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Advanced manufacturing machines (AMMs) are searched as a momentous asset across the manufacturing societies for quenching and addressing the production units under economical circumstances, i.e. production of high-quality of goods under feasible cost. AMMs are significant in holding the managers against their rivals and competitors with high profit margins. The authors developed the decision support mechanism/portfolio (DSM-P) consist of knowledge-based cluster approach with a dynamic model. The purpose of research work is to measure overall economic worth of AMMs under objective and grey-imperfect (mixed) data by exploring the proposed DSM-P.

The authors developed the DSM-P that consist of knowledge-based cluster, three multi-criteria decision-making (MCDM) techniques-1-2-3 with complementary grey relational analysis-4(GRA), approach with a dynamic model (complied by technical plus cost and agility measures of AMMs). The proposed DSM-P enables the manager to map the overall economic worth of candidate AMMs under objective and grey-mixed data.

The presented DSM-P assist the managers for handling the selection problem of AMMs, i.e. CNCs, robots, automatic-guided vehicle, etc under mixed (objective cum grey) data. To enable the readers for intensely understand the work, the utility of proposed approach is displayed by illustrating a polar robot evaluation and selection problem. It is ascertained that the robot candidate-11 alternative is fulfilling the entire technical cum cost and agility measures.

The DSM-P provides more precise and reliable outcomes due to a usage of the dominance theory. Under the dominance theory, the ranks are obtained by MCDM techniques-1-2-3 are compared with ranks gathered by the GRA-4 under objective cum grey data, formed the novelties in presented research work. From a future perspective, the grey-based models in DSM-P can be built/extended/constructed more extensive and can be simulated by the same approach.

Starting from a critical analysis of the main criteria currently used to identify marginal areas, this paper aims to propose a new classification model of such territories by leveraging knowledge discovery approaches and knowledge visualization techniques, which represent a fundamental pillar in the knowledge-based urban development process.

The methodology adopted in this study relies on the design science research, which includes five steps: problem identification, objective definition, solution design and development, demonstration and evaluation.

Results demonstrate how to exploit knowledge discovery and visualization to obtain multiple mappings of inner areas, in the aim to identify good practices and optimize resources to set up more effective territorial development strategies and plans. The proposed approach overcomes the traditional way adopted to map inner areas that uses a single indicator (i.e. the distance between a municipality and the nearest pole where it is possible to access to education, health and transportation services) and leverages seven groups of indicators that represent the distinguishing features of territories (territorial capital, social costs, citizenship, geo-demography, economy, innovation and sustainable development).

The proposed model could be enriched by new variables, whose value can be collected by official sources and stakeholders engaged to provide both structured and unstructured data. Also, another enhancement could be the development of a cross-algorithms comparison that may reveal useful to suggest which algorithm can better suit the needs of policy makers or practitioners.

This study sets the ground for proposing a decision support tool that policy makers can use to classify in a new way the inner areas, thus overcoming the current approach and leveraging the distinguishing features of territories.

This study shows how the availability of distributed knowledge sources, the modern knowledge management techniques and the emerging digital technologies can provide new opportunities for the governance of a city or territory, thus revitalizing the domain of knowledge-based urban development.

In the landscape created by digital transformation, developing the ability to adapt and innovate by absorbing and generating new knowledge has become a strategic priority for organizations. The theory of dynamic capabilities, especially from a knowledge-based perspective, has proven particularly useful in studying the phenomena of transformation and change. Moving from this premise, this paper aims to map the state of research and to define guidelines for the actualization of dynamic capabilities theory in the digital transformation era.

A structured literature review of 75 papers, using descriptive, bibliographic and content analysis, was performed to analyze the evolution of dynamic capabilities in the context of digital transformation.

Studies concerning knowledge-based dynamic capabilities for digital transformation have been clustered into five main research areas: the micro-foundation of dynamic capabilities for digital transformation; dynamic capabilities for value creation in digital transformation; dynamic capabilities for digital transition in specific industries; dynamic capabilities for “data-driven organizations”; and dynamic capabilities for digital transformation in SMEs and family firms. A future research agenda for scholars in strategic management is presented.

A conceptual framework and a future research agenda are presented to highlight directions for this promising research field concerning the renewal of dynamic capabilities in the context of digital transformation.

The originality of the paper lies in the conceptual framework aiming to systematize current research on knowledge-based dynamic capabilities for digital transformation and to provide a new conceptualization of digital dynamic capabilities, clarifying how organizations create and share knowledge in the era of digitalization.

Several manuscripts are adopting knowledge-based dynamic capabilities (KBDCs) as their main theoretical lens. However, these manuscripts lack consistent conceptualization and systematization of the construct. Consequently, the purpose of this study is to advance the understanding of KBDCs by clarifying the dominant concepts at the junction of knowledge management and dynamic capabilities domains, identifying which emerging themes are gaining traction with KBDCs scholars, demonstrating how the central thesis around KBDCs has evolved and explaining how can KBDCs scholars move towards finding a mutually agreed conceptualization of the field to advance empirical assessment.

The Clarivate Analytics Web of Science Core Collection database was used to extract 225 manuscripts that lie at the confluence of two promising management domains, namely, knowledge management and dynamic capabilities. A scientometric analysis including co-citation analysis, bibliographic coupling, keyword co-occurrence network analysis and text mining was conducted and integrated with a systematic review of results to facilitate an unstructured ontological discovery in the field of KBDCs.

The co-citation analysis produced three clusters of research at the junction of knowledge management and dynamic capabilities, whereas the bibliographic coupling divulged five themes of research that are gaining traction with KBDCs scholars. The systematic literature review helped to clarify each clusters’ content. While scientific mapping analysis explained how the central thesis around KBDCs has evolved, text mining and keyword analysis established how KBDCs emerge from the combination of knowledge management process capabilities and dynamic capabilities.

Minimal attention has been paid to systematizing the literature on KBDCs. Accordingly, KBDCs view has been investigated through complementary scientometric methods involving machine-based algorithms to allow for a more robust, structured, comprehensive and unbiased mapping of this emerging field of research.

The purpose of this article is to investigate the engineering of creative urban regions through knowledge‐based urban development. In recent years city administrators realised the importance of engineering and orchestrating knowledge city formation through visioning and planning for economic, socio‐cultural and physical development. For that purpose a new development paradigm of “knowledge‐based urban development” is formed, and quickly finds implementation ground in many parts of the globe.

The paper reviews the literature and examines global best practice experiences in order to determine how cities are engineering their creative urban regions so as to establish a base for knowledge city formation.

The paper sheds light on the different development approaches for creative urban regions, and concludes with recommendations for urban administrations planning for knowledge‐based development of creative urban regions.

The paper provides invaluable insights and discussion on the vital role of planning for knowledge‐based urban development of creative urban regions.

The purpose of this paper is to develop a novel hybrid multi-criteria decision-making (MCDM) model to help organizations select their knowledge-based strategy effectively. Knowledge management (KM) initiatives are often started with the selection of a strategy, which is a critical decision for a successful KM implementation.

KM initiatives are often started with the selection of a strategy, which is a critical decision for a successful KM implementation. Thus, the aim of this paper is to develop a novel hybrid MCDM model to help organizations select their knowledge-based strategy effectively.

Results illustrate that the proposed model is efficient to consider the complex interactions among criteria and provides a consistent decision with less pair-wise comparisons. Furthermore, a case study indicates that a “codification versus tacitness” strategy is preferred over other strategies considering nine main domain criteria.

The contribution of this paper is threefold: it addresses the gaps in KM literature on the effective and efficient assessment of KM strategy selection; it provides a comprehensive and systematic framework that combines analytic network process (ANP) and consistent fuzzy preference relations (CFPR) to assess KM implementation strategy; and it illustrates a real-world study to exhibit the applicability of the proposed approach and the efficacy of the framework.

The purpose of this paper is to focus on one of the most recognized knowledge management enablers, information technology (IT), and explores how IT influences on knowledge base capabilities in product development – specifically knowledge exploitation and exploration.

The proposed hypotheses are empirically tested through the analysis of 80 product developments, and valid and reliable measures for each variable are developed. The research methodology uses a two‐step approach.

The study shows that product development processes can be categorized within three IT configurations: balanced IT configuration, convergent‐based IT configuration and divergent‐based IT configuration. These results show that differences in IT configurations in product development may lead to differences in terms of knowledge exploitation and show the advantages of the balanced IT configuration that combines both dimensions of IT.

The sample size is not large and the scope of this study is limited to firms located in the Madrid area, Spain. The measurement effort represents an advance for research but, nonetheless, they measure facts that are neither fully nor easily measurable. All data were collected from the same respondent, using the same perceptual measurement technique.

This paper suggests that product development managers would be advised to consider knowledge based goals when selecting their IT tools. It also highlights that product development efforts should not involve an excluding trade‐off between the convergent and the divergent dimensions of IT (whereby one is at the expense of the other), but a balance of both dimensions.

The paper differs from previous research in a number of important ways. First, it focuses on benefits to product development. Second, it measures the impact of IT on knowledge base capabilities. Third, it focuses on the configuration of IT and an evaluation of how different configurations impact on different types of knowledge based capabilities.

This paper aims to assess the dissemination of cluster ideas advanced in the “Competitive Advantage of Nations” and three subsequent national studies and the reasons for their substantial public policy impact in Norway.

The paper presents the theoretical and methodological novelties of each of the national studies, the inclusive study-organizing principle used and public policy impact.

The papers finds that the dissemination of cluster thinking and the development of a successful cluster-based industrial policy in Norway is largely a function of the nature and extent of the cluster research efforts that took place in Norway. The national cluster research projects mobilized all the key industrial and governmental actors in a very effective way, making studies with both rigor and relevance. Due to advanced and demanding policy makers, the studies also evolved in terms of the theoretical models and methodologies used.

The paper contributes by illuminating the direct and indirect impact of the “Competitive Advantage of Nations” on both academic endeavors and public policies in Norway and by explicating how studies that make it possible for academics and practitioners to work in tandem substantially affect public policy.

This study is intended to work out a bottleneck in the comprehension of the relational nexus which links the set of key strategic resources (SRs) of a company, represented by the uncertain recognition and the ambiguous clustering of their intangible components. The purpose of this paper is to provide a candidate solution for a rational appraisal of the inventory of the knowledge-based resources held by a company, which synergically form its Intellectual Capital (IC).

This goal is achieved by the means of a qualitative/quantitative approach composed of sequential phases, intended to: atomize the value domain of the firm into its basic building blocks; gauge their mutual interactions and impacts; re-aggregate those involved entities accordingly; cluster them into a collection of identified and validated Intangible Assets (IAs). Never giving any direct judgment on the IAs themselves (whose extension can be fuzzy or unknown). But on the impacts between the value drivers they are built on.

The proposed procedure, step-by-step illustrated by means of a numerical simulation, out of the amorphous mass of the SRs, returns an analytic picture of its composing elements keeping track of their intertwined connections and mutual influence. Consequently, allowing the comprehension of the actual framing and of the relational positioning and magnitude of such entities.

This risk-mitigated rational identification of IAs allows the analyst to target a proper evaluation technique on them. And the management of the company to mindfully allocate/leverage on them to improve business performance and strategy alignment. The implementation returns some analytic tools which render a diagnostic snapshot of the composing elements of the IC, increasing the awareness of such entities and allowing internal/external benchmarking.

The suggested methodology mitigates the risk of discretionality in the definition of the perimeter of each target-entity, by avoiding any direct biased judgment on them. So that each asset gets unambiguously identified within a network-logic and the interlinked portfolio of knowledge-based resources can be assessed and managed in an rational and traceable way.

This paper aims to identify, evaluate and integrate the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. To achieve this aim, the authors use systematic literature review (SLR) as a powerful method to collect and critically analyze the research papers. Also, the authors discuss the selected recommender systems and its main techniques, as well as their benefits and drawbacks in general.

In this paper, the SLR method is utilized with the aim of identifying, evaluating and integrating the findings of all relevant and high-quality individual studies addressing one or more research questions about recommender systems and performing a comprehensive study of empirical research on recommender systems that have been divided into five main categories. Also, the authors discussed recommender system and its techniques in general without a specific domain.

The major developments in categories of recommender systems are reviewed, and new challenges are outlined. Furthermore, insights on the identification of open issues and guidelines for future research are provided. Also, this paper presents the systematical analysis of the recommender system literature from 2005. The authors identified 536 papers, which were reduced to 51 primary studies through the paper selection process.

This survey will directly support academics and practical professionals in their understanding of developments in recommender systems and its techniques.