A case-based reasoning recommender system for sustainable smart city development

With the deployment of information and communication technologies (ICTs) and the needs of data and information sharing within cities, smart city aims to provide value-added services to improve citizens' quality of life. But, currently city planners/developers are faced with inadequate contextual information on the dimensions of smart city required to achieve a sustainable society. Therefore, in achieving sustainable society, there is need for stakeholders to make strategic decisions on how to implement smart city initiatives. Besides, it is required to specify the smart city dimensions to be adopted in making cities smarter for sustainability attainment. But, only a few methods such as big data, internet of things, cloud computing, etc. have been employed to support smart city attainment. Thus, this study integrates case-based reasoning (CBR) as an artificial intelligence technique to develop a recommender system towards promoting smart city planning. CBR provides suggestions on smart city dimensions to be adopted by city planners/decision-makers in making cities smarter and sustainable. Accordingly, survey data were collected from 115 respondents to evaluate the applicability of the implemented CBR recommender system in relation to how the system provides best practice recommendations and retaining of smart city initiatives. Results from descriptive and exploratory factor analyses suggest that the developed system is applicable in supporting smart city adoption. Besides, findings from this study are expected to provide valuable insights for practitioners to develop more practical strategies and for researchers to better understand smart city dimensions.