Collective Intelligence and Crowds Minitrack
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This minitrack is open to analysis of collective intelligence, new sociotechnical configuration of knowledge creation, and crowdsourcing. Included also is the analysis of social interaction as a way of describing underlying social structure, and in particular the social construction of identity and roles. Thus the minitrack invites a range of content areas that lend themselves to the analysis of relations between people, collectives, and machines, as well as the products produced as a result of these sociotechnical relations.
We live surrounded by socially constructed identities – organizations, nations, websites – all of which are constituted through a complex interplay of interactions, a kind of distributed cognition. To allow for these collectives to evolve, it is necessary to have not only a representation in an individual’s mind but also the knowledge that similar representations exist in the minds of others. The way we can create shared representations have changed with the proliferation of a wide range of Internet platforms. These Internet platforms allow people to aggregate knowledge from socially distant areas. They also allow diverse groups of people – and maybe machines in the form of artificial intelligences – to negotiate identities. With these socio-technical configurations we can build collective intelligences that themselves will steer the quest for knowledge. These collectives can be self-catalyzing, deciding individually or collaboratively what to do next, out of which novel and practical ideas emerge.
While these open design collectives rely on organic growth and slow embedding of members in the network, alternative structures based on crowds can be assembled more rapidly. Between the two extremes are a host of different organizational and social structures, in which committed members of a community create, improve, and share ideas. The output of these socio-technical systems often takes the form of digital media, and their traces are varied, ranging from ephemeral short messages to curated collaborative knowledge repositories.
We are interested in 1) papers that observe, analyze, or visualize these socio-technical structures and their outputs; 2) papers that analyze the phenomena of crowdsourcing, collective intelligence and collaborative mass knowledge production; 3) design research that creates and evaluates new tools and processes; and 4) papers that simulate the production processes and outcomes through software.
We are looking for papers about the mechanisms that explain the emergence of collective identity. Particularly we are open to papers that explore unusual ways of modeling emergent organizations: models that demonstrate or reflect the influence of social systems on user behaviors, models that consider the multiple connections between people, technology, and institutions, models of technological and social affordances, models that break personal identity into sub-relations, models that examine the emergence of roles, identity, and institutions, as well as socio-technical models of deviance and disruption. We are interested in applying the ideas of James March, Mark Granovetter, Harrison White, Charles Tilly and related scholars to information systems.
In sum, the content of the minitrack is open to analysis of collective intelligence, new sociotechnical configuration of knowledge creation, and crowdsourcing. Included also is the analysis of social interaction as a way of describing underlying social structure, and in particular the social construction of identity and roles. Thus the track is open to a wide range of content areas that lend themselves to the analysis of relations between people, collectives, and machines, as well as the products produced as a result of these sociotechnical relations.
We are looking for papers that analyze collective intelligence, knowledge creation, and crowdsourcing. We also encourage the submission of papers that design and implement technologies that create new kinds of collectives. In addition, we invite papers that analyze social interaction as a way of better understanding the underlying social structures, and in particular the social construction of identity and roles. In sum, we are open to a wide range of research that addresses relations between people, collectives, and machines, as well as the products produced as a result.
Pnina Fichman (Primary Contact)
Indiana University, Bloomington
Stevens Institute of Technology
Institute for Social Network Analysis of the Economy
ItemUnusual Spatial Patterns of Industrial Firm Locations Uncover their Social Interactions( 2017-01-04)In this paper we report evidence from the Italian industrial sectors whereby firms that buy and sell are spatially distributed with a pattern that reflects the microeconomic powers at play. The main finding is that firms are neither clustered around population centers nor are they situated at random. Although geography has an important role in shaping the population map of Italy, the reasons for the positional pattern of buyers and sellers appear to be social. Geographic proximity between sellers and their buyers is supported by the excess in short-distance social ties. \
ItemTapping into the Collective Creativity of the Crowd: The Effectiveness of Key Incentives in Fostering Creative Crowdsourcing( 2017-01-04)To better understand the conditions that most effectively stimulate creative participation online, a crowdsourcing project was implemented on Amazon’s Mechanical Turk, collecting 4200 written and visual submissions from online participants. An experimental research design tested the impact of specific incentive structures (i.e. financial rewards, bonuses, specification of project purpose, attribution of authorship credit) on the outcomes of creative participation (quantity of submissions, quality of submissions, time spent on task). The study found that extrinsic rewards (i.e. higher pay and bonuses) are effective in encouraging participants to accept the creative task, whereas the strategies that boost the creativity of the submissions are: offering a bonus, mentioning a charitable purpose, and giving contributors authorship credit. These findings help illuminate the factors that have the greatest impact on the quality and quantity of online creative participation, thus making a vital contribution to our understanding of digital creativity.
ItemCrowdIQ: A New Opinion Aggregation Model( 2017-01-04)In this study, we investigate the problem of aggregating crowd opinions for decision making. The Wisdom of Crowds (WoC) theory explains how crowd opinions should be aggregated in order to improve the performance of decision making. Crowd independence and a weighting mechanism are two important factors to crowd wisdom. However, most existing crowd opinion aggregation methods fail to build a differential weighting mechanism for identifying the expertise of individuals and appropriately accounting for crowd dependence when aggregating their judgments. We propose a new crowd opinion aggregation model, namely CrowdIQ, that has a differential weighting mechanism and accounts for individual dependence. We empirically evaluate CrowdIQ in comparison to four baseline methods using real data collected from StockTwits. The results show that, CrowdIQ significantly outperforms all baseline methods in terms of both a quadratic prediction scoring measure and simulated investment returns.
ItemA Conceptual Framework for Investigating Organizational Control and Resistance in Crowd-Based Platforms( 2017-01-04)This paper presents a research agenda for crowd behavior research by drawing from the organizational control literature. It addresses the need for research into the organizational and social structures that guide user behavior and contributions in crowd-based platforms. Crowd behavior is situated within a conceptual framework of organizational control. This framework helps scholars more fully articulate the full range of control mechanisms operating in crowd-based platforms, contextualizes these mechanisms into the context of crowd-based platforms, challenges existing rational assumptions about incentive systems, and clarifies theoretical constructs of organizational control to foster stronger integration between information systems research and organizational and management science.