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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.
... 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]. ...
Disorganised and self-organised crowdsourcing activities that harness collective behaviours to achieve specific level of performance and task completeness are not well understood. Such phenomena become indistinct when highly varied environments are present, particularly for crowdsourcing photogrammetry-based 3D models. Mass photogrammetry can democratise traditional close-range photogrammetry procedures by outsourcing image acquisition tasks to a crowd of non-experts to capture geographically scattered 3D objects. To improve public engagement, we need to understand how individual behaviour in collective efforts work in traditional disorganised crowdsourcing and how it can be organised for better performance. This research aims to investigate the effectiveness of disorganised and self-organised collaborative crowdsourcing. It examines the collaborative dynamics among participants and the trends we could leverage if team structures were incorporated. Two scenarios were proposed and constructed: asynchronous crowdsourcing, which implicitly aggregates isolated contributions from disorganised individuals; and synchronous collaborative crowdsourcing, which assigns participants into a crowd-based self-organised team. Our experiment demonstrated that a self-organised team working in synchrony can effectively improve crowdsourcing photogrammetric 3D models in terms of model completeness and user experience. Through our study, we demonstrated that this crowdsourcing mechanism can provide a social context where participants can exchange information via implicit communication, and collectively build a shared mental model that pertains to their responsibilities and task goals. It stimulates participants’ prosocial motivation and reinforces their commitment. With more time and effort invested, their positive sense of ownership increases, fostering higher dedication and better contribution. Our findings shed further light on the potentials of adopting team structures to encourage effective collaborations in conventionally individual-based voluntary crowdsourcing settings, especially in the digital heritage domain.
... 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. ...
The value of crowdsourcing in solving problems is its ability to aggregate diversity from a crowd. The literature tends to define diversity as a general attribute of the crowd, without unpacking it as a multidimensional concept or measuring it as an attribute of members within a crowd. This study examines collaborative crowdsourcing where people form virtual bona fide groups to win social issue contests. Guided by the literature on generative co-creation and Social Identity Theory, this paper investigates how diversity at the team level affects team winning. With behavioral data from a global crowdsourcing community Openideo, this study finds that teams with higher diversity in members’ expertise, winning experience, and geolocation distribution are more likely to win crowdsourcing contests. However, higher team diversity attributed to members’ community tenure could lead to worse team performance. Discussion on how to leverage the benefits of diversity in collaborative crowdsourcing while avoiding disruptive influences are provided.
... 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). ...
Crowdsourcing social innovation refers to utilization of crowdsourcing to solve social issues. It faces two organizational communication challenges to attract contributions: the public’s short attention span and concerns about a project’s feasibility. Guided by configurational thinking, we combine agenda setting theory and signaling theory to explore how combinations of four factors—media coverage, project duration, number of partners, and cross-sectoral partnership—can complement or substitute for one another to explain high and low crowd contributions solicited. With 53 cases from Openideo.com, we employ a fuzzy-set qualitative comparative analysis to identify two pathways to high contributions and two pathways resulting in low contributions. Implications on how organizations may design their crowdsourcing projects to attract more contributions are provided.
... 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. ...
(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. ...
Modern crowdsourcing offers the potential to produce solutions for increasingly complex tasks requiring teamwork and collective labor. However, the vast scale of the crowd makes forming project teams an intractable problem to coordinate manually. To date, most crowdsourcing collaborative platforms rely on algorithms to automate team formation based on worker profiling data and task objectives. As a top-down strategy, algorithmic crowd team formation tends to alienate workers causing poor collaboration, interpersonal clashes, and dissatisfaction. In this paper, we investigate different ways that crowd teams can be formed through three team formation models namely bottom-up, top-down, and hybrid. By simulating an open collaboration scenario such as a hackathon, we observe that the bottom-up model forms the most competitive teams with the highest teamwork quality. Furthermore, we note that bottom-up approaches are particularly suitable for populations with high-risk appetites (most workers being lenient toward exploring new team configurations) and high degrees of homophily (most workers preferring to work with similar teammates). Our study highlights the importance of integrating worker agency in algorithm-mediated team formation systems, especially in collaborative/competitive settings, and bears practical implications for large-scale crowdsourcing platforms.
... 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. ...
Moral framing; content analysis; moral foundation theory; digital activism
ABSTRACT
Moral framing is a mobilizing strategy in digital activism to raise awareness of a social issue. This essay discussed the strengths and weaknesses of multiple methods of extracting moral framing from media content. Two issues were highlighted from crowd- coding and automated coding using computational approaches: context sensitivity and the handling of coding discrepancies. We compared results from manual content coding and automated analysis using the eMFD, urging scholars to incorporate multiple methods to uncover the meanings behind each moral value. We argued that crowdcoding, computational approaches and manual coding are not exclusive of one another, and encouraged scholars to diversify their methodological toolbox and choose what to use based on the research context.
... 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. ...
This article investigates (a) how serial idea generators in crowdsourcing contests adjust strategies (e.g., exploiting existing expertise and exploring new approaches) over time in response to the relative success of their prior creative ideas, and (b) how these changes in competitive strategies influence subsequent creativity performance. The research was conducted on Threadless, a website that crowdsources the creation of graphic designs through an ongoing competition. Empirical results from a longitudinal sample of 86 serial designers and their 1,714 submissions indicated that a higher level of design expertise was related to less exploration and greater exploitation behavior. Exploitation was also found to contribute to generating ideas that were highly rated by the crowd. For the crowdsourcer's decision as to whether to transform a submitted idea into a product for sale, neither exploration nor exploitation was found to be significant when controlling for crowd rating. Theoretical and practical implications for crowdsourcing are discussed.
Purpose
The purpose of this study is to understand the scope and nature of information sharing as a form of workplace learning among gig workers.
Design/methodology/approach
Data were collected from public social media communities of gig workers in Thailand. In total, 338 posts and 3,022 comments on the posts were analyzed (data corpus N = 3,360). Thailand was selected for the context of this study given its high level of social media penetration, a high percentage of digital service consumption of internet users and the prevalence of app-based gig workers. This study used thematic analysis using inductive and semantic coding to generate themes.
Findings
Findings showed two overarching themes of information sharing, which included on-the-job experience and inquiries. One surprising finding was the extent to which gig workers used social media to help others even when it potentially undermined their success.
Research limitations/implications
This study adds evidence to the role of information sharing in workplace learning and illustrates how gig workers who do not have access to traditional training and learning opportunities use social media communities to fill this need.
Originality/value
Given the surge of digitalization and internet infrastructure leading to the rise of gig work worldwide, this study provides a closer look at how gig workers are using social media communities to facilitate workplace learning and support one another amid otherwise difficult and insecure working conditions. It also discusses the role that culture plays in facilitating a cooperative rather than a competitive environment among drivers.
Purpose
Guided by the collective action theory, signaling theory and social identity approach, this study examines backing behavior by individuals who have created projects under CC licenses. Two motivational mechanisms were examined: (1) identification via common interests in the CC space; (2) resource signaling by other users via their diverse project creation experience, funding or commenting activity.
Design/methodology/approach
Data were collected from Kickstarter.com. Exponential random graph modeling was used to examine how the two reviewed mechanisms influence the tie formation probability between Creative Commons (CC) project creators and other creators. The analysis was conducted on two subnetworks: one with ties between CC creators; and one with ties from CC creators to non-CC creators.
Findings
The study found that CC creators exhibit distinct backing patterns when considering funding other CC creators compared to non-CC users. When considering funding their peer CC creators, CC identity can help them allocate and support perceived in-group members; when considering funding non-CC creators, shared common interests in competitive project categories potentially triggers a competition mindset and makes them hold back when they see potential rivals.
Originality/value
This study makes three contributions. First, it draws from multiple theoretical frameworks to investigate unique motivations when crowdfunders take on dual roles of creators and funders and offered implications on how to manage competition and collaboration simultaneously. Second, with network analysis our study not only identifies multiple motivators at work for collective action, but also demonstrates their differential effects in crowdfunding. Third, the integration of multiple theoretical frameworks allows opportunities for theory building.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2020-0166 .
Task allocation plays a vital role in crowd computing by determining its performance. The power of crowd computing stems from a large number of workers potentially available to provide high quality of service and reduce costs. An important challenge in the crowdsourcing market today is the task allocation of crowdsourcing workflows. Task allocation aims to maximize the completion quality of the entire workflow and minimize its total cost. Trust can affect the quality of the produced results and costs. Selecting workers with high levels of trust could provide better solution to the workflow and increase the budget. Crowdsourcing workflow needs to balance the two conflicting objectives. In this paper, we propose an alternative greedy approach with four heuristic strategies to address the issue. In particular, the proposed approach aims to monitor the current status of workflow execution and use heuristic strategies to adjust the parameters of task allocation. We design a two-phase allocation model to accurately match the tasks with workers. T-Aware allocates each task to the worker that maximizes the trust level, while minimizing the cost. We conduct extensive experiments to quantitatively evaluate the proposed algorithms in terms of running time, task failure ratio, trust and cost using a customer objective function on WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that T-Aware outperforms other optimal solutions on finding the tradeoff between trust and cost, which is 3 to 6% better than the best competitor algorithm.
The recent emergence of low-cost microsensors measuring various air pollutants has significant potential for carrying out high-resolution mapping of air quality in the urban environment. However, the data obtained by such sensors are generally less reliable than that from standard equipment and they are subject to significant data gaps in both space and time. In order to overcome this issue, we present here a data fusion method based on geostatistics that allows for merging observations of air quality from a network of low-cost sensors with spatial information from an urban-scale air quality model. The performance of the methodology is evaluated for nitrogen dioxide in Oslo, Norway, using both simulated datasets and real-world measurements from a low-cost sensor network for January 2016. The results indicate that the method is capable of producing realistic hourly concentration fields of urban nitrogen dioxide that inherit the spatial patterns from the model and adjust the prior values using the information from the sensor network. The accuracy of the data fusion method is dependent on various factors including the total number of observations, their spatial distribution, their uncertainty (both in terms of systematic biases and random errors), as well as the ability of the model to provide realistic spatial patterns of urban air pollution. A validation against official data from air quality monitoring stations equipped with reference instrumentation indicates that the data fusion method is capable of reproducing city-wide averaged official values with an R² of 0.89 and a root mean squared error of 14.3 μg m⁻³. It is further capable of reproducing the typical daily cycles of nitrogen dioxide. Overall, the results indicate that the method provides a robust way of extracting useful information from uncertain sensor data using only a time-invariant model dataset and the knowledge contained within an entire sensor network.
This study applied collective action theories and network theories to examine the information sharing patterns among Twitter users to obtain sociopolitical legitimacy of their collective goal. The role of Twitter in facilitating private–public boundary crossing was defined in relation to main challenges of collective action. The hypotheses and research question were examined using Twitter data collected from an online campaign, which was created to bring about the release of a detained Syrian activist. Network analysis results showed significant geographic homophily effect, that is, participants located in the same region tended to share information with each other. In addition, the results indicated that more influential Twitter users tended to connect with less influential users to help spread information on the movement. Further content analysis showed that to better mobilize potential collective action participants, Twitter users utilized strategies to draw attention from citizen news media organizations, nonprofit advocacy organizations, public figures, and corporations. Traditional collective action literature has taken an organization-centric view, with an emphasis on the role of formal organizations or leadership in coordinating resources for the collective goal (Bimber et al., 2012). With the rise of new online technologies, contrasting
Virtual worlds are Internet-based three-dimensional (3D) computer-generated environments where users interact through "avatars" - a computer-generated representation of themselves that they control in terms of appearance and behavior. In recent years, virtual worlds have evolved into sophisticated social systems where millions of people regularly collaborate. For dispersed organizational teams, they represent a viable collaboration environment in which users can integrate different communication channels and manipulate digital artifacts that represent actual team deliverables. In this editorial to the Special Issue on Team Collaboration in Virtual Worlds, we discuss past research and highlight key findings with respect to five dimensions of collaborative work: technology, people, information, process, and leadership. We conclude with a discussion of the key research challenges that lie ahead to shape the research agenda for team collaboration in virtual worlds and metaverses.
Distance has long been a powerful force that influences the ways in which we organize our personal relationships and collaborations. In the past two decades, globalization and the advent of Information and Communication Technologies (ICTs) have brought profound changes to the formation and maintenance of communication networks in contemporary work and social settings by eliminating the constraints of physical distance on human interaction. To study the role of distance in ICT-enabled virtual worlds, we propose three dimensions of proximity—space, time, and homophily—and analyze the impact of distance, time zones, gender, age, and game age on building collaborative relations in online games. The results show that spatial proximity, temporal proximity, and homophily in age and game age still have a strong impact on players' behavior in creating online relations in virtual worlds, however, there is no evidence of gender homophily. This study suggests that offline proximity plays an important role in bringing people together in virtual worlds.
Many data processing tasks such as semantic annotation of images, translation of texts in foreign languages, and labeling of training data for machine learning models require human input, and, on a large scale, can only be accurately solved using crowd based online work. Recent work shows that frameworks where crowd workers compete against each other can drastically reduce crowdsourcing costs, and outperform conventional reward schemes where the payment of online workers is proportional to the number of accomplished tasks (“pay-per-task”). In this paper, we investigate how team mechanisms can be leveraged to further improve the cost efficiency of crowdsourcing competitions. To this end, we introduce strategies for team based crowdsourcing, ranging from team formation processes where workers are randomly assigned to competing teams, over strategies involving self-organization where workers actively participate in team building, to combinations of team and individual competitions. Our large-scale experimental evaluation with more than 1,100 participants and overall 5,400 hours of work spent by crowd workers demonstrates that our team based crowdsourcing mechanisms are well accepted by online workers and lead to substantial performance boosts.
Crowdsourcing systems of the future (e.g., Social Compute Units — S-CUs, collective adaptive systems) need to support complex collaborative processes , such as software development. This presupposes deploying ad-hoc assembled teams of human and machine services that actively collaborate and communicate among each other, exchanging different artifacts and jointly processing them. Major challenges in such environments (e.g., team formation, adaptability, runtime management of data-flow and collaboration patterns) can be somewhat alleviated by delegating the responsibility and the know-how needed for these duties to the participating crowd members, while indirectly controlling and stimulating them through appropriate incentive mechanisms. Existing process-centric collaboration modeling approaches (e.g., workflows) are incapable of encoding such incentive mechanisms. Therefore, in this paper we analyze different interaction aspects that incentive mechanisms cover and formulate them as requirements for future systems to support. We then propose an artifact-centric approach for modeling incentives in rich crowdsourcing environments that meets these requirements.
This study proposes a rationale by which researchers can use network analysis tools to improve policies and guidelines for the formation of teams within a particular community. The study begins by defining voluntary collaborative project teams (VCPTs) as teams where members have semi-autonomous discretion over whether to join or participate. It is argued that, with reference to VCPTs, those making policies or guidelines appear to face both an information disadvantage and possess an information advantage relative to individual team members, stemming from differing positions regarding emergent qualities of teams. While team members have access to team-specific information, policy-makers can use information regarding norms within the community as a whole. It is then argued that network analysis is a useful tool for uncovering these community-wide tendencies. An example analysis is then performed using exponential random graph modeling of a network to uncover the underlying logics of team construction within nanoHub.org, an online community that fosters collaboration amongst nanoscientists. Results suggest that the technique is promising, as a normative tendency which undermines team performance is uncovered. Introduction As many industries migrate towards a network-based production implemented by short-term, fluid teams which are permeable, interconnected, and modular (Schilling and Steensma, 2001), the performance of teams where members relatively freely choose their collaborators is becoming increasingly important. Settings in which semi-autonomous team assembly is particularly prevalent include the development of software in the open source movement, collaboration in large online multiplayer games, scientific co-authorship, and artistic collaborations (Amaral, 2005; Guimerà et al., 2005; Uzzi and Spiro, 2005).
"Does placing workers together based on their personality give better
performance results in cooperative crowdsourcing settings, compared to
non-personality based crowd team formation?" In this work we examine the impact
of personality compatibility on the effectiveness of crowdsourced team work.
Using a personality-based group dynamics approach, we examine two main types of
personality combinations (matching and crashing) on two main types of tasks
(collaborative and competitive). Our experimental results show that personality
compatibility significantly affects the quality of the team's final outcome,
the quality of interactions and the emotions experienced by the team members.
The present study is the first to examine the effect of personality over team
result in crowdsourcing settings, and it has practical implications for the
better design of crowdsourced team work.
Crowdsourcing is one of the emerging Web 2.0 based phenomenon and has attracted great attention from both practitioners and scholars over the years. It can facilitate the connectivity and collaboration of people, organizations, and societies. We believe that Information Systems scholars are in a unique position to make significant contributions to this emerging research area and consider it as a new research frontier. However, so far, few studies have elaborated what have been achieved and what should be done. This paper seeks to present a critical examination of the substrate of crowdsourcing research by surveying the landscape of existing studies, including theoretical foundations, research methods, and research foci, and identifies several important research directions for IS scholars from three perspectives—the participant, organization, and system—and which warrant further study. This research contributes to the IS literature and provides insights for researchers, designers, policy-makers, and managers to better understand various issues in crowdsourcing systems and projects.
While most think of innovation’s insights coming in a flash of inspiration, Eric Dahlin uses multidisciplinary research to show that advances, big and small, more often result from collaborative, incremental efforts. To understand and spur innovation, then, scholars and practitioners must abandon the romantic notion of the lonely genius in favor of the wisdom of the collective.
Sociologists and historians of science have frequently used the concept of the `marginal man' to describe those most likely to contribute major scientific innovations. An examination of X-ray astronomy during its formative years (1960-75) suggests that `marginal' scientists are no more likely than others to contribute innovations, a finding contrary to most other studies. Moreover, the concept of marginality is, in its present use, so ambiguous as to be almost worthless as a conceptual tool for systematic inquiry into the sources of scientific innovation.
The authors revisited the demographic diversity variable and team performance relationship using meta-analysis and took a significant departure from previous meta-analyses by focusing on specific demographic variables (e.g., functional background, organizational tenure) rather than broad categories (e.g., highly job related, less job related). They integrated different conceptualizations of diversity (i.e., separation, variety, disparity) into the development of their rationale and hypotheses for specific demographic diversity variable—team performance relationships. Furthermore, they contrasted diversity with the team mean on continuous demographic variables when elevated levels of a variable, as opposed to differences, were more logically related to team performance. Functional background variety diversity had a small positive relationship with general team performance as well as with team creativity and innovation. The relationship was strongest for design and product development teams. Educational background variety diversity was related to team creativity and innovation and to team performance for top management teams. Other variables generally thought to increase task-relevant knowledge (e.g., organizational tenure) and team performance were unrelated to team performance, although these variables were almost never studied as the variety conceptualization (i.e., the conceptualization that can reflect the breadth of knowledge that can be applied to the task). Team mean organizational tenure was related to team performance in terms of efficiency. Race and sex variety diversity had small negative relationships with team performance, whereas age diversity was unrelated to team performance regardless of diversity conceptualization. Implications for staffing teams and future research are discussed.
Addressing global design challenges in the environment and underserved communities requires a cooperative approach towards sustainable design innovation, one that embraces multidisciplinary expertise, participatory design and rapid dissemination of critical innovations in the field. How can a rural farmer in Botswana cooperatively develop appropriate solutions for his community with external research expertise? How can a doctor in Sao Paulo access a network of medical device companies to help manufacture her design innovation? While there is a great emphasis on large breakthrough R&D innovations, there is often little support for developing and disseminating small-scale, affordable, and locally sustainable designs. The open source phenomenon has been influential in the software community, however distributed collaboration in engineering design requires awareness and sharing of physical artifacts, design tools and working environments as well as novel mechanisms to support social norms, communities of practice, and intellectual property rights for product innovations. ThinkCycle was created as a web-based collaboration platform with tools and shared online spaces for designers, domain experts and stakeholders to discuss, develop and peer-review evolving design solutions in critical domains. Over 2000 users worldwide access and contribute hundreds of concepts, resources, projects and publications on the site. ThinkCycle is emerging as a collaborative platform, open design repository and global community for innovations in sustainable design: http.//www. thinkcycle.org. Studies were conducted on the nature of design interaction, learning and intellectual property emerging from studio courses run at MIT in 2001-2002.
This article reflects critically on forms of openness and participation emerging from a collaborative network using information technologies for knowledge sharing on climate change and international development. It explores how multiple interpretations of these concepts coalesce around a particular initiative, shaping ways of working and understanding across different epistemic cultures (Knorr-Cetina, 1999) in the network. The resultant shared meanings and practices, it is argued, are a product of existent epistemic and participatory cultures, internal and external dynamics and economies of power, and emergent ways of working that are further shaped by engagement with particular information technologies and protocols. The process through which these shared meanings are constructed, however, is rarely transparent or openly reflected on, but rather, it emerges through the normalization of particular practices that “organize” our social relations. This limits our understanding of how a given “architecture of participation” has been constructed, or how it has situated those working in it. I consider the influence that these processes of meaning-making have had on the present shape of the network and reflect on what this means for such forms of collaboration more generally.
Advances in communication and computational technology have drastically increased opportunities for the public to participate in governing activities. Well-designed responsive participatory pathways can encourage individuals to take action on issues that are important to them while receiving value and enjoyment in contributing to the overall welfare of the state. However, many crowdsourcing programs do not achieve their intended targets or goals in part because people have little motivation to participate. Serious games are examined to address this challenge as they involve the integration of intrinsic human motivators with game mechanics or game dynamics into a website, service, community, campaign, or application to drive participation and engagement. Governments can supplement open governance platforms with characteristics of games to reshape and invigorate public participation if individuals have opportunities to participate through channels that are collectively and individually rewarding. Drawing on theory and an emerging body of examples this manuscript creates a framework for how governance organizations can integrate serious games into the design of open governance platforms to improve the understanding of how the platform affects the working relationship with the public when solving collective challenges. Contributions of serious games are how to foster dialog, educate, solicit feedback, and mobilize individuals. In identifying the characteristics that differentiate early projects that have succeeded with those that have struggled, we provide recommendations of how to cultivate voluntary and enthusiastic participation in open governance platforms– giving the public the opportunity and motivation to have an impact on the communities in which they live just as many already do in the virtual worlds in which they play.
Recent advances on the Web have generated unprecedented opportunities for individuals around the world to assemble into teams. And yet, because of the Web, the nature of teams and how they are assembled has changed radically. Today, many teams are ad hoc, agile, distributed, transient entities that are assembled from a larger primordial network of relationships within virtual communities. These assemblages possess the potential to unleash the high levels of creativity and innovation necessary for productively addressing many of the daunting challenges confronting contemporary society. This article argues that Web science is particularly well suited to help us realize this potential by making a substantial interdisciplinary intellectual investment in (i) advancing theories that explain our socio-technical motivations to form teams, (ii) the development of new analytic methods and models to untangle the unique influences of these motivations on team assembly, (iii) harvesting, curating and leveraging the digital trace data offered by the Web to test our models, and (iv) implementing recommender systems that use insights gleaned from our richer theoretical understanding of the motivations that lead to effective team assembly.
This study explores the premise that knowledge in new product development proves both a barrier to and a source of innovation. To understand the problematic nature of knowledge and the boundaries that result, an ethnographic study was used to understand how knowledge is structured differently across the four primary functions that are dependent on each other in the creation and production of a high-volume product. A pragmatic view of 'knowledge in practice' is developed, describing knowledge as localized, embedded, and invested within a function and how, when working across functions, consequences often arise that generate problematic knowledge boundaries. The use of a boundary object is then described as a means of representing, learning about, and transforming knowledge to resolve the consequences that exist at a given boundary. Finally, this pragmatic view of knowledge and boundaries is proposed as a framework to revisit the differentiation and integration of knowledge.
K nowledge differences impede the work of cross-functional teams by making knowledge integration difficult, especially when the teams are faced with novelty. One approach in the literature for overcoming these difficulties, which we refer to as the traverse approach, is for team members to identify, elaborate, and then explicitly confront the differences and dependencies across the knowledge boundaries. This approach emphasizes deep dialogue and requires significant resources and time. In an exploratory in-depth longitudinal study of three quite different cross-functional teams, we found that the teams were able to cogenerate a solution without needing to identify, elaborate, and confront differences and dependencies between the specialty areas. Our analysis of the extensive team data collected over time surfaced practices that minimized members' differences during the problem-solving process. We suggest that these practices helped the team to transcend knowledge differences rather than traverse them. Characteristic of these practices is that they avoided interpersonal conflict, fostered the rapid cocreation of intermediate scaffolds, encouraged continued creative engagement and flexibility to repeatedly modify solution ideas, and fostered personal responsibility for translating personal knowledge to collective knowledge. The contrast between these two approaches to knowledge integration—traverse versus transcend— suggests the need for more nuanced theorizing about the use of boundary objects, the nature of dialogue, and the role of organizational embeddedness in understanding how knowledge differences are integrated.
Examines how achieved and attributed characteristicsof entrepreneurs affect the composition of founding teams, and how thesecharacteristics are affected by the social context of the entrepreneurialenterprise. Limitations of previous studies are identified: their lack ofgeneralizable findings and their "success bias." To avoid theselimitations, the present study tracks entrepreneurs from the first steps atfirm founding. To avoid success bias a methodological innovation that considersall possible combinations of group members (not just those in a given sample)is used. Structural event analysis is used to generate possible teams, and tocompare chance expectations with empirical teams. The sociological literature identified five mechanisms of group composition:homophily, functionality, status expectations, network constraint, andecological constraint. Sample was 816 teams drawn from the Panel Study ofEntrepreneurial Dynamics (PSED) compiled between 1998 and 2000. Analyzed werethe compositional properties of organizational founding teams. Found thathomophily (respecting both attributed and achieved characteristics) stronglyinfluences group composition. Found that influence of "strong" ties(romantic or family relations) was pronounced; but "weak" ties (such asbusiness acquaintances) imposed no significant network constraint. Ecologicalconstraint leads to disproportionate isolation of minorities such as women andblue-collar workers, but segregation by industry does not strongly affect teamhomophily.(TNM)
It is often presumed that bringing more members on board a multi-sided platform will stimulate value creation. Here I study the thousands of software producers building applications (“apps”) on leading handheld computer platforms (1999-2004). Consistent with past theory, I find a lock-step link between numbers of producers and varieties of software titles. The narrow, unchanging scope of producers and a series of other patterns are consistent with a pronounced role of specialization and heterogeneity of producers. I also find that while adding producers making different types of software stimulated investment incentives, consistent with network effects, adding producers making similar software crowded-out innovation incentives. The latter of these two effects dominates in this context. The patterns also indicate non-random generation and sorting of producers onto platforms, with later cohorts generating systematically less compelling software than earlier cohorts of entrants. Overall, added producers led innovation to become more dependent on population-level diversity, variation and experimentation - while drawing less on the heroic efforts and investments of any one individual innovator.
The sociological study of intellectual innovation has long been polarized between romantic notions of the creative marginal intellectual and competing accounts stressing the benefits of national, organizational and network centrality in the production of knowledge. I offer the concept of “optimal marginality’ as an attempt to move beyond this longstanding but increasingly stale debate. The relationship between a certain type of marginality and intellectual creativity is discussed in the context of a case study on innovation within psychoanalysis. German psychoanalyst Erich Fromm's contributions to the modern revision of Freudian theory is highlighted to illustrate the conditions under which marginality is likely to lead to innovations within theoretical systems and intellectual organizations. What types of marginality lead to innovation? Under what conditions does marginality lead to insight, and when does it lead to marginal ideas? Four ideal types are outlined and a research agenda is called for that operationalizes and tests these theoretical ideas in the context of comparative sociological analysis of intellectual creativity.
The word Crowdsourcing –a compound contraction of Crowd and Outsourcing, was used by Howe in order to define outsourcing to the crowd. Beyond cost, benefits for the company can be substantial. It can externalize the risk of failure and it only pays for products or services that meet its expectations. The Crowdsourcing phenomenon covers heterogeneous situations and it has inspired a number of authors. However, we are still lacking a general and synthetic view of this concept. The aim of our work is to characterize Crowdsourcing in its various aspects. First we define of Crowdsourcing, and provide examples that illustrate the diversity of Crowdsourcing practices and we present similarities and differences between Crowdsourcing and established theories (Open Innovation, User Innovation and Open Source Software). Then, we propose and illustrate a typology of Crowdsourcing practices based on two criteria: the integrative or selective nature of the process and the type of tasks that are crowd sourced (simple, complex and creative tasks). In either case, the client firm seeks to mobilize external competencies. Relying upon the crowd can be an adequate method, because of its unique characteristics that are fostered by the Internet. Finally, we present some potential benefits and pitfalls of Crowdsourcing.
JEL Codes: O32, L17, L24, L29, M19
Experts explore current theory and practice in the application of digitally enabled open networked social models to international development.
The emergence of open networked models made possible by digital technology has the potential to transform international development. Open network structures allow people to come together to share information, organize, and collaborate. Open development harnesses this power, to create new organizational forms and improve people's lives; it is not only an agenda for research and practice but also a statement about how to approach international development. In this volume, experts explore a variety of applications of openness, addressing challenges as well as opportunities.
Open development requires new theoretical tools that focus on real world problems, consider a variety of solutions, and recognize the complexity of local contexts. After exploring the new theoretical terrain, the book describes a range of cases in which open models address such specific development issues as biotechnology research, improving education, and access to scholarly publications. Contributors then examine tensions between open models and existing structures, including struggles over privacy, intellectual property, and implementation. Finally, contributors offer broader conceptual perspectives, considering processes of social construction, knowledge management, and the role of individual intent in the development and outcomes of social models.
ContributorsCarla Bonina, Ineke Buskens, Leslie Chan, Abdallah Daar, Jeremy de Beer, Mark Graham, Eve Gray, Anita Gurumurthy, Havard Haarstad, Blane Harvey, Myra Khan, Melissa Loudon, Aaron K. Martin, Hassan Masum, Chidi Oguamanam, Katherine M. A. Reilly, Ulrike Rivett, Karl Schroeder, Parminder Jeet Singh, Matthew L. Smith, Marshall S. Smith
Copublished with the International Development Research Centre of Canada (IDRC)
Organizations have been experimenting with intraorganizational crowdsourcing (IOC), yet the mechanisms of IOC production remain an underresearched topic. Drawing on a 2-mode ERGM, we examine structural mechanisms and individual-level factors that shape the network structure of idea generation and selection yielded by an IOC idea challenge in a global IT corporation. Results show a Matthew effect leading to 1) highly centralized employee participation around a few "superactive" employees who engage with many ideas and 2) highly centralized idea popularity with a few ideas attracting most employee attention. We find support for shared affiliations among employee-idea clusters in the first half of the participation, which is, however, less likely in the second half. We also find support for geographic homophily.
Research summary : Innovation requires inventors to have both new knowledge and the ability to combine and configure knowledge (i.e., combinatory knowledge), and such knowledge may flow through networks. We argue that both combinatory knowledge and new knowledge are accessed through collaboration networks, but that inventors' abilities to access such knowledge depends on its location in the network. Combinatory knowledge transfers from direct contacts, but not easily from indirect contacts. In contrast, new knowledge transfers from both direct and indirect contacts, but is far more likely to be new and useful when it comes from indirect contacts. Exploring knowledge flows in 69,476 patents and 89,930 unique inventors reveals evidence that combinatory knowledge from direct contacts and new knowledge from indirect contacts significantly affects innovative performance .
Managerial summary : Inventors often combine ideas to create innovations. To do this, they need ideas to combine and they need the ability to combine those ideas. Inventors can get ideas to combine as well as the ability to combine ideas through prior co‐workers. Prior co‐workers can share ideas that may be relevant for the inventor's project and can tell the inventor about other things that other people are working on, especially people the inventor may not know. This can help inventors easily learn about ideas from friends‐of‐friends. The ability to combine ideas, however, is much harder to pass on. Prior co‐workers must carefully work with the inventor to teach him or her the complex processes of combining ideas. This means that it is very hard to learn how to combine knowledge from a friend‐of‐a‐friend, but it may be possible to learn from prior co‐workers. We explore this phenomenon in the social relationships of software inventors . Copyright © 2015 John Wiley & Sons, Ltd.
Whereas recent research on organizational innovation suggests that there is an ecology of roles supporting the innovation process, the majority of network research has concentrated on the role of inventors. In this paper, we contribute to research on organizational innovation by studying the social structural conditions conducive to individuals supporting, facilitating, and promoting the innovativeness of their colleagues-a role we refer to as catalysts of innovation. We consider an individual's network position and the type of knowledge available to her through her network as key enabling conditions. We argue that the unique configuration of having access to diverse knowledge through a closed network enables individuals to act as innovation catalysts. Based on a study of 276 researchers in the research and development division of a large multinational high-tech company, we find strong support for our prediction and demonstrate that catalysts make important contributions to the innovative outputs of other researchers in terms of their colleagues' patent applications.
For a country to become fully democratic, the majority of the population needs to be involved in the politics of that country. This requires people to be aware of what is happening, and to be able to participate in the country's political processes. VotoSocial is a crowdsourcing system launched during the 2013 Honduran government elections. It retrieved the official polling table records in digitized form from the Supreme Electoral Tribunal (the Government's elections authority) and provided them to the Internet community to be counted and the digitizations verified. VotoSocial was thereby able to verify the accuracy of the official polling results. We found no evidence of fraud in the digitization process, but statistical analysis revealed a data behavior usually associated with incremental fraud in an electoral process. VotoSocial shows that social media-powered crowdsourcing systems can increase a population's political awareness during elections, thus providing a motive to build similar platforms in other countries with political systems marked by electoral fraud.
In this study we investigate the interplay between knowledge workers’ formal project team memberships and their informal interactions from a multilevel network perspective. Conceptualizing knowledge workers’ affiliation with project teams as a membership network and their interactions as an advice network, we discuss how shared project team memberships as well as multiple memberships influence patterns of informal exchange in knowledge-intensive organizations. To empirically determine the impact of formal organization on informal exchange we apply exponential random graph models for multilevel networks to relational data collected on 434 R&D employees working on 218 project teams in a high-tech firm in Germany. Our results show that employees sharing project memberships create advice ties to each other but do not exchange advice reciprocally. In addition, we find a negative relationship between having a high number of project memberships and informally seeking or providing advice.
In this paper, we review the development of dependence structures for exponential random graph models for bipartite networks, and propose a hierarchy of dependence structures within which different dependence assumptions may be located. Based on this hierarchy, we propose a new set of model specifications by including bipartite graph configurations involving more than four nodes. We discuss the theoretical significance of the various effects that the extended models afford, and illustrate application of this hierarchy of models to several bipartite networks related to the political mobilization in Brazil in the early 1990s (Mische, 2007).
This study investigates the self-assembly mechanisms of ad hoc project teams using a bipartite network perspective. Individuals and projects are modeled as two types of nodes and team membership as relations between them. This approach enables us to investigate factors that impact voluntary team assembly at the individual, dyadic, and team levels simultaneously. Using Exponential Random Graph Models (ERGM/p*), we study players' combat teams in a Massively Multiplayer Online Role-Playing Game (MMORPG) as a case of self-assembled project teams. Empirical results show that individuals are motivated to join ad hoc teams to complete difficult projects but not projects with long durations. We also found that individuals tend to collaborate with specific teammates who have complementary skills, those who have similar age or skill level, and those who are affiliated with the same organizational entity.
Crowdsourcing for innovation is typically conducted as an "innovation challenge." Despite the popularity of innovation challenges, there appears to be a growing consensus that innovation challenges do not succeed at generating solutions with competitive advantage potential. This article presents three ways in which managers can assure that their innovation challenges are fruitful: foster different crowd roles to encourage contribution diversity; offer knowledge integration instructions and dual incentives; and offer explicit instructions for sharing different types of knowledge. (I nnovation challenges, also known as innovation tournaments and idea con-tests, are a manifestation of crowdsourcing. 1 When running an innovation challenge, a company posts an open call on the Web to solicit solutions from a diverse range of individuals. For example, GE's Innovation Challenge soli-cited new technologies for its sustainability product line and a Lego Challenge asked the public to suggest unique Lego products as new revenue streams. By 2017, over half of consumer goods producers are projected to employ crowdsourcing for 75% of their consumer innovations. 2
Following the recent remarkable successes of crowdsourcing, there have been attempts to apply it to design. However a design problem is often too complex and difficult to break down into simpler, distributable tasks as required by the conventional crowdsourcing model. In this paper, we present Crowd vs. Crowd (CvC), a novel design crowdsourcing method, where several design teams made up of designers and crowd compete with each other. In each team, a designer coordinates effective communication between the crowd members and takes responsibility for the final design output, and the crowd contributes at different stages of design. We conducted an initial evaluation of CvC in comparison with other collaborative design methods, and found that: CvC can attract more people to participate; the crowd can make useful contribution in CvC; CvC can produce competent design outputs. We then applied CvC to two real-life design problems: first, designing a new logo for a university department; second, for a small tech company. With quantitative and qualitative analyses on these applications, we observed that the elements of competition and collaboration helped to sustain the crowd's motivation to participate, and to produce quality design outcomes with higher level of satisfaction for the stakeholders.
Interdisciplinary teams are assembled in scientific research and are aimed at solving complex problems. Given their increasing importance, it is not surprising that considerable attention has been focused on processes of collaboration in interdisciplinary teams. Despite such efforts, we know less about the factors affecting the assembly of such teams in the first place. In this paper, we investigate the structure and the success of interdisciplinary scientific research teams. We examine the assembly factors using a sample of 1103 grant proposals submitted to two National Science Foundation interdisciplinary initiatives during a 3-year period, including both awarded and non-awarded proposals. The results indicate that individuals’ likelihood of collaboration on a proposal is higher among those with longer tenure, lower institutional tier, lower H-index, and with higher levels of prior co-authorship and citation relationships. However, successful proposals have a little bit different relational patterns: individuals’ likelihood of collaboration is higher among those with lower institutional tier, lower H-index, (female) gender, higher levels of prior co-authorship, but with lower levels of prior citation relationships.
After many years of developing in small islands scattered around different disciplines, small group research has reached a point where interdisciplinary scholarship has the potential to foster major progress. The goal of this special issue on interdisciplinary perspectives is to capitalize on the theoretical advances made over the last 50 years by synthesizing and integrating models and theories on small groups proposed by various disciplines into a set of general theoretical perspectives. In this introduction, the authors identify nine general theoretical perspectives from which small groups have been examined: the psychodynamic, functional, temporal, conflict-power-status, symbolic-interpretive, social identity, social-evolutionary, social network, and feminist perspectives. This article summarizes each theoretical perspective briefly and then offers some observations about the perspectives as a whole. Articles describing three of these interdisciplinary perspectives appear in this special issue, and four other perspectives will be introduced in the next issue.
Similarity breeds connection. This principle - the homophily principle - structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people's social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localized positions) within social space. We argue for more research on: (a) the basic ecological processes that link organizations, associations, cultural communities, social movements, and many other social forms; (b) the impact of multiplex ties on the patterns of homophily; and (c) the dynamics of network change over time through which networks and other social entities co-evolve.
This article provides an introductory summary to the formulation and application of exponentialrandomgraphmodels for socialnetworks. The possible ties among nodes of a network are regarded as random variables, and assumptions about dependencies among these random tie variables determine the general form of the exponentialrandomgraphmodel for the network. Examples of different dependence assumptions and their associated models are given, including Bernoulli, dyad-independent and Markov randomgraphmodels. The incorporation of actor attributes in social selection models is also reviewed. Newer, more complex dependence assumptions are briefly outlined. Estimation procedures are discussed, including new methods for Monte Carlo maximum likelihood estimation. We foreshadow the discussion taken up in other papers in this special edition: that the homogeneous Markov randomgraphmodels of Frank and Strauss [Frank, O., Strauss, D., 1986. Markov graphs. Journal of the American Statistical Association 81, 832–842] are not appropriate for many observed networks, whereas the new model specifications of Snijders et al. [Snijders, T.A.B., Pattison, P., Robins, G.L., Handock, M. New specifications for exponentialrandomgraphmodels. Sociological Methodology, in press] offer substantial improvement.
This article reviews how current social network analysis might be used to investigate individual and group behavior in sporting teams. Social network analysis methods permit researchers to explore social relations between team members and their individual-level qualities simultaneously. As such, social network analysis can be seen as augmenting existing approaches for the examination of intra-group relations among teams and provide detail of team members' informal connections to others within the team. Social network analysis is useful in addressing the issue of interdependencies in the data inherent in team structures. Social network terms are introduced and explained by way of an example team, software and resources are discussed, and a statistical approach to social network analysis is introduced.
Personal network analysts usually want to know which types of people are in such network, what kinds of relationships they contain, and what kinds of resources flow through different kinds of networks. These research interests affect how analysts define populations, approach gathering data, and obtain information. Analysts want to identify ties wherever they lead, without being confined to groups, neighborhoods or organizations. All of the articles in this issue use the network paradigm to analyze community and social support. The article's authors did more than 100 interviews with newspapers, magazines, television and radio. This special issue builds on classical personal network research into community and social capital. The basis of this special issue are the three papers presented at a personal network session of the 2004 Sunbelt Social Network Conference. The articles reflect the continuing personal network concern with the "community question" about the interrelationship between large-scale structural phenomena (such as urbanization and technological change) and the composition, structure and content of interpersonal relationships. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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.
A number of factors cause individuals to use diverse strategies to solve problems. This paper presents a methodology for examining these differences in strategy. Verbal protocols are elicited to collect data on the cognitive processes occurring during problem solving. These data, codified into propositional representations, and non-parametric statistical comparisons are then used to evaluate the significance of strategy differences. These strategies are then mapped with dynamical graphs, with which we examine the task-independent and the task-specific cognitive representations the participants used. As an illustrative example we apply this methodology to study the influence of two contributing factors, professional training and national culture, on the strategies adopted by professionals to solve a complex and ill-structured problem (hunger in a country). The problem-solving strategies of professionals from different countries and trained in architecture, engineering, law or medicine are analyzed to show some intriguing differences in the general strategies adopted by individuals belonging to different professions, and the outcomes from using these strategies.
Appointing a good leader to the position of team manager and having competent workers collaborate as team members is a key to success in business activities of an enterprising institution. The traditional methodologies of human resource management have defined the required abilities for team managers and team members, and evaluated those abilities of employees. However, it is difficult to consider those abilities systematically in practice. In addition, the current management paradigm undergoes rapid transitions into knowledge management. In step with these trends, this study presents a framework for analyzing the knowledge of the candidates for managers and team members for the new team, and proposes a genetic algorithm and social network measures for choosing a team manager and team members. A prototype was built for testing the feasibility of the model. The testing data are from an R&D institute’s human resource management department. The results show that our proposed approach is a quantitative and systematic method for selecting proper personnel for appropriate teams.
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.