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- Creators: Barrett, The Honors College
- Creators: Budolfson, Arthur
- Creators: Dooley, Kevin
Description
This paper intends to inform the reader about the current state of crowdfunding, also known as crowdsourced funding, as of early May 2014. Crowdfunding has proven to be an interesting alternate to other more common financing vehicles with its ability to unite people over common ideas and projects without requiring the contribution of large amounts of capital. Further, the changing legal landscape invites a new era of deregulation that makes crowdfunding easier than ever before. This paper contains explanations of the different types of crowdfunding, platforms (websites), and the international landscape particularly of the US and Europe as well as statistics regarding the predicted future growth of the industry.
ContributorsMurphy, Kevin Edward (Author) / Budolfson, Arthur (Thesis director) / Schein, Stephen (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor)
Created2014-05
Description
The area of real-time baseball statistics presents several challenges that can be addressed using mobile devices. In order to accurately record real-time statistics, it is necessary to present the user with a concise interface that can be used to quickly record the necessary data during in-game events. In this project, we use a mobile application to address this by separating out the required input into pre-game and in-game inputs. We also explore the use of a mobile application to leverage crowd sourcing techniques, which address the challenge of accuracy and precision in subjective real-time statistics.
ContributorsVan Egmond, Eric David (Author) / Tadayon-Navabi, Farideh (Thesis director) / Wilkerson, Kelly (Committee member) / Gorla, Mark (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
In the modern digital age, new methods of raising capital for entrepreneurs are being explored and developed at a rapid rate. This is in part due to new legislation aimed at democratizing the funding process for startup-sized businesses, and also due to the growing mistrust in the big banks following the recent financial crisis of 2008. Today, many entrepreneurs are turning to the Internet and crowdsourcing in order to raise the funds they need to get their business ideas off the ground. This trend is more commonly known as crowdfunding. Crowdfunding is not as narrow of an industry as some may think. It goes much deeper than just the commonly known platforms such as Kickstarter or Indiegogo. There are four different crowdfunding methods that exist today, as well as hundreds of websites known as crowdfunding platforms created in order to facilitate these methods as a third party intermediary. My thesis aims to research, break down, study, and compare the various methods of crowdfunding. In addition, I explore the modern uses of the more traditional methods of raising capital for entrepreneurs such as angel investors, venture capital, bank/Small Business Association loans, and bootstrapping. This research includes both primary and secondary research. For my primary research, I interviewed three subject matter experts on the capital markets, and conducted two case studies regarding crowdfunding campaigns. In my secondary research, I used credible published studies, blogs and articles with expert testimonials, and other trustworthy resources such as encyclopedias and professional reports. In the end, I compare and contrast the various methods of raising capital explored throughout the paper, and provide my recommendations regarding each method for entrepreneurs interested in raising funds for their next venture. We live in an exciting time, and there are a lot of interesting new developments emerging as the capital markets continue to integrate with the modern digital age. I hope this thesis will help entrepreneurs, investors, and anyone else who may have interest in the modern capital markets or fundraising develop a better understanding of new trends in raising capital today.
ContributorsCohan, Taylor (Author) / Montoya, Detra (Thesis director) / Schlacter, John (Committee member) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Collecting accurate collective decisions via crowdsourcing
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
ContributorsKemmer, Ryan Wyeth (Author) / Escobedo, Adolfo (Thesis director) / Maciejewski, Ross (Committee member) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Crowdsourcing has become a popular method for collecting information from a large group of people at a low cost. This thesis looks at whether crowdsourcing is viable to collect data for Human Computer Interaction research and comparing collaborative crowdsourcing with individual crowdsourcing. It was hypothesized that collaborative crowdsourcing would provide higher quality results than individual crowdsourcing due to intrinsic motivation. The research draws upon the use of three things: top 10 usability problems, heuristic evaluation and WAMMI survey to measure the two groups. The two groups used these tools to analyze the website: Phoenix.Craigslist.com. The results were compared against each other and against a heuristic evaluation score given by an HCI researcher to determine their accuracy. The results of the experiment failed to confirm this hypothesis. In the end, both groups provided accurate results and were only marginally different from each other.
ContributorsGupta, Kartik (Author) / Atkinson, Robert (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This study aims to combine the wisdom of crowds with ML to make more accurate stock price predictions for a select set of stocks. Different from prior works, this study uses different input elicitation techniques to improve crowd performance. In addition, machine learning is used to support the crowd. The influence of ML on the crowd is tested by priming participants with suggestions from an ML model. Lastly, the market conditions and stock popularity is observed to better understand crowd behavior.
ContributorsBhogaraju, Harika (Author) / Escobedo, Adolfo R (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description
Although many examples have demonstrated the great potential of a human crowd as an alternative supplier in creative problem-solving, empirical evidence shows that the performance of a crowd varies greatly even under similar situations. This phenomenon is defined as the performance variation puzzle in crowdsourcing. Cases suggest that crowd development influences crowd performance, but little research in crowdsourcing literature has examined the issue of crowd development.
This dissertation studies how crowd development impacts crowd performance in crowdsourcing. It first develops a double-funnel framework on crowd development. Based on structural thinking and four crowd development examples, this conceptual framework elaborates different steps of crowd development in crowdsourcing. By doing so, this dissertation partitions a crowd development process into two sub-processes that map out two empirical studies.
The first study examines the relationships between elements of event design and crowd emergence and the mechanisms underlying these relationships. This study takes a strong inference approach and tests whether tournament theory is more applicable than diffusion theory in explaining the relationships between elements of event design and crowd emergence in crowdsourcing. Results show that that neither diffusion theory nor tournament theory fully explains these relationships. This dissertation proposes a contatition (i.e., contagious competition) perspective that incorporates both elements of these two theories to get a full understanding of crowd emergence in crowdsourcing.
The second empirical study draws from innovation search literature and tournament theory to address the performance variation puzzle through analyzing crowd attributes. Results show that neither innovation search perspective nor tournament theory fully explains the relationships between crowd attributes and crowd performance. Based on the research findings, this dissertation discovers a competition-search mechanism beneath the variation of crowd performance in crowdsourcing.
This dissertation makes a few significant contributions. It maps out an emergent process for the first time in supply chain literature, discovers the mechanisms underlying the performance implication of a crowd-development process, and answers a research call on crowd engagement and utilization. Managerial implications for crowd management are also discussed.
This dissertation studies how crowd development impacts crowd performance in crowdsourcing. It first develops a double-funnel framework on crowd development. Based on structural thinking and four crowd development examples, this conceptual framework elaborates different steps of crowd development in crowdsourcing. By doing so, this dissertation partitions a crowd development process into two sub-processes that map out two empirical studies.
The first study examines the relationships between elements of event design and crowd emergence and the mechanisms underlying these relationships. This study takes a strong inference approach and tests whether tournament theory is more applicable than diffusion theory in explaining the relationships between elements of event design and crowd emergence in crowdsourcing. Results show that that neither diffusion theory nor tournament theory fully explains these relationships. This dissertation proposes a contatition (i.e., contagious competition) perspective that incorporates both elements of these two theories to get a full understanding of crowd emergence in crowdsourcing.
The second empirical study draws from innovation search literature and tournament theory to address the performance variation puzzle through analyzing crowd attributes. Results show that neither innovation search perspective nor tournament theory fully explains the relationships between crowd attributes and crowd performance. Based on the research findings, this dissertation discovers a competition-search mechanism beneath the variation of crowd performance in crowdsourcing.
This dissertation makes a few significant contributions. It maps out an emergent process for the first time in supply chain literature, discovers the mechanisms underlying the performance implication of a crowd-development process, and answers a research call on crowd engagement and utilization. Managerial implications for crowd management are also discussed.
ContributorsLiu, Zhongzhi (Author) / Kull, Thomas (Thesis advisor) / Dooley, Kevin (Committee member) / Oke, Adegoke (Committee member) / Arizona State University (Publisher)
Created2017