Managing knowledge in the context of smart cities

In recent years, the concept of smart cities has become more and more popular in scientific literature and international policies. Today, more than half of the world’s population (55%) of the world’s population lives in urban areas, a figure that is expected to rise to 68% by 2050. This exponential urbanisation growth in cities around the world resulted in growing challenges in socioeconomic, environmental and governance domains. Meeting the evolving needs of changing demographic while optimising natural resources utilisation, poses significant challenging to public sector planners and decision-makers. This necessitates a shift into a ‘smart way’ to enable both financially and environmentally sustainable transformation of cities infrastructure without compromising the quality of life of citizens. As public sector organisations try to meet this complex challenge, they need to be innovative. This often calls for the creation, use, capture and exploitation of new knowledge. Within the knowledge economy, the management of knowledge is increasingly considered an important source of sustainable competitive advantage. However, previous studies on smart cities have predominantly focused on technological advancement of a cities hard infrastructure systems, overlooking the managerial dynamics and knowledge management (KM) challenges underlying the development of smart city projects. Effective deployment of knowledge management in smart city contexts remains relatively underexplored in existing literature, indicating a gap in understanding and implementation. Therefore, there is an urgent need for more research and strategic approaches to integrate knowledge management effectively into smart city development processes. Therefore, the aim of this research is to explore the adoption knowledge management in the context of smart cities so as to improve its competitiveness. A mixed-methods approach was adopted to collect and analyse data based on 97 online questionnaire surveys and 15 interviewees from seven organisations involved in smart cities initiatives. The research started with a purposive sampling method that was later adapted to snowball. An online questionnaire survey and semi-structured interview were selected as the data collection tools. SPSS was utilised to analyse the quantitative data gathered from the questionnaire, while content analysis was employed to acquire an in-depth knowledge of the interviews. The study used descriptive statistical analysis and the t-test to compare equality of mean responses between public and private sector organisations. A framework was developed as the output of the research findings. To improve knowledge sharing and to prevent knowledge loss were amongst the main drivers that fuelled the implementation of KM initiatives in the context of smart cities. Lack of senior management trust on the importance of KM principles to support successful implementation of smart cities initiatives is one of the key challenges to adopt KM strategies in the context of smart cities. Whilst, improved worker productivity and increased innovation are among the key benefits of integrating KM in smart cities projects. This study concluded that effective adoption of KM in smart cities initiatives requires a change in the organisational culture to promote knowledge sharing and use. The findings of this research provide valuable insights that can help the public sector organisations decision-makers involved in smart cities projects to implement knowledge management strategies and initiatives to improve their competitiveness.

Citation

Abdalla, W. (2025) Managing knowledge in the context of smart cities. University of Wolverhampton. https://wlv.openrepository.com/handle/2436/625939

Publisher

University of Wolverhampton

URI

https://wlv.openrepository.com/handle/2436/625939

Type

Thesis or dissertation

Language

en

Description

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.

Collections

Theses and Dissertations