Design principles for a hybrid intelligence decision support system for business model validation

Dellermann, Dominik; Lipusch, Nikolaus; Ebel, Philipp; Leimeister, Jan Marco

doi:10.1007/s12525-018-0309-2

Cited by 64 publications

(50 citation statements)

References 96 publications

(168 reference statements)

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“…SD thereby allows individuals to overcome cognitive limitations by leveraging the intelligence from multiple managers and computational simulation. Such combinations of computational processing and human cognition capabilities have been demonstrated to facilitate valuable solutions in the context of business model validation (Dellermann et al 2018).…”

Section: Discussionmentioning

confidence: 99%

System dynamics for corporate business model innovation

Moellers

Burg²,

Bansemir

et al. 2019

Electron Markets

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Business model innovation is a process that allows firms to build and maintain competitive advantages. However, it imposes major challenges to managers who rely on incomplete cognitive representations while attempting to understand the environmental dynamics that determine a business model's prospective performance. System Dynamics is a computational approach potentially useful in enhancing managerial understanding and decision-making during business model innovation, yet its effects lack sound empirical evidence. This study of five cases inside BMW assesses the usefulness of System Dynamics along the different phases of business model innovation processes. In order to develop a nuanced understanding we triangulate insights from the multiple-case study approach with results from a Q-Sort exercise. Our emergent theory highlights that System Dynamics enables managers to develop more accurate cognitive representations about their business models. Unexpectedly, we find that this process leads to a cognition gap apparent in the communication with managers not involved in the modelling process. We observe two strategies to overcome this gap. A second key insight is that System Dynamics has a tendency to consolidate mental models by different managers that need to be managed cautiously. We develop a set of 11 propositions that represents the core of our theoretical insights.

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Section: Discussionmentioning

confidence: 99%

System dynamics for corporate business model innovation

Moellers

Burg²,

Bansemir

et al. 2019

Electron Markets

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“…Finally, the fields of AIs and big data develop computational models to handle large amount of information nondeterministically, and they can be applied literally as decision support systems for people running new ventures or innovation projects (cf. Dellermann et al 2018;Sohn and Lee 2013;Babovic 2011, 2012). Future studies can explore how entrepreneurs can use AI nondeterministic models as decision support for pathdependent decision-making under uncertainty.…”

Section: Learning From Ai: Further Researchmentioning

confidence: 99%

Advancing entrepreneurship as a design science: developing additional design principles for effectuation

Zhang

Burg

2019

Small Bus Econ

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Scholars have advocated the development of entrepreneurship as a design science. One foundational challenge in a design science is to identify design principles. We argue that a particular field can draw on a design knowledge from different design sciences to develop design principles. In particular, we show that entrepreneurship research can learn from one branch of artificial intelligence studies called Bgenetic algorithm,ŵ hich is a design field that creates solutions for complex, nonanalytical, and ill-structured problems. We illustrate the analogous transfer process by identifying complementary design principles for one exemplary entrepreneurship theory, namely effectuation. In turn, these additional effectual design principles further effectuation theory as a design science and help advance entrepreneurship as a nascent design science. Imaginative thinking informed by a variety of (embodied) knowledge to create new and transform existing artifacts.

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“…This is relevant, because automation is a key pillar of the current discourse on future work, which is increasingly performed by machines . In this context, prior research has discussed how big data analytics technologies become generative digital technologies that enable service innovation (Lehrer et al 2018), how hybrid intelligent decision support systems should be designed (Dellermann et al 2018), and how the underlying logic of work is changing (Tumbas et al 2018). In addition, we contribute a novel perspective on the interplay of people and machines that extends the ongoing discourse on hybrid arrangements of work.…”

Section: Implications For Theorymentioning

confidence: 99%