1 Introduction

The digital revolution has transformed policy practice, public service delivery, and the function of governments more generally. Advancements in information and communications technology (ICT) support applications like big data platforms for urban analytics (Ju et al., 2018; Shin, 2016), smartphone-based solicitation of public feedback (Lin, 2018; Moran & Bui, 2019), and 5G wireless networks and services (Frias & Martínez, 2018; Rendón Schneir et al., 2018). Such innovations substantially impact economic growth (Vu et al., 2020) and urban spatial structure (Yousefi & Dadashpoor, 2020). When focusing on public sector efficiency and responsiveness in urban settings, these often disparate technologies have coalesced into the concept of ‘smart cities’—defined by Vu and Hartley (2018) as “the institutionalized and integrated application of smart technologies with a digital age mindset to the tasks and challenges of urban management” (p. 849). Examples are the ‘Smarta Kartan’ (‘smart map’) citizen sharing initiative in Gothenburg, Sweden, internet-of-things (IoT) sensors to improve parking services in Seoul, South Korea, and 3D simulations to assist urban planning in Singapore. Given the rise of public and private investment in smart cities and its impact on urban life, the smart city revolution calls for deeper interdisciplinary research into related political and social issues (Kuecker & Hartley, 2020). Heeding this call, this study examines public perceptions about smart cities in the context of governance effectiveness and quality-of-life factors.

This survey-based study examines Hong Kong as illustrative of the smart city revolution in a highly developed and institutionally mature setting. With a GDP per capita of nearly USD 50,000 in 2021 (in current USD; World Bank, 2023), Hong Kong is an advanced economy not only in terms of wealth but also across some measures of global competitiveness, including human development (#4; UN, 2021) and innovation overall (#14; Global Innovation Index, 2022). Additionally, the city has historically been a hub of communications and technology (Lam, 1998). Regarding smart cities, Hong Kong has been variously ranked: #2 in technology by IMD’s (2022) ‘Digital Competitiveness Ranking,’ #10 by IESE’s (2020) smart city-based ‘Cities in Motion’ index, and #41 by Eden Strategy Institute’s (2021) top smart city government rankings (down from #18 in 2018). Hong Kong has shown a well-funded commitment to smart cities (see Government of Hong Kong, 2020 for details), and the city’s recent initiatives include a smart port, smart traffic fund, 3D digital maps, smart farm management, platforms for individual access to public services, and rollout of 5G infrastructure (Government of Hong Kong, 2019, 2022).

Based on these justifications for Hong Kong as an instructive case for smart city research, this study collected data through a survey of more than 800 members of the general public. The survey measured public perceptions and expectationsFootnote 1 about smart city benefits, with a focus on five measures of governance quality (participation, transparency, public services, communication,Footnote 2 and fairness) and five measures of quality-of-life (buildings, energy-environment, mobility-transportation, education, and health). The univariate and multivariate analyses reveal higher expectations about the ability of smart cities to improve quality-of-life factors than about the ability of smart cities to improve governance factors. Further, the association between awareness about the smart city concept and expectations about smart city benefits is notable for its sensitivity to education levels and income. Given these findings and, more broadly, the quality-of-life and governance variables measured, this study is among the first such efforts undertaken in Hong Kong and is generalizable to cities with similar developmental and governance contexts.

This article proceeds with a literature review focusing on the relationship between smart cities and both governance quality and quality-of-life. A description of the methodology follows, including the analytical framework, details about the survey and data collection process, and respondent characteristics. Findings are presented, followed by a conclusion that outlines policy recommendations and avenues for further research.

2 Literature Review

The implementation of policy initiatives, including smart cities, depends in part on participation and buy-in from citizens (Cardullo & Kitchin, 2019). As such, governments may seek to understand public perceptions about smart cities as one input in the policymaking process. Insights from Hong Kong, where substantial investments in smart city initiatives and infrastructures are being made, may also be used by urban governments in similarly situated settings. The literature review for this study found only three published, systematic, large-scale peer-reviewed surveys related to public perceptions about smart cities in Hong Kong (Cole & Tran, 2022; Hartley, 2021; Lai & Cole, 2022). Other Hong Kong-based surveys examine aspects or sub-dimensions of smart cities, including ‘smart campus’ (Zhang et al., 2020) and smart buildings (To et al., 2018). Chan et al. (2019) examine perceptions of smart cities held by inbound visitors to Hong Kong. Additionally, consulting-based surveys have examined this topic by focusing on perceptions about the mechanics and usefulness of smart cities, but these studies fail to connect the smart city imaginary with perceptions about governance and legitimacy. A study investigating such factors, in the context of Hong Kong, can provide insights into smart city development across advanced and innovation-based urban economies.

This study’s examination of public perceptions focuses on expectations for how smart cities can improve governance and quality-of-life, and this review provides a brief overview of related literature. Governance aspects of smart cities have been variously explored in the literature (Anand & Navío-Marco, 2018; Golubchikov & Thornbush, 2022; Lim et al., 2023; Nastjuk et al., 2022; Pereira et al., 2018; Reardon et al., 2023; Ruhlandt, 2018). These include public inclusion and participation (Becker et al., 2023; Bouzguenda et al., 2019; Castelnovo et al., 2016; Granier & Kudo, 2016; López-Quiles & Bolívar, 2018; Rodríguez Bolívar, 2018) and the design, delivery, and quality of public services as enabled by smart city technologies (Anttiroiko et al., 2014; Ben, 2020; Letaifa, 2015; Lulin, 2017; Miranda et al., 2023; Oschinsky et al., 2022; Reiche, 2020). Smart city governance issues receiving relatively less coverage are transparency in policymaking (Cucciniello et al., 2016; David et al., 2018; Jacobs et al., 2022; Lnenicka et al., 2022; Yadav, 2015), government communication to the general public (Laenens et al., 2018; Nadapdap et al., 2016), and more abstract concepts like fairness and equity in smart city development (Gibbs et al., 2013; Kitchin, 2019; Kontokosta & Hong, 2021; Mouton et al., 2019; Sarkar, 2020; Yigitcanlar et al., 2021).

The relationship between smart cities and quality-of-life factors has likewise received some attention in the literature (Chen & Chan, 2023; Giffinger & Gudrun, 2010; Macke et al., 2018; Michaela & Horák, 2019; Přibyl & Horák, 2015; Rodríguez Bolívar, 2019; Šulyová & Kubina, 2022). Proposing a smart city strategic framework, Kim et al. (2022) include quality-of-life as one of four ‘core values’ of smart cities (the others being sustainability, economic competitiveness, and innovation and growth). Operationalizing the concept of ‘quality-of-life’ can be done numerous ways, and for this study centers on five dimensions of the public user experience with smart cities: technologies for smart buildings, energy and the environment, mobility and transportation, education, and health.Footnote 3 The literature exploring how smart cities contribute to these operationalizations is growing: buildings (Chang et al., 2020; Kumar et al., 2017; Kylili & Fokaides, 2015; Moreno et al., 2014; Morvaj et al., 2011; Roccotelli & Mangini, 2022), energy (Anthony et al., 2020; Brenna et al., 2012; Calvillo et al., 2016; Cortese et al., 2022; Strielkowski et al., 2020), natural environment (Bacco et al., 2017; Lin & Cheung, 2020; Montori et al., 2017; Nold, 2020; Viitanen & Kingston, 2014; Zhang, 2023), mobility and transportation (Al Nuaimi et al., 2015; Cassandras, 2017; Oliveira et al., 2020; Persaud et al., 2017; Rani & Sharma, 2023), education (Gomede et al., 2018; Hughes, 2014; Namiot et al., 2017; Sadeh et al., 2020; Tham & Verhulsdonck, 2023), and health (Hilal et al., 2022; Paolini et al., 2016; Pérez-Roman et al., 2020; Pramanik et al., 2017; Sampri et al., 2016; Singh, 2022; Solanas et al., 2014). These applications of smart city technologies arguably encompass most facets of urban life, and their relevance as a policy issue is clearly expressed in the literature.

Fewer in number are published studies empirically examining public perceptions about smart city governance. Several studies from around the world form the basis of this small but growing strand of literature. Shibuya and Suzuki (2022) find through survey-based research in Japan that perceptions about smart city characteristics impact respondents’ willingness to live in them; characteristics impacting the quality of daily life had the most explanatory power. Examining public perceptions about smart cities in Australia through an analysis of social media content, Yigitcanlar et al. (2022) find that perceptions are centered around the concepts of innovation, governance, and sustainability. Novita and Suryani (2019) examine public perceptions about smart cities in a study of Bekasi Smart City in Indonesia, finding that awareness about smart cities differs across population subgroups and is limited only to certain aspects of smart city initiatives; further, the public perceives that communication and information dissemination efforts by the government concerning such initiatives (e.g., information campaigns, exhibitions, and seminars) are inadequate. Similarly, in a study by Georgiadis et al. (2021), Greek and Cypriot respondents perceive that no efforts at becoming ‘smart’ have been made by their cities and that public–private cooperation is the greatest barrier to smart city development. At the same time, some sentiments have been found ‘unjustifiably’ positive. Using Twitter data, as did Yigitcanlar et al. (2022), Arku et al. (2022) find evidence of public anticipation, trust, and joy—even as smart city projects had ‘underwhelming’ outputs. The authors label this phenomenon ‘smart city mirages.’ Finally, survey-based research shows that privacy is a commonly referenced public concern about smart cities (Bannerman & Orasch, 2020; van Heek et al., 2016; van Zoonen, 2016; van Zoonen et al, 2022; Virkki & Chen, 2013).

Other studies examine perceptions held by experts and practitioners. A study by Vu and Hartley (2018) draws from survey data to identify perceptions among private and public sector managers about the preparedness of Vietnam cities for smart city development; the authors find that respondents harbor concerns about corruption, lack of resources, and absence of strategic vision. In a similar study of expert and practitioner perceptions based in Malaysia, Lim et al. (2021) find that respondents were more sanguine about the smart city elements of economy, living, people, and governance than those of environment and digital infrastructure.

Three peer-reviewed survey-based studies address public perceptions about smart cities in Hong Kong. According to Hartley (2021), trust for smart city initiatives can be distinguished between the technical and the managerial: “respondents may trust the underlying technologies of smart cities (as a largely “mechanical” issue) while harboring concerns about security and privacy (as issues under political or policy influence)” (p. 1299). The survey did not include perceptions about quality-of-life factors or specific operationalizations of governance functions. Meanwhile, Cole and Tran (2022) find the same ‘paradox,’ namely that trust in the relevant technology is higher than trust in government. The authors state that trust in smart cities (e.g., data-collection smartphone applications) is influenced by trust in government overall: “generally positive attitudes towards the smart city are mediated by more divided views towards smart city intermediaries, first and foremost public authorities in which trust is generally low” (p. 11). While the study usefully and insightfully examines aspects of quality-of-life, the scope of the study did not include individual operationalizations of governance effectiveness related to smart cities, such as public participation, transparency, public service quality, communication, and fairness. Lai and Cole (2022) find that respondents’ pre-existing level of trust in the Hong Kong government (legislative or district council) associates positively with their perception about Hong Kong’s quality of development as a smart city and their trust in associated technologies (e.g., the ‘LeaveHomeSafe’ Covid-19-tracing mobile application). The relationship between levels of trust in general governance and perceptions about smart cities has scope for further exploration in more intricately operationalized dimensions of smart cities (e.g., buildings, environment, and transportation). Finally, in a survey-based study of smart city perceptions in Macau, a context similar to that of Hong Kong historically and developmentally, Chen and Chan (2022) find that age (older), education level (higher), and use of technology (more frequent) associate with more support for smart cities.

Aside from the three academic studies focused solely on Hong Kong, the most ambitious efforts at measuring public perceptions through surveys are conducted outside of academia (see a discussion by Mora et al. (2017) about ‘grey literature’). Addressing the issue of smart cities in Hong Kong, a KPMG study (2020a) surveys 430 executives from the business, nonprofit, and government sectors. The most pressing smart city-related policy issues identified are provision of affordable housing, efficient use of land and buildings, development of a ‘future-focused’ workforce, and economic growth and job creation (p. 13). With over 80% of respondents holding senior management positions, the KPMG survey identifies insights of a subgroup with substantial control over the smart city agenda and narrative. However, it does not present itself as a survey of public opinion. A second and more broadly focused KPMG survey (2020b) addresses public perceptions about smart cities in Hong Kong and four other Asia–Pacific cities, with Hong Kong respondents viewing the following as having positive impacts on their personal lives: ‘interactive mobile applications for transportation and mobility’ (66%), ‘electronic payment technology and mobile applications’ (61%), ‘e-billing for utilities or public services’ (57%), and ‘online tax reporting and payment services’ (57%). The survey also measures public awareness of smart city technologies (the top being ‘electronic payment technology and mobile applications’ and ‘e-billing for utilities or public services’) and the public’s expected benefits of being a ‘smarter’ city (the top being ‘less wasted resources,’ ‘improved delivery and management of public services,’ and ‘economic growth’).

3 Methodology

3.1 Analytical Framework

This study’s analytical framework is based on the Smart City Strategy Index (SCSI; Roland Berger, 2019). The SCSI’s assessment framework is structured around three smart city dimensions (action fields, planning enablers, and infrastructure and policy enablers), 12 criteria, and 31 sub-criteria. This study explores two clusters of variables that are generally seen to benefit from smart city initiatives: quality-of-life factors (buildings, energy and environment, mobility and transportation, education, and health; drawn from five SCSI ‘action fields’) and governance (public participation, government transparency, efficiency and effectiveness of public services, effectiveness of communication, and fairness in the function of government overall). The SCSI’s sixth ‘action field,’ governance, is given its own cluster in this study and operationalized through five variables reflecting policy issues common to smart cities.Footnote 4 In addition to their role in research, these variables also appear in smart city strategy and practice; for example, the study’s quality-of-life measures overlap with those of Gothenburg’s smart city initiative, which addresses ‘smartness’ in energy, mobility, buildings, healthcare, governance, and infrastructure.Footnote 5

3.2 Data Collection Procedure

A polling firm in Hong Kong administered the survey through a proprietary Web-based Computer Assisted Telephone Interview system (Web-CATI) that captures and consolidates data in real-time. The Numbering Plan provided by the Hong Kong Office of the Communications Authority was used to randomly select landline (or ‘fixed line’) and mobile telephone numbers. The survey was administered from October to November 2019. Before data cleaning, 1,017 valid responses were obtained from 505 landlines and 512 mobile phone lines, with an effective response rate of 60.4% and a standard sampling error of less than 1.6%age points (± 3.1 percentage points at a 95% confidence level). To remove invalid cases, a quality-control question was included. Cases with more than 30% of opinion questions unanswered were deemed unsuccessful and removed. Standard data verification and logical checks revealed no validity concerns.

3.3 Variable Selection

The concepts of governance and quality-of-life are operationalized through clusters of five variables each, and these variables are treated as dependent in the study’s multivariate regression analysis. Governance is operationalized through five-point Likert-scale variables Participation, Transparency, Public services, Communication, and Fairness. Quality-of-life is operationalized through five-point Likert-scale variables Buildings, Energy-Environment, Mobility-Transportation, Education, and Health.Footnote 6 Table 1 presents the survey questions associated with these variables.

Table 1 Survey questions associated with variables
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3.4 Respondent Profile

The survey targeted Cantonese-speaking Hong Kong residents aged 18 or above. Graphical representations of demographic data for the survey sample are presented in Fig. 1. The sample includes a slightly higher share of females than of males (53% to 47%) and skews relatively older (nearly one third aged 60 or above). Regarding occupation, a majority of respondents hold clerical and service jobs (27%) and administrative and professional jobs (25%), 20% are retired, 11% each are home-makers and production workers, 5% are students, and 2% are unemployed. Monthly income skews towards the low end of the classifications used; 30% earn less than HKD 10,000 per month, 25% between 10,000 and 19,999, 20% between 20,000 and 29,999, 10% between 30,000 and 39,999, and 14% 40,000 or above. Nearly half of respondents (47%) have a secondary or high school education, 27% a tertiary qualification (e.g., associate’s or bachelor’s degree), 19% a primary education or below, and 6% at least one postgraduate qualification (e.g., master’s degree, professional degree, or doctorate). For consideration of lifestyle types, the survey also collected data about travel patterns; 59% of respondents travel outside of Hong Kong more than once per year, 34% once or less, and 7% never.

Fig. 1
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Demographic data for survey sample

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4 Findings

4.1 Summary Data

Table 2 presents descriptive statistics for the five governance variables used in the univariate analysis and in the first series of multivariate regressions. Table 3 presents the same for the five quality-of-life variables used in the univariate analysis and in the second series of multivariate regressions. The number of observations used for the analysis differs between the two clusters of variables (874 for governance and 897 for quality-of-life). This difference is the consequence of a data-cleaning exercise used to achieve a balanced dataset for the multivariate regression analysis. Fields in which respondents gave non-responses to one or more survey questions were removed; there were more non-responses for questions pertaining to governance than there were for questions pertaining to quality-of-life.

Table 2 Descriptive statistics (governance).
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Table 3 Descriptive statistics (quality-of-life).
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4.2 Univariate Analysis

He’s (2018) study of public satisfaction with health systems in Hong Kong is the model around which this study’s univariate and multivariate analyses are structured; it is deemed an appropriate model as it addresses a similar issue (public services), uses a similar type of data (survey-based perceptions), and has a similar number of respondents polled through the same methods (above 1,000, by telephone). The first part of this univariate analysis focuses on two variables later used as predictors in the multivariate regression analysis: SC-Aware (“I am aware of the concept of smart cities”; Fig. 2) and Gov-QOL (“It is the government’s responsibility to improve my quality of life”; Fig. 3). The second part of the analysis illustrates levels of respondent agreement across the five variables representing governance and five quality-of-life.

Fig. 2
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Level of awareness about concept of smart cities

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Fig. 3
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Perceptions about government responsibility for quality-of-life

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Regarding variable SC-Aware, the results are relatively normally distributed, with the highest share of responses (34%) expressing neutrality in awareness of smart cities. This may be the consequence of the term ‘smart cities’ being ambiguous or nebulous (Albino et al., 2015). ‘Very disagree’ and ‘agree’ both account for the second-highest share (19% each), followed by ‘disagree’ (15%) and ‘very agree’ (11%). Given that high awareness about smart cities has the lowest share among the five answer options, it is prudent to consider the degree and effectiveness of communication about the concept. For this purpose, respondents were asked to indicate, on a five-point scale, their level of agreement with the statement “The Hong Kong government is good at keeping me informed about its smart city and technology policies (e.g., through media).” The highest shares of responses are ‘very disagree’ (32%) and ‘neutral’ (23%). It could be inferred that low levels of awareness about the concept of smart cities associate with low perceived levels of effectiveness in the government’s smart cities information dissemination efforts. This finding about low levels of communication reflects that of El Hilali and Azougagh (2021), whose netnographic study of smart cities in Morocco finds that respondents are concerned about lack of communication from government and about insufficient opportunities to provide feedback.

Regarding variable Gov-QOL, the highest shares of responses are ‘very agree’ (42%) and ‘agree’ (24%). It is notable that two thirds of respondents view government having responsibility for determining the quality of their lives, a finding that invites comparative analysis about socio-cultural factors (for example, through Hofstede’s (1982) dimensions of national culture: individualism versus collectivism, power distance, and others). Gov-QOL is explored here because it is used as a control variable in the multivariate regression analysis and because it provides contextual insight about personal value-frames and ideological orientations across the survey sample. When interpreting such factors and situating them within a socio-political milieu, it is prudent to acknowledge the presence of political tensions in Hong Kong at the time of the survey (October and November 2019; see Shek (2020) for more detail). As visibly manifest in protests occurring from mid- to late-2019, it can be inferred that political tensions may have influenced respondent attitudes and perceptions about quality-of-life, wellbeing, and the role of government.

The second part of the univariate analysis uses stacked bar charts to compare survey results for variables operationalizing governance and quality-of-life (Figs. 4, 5). Regarding governance factors that can be improved by smart cities, those drawing the highest level of agreement (‘agree’ and ‘very agree’ combined) are public services (50%), communication (35%), and transparency (32%); those drawing the lowest level of agreement are participation and fairness (25% each). Regarding quality-of-life factors that can be improved by smart cities, those drawing the highest level of agreement (‘agree’ and ‘very agree’ combined) are mobility and transportation (59%) and health (53%), followed by education (47%), energy and environment (46%), and buildings (35%).

Fig. 4
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Governance factors improved by smart cities

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Fig. 5
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Quality-of-life factors improved by smart cities

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Comparing findings across variables for governance and quality-of-life reveals a notable difference in how the public perceives the role of smart cities. The total number of percentage points representing ‘agree’ and ‘very agree’ is 240 for quality-of-life factors and 167 for governance factors. This suggests that individuals are 43% more optimistic about the quality-of-life-improvement capabilities of smart cities than they are about the governance-improvement capabilities of smart cities. This finding confirms that of Vu and Hartley (2018) in a survey of perceptions about smart city development in Vietnam; the authors find that, in general, smart city initiatives are seen to address rudimentary operational issues related to public services (e.g., transportation) more effectively than they address governance issues (e.g., transparency and corruption). The findings of both studies thus have broader implications for the ability of smart cities to gain political legitimacy in some settings—a matter for further research. According to Vu and Hartley (2018), “for SCD [smart city development] and ICT initiatives in developing countries, hardware is not enough; cities must prepare to leverage SCD by improving governance. New technology does not excuse governments from reform; without good governance, returns on ICT investment will lag” (p. 859).

4.3 Multivariate Analysis: Governance

The multivariate regression analysis proceeds by regressing two clusters of dependent variables: governance (participation, transparency, public services, communication, and fairness) and quality-of-life (buildings, energy-environment, mobility-transportation, education, and health). As these dependent variables are ordinal (five-point Likert scale), ordered probit regression is used. The model’s treatment of the ordinal dependent and independent variables as continuous assumes that categories have a perceptible and intuitive order (e.g., low to high) and that differences between a category and its contiguous categories are perceived by respondents to be roughly equal in all cases. All models use the following independent variables (see Tables 2 and 3 for descriptive statistics): SC-Aware, included to measure the association between variation in awareness about smart cities (as previously discussed) and dependent variables representing governance and quality-of-life; Gov-QOL, included to control for the effect of personal value-frames and ideological orientations concerning the role of government in shaping public life and individual quality-of-life; Education (level of attainment) and Income serve as controls.

Table 4 presents the results of regressions on governance variables (Models 1–5). The average effect of SC-Aware (public awareness about smart cities) is positive and significant in all models (p < 0.01), while the magnitude of the effect is highest on public services (Model 3) and lowest on public participation (Model 1). These findings suggest that, controlling for ideology about the role of government, greater awareness about the smart city concept associates with higher expectations about the governance benefits of smart cities. The policy implication is that awareness campaigns can be an effective way to build political support for smart cities. The effect of the variable Gov-QOL (view about the responsibility of government to improve quality-of-life), which is significant across all models, is also notable. Although the variable, as a measure of ideological orientation, does not represent a factor within the direct purview of public policy, it offers a revealing implication: increased ideological belief that a responsibility of government is to improve quality-of-life positively and significantly associates with higher expectations about the ability of smart cities to improve governance. This finding suggests that expectations about the governance benefits of smart cities are higher in settings with statist government orientations (e.g., relatively high public sector provision; Doucette & Park, 2018); the research opportunities for cross-country comparisons are clear.

Table 4 Multivariate analysis for governance variables.
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Of secondary note are the negative and significant effects of education and income in all models (by contrast, Chen and Chan (2022) find the opposite effect for education). As education and income increase, expectations about the governance benefits of smart cities decline; it may be inferred that respondents with more education are more skeptical about claims concerning the benefits of smart cities than are respondents with less education. While there are no identified studies focused specifically on the association between level of educational attainment and expectations about the governance benefits of smart cities, this finding can be interpreted against studies about education and trust in government more generally—a relationship that has seen mixed results. For example, Christensen and Lægreid (2005) find in a single-year study of Norwegian citizens that level of education and trust in government are positively associated. However, in a study of the same relationship in the United States between 1958 and 2000, Dalton (2005) finds that the relationship was positive early in the study period but turned negative; that is, over time, more educated respondents were increasingly skeptical of government. The latter is consistent with this study’s findings about smart cities.

4.4 Multivariate Analysis: Quality-of-Life

Table 5 presents the results of regressions on quality-of-life variables (Models 6–10). The average effect of SC-Aware (public awareness about smart cities) is positive and significant in all models (p < 0.01), while the magnitude of the effect is highest on mobility and transportation (Model 8) and lowest on buildings (Model 6). These findings suggest that, controlling for ideology about the role of government, greater awareness about the smart city concept associates with higher expectations about the quality-of-life benefits of smart cities. Similar to the policy implication for Models 1–5, the policy implication for this variable cluster is that awareness campaigns can be an effective way to build political support for smart cities. The behavior of variable Gov-QOL (view about the responsibility of government to improve quality-of-life) and its policy implications are the same as those observed in Models 1–5.

Table 5 Multivariate analysis for quality-of-life variables.
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Of secondary note are changes in the statistical effects of education and income in Models 6–10 as compared to those in Models 1–5. Education loses its significance entirely in Models 7 and 8, becomes less significant in Models 9 and 10, and gains significance in Model 6; the net effect is a general loss of significance overall. Income loses its significance entirely in all models except Model 7 (energy and environment), where it is significant only at the (p < 0.10) level. As education increases, expectations about the education, health, and buildings benefits of smart cities decline; education has no effect on the energy-environment and mobility-transportation benefits of smart cities. The implications of the education variable’s significance across Models 1–5 likewise hold for Models 6, 9, and 10: respondents with more education are relatively more skeptical about some (but not all) claims concerning the quality-of-life benefits of smart cities than are respondents with less education. Reflecting the analysis for Models 1–5, this finding can be interpreted against studies that address the relationship between education and trust in government. A plausible explanation for the overall changes in significance of education and income between the two sets of models is that expectations about governance issues pertaining to abstract concepts (e.g., fairness, participation, and transparency) are more sensitive to education levels than are expectations about quality-of-life issues pertaining to rudimentary and tangible concepts (e.g., buildings and transportation).

5 Policy Implications and Conclusion

This study has examined public perceptions about smart cities in Hong Kong, finding that expectations about the claimed benefits of smart cities are generally higher for quality-of-life factors than for governance factors. Through a survey of more than 800 individuals in Hong Kong conducted in 2019, the study focused on ten measures across both factors and controlled for individual ideology and demographic characteristics. Findings are consistent between the study’s univariate and multivariate analyses, confirming some findings in the literature about levels of trust in government and their mediation of levels of trust in government technology. In addition to this contribution, the study’s case focus makes a novel contribution to the empirical academic literature, which largely lacks a systematic survey of public perceptions about detailed operationalizations of smart city benefits in Hong Kong. Specifically, the study is among the first surveys of public perceptions about smart cities in Hong Kong from the perspective of quality-of-life benefits across multiple service sectors (e.g., environment, transportation, education, and health) and softer dimensions of governance (e.g., public participation, transparency, communication, and fairness). While such issues have been studied in other case contexts, Hong Kong is characterized by a high level of wealth, a governance legacy of both state interventionism and market liberalism, and a knowledge- and innovation-based economy in which technology plays a substantial role in personal life, commerce, and governance. The findings are thus applicable to similarly situated settings in Asia like Taipei, Singapore, Seoul, Tokyo, Shanghai, and other cities outside the region.

In light of these findings, governments may be advised to more clearly articulate connections between smart city initiatives and improvements in the institutional and equity dimensions of governance. Macke et al. (2018) propose that the ability of smart cities to improve quality-of-life is dependent on government taking a ‘citizen-centric’ approach, with a strategy that “encompasses different knowledge techniques, including community policy forums, action researchers’ interventions and policy formulation and implementation” (p. 724). There may be initial public interest and even optimism about the potential of smart cities to improve quality-of-life and governance, despite a lack of full understanding by the public about technical or operational aspects (Georgiadis et al., 2021). At the same time, public perceptions may also be mixed, based on political framing and source-effects (Martin et al., 2022); further, practitioners and experts may diverge on their perceptions about the quality and effectiveness of smart city programs (Lim et al., 2021; Vu & Hartley, 2018). While smart city applications are often applied to the types of operational tasks that can demonstrate immediate and measurable impacts (e.g., transportation, e-governance portals, and efficiency in public service delivery), the ability of smart cities to improve the underlying functionality and quality of governance—including democratic legitimacy and representativeness—may be more difficult to measure and conceptualize for public understanding. This reality raises the need for smart city research to better examine social and political factors. As observed by Cole and Tran (2022), high trust in government technology in conjunction with low trust in government itself constitutes a paradox that may impact the viability of smart city narratives.

This study’s identified connection between awareness and positive perceptions about smart cities implies that governments should inform the public about the function of proposed smart cities projects, but only as part of a collaborative and participatory approach. Survey results suggest that respondents are generally dissatisfied with the communication they receive from government about smart city projects, reflecting the same finding by Novita and Suryani (2019). This finding highlights communications and narrative-building as topic of growing relevance in smart cities research. Relatedly, Cole et al. (2023) describe how a policy narrative acquires meaning through policy communications that have originality, sincerity, and extension or relevance to the ‘outside world.’ These elements provide a useful template for government communications strategies regarding the benefits of smart cities. It is crucial, however, to avoid what Arku et al. (2022) label ‘smart city mirages’—a phenomenon in which communication efforts draw attention only to benefits and away from policy challenges. The authors advocate for robust public access to quality information, a multi-dimensional media profile (e.g., websites and press releases), and clarity of message. Indeed, governments should be forthcoming about the limits of smart city capabilities. The current wave of techno-fetishization can lead governments to over-promise about the benefits of smart cities and double-down on a narrow and technocratic approach to measuring and framing policy problems. Kuecker and Hartley (2020) argue that “the technocrat’s ability to produce knowledge becomes a gesture through which power guides discourse about normative goals” (p. 522). Ensuring that the public has a role in shaping the purpose and ambitions of smart city projects and in establishing the definitions and measurement methods of their success (see a study by Joss et al. (2017) of citizen-centric smart cities) can ensure that the narrative around technology in public life avoids capture by market opportunists and technocratic elites.

There are several avenues for further research. First, comparative studies using the same survey and methodology would allow differing contextual variables to be tested for their association with public expectations about smart city benefits. As a case, ASEAN presents one opportunity for such a study. Inaugurated in 2018 by the ASEAN Secretariat, the ASEAN Smart Cities NetworkFootnote 7 seeks to adopt “an inclusive approach to smart city development that is respectful of human rights and fundamental freedoms as inscribed in the ASEAN Charter.” This declaration suggests the importance of understanding public perceptions for both practical and analytical purposes. However, there are few empirical studies on this topic, and none focused on an intra-ASEAN comparative context. This study of Hong Kong can thus serve as a template for region-wide and multi-country studies. Second, this survey should be repeated yearly to build a longitudinal dataset for Hong Kong or a panel dataset for multiple other cases. Third, qualitative interview-based studies should seek explanations for why respondents appear to have higher expectations about the quality-of-life benefits of smart cities than about governance benefits. Findings would be a contribution to literature about the political legitimacy of smart city initiatives during an era when technology shapes public life and the function of governments in increasingly consequential ways. A final avenue for future research extends from a limitation of this study: it was conducted by phone. Although 91.5% of Hong Kong residents over the age of 10 and two thirds over the age of 65 possess a smartphone (based on a 2019 survey for the Hong Kong Government),Footnote 8 the data collection method misses the opportunity to obtain responses from individuals owning neither a smartphone nor a landline. This omission risks biasing the sample pool in favor of respondents with certain characteristics−in this case, having the technological skill or financial ability to own a phone. As such, future surveys should adopt multiple data-collection methods, including direct personal interface and netnography (El Hilali & Azougagh, 2021). In this way, a more inclusive understanding of public perceptions about smart cities can be obtained.