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This paper aims to introduce the concept of Entrepreneurial Collective Intelligence (ECI) as a means of understanding how communities of entrepreneurial actors learn to act both collectively and knowingly. It explores how connections between processes of CI, agency and action can explain and enable the development entrepreneurial community organisations.

There is a selective literature review of prior works on the related fields of community and collective entrepreneurship; collectives and intelligence; agency and action. The review is used to propose a framework of collective entrepreneurial intelligence, agency and action. An interpretive approach is used to research four case studies of community organisations which use CI to generate entrepreneurial outcomes.

The cases are compared with themes from prior literature to develop a conceptual model of four ECI processes which enable intelligence, agency and action: collaborative processes; distributed working; intelligence representations and organisation of infrastructures. These are theorised to discuss ideas, challenges, methods and questions to enhance entrepreneurial actions, based on sharing knowledge and learning, in the context of collective agency, action and intelligence.

The four processes, both together and separately, represent a coherent framework useful for further studies on the role of collectives in enterprising communities.

The four processes each represent a central area of attention, not only for development, learning, decision-making and leadership within enterprising communities but also for entrepreneurship education in terms of alternative didactics, pedagogies and learning forms.

The improved knowledge on the role of collective agency and CI within entrepreneurial processes is useful for strengthening civil activism and other fruitful forms of entrepreneurial collective processes. This may help solve complicated societal problems where traditional conceptions of entrepreneurship fail.

The conceptual contribution is to explain the dynamic relationships between ECI and action, mediated by collective agency. The role of CI in informing entrepreneurial communities is explored and four enabling processes are proposed. This coherent framework is useful for further studies on the role of collectives in enterprising communities, whilst informing their learning, decision-making and leadership.

The purpose of this paper is to provide a new framework for managing intellectual capital (IC) inside a university considering the collective intelligence perspective.

The research method uses the fourth stage of IC research and adopts the collective intelligence approach. The underlying assumption behind the framework is to consider the university as a collective intelligence system in which the tangible and intellectual assets are coordinated towards the achievement of strategic goals.

The conceptual framework for IC management harnesses the power of IC, collectively created by the engagement of multiple stakeholders inside the university network. The main components are the final goal of a university (what); the collective human capital to achieve the goal (who); the processes activated inside the university (how); and finally the motivations behind the achievement of the goal (why).

The research is exploratory and the framework offers opportunities for refinement. Future research is needed to verify the application of the framework to other organisations in the public sector intended as collective intelligence systems. A new perspective for managing IC in universities adopting the collective intelligence approach is developed. Contribution to the fourth stage (ecosystem) of IC research is highlighted, expanding the concept of IC value creation beyond the university into wider society.

The framework can be used to manage IC strategically in all the systems interpreted as collective intelligence systems in which the role of IC creation from multiple actors is relevant. This makes possible the understanding of how IC helps create value for the society and the region in which the university operates.

The originality of the paper is in bringing together issues usually dealt within the literature in separate domains, such as IC management and collective intelligence perspective. The concept of collective intelligence remains an unexplored field in relation to IC management in the public sector. The collective intelligence approach provides a novel contribution to managing IC and is intended to inspire future research.

The purpose of this study is to explore the pivotal role that collective teaching plays in knowledge transfer between new product development teams. This study develops a theoretical model of collective teaching, where team intelligence is its consequence and learning orientation cognitive skills are moderators.

Based on a questionnaire survey of 156 pairs of new product development project teams of information technology firms, the authors used partial least squares to test the study’s hypotheses.

The findings reveal that the use of collective teaching is positively related to team intelligence of recipient teams. In addition, T-shaped skills of source teams exert positive moderating influence on this relationship and so does a learning orientation of recipient teams.

First, the sample firms used in the study are from the IT industry, which is characterized by extremely short product life cycles, thereby limiting the generalizability of the study’s findings. Second, the authors did not examine whether the effect of T-shaped skills is different at various NPD stages; the contributions of each functional expertise may vary depending on the NPD stage (e.g. the idea generation or pre-launch stage). Third, the use of cross-sectional design precludes a causal inference. The role of focal constructs and moderators and their consequent effects would benefit from more stringent, longitudinal research. Finally, the authors controlled for only a limited set of factors of team intelligence because other potential antecedents of this variable still await identification by future studies.

This study suggests that the implementation of collective teaching can enhance the capacity of a project team as a whole to manage and innovate information, namely, team intelligence. The study’s findings also suggest that the management must recognize the significance of teams’ learning orientation and thereby proactively develop teams’ learning culture by redesigning work, reward systems or performance evaluation to promote learning. Additionally, it is prudent for managers to reconsider their recruitment criteria to incorporate T-shaped skills.

This study represents the first step in developing an empirically grounded framework linking collective teaching with team intelligence. Additionally, the authors confirm that team intelligence is a four-dimensional construct.

This study aims to investigate the influence of social intelligence and collective efficacy on the fearlessness of change. Furthermore, this study investigates the mediation effect of collective efficacy and moderating role of management commitment to change in the relationship between social intelligence and fearlessness of change.

The analysis is based on data collected from 296 members of information technology and Banking Financial Services Industry organisations using a survey questionnaire. Hypotheses have been tested using structural equation modelling.

The findings show that social intelligence and collective efficacy positively influence fearlessness of change. Social intelligence also impacts collective efficacy positively. Further, collective efficacy acts as a mediator and management commitment to change acts as a moderator in the relationship between social intelligence and fearlessness of change.

This study highlights the relevance of social intelligence in fostering a fearless attitude towards change for easy transition from the current organisational state to a new or desired state.

Organisational leaders must strive to develop a climate of fearlessness in organisations undergoing change so that the employees acquire this attitude of fearlessness and face the hurdles that come with change with a positive mind set.

This study is amongst the few such studies that examined the relationship amongst variables of this study.

Even the smartest organizations believe that “good enough is never good enough.” Highly intelligent people may be able to do important things individually; still, it is their accumulated brainpower that allows them to do great things. Collective intelligence means when a group of people do things that seem intelligent. On the other hand, when intelligent people are gathered or hired in an organization, they tend toward collective stupidity and slow-wittedness. Therefore, the purpose of the present study is to develop a model of the factors affecting and affected by collective stupidity.

This study was conducted using a mixed-method approach and in two phases: qualitative and quantitative. First, in the qualitative phase and in order to achieve new findings, semi-structured interviews with experts from 12 knowledge-based companies were used to design a conceptual model and formulate the hypotheses. At the end of the qualitative phase, the conceptual model and relationships between variables were drawn. Then, in the quantitative phase, by running structural equation modeling, the antecedents and consequences of collective stupidity derived from the qualitative phase findings were analyzed and the research hypotheses were tested in 110 industrial knowledge-based companies.

The results of the qualitative phase revealed that individual, group and organizational factors were the antecedents of collective stupidity, with individual factors having three dimensions, collective ones including two dimensions and organizational ones focusing on the characteristics of managers/companies. It was also found that collective stupidity had individual consequences in three dimensions and organizational consequences in five dimensions; and one-sidedness, non-strategic thinking, organizational injustice and weakness in the management of key personnel were extracted as mediators. The results of the quantitative phase confirmed the research model and showed that individual factors and organizational consequences had the lowest (0.037) and highest (1.084) effect sizes on collective stupidity, respectively.

The study of the phenomenon in a particular context and the difficulty of generalizing the findings to other situations, the small size of the study population due to the disproportionate number of experts to the total staff in the qualitative phase, and the limited participation of experts due to the negative nature of the issue in the quantitative phase. Taking advantage of the diversity of experts' mental abilities through equipping the organization with tools for recognizing collective stupidity, improving collective decision-making, enhancing the efficiency of think tanks and organizational prosperity in the age of knowledge economy, preventing damage to the body of knowledge of the company and reducing social loafing can be the main operational implications of this study.

Using a mixed-method approach for analyzing the antecedents and consequences of collective stupidity in this study and examining such a phenomenon in a knowledge-based organizational context and its implementation in a developing country can be among the innovations of the present research. By following a few studies conducted in this field in addition to the organizational pathology of the phenomenon using a fundamental method, this study obtained deep results on how to make the maximum use of experts’ capacities. This was to the extent that in all of the three sections, i.e. the antecedents and consequences of collective stupidity, a new factor emerged.

The current study aims to explain the influence of technological interactivity and media sociability on sport consumer value co-creation behavior via collective efficacy and collective intelligence.

Subjects were individuals who have a mobile smart device and experience of accessing sport-related community websites (n = 513). A face-to-face self-administered survey was employed based on the convenience sampling method. A structural equation modeling test was conducted to examine the relationships between the variables.

Technological interactivity and media sociability based on mobile smart devices improve sport consumers' collective efficacy, and media sociability positively affects collective intelligence. Also, perceived collective efficacy and collective intelligence in virtual communities induce consumers to engage in the value co-creation process more actively (i.e. consumer participation behavior and citizenship behavior). One of the key findings includes the significant role of media sociability in the process of value co-creation between consumers and sport organizations. Media sociability is more likely to contribute to improving sport consumers' collective efficacy and the development of intelligence than the properties of technological interactivity.

Research findings contribute to extending the body of knowledge in interactivity studies related to sport consumers' value co-creation behaviors in the virtual environment context.

The purpose of this paper is to focus on the underpinning dynamics that explain collective intelligence.

Collective intelligence can be understood as the capacity of a collective system to evolve toward higher order complexity through networks of individual capacities. The authors observed two collective systems as examples of the dynamic processes of complex networks – the wiki course PeSO at the Universidad de Los Andes, Bogotá, Colombia, and an agent-based model inspired by wiki systems.

The results of the wiki course PeSO and the model are contrasted with a random network baseline model. Both the wiki course and the model show dynamics of accumulation, in which statistical properties of non-equilibrium networks appear.

The work is based on network science. The authors analyzed data from two kinds of networks: the wiki course PeSO and an agent-based model. Limitations due to the number of computations and complexity appeared when there was a high order of magnitude of agents.

Better understanding can allow for the measurement and design of systems based on collective intelligence.

The results show how collective intelligence emerges from cumulative dynamics.

The purpose is to examine how collective intelligence initiatives can help address the challenge of water supply globally.

The methodology used was desk research and interview. The paper commences with a review of collective intelligence as a subject area. It then assesses water as an issue, paying particular attention to the new set of World Water Scenarios currently being undertaken by the World Water Assessment Program of UNESCO and its latest World Water Development Report (2012). It considers the application of collective intelligence systems to the challenge of water provision with particular reference to a recent initiative led by the International Water Association (IWA), the IWA WaterWiki. It examines potential future developments from a collective intelligence viewpoint.

It is increasingly recognised that the drivers that affect water provision are linked to the change drivers of other global issues. There is a need to deal with the inadequacies of currently available water data. Online collective intelligence systems make it easier to set practical goals and monitor progress and send, receive and act on the latest research and new information. Complementary collective intelligence systems in other areas are needed.

The paper deals with current developments in the water sector. It will be of interest to all foresight professionals involved with this sector or collective intelligence.

Collective intelligence has drawn many scientists’ attention in many centuries. This paper shows the collective intelligence study process in a perspective of crowd science.

After summarizing the time-order process of related researches, different points of views on collective intelligence’s measurement and their modeling methods were outlined.

The authors show the recent research focusing on collective intelligence optimization. The studies on application of collective intelligence and its future potential are also discussed.

This paper will help researchers in crowd science have a better picture of this highly related frontier interdiscipline.

The purpose of this paper is to analyse the role of computational intelligence techniques in the process of communities' formation.

The paper develops a high performance genetic algorithm for community formation based on collective intelligence capacity. An experimental study is presented to illustrate the algorithm.

Collective intelligence does not represent the sum of individual intelligences, it is the ability of the community to complete more tasks than single individuals. The paper reveals the need for mechanisms that allow a large group of professionals to make decisions better than single individuals.

The genetic algorithm proposed in the paper may be used to obtain the optimal structure of a community, in terms of number of members and their role in the community.

The key concept is a new fitness index, an intelligence index, which is the optimal combination between intelligence and cooperation, and allows not only community formation, but also intelligence to be the driving principle in the community formation process.