Smart city development in Hong Kong

Purpose This paper aims to discover why such a public partnership project had been successful with a non-profit third-party alliance such as a smart city consortium (SCC) promoting smart city development. Design/methodology/approach This descriptive case study is primarily based on analysing data collected from various texts, public statements, media interviews and three semi-structured interviews with key members involved in the Covid-19 dashboard project. Findings The data and analysis reviews that both interpersonal and interorganisational trust, dedication and proactiveness of the leaders at SCC were major contributing factors to why SCC was able to partner with the Hong Kong Government in the Covid-19 dashboard in the first place and that the success was also a direct outcome of effective mass collaborative knowledge management activities. Research limitations/implications The research in leadership attributes and activities in the non-profit alliance has been few and this collaborative partnership between the alliance and the government is an example of the importance of further research in smart city leadership. Practical implications In deploying projects for mass collaboration and knowledge sharing in smart city development (which is multi-disciplinary in nature). there are still many new and evolving organisational practices and leadership matters that many business leaders and city managers can learn from. Social implications Smart city development projects involve the notion of sharing data in an open environment enabled by software and mediating tools. Successful projects such as this Hong Kong Covid-19 dashboard which serves a diverse audience can further promote the importance of an open data policy regime for the benefit of the public. Originality/value This case study covers a highly original and unique case study with the leaders at the SCC and representatives from the Hong Kong Government.

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Unmanned Shop Laboratory: An Extensible and Scalable Research, Teaching and Learning Smart Space System : Project S-SHOP@TMIT in Response to Emerging Challenges for Hong Kong Smart City Development

2020 IEEE 3rd International Conference on Electronics Technology (ICET) ◽

10.1109/icet49382.2020.9119529 ◽

2020 ◽

Cited By ~ 1

Author(s):

Peter ChunYu Yau ◽

Woo Hok Luen ◽

Dennis Wong ◽

Joe Wai Kin Kan

Keyword(s):

Hong Kong ◽

Smart City ◽

Teaching And Learning ◽

Smart Space ◽

City Development ◽

Space System ◽

System Project

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Potential pitfalls of smart city development: A study on parking mobile applications (apps) in Hong Kong

Telematics and Informatics ◽

10.1016/j.tele.2018.04.004 ◽

2018 ◽

Vol 35 (6) ◽

pp. 1580-1592 ◽

Cited By ~ 7

Author(s):

Ruiqu Ma ◽

Patrick T.I. Lam ◽

C.K. Leung

Keyword(s):

Hong Kong ◽

Mobile Applications ◽

Smart City ◽

City Development

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A Review of Human Mobility Research Based on Big Data and Its Implication for Smart City Development

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010013 ◽

2020 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

Anqi Wang ◽

Anshu Zhang ◽

Edwin H. W. Chan ◽

Wenzhong Shi ◽

Xiaolin Zhou ◽

...

Keyword(s):

Big Data ◽

Hong Kong ◽

Smart City ◽

Data Analytics ◽

Human Mobility ◽

Management Policy ◽

Urban Systems ◽

City Management ◽

City Development ◽

Quality Of Data

Along with the increase of big data and the advancement of technologies, comprehensive data-driven knowledge of urban systems is becoming more attainable, yet the connection between big-data research and its application e.g., in smart city development, is not clearly articulated. Focusing on Human Mobility, one of the most frequently investigated applications of big data analytics, a framework for linking international academic research and city-level management policy was established and applied to the case of Hong Kong. Literature regarding human mobility research using big data are reviewed. These studies contribute to (1) discovering the spatial-temporal phenomenon, (2) identifying the difference in human behaviour or spatial attributes, (3) explaining the dynamic of mobility, and (4) applying to city management. Then, the application of the research to smart city development are scrutinised based on email queries to various governmental departments in Hong Kong. The identified challenges include data isolation, data unavailability, gaming between costs and quality of data, limited knowledge derived from rich data, as well as estrangement between public and private sectors. With further improvement in the practical value of data analytics and the utilization of data sourced from multiple sectors, paths to achieve smarter cities from policymaking perspectives are highlighted.

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