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The purpose of this paper is to explore whether and how the intellectual capital (IC) approach and concepts could be fruitfully adapted to study the smart city phenomenon from a managerial point of view.

This study is based on a long-term, in-depth ethnographic exploration of the vast global community, which is created around the smart city movement.

The analysis suggests that, in order to effectively analyse a smart city context through the IC lens, the traditional IC framework needs to be extended for: expected outcomes, which should also include sustainability, resilience and quality of life; categories of key resources, which should also include institutional capital and environmental capital; units of analysis, which should also include territorial systems, such as transportation or waste; and key managerial challenges implied. As a final result, a smart city intellectual capital (SC-IC) framework is proposed.

Most of the cases analysed in this study are European; further studies are advisable to better investigate non-European smart city contexts.

The paper suggests that the knowledge management, project portfolio management and network management approaches are crucial to better support managerial practices in smart city organizations.

The SC-IC framework allows for a clear definition of the smart city organization, as a new knowledge-based, project-oriented, network-shaped type of organization. Therefore, the SC-IC framework provides smart city research with a consistent rooting in management studies. Further, this paper contributes to the fourth stage of IC research.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

The subject of the article is the concept of augmented intelligence, which constitutes a further stage in the development of research on artificial intelligence. This is a new phenomenon that has rarely been considered in the subject literature so far, which may be interesting for the fields of social sciences and humanities. The aim is to describe the features of this technology and determine the practical and ethical problems associated with its implementation in libraries.

The method of literature review was used. Systematic searches according to specific questions were carried out using the Scopus and Web of Science scientific databases, as well as Google Scholar and the LISTA abstract database.

The results established that the issue of augmented intelligence has barely been discussed in the field of librarianship. Although this technology may be interesting as a new area of librarian research and as a new framework for designing innovative services, deep ethical consideration is necessary before this technology is introduced in libraries.

The article deals with some of the newest technologies available, and this topic is generally very rarely discussed in scientific publications in either the social sciences or humanities. Therefore, due to the limited availability of materials, the findings presented in the article are primarily of a conceptual nature. The aim is to present this topic from the perspective of librarianship and to create a starting point for further discussion on the ethical aspects of introducing new technologies in libraries.

The results can be widely used in practice as a framework for the implementation of augmented intelligence in libraries.

The article can help to facilitate the debate on the role of implementing new technologies in libraries.

The problem of augmented intelligence is very rarely addressed in the subject literature in the field of library and information science.

This study aims to show the inconsistency of the approach to the development of artificial intelligence as an independent tool (just one more tool that humans have developed); to describe the logic and concept of intelligence development regardless of its substrate: a human or a machine and to prove that the co-evolutionary hybridization of the machine and human intelligence will make it possible to reach a solution for the problems inaccessible to humanity so far (global climate monitoring and control, pandemics, etc.).

The global trend for artificial intelligence development (has been) was set during the Dartmouth seminar in 1956. The main goal was to define characteristics and research directions for artificial intelligence comparable to or even outperforming human intelligence. It should be able to acquire and create new knowledge in a highly uncertain dynamic environment (the real-world environment is an example) and apply that knowledge to solving practical problems. Nowadays artificial intelligence overperforms human abilities (playing games, speech recognition, search, art generation, extracting patterns from data etc.), but all these examples show that developers have come to a dead end. Narrow artificial intelligence has no connection to real human intelligence and even cannot be successfully used in many cases due to lack of transparency, explainability, computational ineffectiveness and many other limits. A strong artificial intelligence development model can be discussed unrelated to the substrate development of intelligence and its general properties that are inherent in this development. Only then it is to be clarified which part of cognitive functions can be transferred to an artificial medium. The process of development of intelligence (as mutual development (co-development) of human and artificial intelligence) should correspond to the property of increasing cognitive interoperability. The degree of cognitive interoperability is arranged in the same way as the method of measuring the strength of intelligence. It is stronger if knowledge can be transferred between different domains on a higher level of abstraction (Chollet, 2018).

The key factors behind the development of hybrid intelligence are interoperability – the ability to create a common ontology in the context of the problem being solved, plan and carry out joint activities; co-evolution – ensuring the growth of aggregate intellectual ability without the loss of subjectness by each of the substrates (human, machine). The rate of co-evolution depends on the rate of knowledge interchange and the manufacturability of this process.

Resistance to the idea of developing co-evolutionary hybrid intelligence can be expected from agents and developers who have bet on and invested in data-driven artificial intelligence and machine learning.

Revision of the approach to intellectualization through the development of hybrid intelligence methods will help bridge the gap between the developers of specific solutions and those who apply them. Co-evolution of machine intelligence and human intelligence will ensure seamless integration of smart new solutions into the global division of labor and social institutions.

The novelty of the research is connected with a new look at the principles of the development of machine and human intelligence in the co-evolution style. Also new is the statement that the development of intelligence should take place within the framework of integration of the following four domains: global challenges and tasks, concepts (general hybrid intelligence), technologies and products (specific applications that satisfy the needs of the market).

The purpose of this paper is to discuss a Multi-perspective approach to knowledge production in terms of a set of cybernetic concepts relevant to the approach; to describe a software system that computationally embodies the approach; and to articulate a research project that pragmatically employs the approach.

A definition is provided. The paper uses a survey methodology, exploring relevant cybernetic and contemporary technological concepts. An operational software mechanism (The Insight Engine) is discussed that enables the bridging of transdisciplinary concepts by a user in the service of accretive research –Recombinant Informatics.

Many cybernetic concepts are relevant to contemporary research into cognition and Neosentience research. More study needs to be undertaken related to historical BCL projects in terms of articulating relevance to contemporary research.

Future research seeks to extend the computational functionality of “The insight engine”, as well as uncover relevant BCL/cybernetic materials.

The software is unique in the field and already there is interest in its use by differing research communities including the Duke Institute for Brain Sciences, and at Stanford, research under Ian Hodder.

The Insight Engine has potential to be used as a multi-perspective tool for many different fields enabling different forms of distributed, transdisciplinary team-based research.

This text is valuable to researchers interested in new forms of interface, augmentation of thought and learning via computational approaches; and the development of bridges between novel research areas, including contemporary, historical BCL, and other cybernetic inquiry.

This article aims to consider what libraries might be used for after machines become smarter than humans.

The article considers how Vernor Vinge's novel Across Realtime and his essay “The coming technological singularity” inform possible library futures.

Libraries might become havens for humans left out of the high‐tech future, or museums for defunct technologies, or vanish completely.

The article expands discussion of the library's future beyond the foreseeable.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

Exploratory search activities are ubiquitous in various information systems. Much potentially useful or even serendipitous information is discovered during the exploratory search process. Given its irreplaceable role in information systems, exploratory search has attracted growing attention from the information system community. Since few studies have methodically reviewed current publications, researchers and practitioners are unable to take full advantage of existing achievements, which, in turn, limits their progress in this field. Through a literature review, this study aims to recapitulate important research topics of exploratory search in information systems, providing a research landscape of exploratory search.

Automatic and manual searches were performed on seven reputable databases to collect relevant literature published between January 2005 and July 2023. The literature pool contains 146 primary studies on exploratory search in information system research.

This study recapitulated five important topics of exploratory search, namely, conceptual frameworks, theoretical frameworks, influencing factors, design features and evaluation metrics. Moreover, this review revealed research gaps in current studies and proposed a knowledge framework and a research agenda for future studies.

This study has important implications for beginners to quickly get a snapshot of exploratory search studies, for researchers to re-align current research or discover new interesting issues, and for practitioners to design information systems that support exploratory search.