with input for product recommendations and e-commerce or input from human non-experts accessed through crowd work platforms [64,58,65]. More recent endeavors, however, focus on the integration of domain experts in hybrid intelligence architectures that leverage the profound understanding of the semantics of a problem domain to teach a machine, while not requiring any ML expertise [22,66,67].…”

Section: Human-ai Interactionmentioning

confidence: 99%

See 1 more Smart Citation

The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems

Dellermann¹,

Calma²,

Lipusch³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

139

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Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications.

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Section: Human-ai Interactionmentioning

confidence: 99%

“…On the other hand, collective human input combines the input of several individual humans by leveraging mechanisms of human computation (e.g. [68,66,67]). This approach allows to reduce errors and biases of individual humans and the aggregation of heterogeneous knowledge [45,69,65].…”

Section: Amount Of Human Inputmentioning

confidence: 99%

The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems

Dellermann¹,

Calma²,

Lipusch³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

139

View full text Add to dashboard Cite

show abstract

“…Fortschreitende Digitalisierung bietet der Gesellschaft vielfältige Chancen und Herausforderungen [7]. Digitalisierung umfasst nicht nur die Umstellung von analogen Technologien auf digitale Alternativen [8], sondern im Rahmen von Hochschulen auch Veränderungen der Organisationsstrukturen und Rahmenbedingungen [3,4].…”

Section: Digitale Transformation In Hochschulenunclassified

Ein Anreizsystem zur erfolgreichen Implementierung eines digitalen Studienassistenten

König¹,

Guhr²,

Breitner³

2020

WI2020 Community Tracks

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Unterschiedliche Anreizstrukturen und damit auch verschiedene Nutzungsanreize wirken auf das Handeln der beteiligten Akteure. Insbesondere, wenn die Einführung eines neuen Informationssystems wie eines digitalen Studienassistenten bevor steht, sind Anreizstrukturen für die Implementierung und spätere Nutzung weitgehend unbekannt und können zu einer mangelnden Motivation der beteiligten Akteure führen. Unser Forschungsziel ist, verschiedene Anreize für Hochschuleinrichtungen (HE) zu verstehen und in ein Anreizsystem für einen individuellen, digitalen und datengestützten Studienassistenten zu überführen. Durch qualitative Forschung mit Experteninterviews aus einer Hochschule diskutieren wir ein generisches Anreizsystem unter Berücksichtigung des gewachsenen Stellenwerts von digitalen Studienassistenten an Hochschulen. Die Ergebnisse und Erkenntnisse zeigen, dass unterschiedliche Bedürfnisse, implizite und explizite Motive und Ziele von HE in Kombination mit situationsbezogenen Einflüssen berücksichtigt werden müssen, um entsprechendes Handeln, Nutzung und Erfolg zu erzielen.

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“…In order for a technical system, such as a digital study assistant, to be used sustainably, it is essential that it is accepted by the user group [10]. As Abbad et al already pointed out, a solid understanding of user acceptance processes and knowledge about how to convince students to engage with new technologies is necessary for a successful implementation and adoption of a system [11]. This is where our survey comes in.…”

Section: Introductionmentioning

confidence: 98%

Digital assistance in higher education – requirements for the development of a digital study assistant

Greiff¹,

Tenspolde²,

Hoppe³

2020

WI2020 Zentrale Tracks

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Digitalization in higher education sets a trend of digital support in students' and teachers' daily working routine. New ways of accessing teaching materials, simplifying semester planning or structuring learning materials are opened up by technologies such as digital study assistants (DSA). The DSA represents a new and innovative technology in higher education and is still in development. For this reason, it is unclear which exact processes and tasks this application should take over. In software development, it is essential to define the requirements precisely to carry out the subsequent development steps and implementation correctly. These requirements must be derived from the needs of the stakeholders to ensure acceptance and willingness for sustainable use. Therefore, a student survey was conducted via semi-structured qualitative interviews, to identify important requirements for a DSA. Based on our research results, we provide indications for the development and further research of digital study assistance systems.

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Developing Design Principles for a Crowd-Based Business Model Validation System

Cited by 12 publications

References 58 publications

The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems

The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems

Ein Anreizsystem zur erfolgreichen Implementierung eines digitalen Studienassistenten

Digital assistance in higher education – requirements for the development of a digital study assistant

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