Effects of Gamification Elements on Crowdsourcing Participation: The Mediating Role of Justice Perceptions

Justice perceptions have been regarded as an important influencing factor for solvers’ (i.e., users who solve tasks on the crowdsourcing platforms) continued participation in crowdsourcing. However, researchers and practitioners still lack of sufficient understanding on the design of crowdsourcing platform that can effectively foster solvers’ justice perceptions. By synthesizing theory of organizational justice and the literature on gamification, we examine the effects of solvers’ gamification element perceptions on their crowdsourcing participation through justice perceptions. Specifically, we propose a research model to explain the effects of three gamification element perceptions (i.e., point, feedback, social network) on solvers’ distributive, interactional, and informational justice perceptions which, in turn, foster their crowdsourcing participation. By collecting survey data from 295 solvers and analyzing the data with the partial least squares-structural equation modeling (PLS-SEM) approach, our study finds that point fosters crowdsourcing participation through distributive and interactional justice. Feedback enhances participation through distributive, interactional and informational justice. While social network strengthens participation via interactional and informational justice. Our study offers significant theoretical contributions and practical implications for the gamified crowdsourcing and organizational justice literatures

Entity Resolution using Convolutional Neural Network

Entity resolution is an important application in field of data cleaning. Standard approaches like deterministic methods and probabilistic methods are generally used for this purpose. Many new approaches using single layer perceptron, crowdsourcing etc. are developed to improve the efficiency and also to reduce the time of entity resolution. The approaches used for this purpose also depend on the type of dataset, labeled or unlabeled. This paper presents a new method for labeled data which uses single layered convolutional neural network to perform entity resolution. It also describes how crowdsourcing can be used with the output of the convolutional neural network to further improve the accuracy of the approach while minimizing the cost of crowdsourcing. The paper also discusses the data pre-processing steps used for training the convolutional neural network. Finally it describes the airplane sensor dataset which is used for demonstration of this approach and then shows the experimental results achieved using convolutional neural network

How to invent a new business model based on crowdsourcing : the Crowdspirit ® case

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 industrial R&D is undergoing a paradigm shift from the closed to the open model. Information and communication technologies and especially web 2.0 technologies accelerate this shift in so far they provide access to collective and distributed intelligence disseminated in the “crowd”. This phenomenon named “crowdsourcing” is defined by Jeff Howe as “the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined - and generally large – network of people in the form of an open call.” Though this approach may sound appealing to firms and R&D organizations, there is little research available about the strategic use of crowdsourcing for innovation processes. In this paper we develop the argument that crowdsourcing raises a certain number of strategic issues that we discuss on the basis of a real size crowdsourcing experiment. We were associated in the project from the very outset up to the strategic analysis of the company. Our data is made up of the minutes of three strategic workshops with the managers that we completed step by step by additional theoretical study and some benchmarking of crowdsourcing experiments on the web. Although we started this collaboration with no other objectives than to help this company to design its optimal business model, this action research process has led us to address the following research questions: how can a firm create and capture value by means of a strategy based on crowdsourcing? What are the main strategic issues to be considered when a firm intends to open its innovation process through crowdsourcing? Due to the action research approach used, we do not dissociate the theoretical part from the empirical data, but rather to present our research process step by step. We therefore successively present the three main phases of the strategic analysis carried out with the Crowdspirit team: (1) elaboration of Crowdspirit business model; (2) value creation process related to profiles of crowdspirit community of contributors (3)Theoretical framework on business models based on crowdsourcing. In the conclusion we summarize the main strategic issues that emerged during this 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 Chesbrough's open innovation approach and Nambissan and Sawney network-centric innovation model by introducing new options for companies whose strategy is based on crowdsourcing.Open innovation, crowdsourcing, business models

How to invent a new business model based on crowdsourcing: the Crowdspirit ® case

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 industrial R&D is undergoing a paradigm shift from the closed to the open model. Information and communication technologies and especially web 2.0 technologies accelerate this shift in so far they provide access to collective and distributed intelligence disseminated in the “crowd”. This phenomenon named “crowdsourcing” is defined by Jeff Howe as “the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined - and generally large - network of people in the form of an open call.”Though this approach may sound appealing to firms and R&D organizations, there is little research available about the strategic use of crowdsourcing for innovation processes. In this paper we develop the argument that crowdsourcing raises a certain number of strategic issues that we discuss on the basis of a real size crowdsourcing experiment. We were associated in the project from the very outset up to the strategic analysis of a start-up: Crowdspirit. The company's concept is based on the outsourcing of the entire R&D process to a community of designers and users, in the domain of consumer electronics. Our data is made up of the minutes of three strategic workshops with the managers that we completed step by step by additional theoretical study and some benchmarking of crowdsourcing experiments on the web. Although we started this collaboration mainly to help the company design its optimal business model, this action research process has led us to address the following research questions: how can a firm create and capture value by means of a strategy based on crowdsourcing? What are the main strategic issues to be considered when a firm intends to open its innovation process through crowdsourcing? Due to the action research approach used, we do not dissociate the theoretical part from the empirical data, but rather to present our research process step by step. We therefore successively present four main phases of the strategic analysis carried out with the 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 Chesbrough's open innovation approach and Nambissan and Sawney network-centric innovation model by introducing new options for companies whose strategy is based on crowdsourcing.Open innovation, crowdsourcing, business models

Ability Discovery and Weak Centralized Based Crowdsourcing Service Release System in Social Network

Crowdsourcing developed rapidly for its inspiring public abilities. But how to effectively find qualified participants and how to find and prevent malicious workers may be the main difficulties to ensure the crowdsourcing quality. In this paper, the related theories of social network were used in crowdsourcing services, the task publisher (Seeker) was regarded as the network center, his Abilities Set (AS) would be quantified and his Friends Abilities Matrix (FAM) would be generated according to the communication between them, thus his social network was re-constructed. Subsequently, some friends that conformed to the ability requirements of the task would be chosen to be the task receivers (Solvers). The natural trust relationship in the social network was fully used to build a crowdsourcing service release system on weak centralization. By using the social network, even the privacy information needn’t to be shared with others, the system could help the seeker find solvers accurately in the seeker’s own social network according to task demands, and then help to reduce fraud and invalid data. The simulation experiments showed that the release system could help the seeker discover his own abilities, construct the FAM, and select the appropriate solvers precisely and automatically

Submitting tentative solutions for platform feedback in crowdsourcing contests: breaking network closure with boundary spanning for team performance

Purpose To obtain optimal deliverables, more and more crowdsourcing platforms allow contest teams to submit tentative solutions and update scores/rankings on public leaderboards. Such feedback-seeking behavior for progress benchmarking pertains to the team representation activity of boundary spanning. The literature on virtual team performance primarily focuses on team characteristics, among which network closure is generally considered a positive factor. This study further examines how boundary spanning helps mitigate the negative impact of network closure. Design/methodology/approach This study collected data of 9,793 teams in 246 contests from Kaggle.com. Negative binomial regression modeling and linear regression modeling are employed to investigate the relationships among network closure, boundary spanning and team performance in crowdsourcing contests. Findings Whereas network closure turns out to be a negative asset for virtual teams to seek platform feedback, boundary spanning mitigates its impact on team performance. On top of such a partial mediation, boundary spanning experience and previous contest performance serve as potential moderators. Practical implications The findings offer helpful implications for researchers and practitioners on how to break network closure and encourage boundary spanning with the establishment of facilitating structures in crowdsourcing contests. Originality/value The study advances the understanding of theoretical relationships among network closure, boundary spanning and team performance in crowdsourcing contests

Crowdsourcing the Collection of Transportation Behavior Data

Understanding the travel behaviors of individuals who use public transit is essential for enhancing the performance, sustainability and efficiency of public transportation. Contemporary methods for collecting data on transportation behavior are focused on manual or automated procedures for counting the number of individual passengers entering or exiting transit vehicles. While such methods provide useful data for understanding transit demand throughout a network, they ignore the important details of how passengers travel to and within a network as well as their personal experiences during their commute, all of which can enrich the ability of transit agencies to provide sustainable transportation. To address this issue, there has been a proliferation of location-based services (LBS) that allow for new methods of data collection involving passengers volunteering data about their commute. In this light, passengers engage in a crowdsourcing effort to generate data about experiences across the network. This project’s objective is to implement and test specific LBS in a bus transit network to better understand their potential and limitations for improving the crowdsourcing of travel behavior data