Show simple item record

dc.contributor.authorVenslavienė, Santautė
dc.contributor.authorStankevičienė, Jelena
dc.date.accessioned2023-09-18T20:44:13Z
dc.date.available2023-09-18T20:44:13Z
dc.date.issued2021
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/152194
dc.description.abstractPurpose – In recent years, crowdfunding platforms have become very popular as intermediaries between fundraisers and funders. However, various campaigns published on the platform might be of bad quality or fraudulent, so the crowdfunding platform must be very careful when choosing the right ones. Also, the proper selection depends on the profits of a crowdfunding platform. In most cases, crowdfunding platforms mainly earn money from transaction and administration fees from successful campaigns on their platforms. While it is very hard to select successful cam-paigns, it is possible to analyse already published campaigns and forecast future revenues of crowdfunding platforms. And based on this, to select new projects which might be successful too. The aim of this work is to develop a model to forecast the revenues of crowdfunding platforms. Research methodology – In this research, comparative and statistical analysis will be used, data structuring, modelling and forecasting, performed with the ARIMA model. Findings – Main findings of this research present the three most successful campaign categories from the Kickstarter platform that receives the highest funding. Fees were calculated from those three campaign categories, and revenues for the platform were forecasted using the ARIMA model. Research limitations – Main limitations are that there were used data from a very short period of time. For better results accuracy, a longer period is needed. Practical implications – this research might be of practical use since the forecasts show that the revenues will continue to grow from the successful campaign categories. Consequently, investors should continue to support technology, games and design categories the most. At the same time, crowdfunding platforms should give more attention to these categories when choosing new projects to launch on the platform.eng
dc.formatPDF
dc.format.extentp. 1-8
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.source.urihttps://doi.org/10.3846/cibmee.2021.595
dc.titleForecasting crowdfunding platform revenues using ARIMA model
dc.typeStraipsnis recenzuotame konferencijos darbų leidinyje / Paper published in peer-reviewed conference publication
dcterms.accessRightsThis is an open-access article distributed under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dcterms.licenseCreative Commons – Attribution – 4.0 International
dcterms.references30
dc.type.pubtypeP1d - Straipsnis recenzuotame konferencijos darbų leidinyje / Article published in peer-reviewed conference proceedings
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyVerslo vadybos fakultetas / Faculty of Business Management
dc.subject.researchfieldS 004 - Ekonomika / Economics
dc.subject.vgtuprioritizedfieldsEV02 - Aukštos pridėtinės vertės ekonomika / High Value-Added Economy
dc.subject.ltspecializationsL103 - Įtrauki ir kūrybinga visuomenė / Inclusive and creative society
dc.subject.encrowdfunding
dc.subject.ena crowdfunding platform
dc.subject.enfunding, revenues
dc.subject.enforecast
dc.subject.enARIMA model
dc.subject.ensuccessful campaigns
dcterms.sourcetitleInternational scientific conference "Contemporary issues in business, management and economics engineering 2021, 13–14 May 2021, Vilnius, Lithuania
dc.publisher.nameVilnius Gediminas Technical University
dc.publisher.cityVilnius
dc.identifier.doi10.3846/cibmee.2021.595
dc.identifier.elaba95116513


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record