“…Thus, based on the smart city domains and after designing two objectives of understanding and acceptance, future studies could explore other qualitative or quantitative methods to justify the results in this study. Other studies that evaluate the implementation of the smart city domain objectives could be conducted, such as using structural equation modelling to assess the implementation of smart city strategies in Greece [139] and acceptance of smart meters in Malaysia [37]. Moreover, future studies could be expanded to capture the voices and perspectives of the general public on national and local smart city strategy and initiatives.…”
Whilst a plethora of research exists on the smart cities and project performance evaluations, only few studies have focused on the smart city policy evaluation from the perspective of its acceptance by practitioners. This paper aims to generate insights by evaluating the smart city policy through a developing country case study—i.e., Malaysia. This study employed a questionnaire survey method for data collection and analyzed the data by using Fuzzy Delphi analysis. A group of 40 practitioners was gathered in a focus group discussion through purposive sampling. The main objectives of this survey were to identify the understanding and acceptance levels of the seven smart city domains and respective strategies that are outlined in the Malaysian Smart City Framework. The results disclosed that the practitioners possessed divergent levels of understanding and acceptance in terms of smart city domains. The study participant practitioners accepted all understanding and acceptance objectives of smart economy, living, people, and governance domains (expert agreement 75–92% and threshold d value 0.123–0.188), but rejected all objectives for both smart environment and digital infrastructure domains (expert agreement 55–74% and threshold d value 0.150–0.212). Along with this, acceptance of smart mobility was also rejected (expert agreement 56% and threshold d value 0.245). The findings reveal that considering all opinions expressing dissensus is essential when building more inclusive smart city strategies. This study contributes to the smart city discourse as being one of the first in capturing professional practitioners’ understanding and acceptance on a national level smart city policy by applying the Delphi method in the smart city context. Most importantly, the study informs urban policymakers on how to capture the voices and perspectives of the general public on national and local smart city strategy and initiatives.
“…Thus, based on the smart city domains and after designing two objectives of understanding and acceptance, future studies could explore other qualitative or quantitative methods to justify the results in this study. Other studies that evaluate the implementation of the smart city domain objectives could be conducted, such as using structural equation modelling to assess the implementation of smart city strategies in Greece [139] and acceptance of smart meters in Malaysia [37]. Moreover, future studies could be expanded to capture the voices and perspectives of the general public on national and local smart city strategy and initiatives.…”
Whilst a plethora of research exists on the smart cities and project performance evaluations, only few studies have focused on the smart city policy evaluation from the perspective of its acceptance by practitioners. This paper aims to generate insights by evaluating the smart city policy through a developing country case study—i.e., Malaysia. This study employed a questionnaire survey method for data collection and analyzed the data by using Fuzzy Delphi analysis. A group of 40 practitioners was gathered in a focus group discussion through purposive sampling. The main objectives of this survey were to identify the understanding and acceptance levels of the seven smart city domains and respective strategies that are outlined in the Malaysian Smart City Framework. The results disclosed that the practitioners possessed divergent levels of understanding and acceptance in terms of smart city domains. The study participant practitioners accepted all understanding and acceptance objectives of smart economy, living, people, and governance domains (expert agreement 75–92% and threshold d value 0.123–0.188), but rejected all objectives for both smart environment and digital infrastructure domains (expert agreement 55–74% and threshold d value 0.150–0.212). Along with this, acceptance of smart mobility was also rejected (expert agreement 56% and threshold d value 0.245). The findings reveal that considering all opinions expressing dissensus is essential when building more inclusive smart city strategies. This study contributes to the smart city discourse as being one of the first in capturing professional practitioners’ understanding and acceptance on a national level smart city policy by applying the Delphi method in the smart city context. Most importantly, the study informs urban policymakers on how to capture the voices and perspectives of the general public on national and local smart city strategy and initiatives.
“…The new smart city trend in which sustainable cities are projected to be supported by all technological developments in clean energy [ 69 , 70 ]. A high quality of life for citizens, automated management, green industry, sustainable habitats, optimal management of public resources and participatory management [ 71 , 72 ]among others are the new paradigms of the city brand. Finally, there are few studies that examine disruptive changes at the technological, social, political and economic levels in the evolution of future cities and their involvement with the city brand [ 47 ].…”
Section: Future Research Agendamentioning
confidence: 99%
“…The new smart city trend [ 67 ] in which sustainable cities [ 69 , 70 ] are projected to be supported by all technological developments in clean energy. A high quality of life for citizens, automated management, green industry, sustainable habitats, optimal management of public resources and participatory management [ 71 , 72 ], pandemic crisis [ 73 ]. among others are the new paradigms of the city brand.…”
“…More specifically, the very small Municipalities correspond to 15% of the sample (15% in the total of the Greek Municipalities), the small Municipalities to 36% (35% in the total of the Greek Municipalities), the medium Municipalities to 33% (34% in the total of the Greek Municipalities) and the large Municipalities at 15% (16% in the total of the Greek Municipalities). Table 3 presents four categories of the Municipalities that were created based on the related research article [2] . Each category may have 2 or more population classes since there is significant variance in the population coverage.…”
The dataset was formulated through field research in the Greek Municipalities. The survey was undertaken with an online questionnaire (available in Greek) and the contact person was the Chief Digital Officer or a person with similar responsibilities. The final sample is 252 out of a total of 325 Municipalities. Each respondent had to answer to 26 questions, including sub-questions, of different types (1) 5-point Likert scale, (2) rating scale, (3) closed type “Yes/no”, (4) open-ended and (5) dropdown, and, depending on them, there are 3 types of variables (1) ordinal, (2) nominal, and (3) scale variables. The concepts included in the questionnaire are a) the level of integration and use of digital technologies, b) the difficulties and the challenges municipality authorities face when trying to implement a smart strategy and c) the level of diverse collaborations and partnerships which are necessary in order to develop a strategy. The data can be used to analyse the strategic capabilities of the Greek Municipalities under the three concepts evaluated in the questionnaire. The data's primary beneficiaries include researchers, public authorities, digital platforms entrepreneurs, smart city specialists and smart city entrepreneurs, as they can use the dataset to develop models against already known results, to identify factors important in an urban strategy and to further study the correlations between the different factors, design and draft an urban strategy on real data.
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