Literature review on collective intelligence: a crowd science perspective

Yu, Chao; Chai, Yi; Liu, Yi

doi:10.1108/ijcs-08-2017-0013

Cited by 31 publications

(21 citation statements)

References 36 publications

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“…In this paper, the relation between crowdfunding as a crowdsourcing category and sustainability in retail has been discussed, while a theoretical framework showing the potential impact of success criteria of equity-and reward-based crowdfunding campaigns on sustainable retail has been developed. The research uncovers key concepts which are directly influencing the longevity of products and services on the market, as well as the economic endurance of companies by making use of crowd intelligence (Yu et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Crowdsourcing in Sustainable Retail—A Theoretical Framework of Success Criteria

Konhäusner

2021

JRFM

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Current research about crowdsourcing covers industries like food systems or logistics, leaving out the possible impact of crowdsourcing on sustainable retail. The debate about the sustainable impact of different industries is ongoing, especially discussing the adaption to the Sustainable Development Goals of the United Nations critically. This paper examines the influence of crowdsourcing on the sustainable aspects of retailing by applying a theoretical derivation as well as an empirical observation. After theoretically discussing the linkage between crowdfunding as a crowdsourcing category and sustainable retail utilizing a literature review, a theoretical framework employing the grounded theory approach is constructed. A total of 24 crowdfunding campaigns aiming at the market introduction of new products or services, each worth over 5 million USD funding volume and run on international crowdfunding platforms, have been taken into consideration. The outcome of the analysis is a theoretical framework presenting three different categories, in which successful crowdfunding campaigns impacting sustainable retail excel: sustainable economic behavior, sustainable community management and sustainable market adaptation. The derived model contributes to the theoretical discussion about the impact of crowdfunding and assists practitioners in reflecting about their approach and goal setting prior to and while crowdfunding.

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Section: Discussionmentioning

confidence: 99%

Crowdsourcing in Sustainable Retail—A Theoretical Framework of Success Criteria

Konhäusner

2021

JRFM

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“…Those evolution algorithms have played a good role in swarm intelligence, which makes us think about whether these methods can be applied to the crowd intelligence (Yu et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

Crowd evolution method based on intelligence level clustering

Yang

2021

IJCS

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Purpose The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm. Design/methodology/approach This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm. Findings Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments. Practical implications The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents. Originality/value This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

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“…From the functional modelling perspective, any innate collective intelligence that groups have [16], [17], [18] might be modelled as executing a set of collective reasoning processes represented by some set of paths in the collective conceptual space. From this same perspective every Collective Intelligence (CI) solution [19], [20], [21], [22] is a tool that automates execution of some set of processes in the collective conceptual space. Implementing a General Collective Intelligence creates the potential to decouple such processes or software into a library that the GCI might use to significantly increase its general problem-solving ability through automating the execution of any such processes.…”

Section: Practical Implicationsmentioning

confidence: 99%

The Peer to Peer Social Fabric as a Platform for General Collective Intelligence

Williams¹

2020

Preprint

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INTRODUCTION: General Collective Intelligence or GCI has been defined as a platform that combines individuals into a collective intelligence with the potential for exponentially greater general problem-solving ability (intelligence) than that of any individual in the group. Cognitive computing applications are executed by intelligent agents on a user’s behalf to optimize individual user outcomes. Cognitive computing platforms are executed by individuals organized by a GCI to achieve greater collective outcomes. The Peer to Peer Social Fabric (P2PSF) is an infrastructure platform proposed to enable the execution of cognitive computing applications or platforms.OBJECTIVES: To explore the functionality required by an infrastructure platform with the capacity to enable the operation of cognitive computing applications or platforms. And to determine whether the functionality of the proposed Peer to Peer Social Fabric is sufficient.METHODS: The requirements of collective cognitive computing were assessed, including the requirement to increase capacity for complexity, capacity to scale number of processes, and capacity to sustain processes. The proposed high-level specifications of the Peer to Peer Social Fabric were compared to those requirements to determine whether that functionality is sufficient.RESULTS: The proposed Peer to Peer Social Fabric appears to meet the requirements of both the operating system for GCI, as well as the requirements of a client enabling individuals and organizations to access either AGI or GCI functionality.CONCLUSION: All of the functionality required by he Peer to Peer Social Fabric might already exist, but an understanding of how that functionality must be combined in order to achieve the exponential increase in general problem-solving ability potentially possible through AGI or GCI is a new and important contribution.

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Literature review on collective intelligence: a crowd science perspective

Cited by 31 publications

References 36 publications

Crowdsourcing in Sustainable Retail—A Theoretical Framework of Success Criteria

Crowdsourcing in Sustainable Retail—A Theoretical Framework of Success Criteria

Crowd evolution method based on intelligence level clustering

The Peer to Peer Social Fabric as a Platform for General Collective Intelligence

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