FinTech Evolution to Revolution in India - From Minicorns to Soonicorns to Unicorns

Kaur, Navleen; Sahdev, Supriya Lamba; Chhabra, Muskaan; Agarwal, Shivang Mohan

doi:10.1109/icrito51393.2021.9596462

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…The findings also underline the crucial role of technology and the Covid Pandemic in driving changes in consumer behaviour in the fintech industry (Kaur et al, 2021). In both cases, we noted that online users exhibited an inclination to reveal personal information with new digital products.…”

Section: A Life Cycle View On Unicorn Startupssupporting

confidence: 59%

“…The dynamics of Internet commerce have evolved as JSBED 30,6 a result of peer-to-peer interactions that occur between customers on business websites, applications and social networks. According to Kaur et al (2021), elements such as improving customer satisfaction, best customer experiences, faster output and operational efficiencies are being sought through digital technology as an important driver for growth.…”

Section: Intersection Of the High-growth Driversmentioning

confidence: 99%

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A life cycle view on unicorn start-ups: drivers of long-term high-growth

Kabbara

Hagen

2023

JSBED

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PurposeThe purpose of this study is to explore the exogenous and endogenous drivers of the high-growth of Unicorn start-ups along their life cycle, with a particular focus on Unicorns in the fintech industry.Design/methodology/approachThe study employs an explorative longitudinal analysis with a matched-pair of two cases of Unicorns start-ups with similar antecedent features to understand holistically drivers over the longer term.FindingsHigh-growth patterns over the longer term are the result of a combined industry- and company-life cycle perspective. Drivers and growth patterns vary significantly according to the time of entry in the industry and its development status. The findings are systematised within a set of propositions to be tested in future research.Research limitations/implicationsThe limitations lie in empirical evidence, as the analysis is limited to one-matched-pair. The revealed Unicorns' drivers for long-term growth might encourage future research to further investigate these drivers on a larger scale.Practical implicationsThe study offers practical recommendations for start-ups with high-growth ambitions and advice to policy makers regarding the development of tailor-made support programs.Originality/valueThe study significantly extends extant work on growth and high-growth by examining endogenous and exogenous triggers over time and by linking the Unicorn-life cycle to the industry life cycle, an approach which has, to the best of the authors’ knowledge, not yet been applied.

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Section: A Life Cycle View On Unicorn Startupssupporting

confidence: 59%

Section: Intersection Of the High-growth Driversmentioning

confidence: 99%

A life cycle view on unicorn start-ups: drivers of long-term high-growth

Kabbara

Hagen

2023

JSBED

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“…Jaccard Score, or Jaccard Similarity Index, is a metric used to measure the similarity between two data sets. It is calculated as the ratio between the number of elements in common in two data sets and the number of unique elements in both sets [21,22,23,24,25]. The Jaccard Score ranges from 0 to 1, with a score of 1 indicating percentage identity (both sets have elements in common) and a score of 0 indicating no elements in common [26,27,28,29].…”

Section: Fig 2 Sentiment Comparison On Datasetmentioning

confidence: 99%

Sentiment Analysis of User Preference for Old Vs New Fintech Technology Using SVM and NB Algorithms

Nurdin,

Alexandri,

Sumadinata

et al. 2023

Management Systems in Production Engineering

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The aim of this study is to use sentiment analysis to compare the efficiency of old and new fintech technologies by collecting data from various sources and analyzing it using the SVM and NB algorithms. The study seeks to identify opinions or feelings from text in order to provide a clear picture of public opinion and the direction of the debate regarding old and new fintech technologies. The results of the study show that the SVM algorithm has an average accuracy of 87.32% and the NB algorithm has an average accuracy of 81.56% in testing the sample data in a comparison of old and new fintech technology on the internet. The study tested data in a comparison of two specific arguments, namely the debate about which technology is more efficient in old and new fintech on the internet. Despite many unresolved arguments, the study successfully proved that new fintech is more preferred than old fintech, with 71% positive sentiment directed towards new fintech. However, the dataset also found that 62% negative sentiment is directed towards new fintech, indicating that although new fintech is more preferred, there are still some issues that need to be addressed. One reason for negative sentiment towards new fintech may be the continued concerns about security and privacy of user data. Furthermore, other factors that may cause negative sentiment towards new fintech include a lack of understanding about how the technology works.

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