Improving human interaction in crowdsensing

South African Computer Journal

In recent years crowdsensing has become a hot topic amongst researchers. Crowdsensing can incentivise and empower citizens to use their mobile phones to collect and share sensed data from their surrounding environments. The purpose of this paper is to report on the application of the incentive theory and the Theory of Planned Behaviour (TPB) as a lens from which to investigate the non-monetary incentives and participation profiles (intentions and motivations) of citizens from around the world, who could participate in a crowdsensing project for water resource monitoring (WRM). The conceptual framework was used in a survey of citizens. The findings revealed that TPB can be successfully used for predicting behavioural intentions and classified several types of motivational factors for participation in crowdsensing projects for WRM. Guidelines for crowdsensing projects are provided that can improve the success rate of WRM projects.

Industry and Innovation, 2021

Research projects that actively involve 'crowds' or non-professional 'citizen scientists' are attracting growing attention. Such projects promise to increase scientific productivity while also connecting science with the general public. We make three contributions. First, we argue that the largely separate literatures on 'Crowd Science' and 'Citizen Science' investigate strongly overlapping sets of projects but take different disciplinary lenses. Closer integration can enrich research on Crowd and Citizen Science (CS). Second, we propose a framework to profile projects with respect to four types of crowd contributions: activities, knowledge, resources, and decisions. This framework also accommodates machines and algorithms, which increasingly complement or replace professional and non-professional researchers as a third actor. Finally, we outline a research agenda anchored on important underlying organisational challenges of CS projects. This agenda can advance our understanding of Crowd and Citizen Science, yield practical recommendations for project design, and contribute to the broader organisational literature.

Organizations rely on crowds for a variety of reasons, e.g. in order to evaluate (Amazon), create content (Threadless) but also to solve given problems (InnoCentive and OpenIDEO). Several studies have examined how to organize problem-solving activities. However, most papers have examined the crowdsourcing process using a partial perspective and a wide-ranging outlook has been missing. This study uses a computer-based simulation model and anecdotal case studies of InnoCentive and OpenIDEO, in order to study the underlying drivers of collective problem solving behavior. Results suggest that dynamics between the number of users, number of iterations and different selection mechanisms impact the ability to find an optimal solution to the given problem.

Crowdsourcing in recent times has become popular among not-for-profits as a means of eliciting members of the public to contribute to activities that would normally have been carried out by staff or by contracting external expertise. The GLAM (galleries, libraries, archives, museums) sector does have a history of involving online volunteers (e.g. reviewing books). Extending that tradition, some GLAM institutions are engaging in crowdsourcing projects to enhance and enrich their collections. But what motivates the public to participate in these crowdsourcing activities? Understanding the unique motivations of participants is needed to establish a motivational framework for GLAM organisations in their not-for-profit context. We present findings from a study of the motivational factors affecting participation in the Australian Newspapers Digitisation Program (ANDP) by the National Library of Australia (NLA). Based on motivational theories and frameworks the study shows that the participants are motivated by a complex framework of personal, collective and external factors. Participants were highly intrinsically motivated, but valued altruistic and community motivations as well. Community and external factors played a vital role in their continued involvement. The paper concludes with a conceptual framework of the motivational factors for crowdsourcing participants in a GLAM context based on the motivational dynamics observed in the ANDP case.

Information Systems Management, 2013

In this article, the authors first provide a practical yet rigorous definition of crowdsourcing that incorporates "crowds," outsourcing, and social web technologies. They then analyze 103 well-known crowdsourcing web sites using content analysis methods and the hermeneutic reading principle. Based on their analysis, they develop a "taxonomic theory" of crowdsourcing by organizing the empirical variants in nine distinct forms of crowdsourcing models. They also discuss key issues and directions, concentrating on the notion of managerial control systems. , who coined the term "crowdsourcing" in a seminal article appearing in Wired magazine , defined it as "the act of taking a job traditionally performed by a designated agent (usually an employee) and outsourcing it to an undefined, generally large group of people in the form of an open call." Howe's definition is sufficiently clear and comprehensive to serve as a useful beginning point. We especially stress how, in the original definition, the intersection of outsourcing and amorphous crowds-or the use of "open" crowds to complete any feasible organizational taskis explicit. Our conceptual elaboration thus builds on this base, with the major initial difference being our explicit incorporation of advanced internet technologies into the definition.

Handbook of Human Computation, 2013

Crowdsourcing has become an increasingly popular means of flexibly deploying large amounts of human computational power. The present chapter investigates the role of microtask labor marketplaces in managing human and hybrid human machine computing. Labor marketplaces offer many advantages that in combination allow human intelligence to be allocated across projects rapidly and efficiently and information to be transmitted effectively between market participants. Human computation comes with a set of challenges that are distinct from machine computation, including increased unsystematic error (e.g. mistakes) and systematic error (e.g. cognitive biases), both of which can be exacerbated when motivation is low, incentives are misaligned, and task requirements are poorly communicated. We provide specific guidance to how to ameliorate these issues through task design, workforce selection, data cleaning and aggregation. Risks and Rewards of Crowdsourcing Marketplaces The present chapter focuses on the risks and rewards of using online marketplaces to enable crowdsourced human computation. We discuss the strengths and limitations of these marketplaces, with a particular emphasis on the quality of crowdsourced data collected from Amazon Mechanical Turk. Data quality is by far the most important consideration when designing computational tasks, and it can be influenced by many factors. We emphasize Mechanical Turk because it is currently one of the most popular and accessible crowdsourcing platforms and offers low barriers of entry to researchers interested in exploring the uses of crowdsourcing. In addition to describing the strengths and limitations of this platform, we provide general considerations and specific recommendations for measuring and improving data quality that are applicable across crowdsourcing markets. Crowdsourcing is the distribution of tasks to a large group of individuals via a flexible open call, in which individuals work at their own pace until the task is completed (for a more detailed definition see Estelles-Arolas & Gonzalez-Ladron-de Guevera, 2012). Crowd membership is fluid, with low barriers to entry and no minimum commitment. Individuals with heterogeneous skills, motivation, and other resources contribute to tasks in parallel. Crowdsourcing leverages the unique knowledge of individual crowd members, the sheer volume of their collective time and abilities, or both to solve problems that are difficult to solve using computers, or smaller and more structured groups. The unique strengths of groups are generally used to solve one of two basic kinds of problems. Some problems have no obvious a priori solution, but correct answers seem obvious once known (e.g. insight problems; Dominowski & Dallob, 1995) or can be verified. In these cases, crowds can generate responses from which the "best" response can be selected according to some criteria. The volume and diversity of workers with different perspectives, strategies and knowledge can lead to quick, unorthodox, and successful solutions. The Internet has furthered this approach to problem solving by creating virtual meeting places where people can post problems for others to solve. For example, Innocentive (Allio, 2004) is a website that has helped companies find solutions to technical challenges like preventing oxygen from passing through rubber, or adding fluoride powder to toothpaste without dispersing it into the air. Often solutions to these specialized, technical problems are provided by amateurs, hobbyists, or experts in apparently unrelated fields (Larkhani, 2008). Tasks that require resources beyond those available to a single individual or work group are also well-suited to crowdsourcing. The compilation of the Oxford English Dictionary is one early example of this approach. A unique feature of this dictionary is that it includes not only definitions, but also published examples of word use. Examples were collected on slips of paper by a large body of volunteers and then aggregated by editors (Winchester, 2004). Advances in machine computation have made it easier to manage projects of this scale. For example, The Open Science Collaboration coordinates the real time collaborative efforts of scientists and citizen-scientists to systematically code, replicate and communicate social scientific findings using freely available web-software (Open Science Collaboration, 2013). A subset of this broad category are tasks that are easy for people to solve, but difficult for machines to solve. These assignments are particularly amenable to crowdsourcing. In many cases, a crowd's responses can be automatically aggregated, eliminating the need to comprehensively review responses. The volume of workers performing each task can allow ideosyncratic perspectives, strategies and knowledge to be homogenized removed through

This paper critically examines the crowdsourcing process through a hands-on field experiment, a first-person chronicle of the interaction among actants in a crowdsourcing network. By looking at crowdsourcing through the Actor-Network Theory framework, the paper aims to identify and analyze the skills and technologies needed to join these contests, including the barriers to participation. Finally, the paper suggests a redefinition of the term crowdsourcing itself, one that takes into consideration the fact that this production model is first and foremost a contest, albeit done online.

2003

2017

IT-mediated crowds are being implemented by organizations for multifarious purposes, using multifarious techniques. With this minitrack we seek to coalesce and support an enduring community of researchers focused on the study of the IT-mediated crowds. Our aim is to harness and focus the very broad inter-disciplinary study of IT-mediated crowds that currently exists, to incite a sharing of results, and a cross-pollination of ideas among researchers currently investigating ITmediated crowds from IS, ISchool, HCI, Computer Science, Marketing, Education, Natural Sciences, Communication, and Technology Innovation perspectives. In this brief introduction, we define the scope of the Crowd Science minitrack while illustrating numerous useful subjects for future research. Introduction In the purview of this mini-track, IT-mediated crowd phenomena can be found in these areas of research; Crowdsourcing [11-14, 18, 64], Crowd Finance (Crowdfunding, Blockchains, Distributed Ledgers) [8, 21, 50]...

Autonomous Agents and Multi-Agent Systems, 2013

Crowdsourcing applications frequently employ many individual workers, each performing a small amount of work. In such settings, individually determining the reward for each assignment and worker may seem economically beneficial, but is inapplicable if manually performed. We thus consider the problem of designing automated agents for automatic reward determination and negotiation in such settings. We formally describe this problem and show that it is NP-hard. We therefore present two automated agents for the problem, based on two different models of human behavior. The first, the Reservation Price Based Agent (RPBA), is based on the concept of a reservation price, and the second, the No Bargaining Agent (NBA) which tries to avoid any negotiation.The performance of the agents is tested in extensive experiments with real human subjects, where both NBA and RPBA outperform strategies developed by human experts.