**4. Results**

In general, the understanding and acceptance of the targeted group of experts in this study were contested. This shows that the community has different perceptions of the smart city domains stated in the MSCF. This divergent phenomenon can be described in two ways. Firstly, from the domain perspective, the majority of domains (i.e., smart economy, living, people, and governance) were accepted, two domains (i.e., smart environment and digital infrastructure) were rejected, while the smart mobility domain was partially accepted. Secondly, from the objective perspective, more than half of the domains were accepted (Table 10).


**Table 10.** Results of Fuzzy Delphi analysis by smart city domains.

Note: U stands for Understanding, A stands for Acceptance. Three conditions to accept an item: threshold value (*d*) ≤ 0.2, percentage of experts' consensus ≥ 75%, and average fuzzy score (*Amax*) ≥ α − cut value = 0.5.

> To accept the criteria of the Fuzzy Delphi analysis, the results must meet three conditions: (a) threshold value, *d* ≤ 0.2, (b) expert agreement percentage ≥75%, and (c) average fuzzy score (*Amax*) ≥ α − cut value = 0.5. Overall, all the domains fulfilled the third criteria, with fuzzy scores equal to or exceeding 0.5. Meanwhile, the threshold value and expert agreement showed mixed results.

> To provide more detail on the item results, as shown in Table 11, the smart economy and living had a 100% acceptance rate for the objective of Acceptance, hinting that these two domains can be implemented directly at ground level with little modification. On the other hand, the smart environment scored the lowest acceptance rates, 22.22% for the Understanding objective and 33.33% for the Acceptance objective. This result indicates that the smart environment domain has experienced great public dissensus and more refinement is needed before its implementation to avoid later failures.


**Table 11.** Results of Fuzzy Delphi analysis by objectives.

Note: Refer Appendix B for detailed calculations.

In general, the results of the analysis on the smart economy, living, people, and governance domains met all three conditions of the Fuzzy Delphi method in terms of Understanding and Acceptance. However, some item details must be addressed (refer to Appendix B).

First, for the Understanding objective of the smart economy, the two rejected items were items 3 (high value-added industry investment, with threshold value *d* = 0.21, and expert agreement at only 33%) and 7 (assistance to business operations, with 73% expert agreement). For the Acceptance objective of the smart economy, all the items were ac-

cepted. For the high value-added industry investment, the respondents did not arrive at a consensus. Some thought that the authorities should focus on the manufacturing sector, especially in suburban and rural areas, instead of prioritizing high value-added industry, which would accelerate the existing urbanization issues in metropolitan Malaysia, such as in Kuala Lumpur and the Klang Valley area.

Second, under smart living, the only problematic Understanding item was item 1 (crime reduction). Respondents were less able to comprehend why Malaysia was stated as having a high, instead of moderate, crime rate, since most of them lived in peaceful environments. Meanwhile, they were inclined to accept that the MSCF would be able to reduce the crime rate effectively through ICT applications, such as the installation of CCTV in public areas.

Third, for the understanding and acceptance of smart people, all four rejected items were due to the 70% to 73% expert agreement. For item 3, the acceptance of the education policy for human capital development, respondents were not fully confident that the restructuring of education at the tertiary level would produce innovative graduates. One respondent commented that the current graduate market indicated that graduates were able to perform at routine levels while lacking innovative thinking and solution-creation skills.

Fourth, for the understanding and acceptance of smart governance, item 3—intergovernmental data sharing—was the only item rejected as the threshold value *d* = 0.224 and 0.202. Respondent feedback suggested that they did not understand how intergovernmental data could be shared in practice, as some were still experiencing issues such as the separate performance of departments, the redundancy of providing data to particular departments, and the inability to receive valid and complete data through a single department enquiry. For example, the Department of Statistics does not provide open demographic data by city or district level so one needs to go to the particular local authorities.

The major focus of this study should be the smart environment and digital infrastructure domains because both were rejected in terms of the understanding and acceptance objectives. In general, for the environment, its threshold (*d*) construct for Acceptance (0.212) was more than 0.2 while both values of expert agreement (57% for Understanding and 55% for Acceptance) were less than 75%. For digital infrastructure, its threshold (*d*) construct for Understanding (0.204) was also more than 0.2 while both values of expert agreement (72% for Understanding and 74% for Acceptance) were also less than 75%. These negative results show that the public remain less likely to understand and accept the components planned in these two domains, smart environment, and digital infrastructure.

In detail, for the smart environment, the three lowest-ranked Understanding items related to items 1 (park and green area management), 8 (non-revenue water management and reporting), and 9 (low-carbon city and carbon emissions). Meanwhile, the three lowest-ranked Acceptance items related to items 7 (readiness towards disaster-resilient cities), 4 (air quality monitoring) and 2 (waste segregation and recycling). From the overall perspective, the environment-related issues worrying the public are broad in scope and a cause for grave alarm. The smart environment domain facings major public understanding and acceptance issues and the authorities should prioritize improvements in this domain.

For the smart digital infrastructure, two items of interest in terms of Understanding are items 6 (cybersecurity) and 5 (personal data protection); for Acceptance, they are items 1 (roles of service providers) and 2 (internet speed). It seems that respondents lacked confidence in the authority's online system security and personal data protection, and felt they were vulnerable to cyber-attacks and personal data leaks. Attention should also be given to the respondents who did not fully accept that private service providers were solely responsible and thought that the government was too. Another important issue involved rural areas with low internet speeds of 4G and below.

For the smart mobility, the result was accepted for Understanding but rejected for Acceptance. The acceptance of respondents was rejected since the threshold (*d*) conduct was 0.245, which is over the 0.2 required; furthermore, the expert agreement of 56% was much less than the 75% required.

Clearly, the rejection phenomenon identified for the Acceptance objective needs attention. A low level of expert agreement was observed for items 6 (electric vehicle), 1 (smart traffic management), 8 (public transport application), and 5 (smart parking infrastructure). These results showed that the respondents were worried about the traffic planning presented in the MSCF and were unconvinced by the solutions related to the issues stated above.
