Article by Mickael Temporão, Yannick Dufresne, Justin Savoie and Clifton van der Linden in International Journal of Forecasting: “People do not know much about politics. This is one of the most robust findings in political science and is backed by decades of research. Most of this research has focused on people’s ability to know about political issues and party positions on these issues. But can people predict elections? Our research uses a very large dataset () collected during ten provincial and federal elections in Canada to test whether people can predict the electoral victor and the closeness of the race in their district throughout the campaign. The results show that they can. This paper also contributes to the emerging literature on citizen forecasting by developing a scaling method that allows us to compare the closeness of races and that can be applied to multiparty contexts with varying numbers of parties. Finally, we assess the accuracy of citizen forecasting in Canada when compared to voter expectations weighted by past votes and political competency….(More)”.
Paper by Christopher Tucci, Gianluigi Viscusi and Heidi Gautschi: “In this article, we explore the use of hackathons and open data in corporations’ open innovation portfolios, addressing a new way for companies to tap into the creativity and innovation of early-stage startup culture, in this case applied to the food and nutrition sector. We study the first Open Food Data Hackdays, held on 10-11 February 2017 in Lausanne and Zurich. The aim of the overall project that the Hackdays event was part of was to use open food and nutrition data as a driver for business innovation. We see hackathons as a new tool in the innovation manager’s toolkit, a kind of live crowdsourcing exercise that goes beyond traditional ideation and develops a variety of prototypes and new ideas for business innovation. Companies then have the option of working with entrepreneurs and taking some of the ideas forward….(More)”.
R.I.Ogie, R.J.Clarke, H.Forehead and P.Perez in Computers, Environment and Urban Systems: “The application of crowdsourced social media data in flood mapping and other disaster management initiatives is a burgeoning field of research, but not one that is without challenges. In identifying these challenges and in making appropriate recommendations for future direction, it is vital that we learn from the past by taking a constructively critical appraisal of highly-praised projects in this field, which through real-world implementations have pioneered the use of crowdsourced geospatial data in modern disaster management. These real-world applications represent natural experiments, each with myriads of lessons that cannot be easily gained from computer-confined simulations.
This paper reports on lessons learnt from a 3-year implementation of a highly-praised project- the PetaJakarta.org project. The lessons presented derive from the key success factors and the challenges associated with the PetaJakarta.org project. To contribute in addressing some of the identified challenges, desirable characteristics of future social media-based disaster mapping systems are discussed. It is envisaged that the lessons and insights shared in this study will prove invaluable within the broader context of designing socio-technical systems for crowdsourcing and harnessing disaster-related information….(More)”.
Alex Papas at LATimes: “At some point in life, almost everyone will have experienced the debilitating effects of a foodborne illness. Whether an under-cooked chicken kebab, an E. coli infested salad or some toxic fish, a good day can quickly become a loathsome frenzy of vomiting and diarrhoea caused by poorly prepared or poorly kept food.
Since 2009, the website iwaspoisoned.com has allowed victims of food-poisoning victims to help others avoid such an ordeal by crowd-sourcing food illnesses on one easy-to-use, consumer-led platform.
Whereas previously a consumer struck down by food poisoning may have been limited to complaining to the offending food outlet, IWasPosioned allows users to submit detailed reports of food-poisoning incidents – including symptoms, location and space to describe the exact effects and duration of the incident. The information is then transferred in real time to public health organisations and food industry groups, who use the data to flag potentially dangerous foodborne illness before a serious outbreak occurs.
In the United States alone, where food safety standards are among the highest in the world, there are still 48 million cases of food poisoning per year. From those cases, 128,000 result in hospitalisation and 3,000 in death, according to data from the U.S. Food and Drug Association.
Back in 2008 the site’s founder, Patrick Quade, himself fell foul to food poisoning after eating a BLT from a New York deli which caused him to be violently ill. Concerned by the lack of options for reporting such incidents, he set up the novel crowdsourcing platform, which also aims at improving transparency in the food monitoring industry.
The emergence of IWasPoisoned is part of the wider trend of consumers taking revenge against companies via digital platforms, which spans various industries. In the case of IWasPoisoned, reports of foodborne illness have seriously tarnished the reputations of several major food retailers….(More)”.
Paper by Regina Lenart-Gansiniec and Łukasz Sułkowski: “Crowdsourcing is one of the new themes that has appeared in the last decade. Considering its potential, more and more organisations reach for it. It is perceived as an innovative method that can be used for problem solving, improving business processes, creating open innovations, building a competitive advantage, and increasing transparency and openness of the organisation. Crowdsourcing is also conceptualised as a source of a knowledge-based organisation. The importance of crowdsourcing for organisational learning is seen as one of the key themes in the latest literature in the field of crowdsourcing. Since 2008, there has been an increase in the interest of public organisations in crowdsourcing and including it in their activities.
This article is a response to the recommendations in the subject literature, which states that crowdsourcing in public organisations is a new and exciting research area. The aim of the article is to present a new paradigm that combines crowdsourcing levels with the levels of learning. The research methodology is based on an analysis of the subject literature and exemplifications of organisations which introduce crowdsourcing. This article presents a cross-sectional study of four Polish municipal offices that use four types of crowdsourcing, according to the division by J. Howe: collective intelligence, crowd creation, crowd voting, and crowdfunding. Semi-structured interviews were conducted with the management personnel of those municipal offices. The research results show that knowledge acquired from the virtual communities allows the public organisation to anticipate changes, expectations, and needs of citizens and to adapt to them. It can therefore be considered that crowdsourcing is a new and rapidly developing organisational learning paradigm….(More)”
Report by the Centre for Policy Innovation and Public Engagement (CPIPE): “In recent years, governments all over the world have been embracing new and innovative ways to develop public policies and design public services, from crowdsourcing to human-centred design thinking. This trend in government innovation has led to the rise of the Policy Innovation Lab (PIL): individual units, both inside and outside of government, that apply the traditional principles of scientific laboratories – experimentation, testing, and measurement – to social problems.
PILs are an increasingly important development in public policy making, with a variety of methods and approaches to building relationships between governments, organizations, and citizens, and generating ideas and designing policy. Yet, these labs are under-researched: many are established without a full understanding of their role and value to the policy community. We aim to address this knowledge gap, and create opportunities where policy innovators can make connections with their peers and learn about the current practices and applications of policy innovation from one another.
This report identifies the innovation labs in Canada, profiling their methodologies, projects, and partners, mapping the policy innovation landscape across the country. Each one-page summary provides a profile for each lab, and highlights the existing innovation practices and networks in the public, academic, non-profit, and private sectors, and identifies methodological and ideological trends across the different labs and networks.
This report is the first of its kind in North America. In this highly dynamic space, new labs are emerging and disappearing all the time. The purpose of this report is to put a spotlight on policy innovations and their successes, and to build and strengthen connections between researchers, policymakers, and policy innovators. Through a strengthened and sustained community of practice, we hope to see governments continue to embrace new approaches for effective policymaking…(More)”.
Melanie Lefkowitz at Cornell Chronicle: “Cornell research has improved bike sharing in New York and other cities, providing tools to ensure bikes are available when and where they’re needed through a crowdsourcing system that uses real-time information to make decisions.
Citi Bike redistributes its bicycles around New York City using a program called Bike Angels, based on research by David Shmoys, the Laibe/Acheson Professor of Business Management and Leadership Studies in the School of Operations Research and Information Engineering.
Through Bike Angels, which Shmoys helped Citi Bike develop three years ago, cyclists earn points adding up to free rides and other prizes by using or returning bikes at certain high-need stations. Originally, Bike Angels awarded points for the same pattern of stations every morning, and a different fixed pattern each afternoon rush; now the program uses an algorithm that continually updates the pattern of stations for which users earn points.
“The ability to make decisions that are sensitive to exactly what are today’s conditions enables us to be much more effective in assigning those points,” said Shmoys, who is also associate director of Cornell’s Institute for Computational Sustainability.
With co-authors Hangil Chung ’18 and Daniel Freund, Ph.D. ’18, Shmoys wrote “Bike Angels: An Analysis of Citi Bike’s Incentive Program,” a detailed report showing the effectiveness of this approach. …(More)”.
Paper by Nuran Acur, Mariangela Piazza and Giovanni Perrone: “Firms are increasingly engaging in crowdsourcing for innovation to access new knowledge beyond their boundaries; however, scholars are no closer to understanding what guides seeker firms in deciding the level at which to acquire rights from solvers and the effect that this decision has on the performance of crowdsourcing contests.
Integrating Property Rights Theory and the problem solving perspective whist leveraging exploratory interviews and observations, we build a theoretical framework to examine how specific attributes of the technical problem broadcast affect the seekers’ choice between alternative intellectual property rights (IPR) arrangements that call for acquiring or licensing‐in IPR from external solvers (i.e. with high and low degrees of ownership respectively). Each technical problem differs in the knowledge required to be solved as well as in the stage of development it occurs of the innovation process and seeker firms pay great attention to such characteristics when deciding about the IPR arrangement they choose for their contests.
In addition, we analyze how this choice between acquiring and licensing‐in IPR, in turn, influences the performance of the contest. We empirically test our hypotheses analyzing a unique dataset of 729 challenges broadcast on the InnoCentive platform from 2010 to 2016. Our results indicate that challenges related to technical problems in later stages of the innovation process are positively related to the seekers’ preference toward IPR arrangements with a high level of ownership, while technical problems involving a higher number of knowledge domains are not.
Moreover, we found that IPR arrangements with a high level of ownership negatively affect solvers’ participation and that IPR arrangement plays a mediating role between the attributes of the technical problem and the solvers’ self‐selection process. Our article contributes to the open innovation and crowdsourcing literature and provides practical implications for both managers and contest organizers….(More)”.
Frank Chaparro at BusinessInsider: “JPMorgan’s corporate and investment bank is best known for advising businesses on billion-dollar acquisitions, helping private unicorns tap into the public markets, and managing the cash of Fortune 500 companies.
But now it is quietly working on a new platform that would go far beyond anything the firm has previously done, using crowdsourcing to accumulate massive amounts of data intended to one day help its clients make complex decisions about how to run their businesses, according to people familiar with the project.
For JPMorgan’s clients like asset-management firms and hedge funds, it could provide new data sets to help investors squeeze out more alpha from their models or better price assets. But JPMorgan is looking to go beyond the buy side to help its large corporate clients as well. The platform could, for example, help retailers figure out where to build their next store, inform manufacturers about how to revamp systems in their factories, and improve logistics management for delivery services companies, the people said.
The platform, called Roar by JPMorgan, would store sensitive private data, such as hospital records or satellite imagery, that’s not in the public domain. Typically, this type of information is exchanged between firms on a bilateral arrangement so it is not improperly used. But Roar would allow clients to tap into this data, which they could then use in a secure fashion to make forecasts and gain business insights….
Right now, the platform is being tested internally with public data and JPMorgan is collaborating with academics to answer questions such as predicting traffic patterns or future air pollution….(More)”.
Guest Editorial to Special Issue of IEEE Internet of Things Journal: “As we become increasingly reliant on intelligent, interconnected devices in every aspect of our lives, critical trust, security, and privacy concerns are raised as well.
First, the sensing data provided by individual participants is not always reliable. It may be noisy or even faked due to various reasons, such as poor sensor quality, lack of sensor calibration, background noise, context impact, mobility, incomplete view of observations, or malicious attacks. The crowdsourcing applications should be able to evaluate the trustworthiness of collected data in order to filter out the noisy and fake data that may disturb or intrude a crowdsourcing system. Second, providing data (e.g., photographs taken with personal mobile devices) or using IoT applications may compromise data providers’ personal data privacy (e.g., location, trajectory, and activity privacy) and identity privacy. Therefore, it becomes essential to assess the trust of the data while preserving the data providers’ privacy. Third, data analytics and mining in crowdsourcing may disclose the privacy of data providers or related entities to unauthorized parities, which lowers the willingness of participants to contribute to the crowdsourcing system, impacts system acceptance, and greatly impedes its further development. Fourth, the identities of data providers could be forged by malicious attackers to intrude the whole crowdsourcing system. In this context, trust, security, and privacy start to attract a special attention in order to achieve high quality of service in each step of crowdsourcing with regard to data collection, transmission, selection, processing, analysis and mining, as well as utilization.
Trust, security, and privacy in crowdsourcing receives increasing attention. Many methods have been proposed to protect privacy in the process of data collection and processing. For example, data perturbation can be adopted to hide the real data values during data collection. When preprocessing the collected data, data anonymization (e.g., k-anonymization) and fusion can be applied to break the links between the data and their sources/providers. In application layer, anonymity is used to mask the real identities of data sources/providers. To enable privacy-preserving data mining, secure multiparty computation (SMC) and homomorphic encryption provide options for protecting raw data when multiple parties jointly run a data mining algorithm. Through cryptographic techniques, no party knows anything else than its own input and expected results. For data truth discovery, applicable solutions include correlation-based data quality analysis and trust evaluation of data sources. But current solutions are still imperfect, incomprehensive, and inefficient….(More)”.