Knowledge-city index construction: An intellectual capital perspective

Ruiz, Víctor Raúl López; Navarro, José Luis Alfaro; Peña, Domingo Nevado

doi:10.1016/j.eswa.2014.02.007

Cited by 73 publications

(31 citation statements)

References 34 publications

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“…There have been increasing demands to construct indicators that can assess urban sustainability, and many lists of urban sustainability indicators have been provided [6][7][8][9]. For example, Shen et al examined and compared nine different lists of urban sustainability indicators used for nine regions/cities, and derived a primary list of urban sustainability indicators on a comparative basis [10].…”

Section: Sustainability and The Transdisciplinary Approachmentioning

confidence: 99%

Visualization of a City Sustainability Index (CSI): Towards Transdisciplinary Approaches Involving Multiple Stakeholders

et al. 2015

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Abstract:We have developed a visualized 3-D model of a City Sustainability Index (CSI) based on our original concept of city sustainability in which a sustainable city is defined as one that maximizes socio-economic benefits while meeting constraint conditions of the environment and socio-economic equity on a permanent basis. The CSI is based on constraint and maximization indicators. Constraint indicators assess whether a city meets the necessary minimum conditions for city sustainability. Maximization indicators measure the benefits that a city generates in socio-economic aspects. When used in the policy-making process, the choice of constraint indicators should be implemented using a top-down approach. In contrast, a bottom-up approach is more suitable for defining maximization indicators because this technique involves multiple stakeholders (in a transdisciplinary approach). Using different materials of various colors, shapes, sizes, we designed and constructed the visualized physical model of the CSI to help people evaluate and compare OPEN ACCESSSustainability 2015, 7 12403 the performance of different cities in terms of sustainability. The visualized model of the CSI can convey complicated information in a simple and straightforward manner to diverse stakeholders so that the sustainability analysis can be understood intuitively by ordinary citizens as well as experts. Thus, the CSI model helps stakeholders to develop critical thinking about city sustainability and enables policymakers to make informed decisions for sustainability through a transdisciplinary approach.

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Section: Sustainability and The Transdisciplinary Approachmentioning

confidence: 99%

Visualization of a City Sustainability Index (CSI): Towards Transdisciplinary Approaches Involving Multiple Stakeholders

et al. 2015

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“…The City Intellectual Capital approach assumes that the traditional dimensions of intellectual capital (human, relational, and structural capital) can help leverage knowledge resources and innovation capacity (Schiuma and Lerro ) to address specific threats and opportunities in city systems, where dimensions such as quality of life, level of participation, identity, and vision assume particular relevance. Several authors have proposed city intellectual capital models: 1) city competitiveness (Schiuma and Lerro ); 2) city's intellectual capital framework (Uziene ); 3) knowledge‐based model of cities (López‐Ruiz, Alfaro‐Navarro, and Nevado‐Peña ); and 4) a management‐oriented view of the smart city (Dameri and Ricciardi ).…”

Section: Intelligent and Smart Citiesmentioning

confidence: 99%

“…Many models have been developed to analyze and measure smart city‐related concepts, such as “Smart City Intellectual Capital” (Dameri and Ricciardi ), “City's Knowledge‐Based Capital” (Schiuma and Lerro ), “City's Intellectual Capital Framework” (Uziene ), “Knowledge City Index” (López‐Ruiz, Alfaro‐Navarro, and Nevado‐Peña ), “Happy City Index” (http://www.happycity.org.uk/), “Life‐satisfaction ranking” (Oswald and Wu ), “European Smart City Model” (Giffinger et al ), “Urban Audit” (http://ec.europa.eu/regional_policy/index.cfm/en/policy/themes/urban-development/audit/), “Business Excellence Attraction Composite Index” (Murillo, Romaní, and Suriñach ), “Global Cities Index” (http://www.foreignpolicy.com), “The Coverage Index” (Neirotti et al ), and “Quality of life and business environment” (Chen and Rosenthal ), among many other models developed by other researchers and international organizations around the world. Whereas all these models vary in depth and breadth, it is well beyond the scope of this particular paper to discuss, compare, assess, sort, or facilitate a choice among alternatives models.…”

Section: Intelligent and Smart Citiesmentioning

confidence: 99%

Toward a Resource‐Based View of City Quality: A New Framework

Carvalho¹,

Costa²,

Marnoto³

et al. 2018

Growth and Change

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Based on an extensive literature review on intelligent cities, smart cities, and happy cities, and on their conceptual connections with citizens' well‐being, quality of life, and happiness, we developed a resource‐based view on City Quality: the PESNAT (political, economic, social, natural, artificial, and technological) framework. The concept of City Quality rests on the idea of cities interconnected sub‐habitats—PESNAT—which are powerful analytical categories needed for understanding cities as complex and intricate loci. This framework eventually aims at assessing the cities' power to attract businesses and people, to contribute to a sustainable development of the city and an increased quality of life. Furthermore, two hypotheses are outlined regarding the level of importance of each sub‐habitat in relation to happiness, and the level of controversy of each one for citizens, city planners, and decision makers.

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“…From the market's point of view, a company's global objective is to maintain a position in the market. In a knowledge-based economy, increasing the company's intellectual capital is a primary element of this strategy (López-Ruiz et al, 2014;Nemetz, 2006). Moreover, from the knowledge perspective, the organization's knowledge worker competences and any related core competences are an important part of intellectual capital (Ulrich, 1998).…”

Section: Knowledge Workermentioning

confidence: 99%

Knowledge workers’ collaborative learning behavior modeling in an organizational social network

Różewski¹,

Jankowski²,

Bródka³

et al. 2015

Computers in Human Behavior

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Computations related to learning processes within an organizational social network area require some network model preparation and specific algorithms in order to implement human behaviors in simulated environments. The proposals in this research model of collaborative learning in an organizational social network are based on knowledge resource distribution through the establishment of a knowledge flow. The nodes, which represent knowledge workers, contain information about workers' social and cognitive abilities. Moreover, the workers are described by their set of competences, their skill level, and the collaborative learning behavior that can be detected through knowledge flow analysis. The proposed approach assumes that an increase in workers' competence is a result of collaborative learning. In other words, collaborative learning can be analyzed as a process of knowledge flow that is being broadcast in a network. In order to create a more effective organizational social network for co-learning, the authors found the best strategies for knowledge facilitator, knowledge collector, and expert roles allocation. Special attention is paid to the process of knowledge flow in the community of practice. Acceleration within the community of practice happens when knowledge flows more effectively between community members. The presented procedure makes it possible to add new ties to the community of practice in order to influence community members' competences. Both the proposed allocation and acceleration approaches were confirmed through simulations.Keywords: organizational social network, computational models, collaborative learning behavior, knowledge flow, community of practice . to maximize worker knowledge level (over a planning horizon) through sharing in different organizational environments was presented by Dong et al. (2012). In this approach, organizations that support multiple skills and have workers with varying levels of knowledge in these skills were examined. The algorithm developed identified the best set of knowledge transfers in each period in order to maximize the total weighted knowledge level of a given organization over a planning horizon. As a result, the mixed integer programming model and its related heuristics were formulated to facilitate the systematic analysis and understanding of effective knowledge flows. Neither of these approaches included a community-of-practice component or roles in the knowledge flow.Depending on the analysis concept, there are different approaches to collaboration network analysis (Różewski, 2010). The queuing theory can be used to efficiently optimize a telecommunications network (Różewski and Ciszczyk, 2009). In such a situation, the node represents various computer stations that are able to signal regeneration or data distribution.Another approach to collaboration network analysis is from the workflow point of view (Wang et al., 2006). In this context the network's unit is a task and we are looking for workload optimization. Moreover, the nodes correspond to work...

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Knowledge-city index construction: An intellectual capital perspective

Cited by 73 publications

References 34 publications

Visualization of a City Sustainability Index (CSI): Towards Transdisciplinary Approaches Involving Multiple Stakeholders

Visualization of a City Sustainability Index (CSI): Towards Transdisciplinary Approaches Involving Multiple Stakeholders

Toward a Resource‐Based View of City Quality: A New Framework

Knowledge workers’ collaborative learning behavior modeling in an organizational social network

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