Need to Crowd Characteristics

The main goal of this paper is to show that crowdworkers collaborate to fulfill technical and social needs left by the platform they work on. That is, crowdworkers are not the independent, autonomous workers they are often assumed to be, but instead work within a social network of other crowdworkers. Crowdworkers collaborate with members of their networks to 1) manage the administrative overhead associated with crowdwork, 2) find lucrative tasks and reputable employers and 3) recreate the social connections and support often associated with brick and mortar-work environments. Our evidence combines ethnography, interviews, survey data and larger scale data analysis from four crowdsourcing platforms, emphasizing the qualitative data from the Amazon Mechanical Turk (MTurk) platform and Microsoft's proprietary crowdsourcing platform, the Universal Human Relevance System (UHRS). This paper draws from an ongoing, longitudinal study of crowdwork that uses a mixed methods approach to understand the cultural meaning, political implications, and ethical demands of crowdsourcing.

Crowdsourcing is a new, online-based, way of outsourcing that relies on large and undefined networks of people. This process has been used by several successful organizations to solve their internal Innovation challenges with very good results. Assuming that, at a fundamental level, Innovation results from the creation and application of organizational knowledge, this work aims to better understand, from the firm's perspective, how can Crowdsourcing be used to enhance the creation of organizational knowledge. Therefore, we analysed Crowdsourcing through the lenses of the theory of organizational knowledge creation. Based on the main elements of the theory, our analysis raised theoretical assumptions that serve to question some practical aspects of the Crowdsourcing initiatives and may serve as basis for future research and practical experiments. Our analysis shows that Crowdsourcing for Innovation, has all the characteristics of a tool for organizational knowledge creation through the endogenisation of knowledge, ideas and expertise of external individuals. Nevertheless, this theoretical perspective of Crowdsourcing highlights a rather complex process that requires organizational effort and resources throughout all the process. Although a critical part of the work, in a Crowdsourcing initiative is developed outside of the company, the seeker should not neglect its efforts, doing so may result in unsuccessful Crowdsourcing experiences.

2016 49th Hawaii International Conference on System Sciences (HICSS), 2016

Crowdsourcing enables organizations to obtain dynamic solutions from large and diverse crowds of individuals. With the advent of IT-mediated ecosystems crowdsourcing initiatives readily possess a means to mediate, regulate and refine crowdsourcing sub processes including the process of vetting and selecting crowd membership (crowd specification sub process). Despite this fact there is ambiguity in current research as to what constitutes a crowd. Furthermore, traditional crowd selection processes show signs of limited application and are often conducted (i) once, at a fixed stage in the process and (ii) by the process initiators. This research-in-progress paper seeks to address constraints caused by the limited application of these processes. Firstly, a definition of the crowd is formulated from concepts identified in literature. Secondly, an iterative conceptual model is advanced from theory that facilitates the creation of a crowd tailored for tasks of varying size and complexity.

2018

The recent shift towards a polycentric perception of business making requires the involvement of stakeholders in an organization’s management. This is a starting point for looking at the idea of crowdsourcing as an innovative business model. The inclusion of stakeholders in the process of developing new products or services forms a general foundation of this notion. Entrepreneurs take advantage of crowdsourcing ventures due to the delegation of tasks to stakeholders in the form of an open call. Crowdsourcing interacts dynamically with interconnected society of today. This trend is symbolized by the turn from a consumer to prosumer and from a content consumer to content creator. The chapter exemplifies the types of crowdsourcing with reference to recent research outcomes. Additionally, it presents crowdsourcing incentives and tackles the problem of ethical and legal risks that face crowdsourcing.

International Journal of Crowd Science

Purpose There is growing interest in innovative online problem-solving models such as crowdsourcing to serve individuals, firms and the society as a whole. Crowdsourcing is the combination of efforts from various sets of individuals who are either volunteering or working part-time for socioeconomic production, basically in the cyber world. This hybrid work model is already in use by businesses and entrepreneurs; some of the platforms include Amazon mechanical Turk, 99designs, Hit RECORD and Design Crowd. Design/methodology/approach Much has been parleyed and published, and this is primarily because of the efficient socioeconomic potentials crowdsourcing offers. Findings This paper addressed the following three questions to help have a better understanding of crowdsourcing: who can perform crowdsourcing? why it is relevant to crowdsource in this present proliferated internet age and if there are going to be some changes in the future and the last but not the least what can be done to...

While the use of social IT-enabled " internal crowdsourcing " with employees in organizations has substantially increased in recent years (e.g., LEGO, IBM), internal crowdsourcing is not well understood from a theoretical point of view. In this research in progress, we build on the literature on new forms of organizing to improve our theoretical understanding of internal crowdsourcing, to consider whether it constitutes a theoretically distinct phenomenon, and to gain insights into its theoretical nature. The paper presents insights from an ongoing interpretivist field study of internal crowdsourcing at the multinational company BOSCH. Theorized as a form of organizing, we find that internal crowdsourcing is a very different form of organizing compared to work based on fixed assignments. Among the key dimensions of organizing, we identify internal crowdsourcing's " open calls " model of work allocation as the key characteristic.

AMS Review, 2013

The use of crowds to solve business problems, or to take advantage of market opportunities, appears to be increasingly popular. The impact of crowdsourcing across the marketing domain, suggests the importance of examining this phenomenon. Firms use crowds of consumers to perform many of the traditional tasks of marketing, such as choosing designs (e.g., Threadless t-shirts), testing offerings (e.g., Microsoft), creating promotions (e.g., Facebook), and determining pricing (e.g., Travelocity). To more effectively harness the power of crowds and to learn from crowds, firms should understand their capacity for absorbing crowd contributions and the motives underlying the participation of the crowd as well as their own participation. An eclectic review of the literature offers a base for distinguishing crowds using a range of motives from intrinsic to extrinsic. Exploration of these dimensions results in a propositional inventory identifying motivations for both crowdsourcing and crowd participation. These inform practitioners of consequences of crowdsourcing decisions and offer future research directions.

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

Foundations of Management, 2013

The aim of this article is to analyse and describe a new trend in obtaining information and solving problems, which is crowdsourcing. Contemporary enterprises face enormous challenges, which are connected with omnipresent changes occurring in the functioning of societies and economies all over the world. Crowdsourcing is a very complex and constantly evolving phenomenon. It is an open subject of multiple discussions in which, due to its multiple factors and variable applications, it is being differently defined. Growing usage of this phenomenon prompts scientists for undertaking research and analysis of this issue as it is strictly connected with dynamically changing attitudes of, not only, entrepreneurs, but also their surroundings. The prime driving force behind undertaking this subject is a fast development of technology, Web 2.0, or globalisation. The mechanism of crowdsourcing’s functioning has been known for ages; however, it was named as a tool for modern enterprises 10 years...