Crowdsourcing Communities Research Articles

Competition and Collaboration in Crowdsourcing Communities: What Happens When Peers Evaluate Each Other?

Crowdsourcing has evolved as an organizational approach to distributed problem solving and innovation. As contests are embedded in online communities and evaluation rights are assigned to the crowd, community members face a tension: They find themselves exposed to both competitive motives to win the contest prize and collaborative participation motives in the community. The competitive motive suggests they may evaluate rivals strategically according to their self-interest, the collaborative motive suggests they may evaluate their peers truthfully according to mutual interest. Using field data from Threadless on 38 million peer evaluations of more than 150,000 submissions across 75,000 individuals over 10 years and two natural experiments to rule out alternative explanations, we answer the question of how community members resolve this tension. We show that as their skill level increases, they become increasingly competitive and shift from using self-promotion to sabotaging their closest competitors. However, we also find signs of collaborative behavior when high-skilled members show leniency toward those community members who do not directly threaten their chance of winning. We explain how the individual-level use of strategic evaluations translates into important organizational-level outcomes by affecting the community structure through individuals’ long-term participation. Although low-skill targets of sabotage are less likely to participate in future contests, high-skill targets are more likely. This suggests a feedback loop between competitive evaluation behavior and future participation. These findings have important implications for the literature on crowdsourcing design, and the evolution and sustainability of crowdsourcing communities. Funding: This work was supported by the National Science Foundation [Grant IIS-1514283] and the U.S. Office of Naval Research [Grant N00014-17-1-2542]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2021.15163 .

An evolutionary mechanism of social preference for knowledge sharing in crowdsourcing communities

Crowdsourcing community, as an important way for enterprises to obtain external public innovative knowledge in the era of the Internet and the rise of users, has a very broad application prospect and research value. However, the influence of social preference is seldom considered in the promotion of knowledge sharing in crowdsourcing communities. Therefore, on the basis of complex network evolutionary game theory and social preference theory, an evolutionary game model of knowledge sharing among crowdsourcing community users based on the characteristics of small world network structure is constructed. Through Matlab programming, the evolution and dynamic equilibrium of knowledge sharing among crowdsourcing community users on this network structure are simulated, and the experimental results without considering social preference and social preference are compared and analysed, and it is found that social preference can significantly promote the evolution of knowledge sharing in crowdsourcing communities. This research expands the research scope of the combination and application of complex network games and other disciplines, enriches the theoretical perspective of knowledge sharing research in crowdsourcing communities, and has a strong guiding significance for promoting knowledge sharing in crowdsourcing communities.

Pickled eggs: GenerativeAIas research assistant or co‐author?

Pickled eggs: Generative<scp>AI</scp>as research assistant or co‐author?

Innovation Crowdsourcing Mechanisms and Innovation Performance: An Empirical Study of a Business Intelligence Community

Purpose: This study aims to develop a research model to investigate how the structure and mechanisms of innovation crowdsourcing influence knowledge management and innovation performance, based on the perspectives of open innovation theory and the knowledge-based view (KBV) of the firm. Design/Methodology/Approach: The research model and associated hypotheses were tested using partial least squares structural equation modelling (PLS-SEM), based on a dataset from the Microsoft Power BI community of business intelligence (BI) and analytics tools. Findings: The results show that both organisational and technical mechanisms of the community positively influence the community structure. The community structure has a positive impact on knowledge acquisition, knowledge transformation and the size and diversity of crowd participation. The mechanisms of innovation crowdsourcing and knowledge transformation in turn have a strong influence on innovation performance. Originality: This study is among the first to provide analytical insights into the mechanisms of innovation crowdsourcing and their underlying impact on innovation performance in the context of BI and analytics tools that exhibit a multiplicity and complexity of functions and capabilities. It therefore provides strategic guidance on how to effectively stimulate crowd intelligence and maximise the collaborative and synergistic effectiveness of innovation crowdsourcing communities, focusing on knowledge management practices and user innovation behaviour and performance.

Innovation in Business Intelligence Systems

Innovation crowdsourcing communities play a central role for companies to advance their innovation capabilities and portfolio by leveraging crowd intelligence and knowledge. However, it remains unclear how the mechanisms and structure of innovation crowdsourcing communities affect firms' innovation performance. Based on the open innovation theory and knowledge-based view (KBV), this study develops a research model to investigate how the structure and mechanisms of innovation crowdsourcing influence firms' knowledge management and innovation performance. The model was tested using structural equation modeling based on a dataset from the Microsoft community for business intelligence tools. The results show that both organizational and technical mechanisms of the community positively influence the community structure. The community structure positively influences knowledge acquisition, knowledge transformation, and the size and diversity of crowd participation. In turn, innovation crowdsourcing mechanisms and knowledge transformation have a strong influence on innovation performance.

The creative touch: the influence of haptics on creativity.

An increasing number of firms rely on consumers to develop new ideas for the marketplace. While many firms rely on online crowdsourcing communities, some have created facilities that encourage in-person ideation through which consumers can interact with product design materials. This research proposes that active touch engenders a positive effect on new product creativity and highlights the importance of touch during idea generation. We further suggest that interacting with an object via active touch increases positive mood, which enhances creative performance. Results from two studies provide support for these hypotheses. Study 1 demonstrates the positive effect of active touch on new product creativity. Study 2 replicates this effect in a different product development context and provides evidence that a positive mood mediates the active touch-creativity relationship.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11002-022-09628-5.

Solver engagement in online crowdsourcing communities: The roles of perceived interactivity, relationship quality and psychological ownership

Crowdsourcing has attracted significant attention from organizations seeking to capture new ideas and solutions, finance new projects, and obtain market feedback about product concepts. The success of online crowdsourcing communities relies on the engagement of solvers that participate in crowdsourcing activities. In line with the Stimulus-Organism-Response (S-O-R) model, this study aims to examine the relationships between the dimensions of perceived interactivity, relationship quality, psychological ownership, and solver engagement. The resulting relationships are examined using both symmetric (PLS-SEM) and asymmetric (fsQCA) approaches, applying survey data from 423 active solvers on two online crowdsourcing communities, Epwk.com and Zbj.com. PLS-SEM identified that high levels of perceived interactivity increased relationship quality. In contrast, the influence of perceived responsiveness on psychological ownership was not statistically significant. Finally, relationship quality and psychological ownership significantly influences solver engagement. The fsQCA results reinforced the PLS-SEM findings and revealed five alternative causal configurations that are sufficient for higher levels of solver engagement.

The Relationship Between Persuasion Cues and Idea Adoption in Virtual Crowdsourcing Communities

Building on the elaboration likelihood model (ELM) and absorptive capacity, this study develops a four-dimensional model of idea adoption in Virtual Crowdsourcing Communities (VCCs) and examines the influence of different persuasion cues on idea adoption. The research model was tested using hierarchical logistic regression based on a dataset from the Tableau community. The results show that both community recognition of users and community recognition of ideas are positively related to idea adoption. Proactive user engagement has a significant positive impact on idea adoption, while reactive user engagement has no significant impact. Idea content quality, represented by idea length and supporting arguments, has an inverted U-shaped relationship with idea adoption. Community absorptive capacity positively moderates the curvilinear relationship between idea content quality and idea adoption. These results contribute to a better elucidation of the persuasion mechanisms underlying idea adoption in VCCs, and thus provide important implications for open innovation research and practice.

Mapping Community Engagement with Urban Crowd-Sourcing

Cities are highly dynamic entities, with urban elements such as businesses, cultural and social Points-of- Interests (POIs), housing, transportation and the like, continuously changing. In order to maintain accurate spatial information in these settings, crowd-sourcing models of data collection, such as in OpenStreetMap (OSM), have come under investigation. Like many crowd-sourcing platforms (e.g., Wikipedia), these geo- wikis exhibit tailing-off activity, bringing into ques- tion their long-term viability. In this paper, we begin an investigation into the sustainability of urban crowd-sourcing, by studying the network structure and ge- ographical mapping of implicit communities of con- tributors in OSM. We observe that spatially clustered crowd-sourcing communities produce higher coverage than those with looser geographic affinity. We discuss the positive implications that this has on the future of urban crowd-sourcing.

Understanding the effects of personality traits on solver engagement in crowdsourcing communities: a moderated mediation investigation

PurposeCrowdsourcing communities enable companies to post challenges that are completed by solvers (workers); their success depends on engagement, requiring both creativity and effort. This study explores solver engagement in online crowdsourcing communities, advancing the theory of trait engagement by investigating the mediating roles of: (a) task-related self-efficacy in linking conscientiousness, neuroticism and extraversion, with solver engagement, and (b) task complexity in influencing the mediation.Design/methodology/approach215 valid responses were obtained from solvers engaged in the popular Chinese crowdsourcing community, Epwk.com, using an online questionnaire. PLS was then used to analyze the data.FindingsResults show that self-efficacy mediates the relationships for conscientiousness, neuroticism and extraversion, with solver engagement. Moderated mediation analysis revealed that self-efficacy mediates the relationships for: (a) conscientiousness and extraversion, for only solvers with high task complexity; and (b) neuroticism, for only solvers with low task complexity.Originality/valueThe authors’ findings underscore the importance of accounting for solvers' situational contexts when examining the relationships between personality, self-efficacy and solver engagement in online crowdsourcing communities.

The role of crowdsourcing in product design: The moderating effect of user expertise and network connectivity

To meet the challenges of customization and personalized design in the context of the digital economy, increasing numbers of small and medium-sized enterprises (SMEs) are using crowdsourcing platforms to seek product design innovation. While crowdsourcing has significant potential for eliciting product ideas, it remains unclear whether it can generate high-quality designs for relatively more complex product design tasks. Based on a leading social new product development website Quirky.com, we explore the antecedents of a firm's adoption decision of crowdsourcing, the impact of crowdsourcing on product design quality, and the moderating effect of user expertise and network connectivity by utilizing treatment effect model. The results indicate that: (1) crowdsourcing has a significant positive impact on product design quality; (2) user expertise positively moderates the role of crowdsourcing in product design quality; (3) network connectivity has an inverted U-shaped moderating effect on crowdsourcing and product design quality relationship; (4) the probability of firm's crowdsourcing adoption is negatively correlated with the complexity and reliability of initial product idea but positively correlated with popularity, novelty, and numbers of images. This study not only enriches the crowdsourcing and social network theory; but also provides guidance for crowdsourcing platform firms to better manage the crowdsourcing process and user crowdsourcing communities.

The Impact of Helping Others in Coopetitive Crowdsourcing Communities

Organizations are increasingly engaging the community through crowdsourcing platforms to evolve innovative solutions to challenging business problems. Participants on such platforms often simultaneously cooperate and compete with one another to earn top honors. This paper addresses the imperative to understand the dynamics of knowledge sharing in such a coopetitive environment. Specifically, our study relies on the conceptual foundations of social exchange and social capital theories to investigate how help rendered (e.g., exchanging ideas or sharing knowledge) by participants in an online coopetitive crowdsourcing setting affects their performance. Furthermore, the study examines the moderating effects of the intensity of competition. Results of our econometrics analyses suggest that help given in a highly competitive contest, as opposed to a less competitive one, is more likely to be reciprocated, but less likely to improve the contributor’s contest performance. In addition, our study found that help received by participants positively impacts their contest performance, and partially mediates the relationship between help rendered and contest performance. This research also provides insight into what motivates participants to share knowledge under conditions of coopetition. The findings of our study have strong implications for both theory and practice.

Idea Recommendation in Open Innovation Platforms: A Design Science Approach

Collaborative crowdsourcing communities help firms obtain ideas generated by the public at a lower cost compared to those generated in-house. However, the growth of these communities has led to a large influx of ideas of mixed quality, which has made it difficult for firm experts to select and implement ideas. In this paper we propose a novel theoretical framework to (1) extract important features from a collaborative crowdsourcing community and (2) apply them to the practice of recommending ideas that are most likely to be implemented in the future. More specifically, we adopt the design science research paradigm, introduce the knowledge persuasion model as the kernel theory, operate users’ persuasive attempts and firm experts’ persuasive coping, and identify a rich set of features as predictors of the likelihood of idea implementation. We test our prediction framework on a large-scale collaborative crowdsourcing community. The results of our data analysis show that the proposed framework is effective and efficient in predicting the likelihood of idea implementation. To increase the interpretability of the prediction model, we also implement the SHapley Additive exPlanations (SHAP) analysis and discuss the relationships between important features and idea implementation. We conclude by discussing the theoretical and practical implications of these findings.

Corrections to “A Social Crowdsourcing Community Case Study: Interaction Patterns, Evolution, and Factors That Affect Them” [Jun 20 659-671

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Creativity of Participants in Crowdsourcing Communities

Organizations can tap the wisdom of the crowd through digital platforms of crowdsourcing for ideation. However, we have limited understanding of factors affecting the innovativeness of ideas and solutions submitted by individual participants. Drawing upon self-determination theory and regulatory focus theory, we investigate how participants' regulatory focus and extrinsic motivation aroused by incentivizing mechanisms affect their creativity in crowdsourcing communities. Based on the data collected from 164 participants in a crowdsourcing platform, we find that promotion focus positively influences participants' creativity, and different types of extrinsic motivation have differential effects. Although external, identified, and integrated motivation positively affect participants' creativity, introjected motivation is not significantly related to participants' creativity. In addition, external and identified motivation strengthen the relationship between promotion focus and creativity. The theoretical contributions and managerial implications of this study are discussed.

The Paradox of Interaction: Communication Network Centralization, Shared Task Experience, and the Wisdom of Crowds in Online Crowdsourcing Communities

Research on the wisdom of crowds (WOC) identifies two paradoxical effects of communication. The social influence effect hampers the WOC, whereas the collective learning effect improves crowd wisdom. Yet it remains unclear under what conditions such communication impedes or enhances collective wisdom. The current study examined two features characterizing communication in online communities, communication network centralization and shared task experience, and their effect on the WOC. Both these features can serve as indicators of the likelihood that underlying communication may facilitate either social influence or collective learning. With an 8-year longitudinal behavioral-trace data set of 269,871 participants and 1,971 crowds, we showed that communication network centralization negatively affected the WOC. By contrast, shared task experience positively predicted the WOC. Shared task experience also moderated the effect of communication network centralization such that centralized communication networks became more beneficial for crowd performance as shared task experience increased.

A Social Crowdsourcing Community Case Study: Interaction Patterns, Evolution, and Factors That Affect Them

A social crowdsourcing community (SCC) is a moderated online community where members participate in activities designed to elicit their opinions concerning topics related to products or services. This emerging paradigm of human computation systems is distinct in which it combines the powers of crowdsourcing and social networking to allow for systematic querying of crowds and synthesizing response data (i.e., contributions) into coherent reports for decision-makers. Since the SCC paradigm is relatively new, we apply methods from social network analysis and related fields to help answer important questions relevant to SCCs. We demonstrate the applicability of these methods by analyzing five years of interactions in a private SCC starting from its inception. This analysis reveals the patterns in members’ interactions within the SCC and key factors that affect them. In addition, the analysis reveals the patterns in the evolution of these interactions.

User idea implementation in open innovation communities: Evidence from a new product development crowdsourcing community

AbstractIn collaborative crowdsourcing communities for open innovation, users generate and submit ideas as idea co‐creators. Firms then select and implement valuable ideas for new product development. Despite the popularity and success of these open innovation communities, relatively little is known about the factors that determine the implementation of the user‐generated ideas. Based on research on individual creativity, we propose a conceptual model integrating users' previous experience, idea presentation characteristics and feedback valence to explain the likelihood of idea implementation. We validate our research model with a panel data analysis of 43 550 ideas submitted by 16 360 users in the MIUI new product development community hosted by Xiaomi, a large electronics manufacturing company in China. We find an inverted U‐shaped relationship between users' past successful experience and idea implementation. Furthermore, the length of ideas is positively associated with the likelihood of idea implementation. There is also an inverted U‐shaped relationship between supporting evidence and idea implementation. Finally, we demonstrate the negative effect of positive feedback and the positive effect of negative feedback on idea implementation. These findings offer rich insights to understand the phenomenon of open innovation better. Theoretical and practical implications are discussed.

Jack of All, Master of Some: Information Network and Innovation in Crowdsourcing Communities

In this paper, we examine information flow across two crowdsourcing communities and provide empirical evidence on how information obtained from a customer support crowdsourcing community can help individuals generate more novel, popular, and feasible ideas in an innovation crowdsourcing community. Our findings inform firms’ decisions about whether to simultaneously host customer support and innovation crowdsourcing communities. Companies such as Dell and BestBuy simultaneously host customer support and innovation crowdsourcing communities. Yet many other companies host only innovation crowdsourcing communities. Our findings suggest that hosting both communities has benefits because a customer support community offers idea generators an opportunity to accumulate current needs information as well as to activate means information from their memory. This needs-and-means information enables idea generators to create high-quality new product/service ideas. Our findings further suggest that those firms that are already hosting two crowdsourcing communities could benefit by tracing individuals’ activities more closely. By collecting data about individuals’ microlevel activities in a customer support community, companies would be able to identify those individuals who have a higher probability of creating better ideas. By mobilizing those individuals, companies can increase the quality of ideas they obtain from innovation crowdsourcing communities.

Towards Quantifying Behaviour in Social Crowdsourcing Communities

We analyze crowdsourcing communities by creating a detailed process for quantifying individual behaviour in online environments. The key feature of our communities is their social interactions so we call these social crowdsourcing communities (SCC). First, we derive factors based on actions captured about textual contributions. We interpret and name these factors. Then we demonstrate their utility in predicting the quality of team contributions. We capture the actions of members using measurable variables and perform factor analysis on these to produce factors of behaviour in SCCs (i.e. dimensions of behaviour). We derive factor scores for each member. An abstract notion of teams is used that is based on the social interactions. Team scores are then determined by the aggregation of the individual factor scores. The relationship between the team-level factor scores and the quality of contributions made by each team are then used as a proxy for team effectiveness. We found that member behaviour has three dimensions/factors: Impact, Activity, Policing/Rowdiness and there is a linear relationship between a team's contribution quality and their Impact scores. We also found a moderate negative linear relationship between the smallest Activity scores in each team with the quality of their individual contributions. This shows that teams that produce higher quality contributions tend to have higher total and maximum Impact score with lower levels of Activity. Thus, we demonstrate that properly aggregated behavioural factors can predict the quality of team-level contributions.