THINGS FROM THE FUTURE How can we crowdsource innovation foresight with games?

In the current world uncertainty is more dominant than it used to be. One of the key forces for constant change is innovation. Innovations can be radical and create surprising effects. Can there be ways of anticipating these unforeseen effects of innovation? Or can the course of future innovations be managed somehow? Innovation foresight processes are required to communicate between different stakeholders on an extensive scale to be able to build comprehensive and understandable future options. Knowledge on future and innovation is no more the exclusive right of experts. This study tries to find new ways of engaging people with the innovation foresight work as well as get new audiences to participate in it. Games and crowdsourcing are two possible solutions to this. Theories covering innovation, foreisght and crowdsourcing are plentiful but scattered, and do not form a coherent framework for innovation foresight. Study is approaching the research topic from two perspectives: what kind of innovation foresight knowledge can we create with games, and what innovation foresight activities can we crowdsource with games? For these targets study has used two different methods, an innovation game case study experiment and a questionnaire targeted to Finnish innovation experts. Game case study consisted of a foresight analysis of 310 ”future thing” ideas generated with an innovation card game. The results revealed that games can enhance the creativity of the players and generate many unexpected uses of future technologies and services. Ideas were also rich with future hopes and fears and they had multidimensional content including different PESTE-variables. Questionnaire was targeted to map views related to the usability of games in different phases of the innovation foresight process. According to responses gaming can be used to observe weak signals, to form wild cards, perceive hopes and fears, and to develop new visions for the future. But games are not seen as suitable for decision-making nor forecasting future trends. Crowdsourcing can enhance the ”crowd wisdom” of the foresight process. Crowd wisdom means that groups are often smarter than the smartest people in them. This phenomenon is based on the thought that “no one knows everything, but everyone knows something”. The challenge in crowdsourcing is to motivate people to participate and engage. Games can be a powerful solution to innovation foresight motivation challenge, and they may also generate different solutions than other methods. But games cannot replace the foresight process. To subject foresight to games and gamification would take too many resources, be expensive, difficult to manage, and results would be risky. Crowdsourcing innovation foresight can often be carried out more effectively when using existing social media platforms such as Facebook, Twitter etc. instead of games. In any case, crowd wisdom is too valuable resource not to be exploited in foresight.siirretty Doriast

Developing a Framework towards Design Understanding for Crowdsourcing Research: A Content Analysis

In the Information Systems (IS) discipline, Design Science Research (DSR) is distinctive; creating knowledge through the design of novel or innovative artefacts, and analysing the artefacts’ use or performance. We present an analysis of DSR doctoral theses published in Australia for the period 2006-2017. Our purpose is to understand the extent and diversity of DSR applied by the Australian IS community in particular by doctoral candidates. We selected the theses from the Australian national repository and analysed their content. The findings suggest that 1) DSR is evolving and maturing in this cohort, 2) DSR theses have resulted in various artefacts and scholarly publications, 3) candidates’ ability to theorize about their work remains a challenge, and 4) nomenclature in DSR remains a problem and the whole IS community should strive for consistency. This paper contributes towards our understanding of DSR as a research approach and offers recommendations to the DSR community

A data-driven game theoretic strategy for developers in software crowdsourcing: a case study

Crowdsourcing has the advantages of being cost-effective and saving time, which is a typical embodiment of collective wisdom and community workers’ collaborative development. However, this development paradigm of software crowdsourcing has not been used widely. A very important reason is that requesters have limited knowledge about crowd workers’ professional skills and qualities. Another reason is that the crowd workers in the competition cannot get the appropriate reward, which affects their motivation. To solve this problem, this paper proposes a method of maximizing reward based on the crowdsourcing ability of workers, they can choose tasks according to their own abilities to obtain appropriate bonuses. Our method includes two steps: Firstly, it puts forward a method to evaluate the crowd workers’ ability, then it analyzes the intensity of competition for tasks at Topcoder.com—an open community crowdsourcing platform—on the basis of the workers’ crowdsourcing ability; secondly, it follows dynamic programming ideas and builds game models under complete information in different cases, offering a strategy of reward maximization for workers by solving a mixed-strategy Nash equilibrium. This paper employs crowdsourcing data from Topcoder.com to carry out experiments. The experimental results show that the distribution of workers’ crowdsourcing ability is uneven, and to some extent it can show the activity degree of crowdsourcing tasks. Meanwhile, according to the strategy of reward maximization, a crowd worker can get the theoretically maximum reward

A review of Citizen Science within the Earth Sciences: potential benefits and obstacles

The field of citizen science is a rapidly evolving type of scientific research focussing on the collaboration of motivated volunteers (citizen scientists) with professional scientists to generate new knowledge and information. In recent years, there has been a steady growth of Earth Science related citizen science projects that aim to build knowledge, awareness and ultimately resilience to key local- to global-scale environmental issues (e.g., geohazards, environmental monitoring). In addition, there has also been progression from small pilot studies to large data collection Earth Science citizen science initiatives that are used to underpin modelling. However, despite this, numerous operational and strategic challenges exist and whilst the awareness of citizen science has improved markedly, it is clear that the direct impact of citizen science on policy and decision making is still limited. Within this paper, we review these challenges alongside defining citizen science itself, and its benefits. The range of methods and applications of citizen science are explored through a series of case studies centred on geohazards, observations & classification, multi-topic, and education/outreach. The paper also explores future citizen science opportunities within Earth Science

Crowdsourcing a text corpus for a low resource language

Low resourced languages, such as South Africa's isiXhosa, have a limited number of digitised texts, making it challenging to build language corpora and the information retrieval services, such as search and translation that depend on them. Researchers have been unable to assemble isiXhosa corpora of sufficient size and quality to produce working machine translation systems and it has been acknowledged that there is little to know training data and sourcing translations from professionals can be a costly process. A crowdsourcing translation game which paid participants for their contributions was proposed as a solution to source original and relevant parallel corpora for low resource languages such as isiXhosa. The objectives of this dissertation is to report on the four experiments that were conducted to assess user motivation and contribution quantity under various scenarios using the developed crowdsourcing translation game. The first experiment was a pilot study to test a custom built system and to find out if social network users would volunteer to participate in a translation game for free. The second experiment tested multiple payment schemes with users from the University of Cape Town. The schemes rewarded users with consistent, increasing or decreasing amounts for subsequent contributions. Experiment 3 tested whether the same users from Experiment 2 would continue contributing if payments were taken away. The last experiment tested a payment scheme that did not offer a direct and guaranteed reward. Users were paid based on their leaderboard placement and only a limited number of the top leaderboard spots were allocated rewards. From experiment 1 and 3 we found that people do not volunteer without financial incentives, experiment 2 and 4 showed that people want increased rewards when putting in increased effort , experiment 3 also showed that people will not continue contributing if the financial incentives are taken away and experiment 4 also showed that the possibility of incentives is as attractive as offering guaranteed incentives

Crowdsourcing data collection through mobile gamification : leveraging the freemium model

Classic ways of gathering data on human behavior, such as laboratory based user studies, can be time-consuming, costly and are subject to limited participant pools. Crowdsourcing offers a reduction in operating costs and access to a diverse and large participant pool, however issues arise concerning low worker pay and questions about data quality. Gamification provides a motivation to participate, but also requires the development of specialized, research-question specific games that can be costly to produce. We provide another alternative that combines gamification and crowdsourcing in a smartphone-based system that emulates the popular Freemium model of micro-transactions to motivate voluntary participation through in-game rewards, using a robust framework to study multiple unrelated research questions within the same system. We deployed our prototype framework on the Android market and gathered data over a period of 5 weeks. We compared this data to that gathered from a gamified laboratory version and a non-gamified laboratory version, and found that players who use the in-game rewards were motivated to do experimental tasks. The data showed that there was no difference between the groups for performance on a motor task; however, performance on a cognitive task was worse for the crowdsourced Android group. We discuss the possible reasons for this and provide options for improving data collection and performance on tasks

Designing for quality in real-world mobile crowdsourcing systems

PhD ThesisCrowdsourcing has emerged as a popular means to collect and analyse data on a scale for problems that require human intelligence to resolve. Its prompt response and low cost have made it attractive to businesses and academic institutions. In response, various online crowdsourcing platforms, such as Amazon MTurk, Figure Eight and Prolific have successfully emerged to facilitate the entire crowdsourcing process. However, the quality of results has been a major concern in crowdsourcing literature. Previous work has identified various key factors that contribute to issues of quality and need to be addressed in order to produce high quality results. Crowd tasks design, in particular, is a major key factor that impacts the efficiency and effectiveness of crowd workers as well as the entire crowdsourcing process. This research investigates crowdsourcing task designs to collect and analyse two distinct types of data, and examines the value of creating high-quality crowdwork activities on new crowdsource enabled systems for end-users. The main contribution of this research includes 1) a set of guidelines for designing crowdsourcing tasks that support quality collection, analysis and translation of speech and eye tracking data in real-world scenarios; and 2) Crowdsourcing applications that capture real-world data and coordinate the entire crowdsourcing process to analyse and feed quality results back. Furthermore, this research proposes a new quality control method based on workers trust and self-verification. To achieve this, the research follows the case study approach with a focus on two real-world data collection and analysis case studies. The first case study, Speeching, explores real-world speech data collection, analysis, and feedback for people with speech disorder, particularly with Parkinson’s. The second case study, CrowdEyes, examines the development and use of a hybrid system combined of crowdsourcing and low-cost DIY mobile eye trackers for real-world visual data collection, analysis, and feedback. Both case studies have established the capability of crowdsourcing to obtain high quality responses comparable to that of an expert. The Speeching app, and the provision of feedback in particular were well perceived by the participants. This opens up new opportunities in digital health and wellbeing. Besides, the proposed crowd-powered eye tracker is fully functional under real-world settings. The results showed how this approach outperforms all current state-of-the-art algorithms under all conditions, which opens up the technology for wide variety of eye tracking applications in real-world settings