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Marginality and Team Building in Collaborative Crowdsourcing

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Abstract

Existing studies on crowdsourcing have focused on analyzing isolated contributions by individual participants and thus collaboration dynamics among them are under-investigated. The value of implementing crowdsourcing in problem solving lies in the aggregation of wisdom from a crowd. This study examines how marginality affects collaboration in crowdsourcing. With population level data collected from a global crowdsourcing community (openideo.com), this study applied social network analysis and in particular bipartite exponential random graph modeling (ERGM) to examine how individual level marginality variables (measured as the degree of being located at the margin) affect the team formation in collaboration crowdsourcing. Significant effects of marginality are attributed to collaboration skills, number of projects won, community tenure, and geolocation. Marginality effects remain significant after controlling for individual level and team level attributes. However, marginality alone cannot explain collaboration dynamics. Participants with leadership experience or more winning ideas are also more likely to be selected as team members. The core contribution this research makes is the conceptualization and definition of marginality as a mechanism in influencing collaborative crowdsourcing. This study conceptualizes marginality as a multidimensional concept and empirically examines its effect on team collaboration, connecting the literature on crowdsourcing to online collaboration.

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... It is a known fact that collaborative teamwork can yield synergetic effects [27] so as to solve more complex problems [6,54]. Some researchers have proposed the implementation of team structures in typical crowdsourcing mechanisms [4,43,59,60,67] to reduce individual workload, shorten the time, boost individual performance, and so on. However, the majority of existing collaborative crowdsourcing approaches fall short of the ability to facilitate productive collaboration due to the inflexible and inactive team mechanisms [66]. ...
... As [18] pointed out, crowdsourcing is not only about how crowd workers are sourced, but more fundamentally to the methods that the crowd can be organised and coordinated. The effectiveness of collaboration among crowdsourcing participants is highly critical and has been increasingly emphasised [67]. Therefore, to fully tap potential of the crowd, the crowdsourcing process, we should impose some organisational control over the implementation that can dictate what the crowd needs to work on during the crowdsourcing processes. ...
... There is a growing body of research that focuses on exploring the potentials of integrating team structures into crowdsourcing to enhance effective collaboration for high-quality crowdsourced data [31,42,54,59,67]. Because team structures can not only stimulate synergetic effects by emphasising the cooperative goal, but more importantly, provide the social context [56] where individuals can share and exchange information as well as adapt their behaviours via observation [37]. ...
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... Scholars argue that it is diversity in a crowd that makes crowdsourcing an innovative problem-solving model (Wang, 2020). However, few studies have examined how team diversity affects crowdsourcing outcomes (see Bayus, 2013 for an exception). ...
... In collaborative crowdsourcing, participants engage interactions in which different ideas and perspectives are discussed and integrated to achieve a collective solution (Wang, 2020). Collaborative crowdsourcing emphasizes the process of participants collaboratively cocreating innovative solutions through offering alternatives, modifying proposals, and generating collective ideas (Majchrzak & Malhotra, 2013). ...
... In crowdsourcing, experience diversity can be derived not only from individuals winning ideas but also winning a variety of challenges. Winning more ideas indicates experience in simply winning more; while winning more challenges would indicate skills in heterogeneous domains and also in navigating the crowdsourcing platform (Wang, 2020). These two dimensions of winning are related but entail different experience diversity. ...
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... These cases covered a variety of social issues including humanitarian crisis, business innovation, climate change, education, public health, and community well-being. Openideo projects are created through collaboration between Openideo and sponsors which can be a nonprofit organization, a government agency, or a for-profit business (see Lakhani et al. (2013) and Wang (2020) for more detail on how Openideo operates). ...
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... Bipartite exponential random graph modeling (ERGM) was conducted to analyze the network patterns between two sets of nodes [27] (see Figure 1 for an illustration of how the ties were coded). ERGM investigates the propensities in a network compared to variables sorted by chance alone, through simultaneously testing the effects of variables from multiple levels [28,29]. It enables the grouped analysis of health organizations across countries and TLD attributes, which reveals whether and how local parameters (at the country and TLD levels) shape the observed network configurations. ...
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(1) Background: This study introduces a novel computational approach to examine government capabilities in information intervention for risk management, influential agents in a global information network, and the socioeconomic factors of information-sharing behaviors of the public across regions during the COVID-19 pandemic. (2) Methods: Citation network analysis was employed to gauge the online visibility of governmental health institutions across regions. A bipartite exponential random graph modeling (ERGM) procedure was conducted to measure network dynamics. (3) Results: COVID-19 response agencies in Europe had the highest web impact, whereas health agencies in North America had the lowest. Various stakeholders, such as businesses, non-profit organizations, governments, and educational institutions played a key role in sharing the COVID-19 response by agencies’ information given on their websites. Income inequality and GDP per capita were associated with the high online visibility of governmental health agencies. Other factors, such as population size, an aging population, death rate, and case percentage, did not contribute to the agencies’ online visibility, suggesting that demographic characteristics and health status are not predictors of sharing government resources. (4) Conclusions: A combination of citation network analysis and ERGM helps reveal information flow dynamics and understand the socioeconomic consequences of sharing the government’s COVID-19 information during the pandemic.
... Although workers can request changes in the original teams, the final decision is made by a small number of experts and the task requester. Aside from skill sets, budget, and time, a small set of recent studies has started proposing team formation algorithms that harness social network qualities such as connectivity (Salehi and Bernstein, 2018), centrality (Hasteer et al., 2015), and marginality (Wang, 2020), as non-trivial parameters affecting teamwork performance across time. In this direction, Jiang et al. (2019) propose a team formation algorithm that instead of forming artificial teams, based on the individual teammates' skills, cost, or other features, utilizes groups that have been naturally organized through social networks, and allocated them to tasks in a priority-based manner based on their capacity to address the task. ...
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... First, it assumes that regardless of coder qualifications, the majority always outperforms the minority. This argument actually counters the findings from the crowdsourcing literature that marginalized members of a crowd are more likely to contribute in a meaningful way (Jeppesen & Lakhani, 2010;Wang, 2020). As Lind et al. (2017) reflected on this issue, they similarly acknowledged that majority rule "assumes all crowd workers to be equally reliable, while it is evident that the quality and performance level differs within the group of workers" (p. ...
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... Given the fluid nature of online communities, it is not surprising that previous research has mainly focused on decision-making processes and behavioral characteristics of temporary crowd members (Bassi et al., 2020;Majchrzak and Malhotra, 2016). While the existing literature has provided great insights into communicative (Xu, 2018), structural (Wang, 2020), motivational (Liu, Yang et al., 2014), cultural (Chua et al., 2015), and architectural (Nov et al., 2015) antecedents of crowdsourcing behaviors and outcomes, less attention has been paid to serial idea generators who persistently contribute ideas to crowdsourced tasks over extended periods of time. The defining features of these participants are their sustained commitment to the online community and self-identification with the crowdsourcing company. ...
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The increased use of teams in organizations, coupled with an increasingly diverse workforce, strongly suggests that we should learn more about how team diversity affects functioning and performance. The purpose of this study was to explore the differential impact of surface-level diversity (gender, ethnicity), deep-level diversity (time urgency, extraversion), and two moderating variables (team orientation, team process) on relationship conflict over time. Hypotheses were tested by tracking 45 student project teams in a longitudinal design. Results revealed that team orientation and team process moderated the diversity–conflict link. Specifically, team orientation helped to neutralize the negative effects of surface-level (gender) diversity on relationship conflict. In a similar manner, team processes worked to weaken the deleterious effects of deep-level diversity (time urgency) on relationship conflict. In addition, relationship conflict resulted in lower perceived performance by team members. Copyright © 2004 John Wiley & Sons, Ltd.
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A substantial amount of economic activity involves problem solving, yet economics has few, if any, formal models to address how agents of limited abilities find good solutions to difficult problems. In this paper, we construct a model of heterogeneous agents of bounded abilities and analyze their individual and collective performance. By heterogeneity, we mean differences in how individuals represent problems internally, their perspectives, and in the algorithms they use to generate solutions, their heuristics. We find that while a collection of bounded but diverse agents can locate optimal solutions to difficult problems, problem solving firms can exhibit arbitrary marginal returns to problem solvers and that the order that problem solvers are applied to a problem can matter, so that the standard story of decreasing returns to scale may not apply to problem solving firms. Journal of Economic Literature Classification Numbers: C6, D2.