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Collaboration is a common phenomenon in human society. The best way of collaborations can make the group achieve the best interests. Because of the low cost and high repeatability of simulation, it is a good method to explore the best way of collaborations by means of simulation. The traditional simulation is difficult to adapt to the crowd intelligence network simulation, so the crowd collaborations simulation is proposed.

In this paper, the atomic swarm intelligence unit and collective swarm intelligence unit are proposed to represent the behavior of individuals and groups in physical space and the interaction between them.

To explore the best collaboration mode of the group, a framework of crowd collaborations simulation is proposed, which decomposes the big goal into the small goals by constructing the cooperation chain and analyzes the cooperation results and feeds them back to the next simulation.

Two kinds of swarm intelligence units are used to represent the simulated individuals in the group, and the pattern is used to represent individual behavior. It is suitable for the simulation of collaboration problems in various types and situations.

For at least three decades, inter-organizational collaboration (IOC) has attracted scholarly attention and many studies have unveiled its inner dynamics. More recently, new phenomena have appeared in the changing landscape of IOC, affecting the way in which organizations are open to interact with, and rely upon, other actors that may be standalone entities as well as representatives of other organizations. These actors operate “betwixt and between” the organizational core and its external environment(s), populating a liminal space located at the organization’s boundary in which activities take place according to non-proprietary and non-employment logics. The authors focus on the forms of collaboration, which blur the lines between organizations, calling into question the fundamental label of crowd-focused IOCs. The authors consider two forms: crowd-open and crowd-based organizations. The authors show the organizational design impact of openness spans from the mere scalability associated with organizational growth to the phenomena of reshaping formalization and standardization of roles and processes, and self-organizing over time.

The purpose of this paper is to study how the online temporary crowd shares knowledge in a way that fosters the integration of their diverse knowledge. Having the crowd integrate its knowledge to offer solution-ideas to ill-structured problems posed by organizations is one of the desired outcomes of crowd-based open innovation because, by integrating others’ knowledge, the ideas are more likely to consider the many divergent issues related to solving the ill-structured problem. Unfortunately, the diversity of knowledge content offered by heterogeneous specialists in the online temporary crowd makes integration difficult, and the lean social context of the crowd makes extensive dialogue to resolve integration issues impractical. The authors address this issue by exploring theoretically how the manner in which interaction is organically conducted during open innovation challenges enables the generation of integrative ideas. The authors hypothesize that, as online crowds organically share knowledge based upon successful pro-socially motivated interaction, they become more productive in generating integrative ideas.

Using a multilevel mixed-effects model, this paper analyzed 2,244 posts embedded in 747 threads with 214 integrative ideas taken from 10 open innovation challenges.

Integrative ideas were more likely to occur after pro-socially motivated interactions.

Ideas that integrate knowledge about the variety of issues that relate to solving an ill-structured problem are desired outcomes of crowd-based open innovation challenges. Given that members of the crowd in open innovation challenges rarely engage in dialogue, a new theory is needed to explain why integrative ideas emerge at all. The authors’ adaptation of pro-social motivation interaction theory helps to provide such a theoretical explanation. Practitioners of crowd-based open innovation should endeavor to implement systems that encourage the crowd members to maintain a high level of activeness in pro-socially motivated interaction to ensure that their knowledge is integrated as solutions are generated.

The present study extends the crowd-based open innovation literature by identifying new forms of social interaction that foster more integrated ideas from the crowd, suggesting the mitigating role of pro-socially motivated interaction in the negative relationship between knowledge diversity and knowledge integration. This study fills in the research gap in knowledge management research describing a need for conceptual frameworks explaining how to manage the increasing complexity of knowledge in the context of crowd-based collaboration for innovation.

This study is a systematic literature review of crowdsourcing that aims to present the research evidence so far regarding the extent to which it can contribute to organisational performance and produce innovations and provide insights on how organisations can operationalise it successfully.

The systematic literature review revolved around a text mining methodology analysing 106 papers.

The themes identified are performance, innovation, operational aspects and motivations. The review revealed a few potential directions for future research in each of the themes considered.

This study helps researchers to consider the recent themes on crowdsourcing and identify potential areas for research. At the same time, it provides practitioners with an understanding of the usefulness and process of crowdsourcing and insights on what the critical elements are in order to organise a successful crowdsourcing project.

This study employed quantitative content analysis in order to identify the main research themes with higher reliability and validity. It is also the first review on crowdsourcing that incorporates the relevant literature on crowdfunding as a value-creation tool.

Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network.

Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy.

By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher.

This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.

The study explores the role of dialogic public communication and information quality (IQ) in evaluating the operational performance of donation-based crowdfunding (DBC) tasks. These tasks are primarily used to support disaster relief operations. The authors also test the influence of cognitive trust and swift trust as moderating variables in explaining the relationship between both IQ and dialogic communication with operational performance.

The authors used a primary survey to test the hypotheses. A total of 203 responses were collected from multiple crowdfunding platforms. The authors used archival data from task creators on donation-based crowdfunding platforms, and a structured questionnaire is also used to collect responses. Data are analyzed using Warp PLS 6.0. Warp PLS 6.0 works on the principle of partial least square (PLS) structured equation modeling (SEM) and has been used widely to test path analytical models.

The authors found out that the operational performance is explained significantly by the quality of information and its association with dialogic public communication. The results support the arguments offered by dialogic public communication theory and trust transfer theory in assessing the operational success of DBC. The study also confirms that cognitive trust positively moderates the relationship between IQ and organizational public dialogic communication and operational performance. It is also revealed that the duration of the DBC task has no significant control over dialogic public communication.

The study lays practical foundations for task creators on DBC platforms and website designers as it sets the importance of both IQ and dialogic communication channels. The communication made by the task creator and/or the DBC platforms with the donors and potential donors in the form of timely and appropriate information forms the key to the success of any DBC task. The study also helps task creators choose a suitable platform to improve performance.

The authors propose a unique framework by integrating two theoretical perspectives: dialogic public relation theory and trust transfer theory in understanding the operational performance of donation-based crowdfunding tasks. The authors address DBC tasks catering to disaster relief operations by collecting responses from task creators on DBC platforms. The study uniquely positions itself in the area of information and communication.

The purpose of this study is to offer implications and future research directions related to new organizational forms like crowds. Organizations are increasingly relying on online crowds to innovate through mechanisms such as crowdsourcing, open innovation, innovation challenges and tournaments. To leverage the "wisdom of crowds", crowdsourcing platforms that enable heterogeneous knowledge sharing in crowds lead to novel solution generation by individuals in the crowd. Based on the associative variety memory model of creativity, the authors hypothesize that when a crowd contributes a heterogeneous knowledge in form of a variety of knowledge associations, individual crowd members tend to generate solutions that are more novel. In contrast to the brainstorming view that focuses on ideas as knowledge, the authors propose, test, find and elaborate on implications of crowd sharing of heterogeneous knowledge for the generation of innovation, i.e. novel ideas. The authors coded and analyzed all the posts in 20 innovation challenges leveraging online temporary crowds that were structured to foster knowledge sharing as part of the idea generation process. The analysis shows a positive relationship between the variety of knowledge associations contributed by the crowd and the generation of novel solutions by individuals in the crowd. Further, the variety of knowledge associations contributed by the crowd has a stronger relationship with novel solution generation than the number of associations generated by the crowd, i.e. variety of knowledge has a greater impact than either the quantity of knowledge or the number of solution-ideas shared. The authors offer four implications and several future directions for research on the new organizational form of online crowds.

The authors coded and analyzed all the posts in 20 innovation challenges. They also designed and ran these challenges in collaboration with corporate sponsors. The ideas in the challenge were rated by senior executive at each company using a creative forecasting method.

The variety of knowledge associations contributed by the crowd has a stronger relationship with novel solution generation than the number of associations generated by the crowd, i.e. variety of knowledge has a greater impact than either the quantity of knowledge or the number of solution-ideas shared.

The authors offer four implications and several future directions for research on the new organizational form of online crowds.

The authors propose several ways in which companies running innovation challenges can moderate and encourage crowd to generate a variety of knowledge.

The authors believe that we are the first empirical paper to emphasize and show that associative variety of knowledge sharing in crowds has impact on novel idea generation by crowds. This view is counter to "electronic brainstorming" view where crowd is asked to just generate these ideas and often just submit their ideas to the sponsor. Their view also goes beyond knowledge refinement of ideas by crowds to more of knowledge integration by crowds.

This paper aims to offer a novel set of insights to understand the role of network ties in pursuit of radical innovation. In this sense, the purpose of the study is to analyze how the heterogeneity in the content of network ties affects radical innovation performance.

Based on a comprehensive review of existing literature, this paper conceptualizes how different types of network ties affect radical innovation performance by deriving five research propositions.

Both buyer-supplier ties and peer collaboration ties are positively related to radical innovation performance, whilst the peer collaboration ties may be further affected by partner similarity. Compared to other two types of network ties, equity ties act as more of moderating roles on spurring radical innovation. Crowding out between network ties prevents firms from knowledge searching within an extensive network scope, reducing the opportunities of mixing and matching different kinds of knowledge needed for radical innovation.

The study suggests a natural way of launching marketing strategy by selectively integrating different sources of knowledge (market, supplier or technology) needed for commercializing radical technologies, highlighting the importance of partner selection for radical innovation among different types of firms surrounding the current market. For managers, it is necessary to identify and select network ties helpful for long-term business and strategic interests.

This paper makes two main contributions. First, it addresses the question of how networks influence radical innovation by identifying three types of network ties and their effects – individual and in combination – on extension of the depth and breadth of knowledge and development of disruptive ideas. Second, it develops the existing literature by demonstrating the crowding-out effect of network ties.

Crowdsourcing – a form of collaboration across organizational boundaries – provides access to knowledge beyond an organization’s local knowledge base. Integrating work on organization theory and innovation, the authors first develop a framework that characterizes crowdsourcing into a main sequential process, through which organizations (1) define the task they wish to have completed; (2) broadcast to a pool of potential contributors; (3) attract a crowd of contributors; and (4) select among the inputs they receive. For each of these phases, the authors identify the key decisions organizations make, provide a basic explanation for each decision, discuss the trade-offs organizations face when choosing among decision alternatives, and explore how organizations may resolve these trade-offs. Using this decision-centric approach, the authors continue by showing that there are fundamental interdependencies in the process that makes the coordination of crowdsourcing challenging.

The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.