Web-based Startup Success Prediction

Sharchilev, Boris; Roizner, M. A.; Rumyantsev, Andrey M.; Ozornin, Denis; Serdyukov, Pavel; Rijke, Maarten de

doi:10.1145/3269206.3272011

Cited by 44 publications

(54 citation statements)

References 15 publications

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“…The definition of success varies across these contributions. Some authors define successful startups as firms that receive a significant source of external funding (this can be additional financing via venture capitalists, an initial public offering, or a buyout) that would allow to scale operations [4,15,87,101,104]. Other authors define successful startups as companies that simply survive [16,59,72] or coin success in terms of innovative capabilities [55,43].…”

Section: Entrepreneurship and Innovationmentioning

confidence: 99%

“…2 To successfully distinguish how to classify successes from failures, algorithms are usually fed with company-, founder-, and investor-specific inputs that can range from a handful of attributes to a couple of hundred. Most authors find the information that relate to the source of funds predictive for startup success (e.g., [15,59,87]), but also entrepreneurial characteristics [72] and engagement in social networks [104] seem to matter. At the project level, funding success depends on the number of investors [41] as well as on the audio/visual content provided by the owner to pitch the project [52], whereas success in R&D projects depends on an interplay between company-, market-, and product-driven factors [79].…”

Section: Entrepreneurship and Innovationmentioning

confidence: 99%

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Supervised Learning for the Prediction of Firm Dynamics

Bargagli-Stoffi

Niederreiter

Riccaboni

2021

Data Science for Economics and Finance

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Thanks to the increasing availability of granular, yet high-dimensional, firm level data, machine learning (ML) algorithms have been successfully applied to address multiple research questions related to firm dynamics. Especially supervised learning (SL), the branch of ML dealing with the prediction of labelled outcomes, has been used to better predict firms’ performance. In this chapter, we will illustrate a series of SL approaches to be used for prediction tasks, relevant at different stages of the company life cycle. The stages we will focus on are (1) startup and innovation, (2) growth and performance of companies, and (3) firms’ exit from the market. First, we review SL implementations to predict successful startups and R&D projects. Next, we describe how SL tools can be used to analyze company growth and performance. Finally, we review SL applications to better forecast financial distress and company failure. In the concluding section, we extend the discussion of SL methods in the light of targeted policies, result interpretability, and causality.

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Section: Entrepreneurship and Innovationmentioning

confidence: 99%

Section: Entrepreneurship and Innovationmentioning

confidence: 99%

Supervised Learning for the Prediction of Firm Dynamics

Bargagli-Stoffi

Niederreiter

Riccaboni

2021

Data Science for Economics and Finance

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“…2. These papers [11][12][13]16,18,19,[21][22][23][24][25][26] use general CSFs and thus not context-based ones.…”

Section: Current Researchmentioning

confidence: 99%

Why context matters for start-ups' critical success factors

Broszat

Fekete

2021

SIER

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Current research in the field of critical success factors of start-ups refers to general factors with which important information about the start-up is lost. Start-ups are too individual for a generalistic assessment, so a novel approach is presented in this paper that allows the context of start-ups to be included in the assessment of critical success factors. This results in the context-based critical success factor, which is defined for the first time in this paper.

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“…The electronic word-of-mouth (eWOM) platforms have a significant economic impact on entrepreneurs and startups. Some recent research [7], [14]- [16] has been conducted to investigate the role of Twitter usage among startups and corresponding financial success.…”

Section: B the Role Of Social Media Usage In Startup Financial Successmentioning

confidence: 99%

“…The number of posts and followers was extracted, and the results suggested a statistically positive correlation between total startup founder presence and the total investment per country. In a similar effort [14], the role of SM in startup outcomes was examined by studying the activities of entrepreneurs which influenced the outcomes of their startups. The empirical analysis was based on Twitter data.…”

Section: B the Role Of Social Media Usage In Startup Financial Successmentioning

confidence: 99%

Startup Initiative Response Analysis (SIRA) Framework for Analyzing Startup Initiatives on Twitter

et al. 2020

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Social Media (SM) platforms, particularly Twitter, have become useful tools for startup companies (henceforth startups) which use the latter to support most of their business activities. As a result, there is a need to gauge the performance of specific business initiatives vis-à-vis public sentiment, or more specifically the spread of such initiatives based on Twitter user-generated content. Previous research which makes use of Twitter analysis to analyze the business activities of startups is minimal, especially for Twitter user content in the Arabic language. Consequently, this paper proposes an analytics-based framework called Startup Initiatives Response Analysis (SIRA) designed to assess the performance of initiatives launched by startups via text classification, sentiment analysis, and statistical analysis techniques. To provide empirical evidence for the viability of the proposed research framework, this paper examined the case of an Arab transportation network startup, carrying out a SIRA analysis of an initiative undertaken by Careem to empower women by encouraging them to work for the company. The results confirm the effectiveness of the proposed framework for statistically measuring the initiative spread and the public feedback based on the user-generated content on the Twitter social platform.

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Web-based Startup Success Prediction

Cited by 44 publications

References 15 publications

Supervised Learning for the Prediction of Firm Dynamics

Supervised Learning for the Prediction of Firm Dynamics

Why context matters for start-ups' critical success factors

Startup Initiative Response Analysis (SIRA) Framework for Analyzing Startup Initiatives on Twitter

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