Crowdsourcing in Business-to-Business Markets

2015

Crowdsourcing is an increasingly popular source of both ideas and funding. Crowdsourcing in a B2B context is less well understood and, as such, much of our discussion will highlight business-to-business crowdsourcing. More generally discussion will address crowdsourcing relative to innovation and technology development and customer relationship management. Examples will be used to derive a general roadmap, and to identify specific areas worthy of increased attention. The roadmap is developed as a six-stage approach that begins with task specification and concludes with management of inputs generated from crowd-sourcing efforts. The roadmap emphasizes on early stages in the overall innovation management activity that is related to development and specification of the task to be crowdsourced, the identification of the crowd, the creation of the environment to connect the crowdsourcer, the task and the crowd, motivation of the crowd, and actual activities in reaching the crowd. Managin...

2012

The aim of this research is to explore the use of crowdsourcing especially from business-to-business companies' innovation perspective, and to create a more comprehensive picture of the possibilities of crowdsourcing for companies operating in business-to-business markets. Business-to-business context was chosen because it is in many ways a very different environment for crowdsourcing than business-to-consumer context, and we found no academic studies on the topic. A systematic literature review ...

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

The paper provides a crowd-sourcing business model (CBM) that integrates the useful components of both crowd-sourcing and business models. The model includes the business strategy that a company decides to implement in accordance with the company's scope, market perspectives and competitors that are important to differentiate their own products. Another component of the crowd-sourcing business model is the workflow management that involves the resources management and the task management. The resource management involves the tools the financial resource and the human resource that ar e needed to achieve the business goal. The resource management influences the task management that is assigned and execute by internal staff and/or by the crowd. The final outcome of the value co-creation process is the definition of a new business strategy for products/services placement and/or new product development efforts.

Academy of Management Review, 2013

2010

Many firms are now asking how they can benefit from the new form of outsourcing labelled “crowdsourcing”. Like many other forms of outsourcing, crowdsourcing is now being “talked up” by a somewhat credulous trade press. However, the term crowdsourcing has been used to describe several related, but different phenomena, and what might be successful with one form of crowdsourcing may not be with another. In this paper the notion of crowdsourcing is decomposed to create a taxonomy that expands our understanding of what is meant by the term. This taxonomy focuses on the different capability levels of crowdsourcing suppliers; different motivations; and different allocation of benefits. The management implications of these distinctions are then considered in light of what we know about other forms of outsourcing.

2008

Chesbrough's work on open innovation provides a theoretical framework to understand how firms can access external knowledge in order to support their R&D processes. The author defines open innovation as a paradigm that assumes that firms can and should use both external and internal ideas and internal and external paths to market. He considers that Crowdspirit team: (1) The emergence of the Crowdspirit business model; (2) The value creation process related to profiles of crowdspirit community of contributors (3) The challenging of the company's initial business model and (4) The creation of a new business model successively open and closed models. In the discussion we summarize the main strategic issues that emerged during the work on Crowdspirit's strategy with its managers, and interpret them on the basis of existing literature on open innovation. This leads us to complete

The paper provides a crowd-sourcing business model (CBM) that integrates the useful components of both crowd-sourcing and business models. The model includes the business strategy that a company decides to implement in accordance with the company's scope, market perspectives and competitors that are important to differentiate their own products. Another component of the crowd-sourcing business model is the workflow management that involves the resources management and the task management. The resource management involves the tools the financial resource and the human resource that ar e needed to achieve the business goal. The resource management influences the task management that is assigned and execute by internal staff and/or by the crowd. The final outcome of the value co-creation process is the definition of a new business strategy for products/services placement and/or new product development efforts.

2012

Open innovation has gained increased attention as a potential paradigm for improving innovation performance. This paper addresses crowdsourcing, an under-researched type of open innovation that is often enabled by the web. We focus on a type of crowdsourcing where financial rewards exist, where a crowd is tasked with solving problems which solution seekers anticipate to be empirically provable, but where the source of solutions is uncertain and addressing the challenge in-house perceived to be too high-risk. There is a growing recourse to crowdsourcing, but we really know little about its effectiveness, best practices, challenges and implications. We consider the shift to more open innovation trajectories over time, define crowdsourcing as an open innovation model, and clarify how crowdsourcing differs from other types of 'open' innovation (e.g. outsourcing and open-source). We explore who is crowdsourcing and how, looking at the potential diversity and core features and variables implicated in crowdsourcing models.

2016

During last years the relevance of crowdsourcing has increased both among scholars and practitioners: about the 85% of the Best Global Brands developed crowdsourcing projects during last 10 years (eYeca, Annual Report 2015) and the bibliometric indexes on the topic show an increasing trend (ISI WEB of Knowledge, 2016) of publications. Collaboration and, more specifically, the integration of resources of different actors (Carida et al., 2014) now arise as pre-conditions for the definition of the process of value creation which business model have to be addressed to (Zott et al., 2011). The crowd is a relevant source of innovation for firms (Brem, Bilgram, 2015) and the collective creativity (Boulaire et al. 2010) that it expresses (Majchrzach, Malorta 2013; Colurcio et al., 2012) is a valuable contribution as it matches knowledge and creative potential belonging to several individuals (Peppler, Solomou, 2011). Collective creativity is a unique and not replicable resource that come fr...