Factors Responsible for the Success of a Start-up: A Meta-Analytic Approach

Pasayat, Ajit Kumar; Bhowmick, Bhaskar; Roy, Ritik

doi:10.1109/tem.2020.3016613

Cited by 20 publications

(11 citation statements)

References 47 publications

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“…PRISMA is suited to this study as it is a widely used method for conducting systematic literature reviews (Pasayat et al, 2020). The databases used in this study include Scopus, Web of Science and Google scholar they are the most widely used databases in academic research.…”

Section: Research Methodology 31 Prisma Techniquementioning

confidence: 99%

“…The PRISMA technique was used to conduct a review of the literature to identify studies that focused on the implementation of ML in the context of occupational accident data analysis. PRISMA is suited to this study as it is a widely used method for conducting systematic literature reviews (Pasayat et al. , 2020).…”

Section: Methodsmentioning

confidence: 99%

“…The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) technique was used to conduct the review of the literature to identify studies that focused on the implementation of ML in the context of accident data analysis. PRISMA is suited to this study as it is a widely used method for conducting systematic literature reviews (Pasayat et al, 2020). The databases used in this study include Scopus, Web of Science, Science Direct, and EBSCO are they are the most widely used databases in academic research.…”

Section: Prisma Techniquementioning

confidence: 99%

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Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Gangadhari

Khanzode

Murthy

et al. 2022

BIJ

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PurposeThis paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry.Design/methodology/approachThe preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India.FindingsThe findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data.Originality/valueThis is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers.

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Section: Research Methodology 31 Prisma Techniquementioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Prisma Techniquementioning

confidence: 99%

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Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Gangadhari

Khanzode

Murthy

et al. 2022

BIJ

View full text Add to dashboard Cite

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“…The work by Tomy and Pardede (2018) and Żbikowski and Antosiuk (2021) further enriched the domain by analyzing uncertainties and employing algorithms on extensive datasets from platforms like Crunchbase. It is important to note the meta‐analytic work by Pasayat et al (2020), which synthesizes factors contributing to startup success, offering a meta‐perspective on the predictive elements. These studies collectively emphasize the role of AI in enhancing the decision‐making process for startup investments, indicating a common trend in the literature toward adopting advanced, data‐centric techniques to improve predictions of startup performance.…”

Section: Related Workmentioning

confidence: 99%

Cost‐sensitive machine learning to support startup investment decisions

Setty,

Elovici,

Schwartz

2024

Intell Sys Acc Fin Mgmt

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SummaryIn 2022, global startup investments exceeded US$445 billion, sourced from entities like venture capital (VC) funds, angel investors, and equity crowdfunding. Despite their role in driving innovation, startup investments often fall short of S&P 500 returns. Surprisingly, the potential of artificial intelligence (AI) remains untapped by investors, despite AI's growing sway in financial decision‐making. Our empirical analysis predicts the success of 10,000 Israeli startups, utilizing diverse machine learning models. Unlike prior research, we employ the MetaCost algorithm to convert models into cost‐sensitive variants, minimizing total cost instead of total error. This innovative approach enables varied costs linked to different prediction errors. Our results underscore that these cost‐sensitive machine learning models significantly reduce risk for VC funds and startup investors compared to traditional ones. Furthermore, these models provide investors with a distinct capability to tailor their risk profiles, aligning predictions with their risk appetite. However, while cost‐sensitive machine learning reduces risk, it may limit potential gains by predicting fewer successful startups. To address this, we propose methods to enhance successful startup identification, including aggregating outcomes from multiple MetaCost models, particularly advantageous for smaller deal flows. Our research advances AI's role in startup investing, presenting a pivotal tool for investors navigating this domain.

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“…PRISMA provides a standard peer accepted methodology that uses a guideline checklist to contribute to the quality of the literature review and to its replicability. Of late, PRISMA framework is being followed in business and management area literature review paper (Pasayat et al , 2020; Satalkina and Steiner, 2020). A review protocol is developed describing the search strategy, article selection criteria, data extraction and data analysis procedures.…”

Section: Selection Literaturementioning

confidence: 99%

Studies on Indian IPO: systematic review and future research agenda

Chatterjee,

Bhattacharjee,

Chakraborty

2023

QRFM

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Purpose This paper aims to review, discuss and synthesize the literature focusing on the Indian initial public offering (IPO) market. Understanding the Indian IPO market can help answer broader corporate finance questions. The growing number of IPOs in the Indian context, coupled with the increasing importance of the Indian economy in the global market, makes this review an essential topic. Design/methodology/approach The systematic literature review methodology was adopted to review 111 papers published between 2002 and 2021. The authors used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach during the review process. Additionally, the authors use a bibliometric review methodology to examine the pattern and trend of research in this area of interest. Furthermore, the authors conduct a critical review and synthesis of the top 20 papers based on citations. The authors also use a co-citation network and manual content analysis method to identify key research themes. Findings This review helps in identifying major themes of research in this area of interest. The authors find that majority of the research has focused on IPO performance whereas post-IPO performance needs critical attention as well. The authors develop a comprehensive framework and future research agenda based on their discussion. Research limitations/implications Meta-analysis of the literature can be conducted to gain better insights into the findings of prior studies. Practical implications This review paper develops a comprehensive overview on Indian IPO market which can be of interest not only to Indian scholarship. India as an economy is increasingly gaining attention at the global level. Hence, the future research objectives as illustrated in the study can be of interest for the global scholarship also. Originality/value To the best of the authors’ knowledge, this is the first comprehensive review paper that examines, synthesizes and outlines the future research agenda on Indian IPO studies. This review can be useful for researchers, business policymakers, finance professionals and anyone else interested in the Indian IPO market.

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Factors Responsible for the Success of a Start-up: A Meta-Analytic Approach

Cited by 20 publications

References 47 publications

Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Cost‐sensitive machine learning to support startup investment decisions

Studies on Indian IPO: systematic review and future research agenda

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