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This conceptual paper aims to explore the possibility of human beings reaching a virtual form of immortality.

The paper is an investigation of the path from an early example of human knowledge to the birth of artificial intelligence (AI) and robots. A critical analysis of different point of views, from philosophers to scientists, is presented.

From ancient rock art paintings to the moon landing, human knowledge has made a huge progress to the point of creating robots resembling human features. While these humanoid robots can successfully undertake risky tasks, they also generate ethical issues for the society they interact with.

The paper is conceptual, and it does attempt to provide one theory by which human beings can achieve the dream of immortality. It is part of a work in progress on the use of AI and the issues related to the creation/use of humanoid robots in society.

This paper provides an overview of some of the key issues and themes impacting our modern society. Its originality resides in the linking of human knowledge to collective knowledge and then of collective mind to the hyper-collective mind. The idea of humans reaching immortality is burdened by the imperative need to define ethical guidelines for the field of AI and its uses.

The aim of the study is to explore the genesis of entrepreneurial ecosystems (EE) and highlight the role played by intellectual capital (IC) in that process. Specifically, the paper adopts the collective intelligence approach, and the study shows how human capital (HC), structural capital (SC) and relational capital (RC) interact to create an entrepreneurial ecosystem.

The paper adopts a single case study of an Italian EE. The data analysis is based upon the collection of different sources of data: semi-structured interviews with representatives of each actor of the ecosystem; email correspondence; meetings report; a 24-months period of direct observation. Given the novelty of the topic, the qualitative method seems well suited for studying innovation-based EE since the method offers rich data about a phenomenon in real-life context.

The case is a top-down, innovation-based EE in which all main components of the IC play a crucial role from the initial stage. Findings show how the constant interchange between IC components occurs at two different levels: the micro and the meso level. HC and RC play major roles at both levels, whilst SC only occurs at a meso level, representing the environment in which the whole ecosystem takes place. Additionally, the use case, a new intangible asset integrating all three components of IC, emerged as one of the main outcomes of this innovation-based EE.

The paper contributes to a rather unexplored topic in the existing literature on EE and IC, namely the formation process of EE and the role played by IC within that process. Additionally, through the application of the collective intelligence approach, the authors shed light on the need to manage IC at both micro and meso level in the creation of an EE.

The purpose of this work is to introduce a generic conceptual and methodological framework for the study of emergent social and intellectual patterns and trends in a diverse range of sense‐and decision‐making activities.

The development of the framework is driven by three motivating challenges: capturing the collective intelligence of science, fostering scientific discoveries in science and e‐Science, and facilitating evidence‐based librarianship (EBL). The framework is built on concepts such as structural holes and intellectual turning points, methodologies and techniques for progressive knowledge domain visualization and differentiation of conflicting opinions, and information integration models to achieve coherent transitions between different conceptual scales.

Structural holes and turning points are detected and validated with the domain of terrorism research as an example. Conflicting opinions are differentiated in the form of a decision tree of phrases with the greatest information gains. Fundamental issues concerning the reliability of common assumptions across multiple levels of granularity are identified. Knowledge diffusion is studied in terms of information integration between a geographic space and an intellectual space.

This study characterizes a holistic sense‐making approach with three exemplar themes. Future research is needed to develop theoretical foundations and corresponding techniques to sustain additional themes.

The work contributes to the practice of improving our understanding of the collective intelligence in science.

The value of the work is the conceptual and methodological contributions to address various phenomena across micro‐ and macroscopic levels.

The purpose of this paper is to explore implicit crowdsourcing, leveraging social media in real-time scenarios for intelligent systems.

A case study using an illustrative example system, which systematically used a custom social media platform for automated financial news analysis and summarisation was developed, evaluated and discussed. Literature review related to crowdsourcing and collective intelligence in intelligent systems was also conducted to provide context and to further explore the case study.

It was shown how, and that useful intelligent systems can be constructed from appropriately engineered custom social media platforms which are integrated with intelligent automated processes. A recent inter-rater agreement measure for evaluating quality of implicit crowd contributions was also explored and found to be of value.

This paper argues that when social media platforms are closely integrated with other automated processes into a single system, this may provide a highly worthwhile online and real-time approach to intelligent systems through implicit crowdsourcing. Key practical issues, such as achieving high-quality crowd contributions, challenges of efficient workflows and real-time crowd integration into intelligent systems, were discussed. Important ethical and related considerations were also covered.

A contribution to existing theory was made by proposing how social media Web platforms may benefit crowdsourcing. As opposed to traditional crowdsourcing platforms, the presented approach and example system has a set of social elements that encourages implicit crowdsourcing. Instances of crowdsourcing with existing social media, such as Twitter, often also called crowd piggybacking, have been used in the past; however, using an entirely custom-built social media system for implicit crowdsourcing is relatively novel and has several advantages. Some of the discussion in context of intelligent systems construction are novel and contribute to the existing body of literature in this field.

The purpose of this paper is to present a case study of a university that uses a collective intelligence approach for managing its intellectual capital (IC). Specifically, the authors investigate how one of Europe’s oldest business schools, Ca’ Foscari University of Venice (Italy), manages IC through stakeholder engagement to achieve academia’s third mission so contributing to social and economic development.

Data are collected through semi-structured interviews and Ca’ Foscari University’s strategic plan. Secundo et al.’s (2016) collective intelligence framework is used to analyse the data. Alvesson and Deetz’s (2000, pp. 19-20) critical management tasks – insight, critique and transformative redefinition – are adopted to frame and discuss the results.

On the assumption that a university is a collective intelligence system, the findings demonstrate that IC management needs to change to incorporate an ecosystem perspective, reflecting the fourth stage of IC research. The IC management at the university incorporates its core goal (what), the collective involvement of internal and external stakeholders to achieve the goal (who), the motivations behind the achievement of the goal (why) and, finally, the processes activated inside the university (how) and indicators to assess value creation.

A new perspective for managing IC in universities that adopts a collective intelligence approach is further developed. Contributions to the fourth stage of IC research – IC in an ecosystem – are highlighted that expand the concept of IC value creation beyond universities into wider society.

Two key consequences of this case study are that more stakeholders have become involved in IC management and that IC management requires critical rethinking, given the universities’ evolving role.

This paper brings together issues that are usually dealt with in separate domains of the literature: IC management and collective intelligence in the university setting.

A pattern of personal intelligence is seen emerging from the concept of agent-mediated personal knowledge management (PKM) in achieving collective organisational goals. The purpose of this paper is to present the results of surveys undertaken to prove this emergence.

A quantitative analysis supported by a qualitative analysis was conducted across three main industries in Malaysia, namely manufacturing, service and education. The triangulation of analysis is based on the four proposed hypotheses.

From these analyses, it was discovered that the emergence of personal intelligence is embedded within the collaborative interactions amongst software agents, and between agents and human knowledge workers. All the hypotheses are supported by the results of the surveys which manifest organisational knowledge management (OKM) practices as a consequence of the agent-mediated PKM processes.

This research focused on the PKM in Malaysia, where the level of KM implementation varies among the organisations. The results may not reflect other developing countries due to the socio-cultural differences amongst the knowledge workers.

The results from this paper can be used either to relook and reanalyse the existing organisational KM system or to plan and design a KM system for organisations that have not implemented any.

The focus on personal intelligence and agent-mediated PKM contribute to further development of agent-based system that animates these theories in the real working environment.

This chapter explains how organizations can build intelligence capabilities into their everyday working environments. The definition of capacity building builds upon the organizational management and the strategic workforce development literatures. This chapter also derives essential guidance from another series focused on critical capabilities and capacity building. The authors highlight the role of a strong intelligence culture and learning in building intelligence capacity. Capacity building is achieved through short- and long-term efforts. This chapter also highlights the importance of balancing capacity building across everyday business operations and specialized intelligence functions.

Chapter 8 synthesizes the research findings from the processes of sensory cognition into the design and configuration of the learning environment. The focus of cognition changes perspective and focus from the attributes of an external stimulus to the internal processes of integration with prior learning and internalization into a new cognition of the individual, which is labeled as the individual's learning ecology. These processes provide a plausible model for the design of the learning environment dimension, which internalizes the learning into transformational and ultimately lifelong learning. The processes of sensory cognition provide a viable and practical model to engineer learning cognition in the same way the brain does with sensory cognition. Like sensory cognition, learning cognition is the result of the structure of the learning environment.

The personal learning environment driven approach to learning suggests a shift in emphasis from a teacher‐driven knowledge‐push to a learner‐driven knowledge‐pull learning model. One concern with knowledge‐pull approaches is knowledge overload. The concepts of collective intelligence and the Long Tail provide a potential solution to help learners cope with the problem of knowledge overload. The paper aims to address these issues.

Based on these concepts, the paper proposes a filtering mechanism that taps the collective intelligence to help learners find quality in the Long Tail, thus overcoming the problem of knowledge overload.

The paper presents theoretical, design, and implementation details of PLEM, a Web 2.0 driven service for personal learning management, which acts as a Long Tail aggregator and filter for learning.

The primary aim of PLEM is to harness the collective intelligence and leverage social filtering methods to rank and recommend learning entities.

For many of the current leadership theories, models, and approaches, the answer to the question posed in the title, “Is leadership more than ‘I like my boss’?,” is “no,” as there appears to be a hierarchy of leadership concepts with Liking of the leader as the primary dimension or general factor foundation. There are then secondary dimensions or specific sub-factors of liking of Relationship Leadership and Task Leadership; and subsequently, tertiary dimensions or actual sub-sub-factors that comprise the numerous leadership views as well as their operationalizations (e.g., via surveys). There are, however, some leadership views that go beyond simply liking of the leader and liking of relationship leadership and task leadership. For these, which involve explicit levels of analysis formulations, often beyond the leader, or are multi-level in nature, the answer to the title question is “yes.” We clarify and discuss these various “no” and “yes” leadership views and implications of our work for future research and personnel and human resources management practice.