Finding the Unicorn: Predicting Early Stage Startup Success Through a Hybrid Intelligence Method

Dellermann, Dominik; Lipusch, Nikolaus; Ebel, Philipp; Popp, Karl Michael; Leimeister, Jan Marco

doi:10.2139/ssrn.3159123

Cited by 52 publications

(29 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At the same time, taking 35 cities in China as research samples, through principal component analysis and cluster analysis, she found the important role of human resources in the cultivation of Unicorns [23]. Delermann developed a hybrid intelligence method based on the concept of human-computer complementarity to identify whether a start-up has grown into a Unicorn and pointed out that talent and policy resources are the most important input elements [24]. Stemler took the Unicorn Airbnb as a case study and then found that cumbersome enterprise regulatory rules hindered its rapid growth.…”

Section: Research Review and Analysis Frameworkmentioning

confidence: 99%

Research on the Development Path and Growth Mechanism of Unicorn Enterprises

Guo

Zhang

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Combining with the growth environment of Unicorns, from the aspects of emerging industries, business environment, platform support, and financial support, we propose an overall analysis framework for the existence or absence of Unicorns, use the fuzzy set qualitative comparative analysis (fsQCA) method to carry out configuration analysis on the status quo of Unicorns in 40 cities in China, and analyze the cultivation path of Unicorns. The research results indicate that the synergy of emerging industries, business environment, platform support, and financial support can foster Unicorns. According to the differences in the core conditions in the configuration and the characteristics of the cases contained, it is divided into two cultivation paths, which are driven by emerging industries and supported by the business environment; combining with the status quo and characteristics of the cities where Unicorns are missing, it provides suggestions for the selection of the cultivation path of Unicorns in different regions.

show abstract

Section: Research Review and Analysis Frameworkmentioning

confidence: 99%

Research on the Development Path and Growth Mechanism of Unicorn Enterprises

Guo

Zhang

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…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: Human-ai Interactionmentioning

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

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.

show abstract

“…Random forest achieves the higher performance compared to other method in sentiment analysis. Dellermann, et al [22] Semi-supervised method is used in this research.…”

Section: Literature Work On Startup Prediction Performancementioning

confidence: 99%

Prediction Models for Startups Success: A Empirical Analysis

Allu*,

Padmanabhuni

2020

IJITEE

View full text Add to dashboard Cite

Small and Medium-Scale Enterprises have been recognized by the government due to their significant role in the country’s economy. The risk of capital investment is high in the enterprises and various factors need to be properly analyzed for the prediction of success of an enterprise. Machine learning techniques can be adopted to predict the success of startups that helps the entrepreneur to make a decision accordingly. In this paper, a details analysis has been carried out on the existing methodologies on startup success prediction to analyze the benefits and limitations. Fewer researches has been carried out in the startup success prediction and achieves the considerable prediction performance. Major limitation has been found among startup success prediction model that use irrelevant features. Some researchers have used social media datasets like Twitter data to increase the performance of the developed method. From existing methods, it has been observed that Random Forest classifiers have been out performed than Logistic Regression method.

show abstract

Finding the Unicorn: Predicting Early Stage Startup Success Through a Hybrid Intelligence Method

Cited by 52 publications

References 71 publications

Research on the Development Path and Growth Mechanism of Unicorn Enterprises

Research on the Development Path and Growth Mechanism of Unicorn Enterprises

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

Prediction Models for Startups Success: A Empirical Analysis

Contact Info

Product

Resources

About