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The ubiquity of digitally intermediated interactions is changing the ways in which social interaction creates the cognitive and institutional underpinnings of new markets. Logics that define markets used to be localized, but they now emerge from crowds that span – and persist – across time and space. This article builds a theory of how crowds emerge and evolve in a way that influences the emergence of shared logics and helps explain why some markets are viable while others are not. What is revealed is that a crowd has a hidden niche structure that determines the fate of a new market.

Purpose: The insurance business is confronted with coordination difficulties that necessitate a high level of mobility, flexibility, and the capacity to analyse heterogeneous, location-dependent data from different sources and qualities. Recent innovations in emerging technologies have given the insurance industry new organisational options. When coupled with data analytics, crowdsourcing in the insurance industry facilitates solving complex issues with the wisdom of crowds. The notion of incorporating crowdsourcing and big data into the mainstream activities of insurance management is developed in this article, as are the ramifications and gains of collective intelligence achieved by Crowdsourcing and the added value of crowdsourcing insurance activities.

Design/methodology/approach: This chapter is a conceptual work that builds on relevant literature.

Findings: This chapter analyses what insurance industry managers should consider when coordinating crowdsourced activities and how they may benefit from collective intelligence combined with data analytics in terms of efficient and real-time response management for the insurance industry. Furthermore, it is demonstrated how they may use crowdsourcing to exploit information and benefit from invoking additional resources and eliminating the institutional voids present in the industry.

Practical implications: Exemplary applications that take advantage of crowdsourcing and data analytics would help the insurance sector respond flexibly, efficiently, and effectively in real time.

Originality/value: This chapter offers new collaborative ways to enhance the decision-making of insurance industry managers. The relevance of overcoming institutional voids is expanded, and repercussions from the given framework are suggested using data analytics.

With the advent of Internet technologies, shorter product life cycles and increasing competition, organisations have started looking for innovation sources outside the organisational boundaries. The external community of crowds can be used as a valuable source of co-creation in a company's innovation process to generate value. Despite its growing popularity, organisations often face difficulty capturing value from crowdsourcing due to the lack of proper mechanisms behind crowdsourcing-based value co-creation between a crowd and an organisation and their impact on organisational learning and innovation performance. The present study seeks to understand the crowdsourcing-based co-creation mechanism that influences knowledge transfer effectiveness and the organisation's absorptive capacity, resulting in improved innovation performance.

The model was empirically tested using online survey data received from 300 managers of IT firms. Partial least squares structural equation modelling was used to test the model.

The empirical results reveal that crowdsourcing-based value co-creation causes structural, cognitive and relational linkages between a crowd and a firm, among which crowdsourcing-based cognitive linkage contributes more to organisational value capture. Further, an organisation's effective knowledge transfer and absorptive capacity play an important role in influencing the crowdsourcing-based-co-creation organisational learning-innovation performance framework.

This is the first and foremost study that has developed an integrated model using social capital dimensions to understand the entire mechanism behind crowdsourcing-based value co-creation between a crowd and an organisation and their impact on organisational learning and innovation performance. The study provides organisations with theoretical and practical implications of using crowdsourcing as a value co-creation tool and its effects on enhancing organisational learning and value capture.

The originality of the crowd cyber system lies in the fact that it possesses the intelligence of multiple groups including intelligence of people, intelligence of objects and intelligence of machines. However, quantitative analysis of the level of intelligence is not sufficient, due to many limitations, such as the unclear definition of intelligence and the inconformity of human intelligence quotient (IQ) test and artificial intelligence assessment methods. This paper aims to propose a new crowd intelligence measurement framework from the harmony of adaption and practice to measure intelligence in crowd network.

The authors draw on the ideas of traditional Confucianism, which sees intelligence from the dimensions of IQ and effectiveness. First, they clarify the related concepts of intelligence and give a new definition of crowd intelligence in the form of a set. Second, they propose four stages of the evolution of intelligence from low to high, and sort out the dilemma of intelligence measurement at the present stage. Third, they propose a framework for measuring crowd intelligence based on two dimensions.

The generalized IQ operator model is optimized, and a new IQ algorithm is proposed. Individuals with different IQs can have different relationships, such as cooperative, competitive, antagonistic and so on. The authors point out four representative forms of intelligence as well as its evolution stages.

The authors, will use more rigorous mathematical symbols to represent the logical relationships between different individuals, and consider applying the measurement framework to a real-life situation to enrich the research on crowd intelligence in the further study.

Intelligence measurement is one of foundations of crowd science. This research lays the foundation for studying the interaction among human, machine and things from the perspective of crowd intelligence, which owns significant scientific value.

This study aims to review the literature on sharing economy logistics and crowd logistics to answer the three following questions: How is the literature on sharing economy logistics structured? What are the main trends in sharing economy logistics and crowd logistics? What are the future research options?

Bibliometric analysis is used to evaluate 85 articles published over the past 12 years; it identifies the top academic journals, authors and research topics contributing to the field.

The sharing economy logistics and crowd logistics literature is structured around several disciplines and highlights that some are more scientifically advanced than others in their subject definitions, designs, modelling and innovative solutions. The main trends are organized around three clusters: Cluster 1 refers to the optimal allocation of costs, prices, distribution and supplier relationships; Cluster 2 corresponds to business related crowdsourcing and international industry practices; and Cluster 3 includes the impact of transport on last-mile delivery, crowd shipping and the environment.

The study is based on data from peer-reviewed scientific journals and conferences. A broader overview could include other data sources such as books, book chapters, working papers, etc.

Future research directions are discussed in the context of the evolution from crowd logistics to crowd intelligence, and the complexities of crowd logistics such as understanding how the social crowd can be integrated into the logistics process. Our results are part of the crowd science and engineering concept and provide some answers about crowd cyber-system questions regarding crowd intelligence in logistic sector.

Colossal information is available in cyberspace from a variety of sources such as blogs, reviews, posts and feedback. The mentioned sources have helped in improving various business processes from product development to stock market development. This paper aims to transform this wealth of information in the online medium to economic wealth. Earlier approaches to investment decision-making are dominated by the analyst's recommendations. However, their credibility has been questioned for herding behavior, conflict of interest and favoring underwriter's firms. This study assumes that members of the online crowd who have been reliable, profitable and knowledgeable in the recent past will continue to be so soon.

The authors identify credible members as experts using multi-criteria decision-making tools. In this work, an alternative actionable investment strategy is proposed and demonstrated through a mock-up. The experimental prototype is divided into two phases: expert selection and investment.

The created portfolio is comparable and even profitable than several major global stock indices.

This work aims to benefit individual investors, investment managers and market onlookers.

This paper takes into account factors: the accuracy and trustworthiness of the sources of stock market recommendations. Earlier work in the area has focused solely intelligence of the analyst for the stock recommendation. To the best of the authors’ knowledge, this is the first time that the combined intelligence of the virtual investment communities has been considered to make stock market recommendations.

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.

To thrive, any individual, organization, or society needs to separate true from false expertise. This chapter provides a selective review of research examining self and social judgments of human capital – that is, expertise, knowledge, and skill. In particular, it focuses on the problem of the “flawed evaluator”: most people judging expertise often have flawed expertise themselves, and thus their assessments of self and others are imperfect in profound and systematic ways.

The review focuses mostly on empirical work specifically building on the “Dunning–Kruger effect” in self-perceptions of expertise (Kruger & Dunning, 1999). This selective review, thus, focuses on patterns of error in such judgments.

Because judges of expertise have flawed expertise themselves, they fail to recognize incompetence in themselves. Because of their flaws, most people also fail to recognize genius in other people and superior ideas.

The review suggests that organizations have trouble recognizing those exhibiting the highest levels of expertise in their midst. People in organizations also fail to identify the best advice and correct flawed ideas. Organizations may also rely on the “wisdom of crowds” strategy in situations in which that strategy actually misleads because too few people identify the best idea available.

On the basis of the Collective Intelligence Genome framework, which was developed to describe private, for profit ventures, this study aims to review the recent public sector initiatives launched by the American federal government and the European Union. The study's goal is to examine if, and how, the Genome construct would apply to not for profit.

This paper builds on an existing classification methodology for collective intelligence initiatives and extends it to pubic sector initiatives.

The findings suggest that, although the framework offers a generally good fit, it does not fully address all the factors at play and the paper proposes expanding the gene pool. In addition, it confirms that Collective Intelligence initiatives do indeed co‐create value and conform to the emerging services dominant logic concept.

With the growing success of profit motivated internet‐based collaborative ventures, including Innocentive, VenCorps, Threadless and many others, governments have taken notice and engaged. Recent public sector initiatives, including Open.gov, Peer 2 Patent, innovation.ED.gov among others, have begun to leverage collaborative internet media through similar means. These initiatives not only engage a broader community in the co‐creation of value, but also foster what has been termed as Collective Intelligence. This paper details one of the first forays into what might be termed sense making within the public sector usage of Collective Intelligence using the Genome framework and, as such, provides researchers and practitioners with a means of assessing value, potential impact and making comparisons.

Reliability and political bias of mass media has been a controversial topic in the literature. The purpose of this paper is to propose and implement a methodology for fully automatic evaluation of the political tendency of the written media on the web, which does not rely on subjective human judgments.

The underlying idea is to base the evaluation on fully automatic comparison of the texts of articles on different news websites to the overtly political texts with known political orientation. The authors also apply an alternative approach for evaluation of political tendency based on wisdom of the crowds.

The authors found that the learnt classifier can accurately distinguish between self-declared left and right news sites. Furthermore, news sites’ political tendencies can be identified by automatic classifier learnt from manifestly political texts without recourse to any manually tagged data. The authors also show a high correlation between readers’ perception (as a “wisdom of crowds” evaluation) of the bias and the classifier results for different news sites.

The results are quite promising and can put an end to the never ending dispute on the reliability and bias of the press.

This paper proposes and implements a new approach for fully automatic (independent of human opinion/assessment) identification of political bias of news sites by their texts.