Abstract
Purpose
This study aims to examine the effects of technology-, organisation- and environment-readiness, smart economic development, change valence, social cohesion and quality of life on citizenship in the context of smart cities.
Design/methodology/approach
The study employed a customized questionnaire which was completed by 280 residents of China’s first-tier cities. This study tested the framework using the partial least squares structural equation modelling (PLS-SEM) technique.
Findings
The results indicated that smart economy development, social cohesion, change valence, technological readiness, organizational readiness and environmental readiness have a significant impact on the quality of life. Quality of life has a positive impact on citizenship.
Originality/value
This study adds new insights to smart city academic discussions. The study addresses a critical gap identified in existing literature which urges the need for a balance between user-centric, organization-centric and technology-centric approaches. It offers a fresh perspective on how the smart economy, social cohesion and readiness factors are interlinked. These elements together shape urban living experiences. For policymakers and urban planners, our findings provide clear guidance. They highlight the complex dynamics that must be considered to build more unified, inclusive and sustainable smart cities.
Keywords
Citation
Huang, S., Huang, H., He, S. and Yu, X. (2025), "Shaping future home: understanding quality of life and citizenship in smart cities", Open House International, Vol. 50 No. 1, pp. 139-157. https://doi.org/10.1108/OHI-12-2023-0289
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited
Introduction
More than 50% of the world’s population currently resides in cities, and by 2050, the urban population is expected to comprise over 70% of the total global population (Huang et al., 2021). This indicates that the process of urbanization will continue to accelerate over the next 30 years (El Barachi et al., 2022). This trend is particularly evident in China. China currently hosts numerous cities with populations exceeding 1 million and possesses one of the highest numbers of such cities worldwide (Wang et al., 2020). This growth can be attributed to China’s large rural population constantly migrating to urban areas. This rapid urbanization presents significant challenges to cities in terms of technological, organizational and environmental preparedness (Huang et al., 2021). Concurrently, population migration will also impact the traditional social ecology within urban areas (Marchesani et al., 2023).
The concept of a “smart city” has gathered significant attention due to the rapid process of urbanization globally. With the emergence and evolution of the internet of Things, the “smart revolution” of cities is changing the track of its operation (Marchesani et al., 2023). The smart city initiative emerged as the times required during the “smart revolution” of the city to address the problems caused by the rapid growth of the urban population and the complexity of the community population (Camboim et al., 2019). Broadly speaking, smart cities leverage the innovative potentials presented by technology advancements, including IoT and AI. They use these opportunities to address severe metropolitan challenges, enrich residents’ life quality and aid in facilitating a more productive governance system (van Twist et al., 2023).
While a number of studies have been carried out to explore the application of artificial intelligence technologies in different sectors to improve the quality of life of the citizens, the effective implementation of the intelligent cities initiative involves extensive cooperation and collaboration with the members of a society (Caputo et al., 2018). Smart cities must be able to show how and when it is able to successfully solve different problems and challenges. United States is an example of such a system, through which the citizens can report their problems directly to the city. This system collects personal data and feedback from the citizens, using this information to develop and implement effective strategies to enhance service delivery and communication with the people at large (Wu, 2020). There is a noticeable rise in academic work aimed at citizen engagement in the development of smart cities. This inclination in interest is particularly related to the uprising of citizens as active participants in the cities’ advancements (Sameer et al., 2023).
Scholars are keen to better understand the effective promotion of smart cities, and several works have been undertaken in researching this subject matter (Ebrahiem et al., 2023; Lapinskaitė et al., 2022). One mainstream of research has elucidated that the concept of smartness, which is a predominant theme in many research areas, includes several key aspects. These encompass the smart city as a whole, emphasizing the role of information and communication technology (ICT), urban transport systems, energy control via smart grid innovations and sensor networks. These studies looked into the specific aspects of technology application in smart cities (Han and Kim, 2021). For example, Manfreda et al. (2021) not only distinguished the defining traits and composition of smart cities but also explored the potential advantages and difficulties they may encounter.
Nonetheless, scholarly research reveals that stakeholders within smart cities (including residents, visitors, users and data contributors) remain unconvinced of the desirability of advanced technologies and the technological utopian vision guiding their application, thus expressing dissatisfaction. Thus, the present study strives to understand further citizenship in smart cities. In addition, we argue that the foundation of smart city citizenship relies on the readiness of technology, organizations and the environment to facilitate a sustainable and holistic smart city ecosystem (Ullah et al., 2021). While the technology organization environment framework has been validated as a robust foundation for studying smart city-related phenomena, there is scant research that comprehensively examines the readiness from three joint aspects of technology, organizations and the environment and their implications on quality of life in smart cities.
The idea of a societal smart city focuses on prioritizing citizens. The current study supports the idea that quality of life can affect citizenship, aligning with the call to prioritize citizens’ needs, rights and participation in smart cities (Alizadeh and Sharifi, 2023a,b). To understand the community dynamics in smart cities, our study supports the idea of Alizadeh and Sharifi (2023a,b) to integrate social elements (i.e. social cohesion) in understanding quality of life and citizenship. This under-researched approach urges for a more balanced smart city model that values social aspects as much as technological advancements. The next aspect that is worth further research is the change valence (the psychological aspect of community dynamics) that pertains to how citizens perceive and respond to the ongoing transformations within smart cities. Understanding this aspect is important as it determines citizens’ appraisal of change (Hussain and Papastathopoulos, 2022). Moreover, past research is mainly constrained to the micro-management level but overlooked the importance of smart economy development as a macro-view (economic aspects of community dynamics) toward understanding smart citizenships.
We further test the moderating roles of technology anxiety and empowerment. We note that quick technological changes may cause worries for citizens. Such concerns can influence their use of smart city technologies (Park et al., 2019). This, in turn, can impact their quality of life and their feeling of belonging to the smart city. On the other hand, we posit empowerment can strengthen the positive impact of quality of life because it boosts perceived control and involvement (Aw et al., 2023; Zhu and Alamsyah, 2022). This unified framework comprehensively investigates how technology infrastructure, governance, environmental sustainability and socio-economic factors in smart cities together affect the quality of life and citizenship. Our model development emphasizes that citizenship in smart cities involves more than just technological or economic changes but needs to consider citizens’ well-being, their ability to adapt and their active involvement in the community (Alizadeh and Sharifi, 2023a,b).
The citizens’ input and active participation form the backbone of smart city development and therefore, cannot be neglected (Bouzguenda et al., 2019). Crucially, with this emphasis on citizen participation, an intriguing question emerges - “How can we effectively stimulate and maintain citizen engagement in the erection and expansion of smart cities?” This question presents a remarkable relevance in laying the groundwork for future successes within the sphere of smart city development (Wang et al., 2023b). However, existing research does not comprehensively focus on smart cities’ strategies and techniques for fostering quality of life and citizenship.
Literature review
Underpinning theories
Earlier research has employed models like the Technology Acceptance Model (TAM), Task Technology Fit (TTF) and Stimulus-Organism-Response (SOR) to clarify the behavioral intentions of citizens in smart cities (Ullah et al., 2021; Wang et al., 2023a). For instance, according to Kashef et al. (2021), smart cities, much like large community organizations, heavily rely on citizen participation for their development (Simonofski et al., 2021). The S-O-R theory provides the theoretical foundations to understand how smart city initiatives enhance citizens’ quality of life and foster a sense of citizenship (Kashef et al., 2021). However, within the smart city framework, these theories solely illustrate a single dimension of the issue, such as technology adoption or human behavior. They do not provide a complete perspective on the integration of citizens with smart cities (Ullah et al., 2021).
Beyond the technical considerations, it becomes imperative to factor in the organizational and environmental aspects within the context of smart cities. To holistically address smart city challenges, Ullah et al. (2021) advocated for the Technology-Organization-Environment (TOE) framework, emphasizing its comprehensive nature in elucidating associated issues. Studies like Mukti et al. (2022) have utilized the TOE framework as a benchmark for evaluating smart rural governance in China. This study chooses to employ the TOE framework to gain a more encompassing understanding of crucial factors influencing citizens’ quality of life. This framework facilitates an examination of the impact of smart city initiatives on citizens’ quality of life from three perspectives: technology, organization and environment. By employing the TOE framework, research can more effectively assess how smart city initiatives impact the overall quality of life of residents. It provides a lens to understand not just the technological advancements but also how these advancements harmonize with organizational and environmental aspects to enhance living conditions. One major limitation of the TOE framework lies in the potential over-generalization and over-simplification, which may impede its direct applicability in a more specific context (e.g. smart cities). Furthermore, the framework might not effectively capture the complexity inherent in the adoption and implementation of technology.
Therefore, this study integrates the socio-technical systems theory for a more robust understanding of the phenomenon. The theory of socio-technical systems highlights the importance of aligning both the social and technological facets in any system design. In addition, designing or modifying a single element of the system, without considering its impact on the other parts will compromise the outcome. The theory prompts this study to include other variables apart from TOE readiness. Smart economic development is included to investigate technology-driven economic growth backs smart cities (quality of life). Change valence is factored in because the social system’s adaptability and receptiveness are vital for technology adoption and implementation. Finally, the socio-technical systems theory highlights the necessity for social cohesion to integrate technological systems within communities successfully.
Following Zhu and Alamsyah (2022), we adopted the empowerment theory (Perkins and Zimmerman, 1995) to understand the role of empowerment. Empowerment theory suggests that the feeling of empowerment manifests a greater sense of control or improved abilities. In the present study, we posit empowerment as a potential augmentor of quality of life, given that Zhu and Alamsyah (2022) revealed that empowerment fosters citizens’ satisfaction in smart cities. Besides, this study examines technology anxiety using the fear acquisition theory (Li and Huang, 2020), which has been widely applied to explain many anxiety disorders. The theory posits that technology anxiety can be innate in nature, representing a characteristic that emerges from experience and innate anxiety concerning the unknown consequences of technology use.
Smart economy development
A smart economy is a paradigm that seamlessly combines both local and global markets to fuel creativity and entrepreneurship, increase productivity and enhance labor market adaptability (Chan et al., 2019). In the context of a smart city, this model includes the use of Information and Communication Technologies (ICTs) to galvanize citizen participation in public services, encourage ICT adoption in business processes and facilitate changes across various service sectors (Dash, 2022). Smart economics, as a component of this model, seeks to foster sustained economic growth, elevate living standards and judiciously manage natural resources, all within a framework of participatory governance (Burns and Welker, 2022). The advent of the smart economy has significantly impacted numerous sectors that directly influence citizens’ lives Parra-Domínguez et al., (2021). By intertwining with various facets of daily life, the development of the smart economy has the potential to enhance the quality of life for citizens dramatically (Dash, 2022). For instance, it can open up new employment opportunities, augment income levels and support personal business activities.
Smart economy development has a positive effect on quality of life.
Change valence
As purported by Meyer and Herscovitch (2001), change valence refers to the extent of favorable understanding that individuals have towards technological transformation. This concept takes on immense significance in the fast-evolving realm of smart cities, where the implications of technological progress are dramatically reshaping our everyday lives (Marchesani et al., 2023). The progression of technology within smart cities not only instigates changes in the public’s roles, but also demands their acceptance of novel technologies. It encourages an alliance between residents and technology and modifies the behavioral interaction within the community (Marchesani et al., 2023; Noori et al., 2020; Sonta and Jiang, 2023). Given the rapidity and broad sweep of these modifications, citizens often need to adjust and occasionally make concessions, leading to a plausible gap between technological acceleration and people’s expectations or comfort zones (Noori et al., 2020). As such, it is crucial to involve the public in this transition actively and foster a favourable perception of these changes (Nobel, 2022). High change valence leads to more likely and effective voluntary involvement in technological alternations, leading to better usage and resultant quality of life improvements that come from smart city technologies. Fundamentally, the relationship between change valence and quality of life within smart cities lies within the principle that a positive mindset towards technological transformations simplifies the shift to new lifestyle norms and interactions. This shift betters the overall life experience, making it more productive, more comfortable and more attuned to modern needs. Hence, promoting high change valence amongst residents is not only advantageous but also vital for the successful attainment of smart city goals and the ensuing enhancement of life quality. Hence, we hypothesize the following:
Change valence has a positive effect on quality of life.
Social cohesion
Social cohesion, as conceptualized by Sonta and Jiang (2023), involves the strength of connections and trust among community members. It plays a pivotal role in various aspects of urban life, notably in enhancing resilience and recovery from infrastructure challenges and natural disasters (Delhey and Dragolov, 2015). In the context of smart cities, where rapid technological changes can bring disruptions or uncertainty, social cohesion acts as a mitigating force, lessening the negative impacts of these changes (Miao et al., 2020). The essence of social cohesion lies in fostering a sense of belonging and mutual trust among citizens. This communal bond encourages spontaneous interactions and the sharing of knowledge and experiences, which in turn contributes to the formation of social capital (Orejon-Sanchez et al., 2022). Strong social cohesion facilitates a harmonious integration of technology in daily life, as it cultivates an environment of mutual respect and trust. This environment is crucial for balancing technological adoption with social interaction and collaborative work, making it easier for citizens to adapt to and embrace new technologies and ways of living. The idea is grounded in the understanding that a cohesive community provides a supportive framework for citizens. In such a community, the challenges of adapting to technological advancements and other urban transformations are more easily navigated, leading to enhanced well-being and quality of life for its members. Social cohesion not only strengthens community bonds but also ensures that the benefits of smart city initiatives are more evenly and effectively distributed, thereby enhancing the overall quality of life for all citizens. Accordingly, the following hypothesis is proposed.
Social cohesion has a positive effect on quality of life.
Technological readiness
In the realm of smart cities, technological readiness is a critical factor, encompassing the essential technical components needed for their successful operation (Ullah et al., 2021). Unlike individual technology readiness (Aw et al., 2023), this readiness pertains to the infrastructure and services related to IT and digital technology, such as widespread Internet access, adoption of smart devices and reliable power supply (Rjab et al., 2023). Additionally, it includes strategic guidelines for the implementation and use of smart technologies, which are crucial for enhancing governance efficiency and service levels (Mukti et al., 2022). These technologies, provided either by government entities or third parties, aim to streamline city operations and improve the delivery of services to citizens. Thus, the hypothesis that technological readiness has a positive effect on quality of life is based on the understanding that a city’s technological infrastructure, coupled with its strategic use and citizen engagement, plays a significant role in improving the daily experiences of its residents. Technological readiness in a smart city context does not merely represent the availability of advanced technology but also signifies a city’s capability to effectively integrate this technology into the fabric of urban life, thereby enhancing the quality of life for its inhabitants.
Technological readiness has a positive effect on quality of life.
Organizational readiness
Organizational readiness, in the context of smart city initiatives, is predominantly driven by governmental entities (Ullah et al., 2021). The government’s role is pivotal in ensuring the successful implementation of smart city strategies. This involves securing funding, advancing technological solutions and fostering social cooperation (Miao et al., 2020). As the central organizing body, the government is tasked with allocating resources to deliver high-quality services to its citizens. This encompasses providing smart services in critical sectors such as finance, healthcare, education and logistics. Additionally, the government plays a crucial role in creating smart technology-related job opportunities and nurturing business ecosystems driven by smart technologies (Rjab et al., 2023; Shee et al., 2021). An effectively organized and resourceful government can significantly enhance the implementation and reach of smart city initiatives. When a government is well-prepared organizationally, it can more effectively deploy smart technologies and services, ensuring that they are accessible, efficient and meet the diverse needs of its citizens. This readiness not only streamlines city operations but also directly contributes to improving the daily lives of residents by providing better services, more job opportunities and fostering a technology-friendly business environment.
Organizational readiness has a positive effect on quality of life.
Environmental readiness
Environmental readiness involves the promotion and inhibition factors related to smart cities, such as social and cultural issues, regulations, access to third-party resources and industry structure (Shee et al., 2021). De Giovanni (2012) highlighted that building a good environment promotes positive actions that positively affect economic performance. For an organization, a good environment helps to enhance the participation of consumers, enterprises and public welfare groups. A good environment can also improve economic performance (Ameer and Othman, 2011). In the context of smart cities, building a good environment is an important prerequisite for improving citizens’ engagement (Shee et al., 2021). If the current environment is conducive to citizens’ adoption of smart technology, the goal of improving their quality of life and economic welfare will be achieved. Hence, the following hypothesis is proposed.
Environmental readiness has a positive effect on quality of life.
Quality of life
Quality of life refers to the total well-being of persons living there, and well-being reflects not only living conditions and control over resources throughout the entire spectrum of life domains but also how people respond to and feel about their lives within those domains (Dash, 2022). When citizens’ quality of life improves, their sense of belonging to the city will increase, especially with the improvement of the quality of life brought about by technological innovation (Lam and Yang, 2019). The high-quality life brought by the smart city initiative to citizens will enhance their sense of dependence and belonging, which may lead to spontaneous participation, maintenance and support of the concept of smart city initiatives. Thus, the following hypothesis is proposed.
Quality of life has a positive effect on citizenship.
Moderating effect of technology anxiety
Technology anxiety is defined as a complex set of emotions, such as tension, uncertainty and fear related to the use and learning of technology. This concept is associated with apprehension about technology usage, such as losing important data or making mistakes (Compeau et al., 1999). In addition, it may also be related to personal potential social and psychological factors, such as cost, social problems, trust and privacy problems (Troisi et al., 2022). In the context of smart cities, the smart city initiative may bring benefits to citizens’ lives in the short term, but it cannot change citizens’ anxiety toward new technologies (Troisi et al., 2022). Citizens who experience higher levels of technology anxiety might be less likely to fully engage with and support smart city initiatives despite the improvements these initiatives bring to their quality of life. Essentially, while citizens may appreciate the tangible improvements in their daily lives brought about by smart city initiatives, their apprehensions and fears about the technology itself can dampen their overall satisfaction and sense of community. Thus, the following hypothesis is proposed.
Technology anxiety moderates the influence of quality of life on citizenship.
Moderating effect of empowerment
Empowerment refers to the process by which individuals, groups and/or communities become capable of controlling their circumstances to achieve their goals, thereby enabling them to work towards maximizing the quality of their lives (Aw et al., 2023; Moedeen et al., 2024; Nikkhah and Redzuan, 2009). Empowerment in smart cities translates to citizens having more agency and control over the technology and services that shape their daily lives. This increased control can lead to a heightened sense of ownership and participation in smart city initiatives, thereby strengthening their citizenship. Thus, the following hypothesis is proposed.
Empowerment moderates the influence of quality of life on citizenship.
Methodology
Data collection and respondent profile
We collected data through a web-based questionnaire. To achieve the goal, we used the professional data collection service hosted by the Wenjuanxing platform, which has been recognized as the largest market survey platform in China. The survey was distributed to respondents with different backgrounds based on the census data. In terms of sampling technique, we employed quota sampling. The choice was driven by the purpose of achieving an evenly distributed sample (25% of respondents from each city) of respondents from each of China’s first-tier cities. This is important to avoid a scenario where the sample becomes biased toward any of the first-tier cities, which could potentially skew the results and compromise the study’s validity. To be eligible for the survey, all respondents are citizens of first-tier cities in China, namely Beijing, Shanghai, Guangzhou and Shenzhen (Zhou et al., 2021). The reason for choosing first-tier cities in China is that these cities have a high penetration rate of smart technologies and facilities, and the community population mobility is high. The dataset consists of 280 responses. The sample size demonstrated sufficient statistical power, surpassing the threshold of 146 responses calculated via the G*power analysis, factoring in an effect size (f2) of 0.15, a significance level (α) of 0.05, a power of 0.95 and the presence of 6 predictive variables (Faul et al., 2007). Furthermore, we conducted a preliminary power analysis using the inverse square root and gamma-exponential methods. The result suggested that a sample size of 160 (based on inverse square root) and 146 (based on gamma-exponential method) respondents, respectively, would be adequate (Kock and Hadaya, 2018).
Measures
The measurement of smart economic development, quality of life and technology anxiety was adapted from Dash (2022), change valence was adapted from Hussain and Papastathopoulos (2022) and the measurement of social cohesion was adapted from Sonta and Jiang (2023). In addition, the measurement of technological readiness, organizational readiness and environmental readiness was adapted from Mukti et al. (2022), while the measurement of citizenship was adapted from Wang et al. (2023b), and empowerment was adapted from El Barachi et al. (2022). All items were measured on a 7-point Likert scale, ranging from 1 “Strongly disagree” to 7 “Strongly agree”.
Results
Common method bias
Due to the cross-sectional nature of this study, there is a potential threat of common method bias. To address this concern, we adopted both procedural and systematic remedies (Podsakoff et al., 2003). The questionnaire utilized in this study was designed after consultation with academics who have ample expertise. In terms of procedural remedy, simple and concise language was adopted in all measurement items. In addition, respondents were reassured that there were no correct or incorrect answers, and that their responses would be confidential (Podsakoff et al., 2003). Moreover, Harman’s single-factor test was performed as a statistical remedy, and the results indicated that one factor could explain 29.568% of the total variance, which is well below the 40% conservative threshold (Babin et al., 2016). The correlation matrix also indicated that the highest inter-construct correlation (0.749) is lower than the threshold value of 0.90 (Bagozzi et al., 1991).
Partial least squares structural equation modeling
PLS-SEM was adopted in the present study to test the research model, as this tool is widely used to predict certain outcome variables (Hair et al., 2017). The statistical technique enables researchers to examine the set of interrelated hypotheses by appraising the relationships among multiple exogenous and endogenous variables in a theoretical framework (Hair et al., 2017). PLS-SEM was adopted for two reasons. Firstly, PLS-SEM is suitable for prediction and theory development in obtaining the maximum variance explained (Hair et al., 2017). This is opposed to Covariance-based Structural Equation Modelling, which is to confirm the fitness of a theoretical foundation with the observed data. Secondly, PLS-SEM is suitable for complex research models with multifaceted constructs (Hair et al., 2017).
Measurement model
We began by confirming the reliability and validity of the variables in our measurement model. All variables had CR values over 0.70, as outlined in Table 1, which suggests a satisfactory internal consistency. We also examined the Average Variance Extracted (AVE). The AVE values exceeded the 0.50 threshold as per Fornell and Larcker (1981). In examining discriminant validity, the criterion proposed by Fornell and Larcker (1981) was adopted. The result demonstrated that all square roots of AVE for each construct were greater than the corresponding correlation coefficients, and thus, DV was ascertained (see Table 2).
Structural model
The findings indicated that the Variance Inflation Factor (VIF) values ranged from 2.178 to 3.266. Since the maximum VIF value did not exceed the 3.3 threshold, multicollinearity was not an issue. The analysis further revealed that the model accounted for 23.50% of the variance in the primary endogenous variable (i.e. citizenship). The predictive capability was conducted using PLSpredict. When comparing the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values associated with the key endogenous variable indicators derived from PLS-SEM to those obtained from a linear regression model, it was found that the PLS-SEM’s values were not exceeded by those of the linear model. Hence, the model demonstrates superior predictive accuracy (Hair et al., 2017).
To evaluate the importance and significance of the hypothesized relationships, we utilized bootstrapping with 5,000 subsamples for the inner structural model analysis (as per Hair et al., 2017). As exhibited in Table 3, the results revealed that smart economy development (β = 0.204, ρ < 0.001), change valence (β = 0.144, ρ < 0.01), social cohesion (β = 0.094, ρ < 0.05), technological readiness (β = 0.207, ρ < 0.001), organizational readiness (β = 0.149, ρ < 0.01) and environmental readiness (β = 0.162, ρ < 0.01) significantly influenced quality of life. Moreover, quality of life (β = 0.485, ρ < 0.001) had a significant effect on citizenship. Hence, H1, H2, H3, H4, H5, H6 and H7 were supported. The results, as displayed in Table 4 and Figure 1 demonstrated that technology anxiety and empowerment do not moderates the relationship between quality of life and citizenship. Hence, H8 and H9 were not supported.
Discussion
The complexity of city planning and implementation is manifested through the diverse viewpoints and debates about smart cities (Al-Sharif and Pokharel, 2022). A significant contributor to this intricacy is the array of stakeholders, each with their different interests and expectations (Clement et al., 2022). Key stakeholders in the construction of smart cities, including governments, communities and third-party organizations, aspire for citizens to not only be passive recipients but active participants, beneficiaries and advocates of these initiatives (Joss et al., 2017). This active participation is crucial because the successful release, promotion and widespread adoption of intelligent technologies in smart cities necessitate a collaborative effort between those implementing these technologies (the promoters) and those who stand to benefit from them (the beneficiaries) (Shami et al., 2022). This study posits that citizenship in smart cities is principally reliant on the apparent enhancements these projects contribute to their lifestyle quality. This proposition is not solely reliant on the presence or intricacy of technology, but rather on how these technological progressions transform into recognizable advantages for the inhabitants.
The first theme of our study is grounded in the community dynamics of smart cities, which comprises of the economic (smart economy development), psychology (change valence) and social (social cohesion) aspects of community dynamics. Smart economy development includes digitalization, intelligent manufacturing and e-commerce to improve employment prospects, boost productivity and typically result in higher income levels (Dash, 2022). This aids in economic growth, efficiency and enhances citizens’ quality of life by offering improved services, increased job opportunities and greater economic stability (Duygan et al., 2022). Additionally, it creates an environment conducive to innovation and entrepreneurship, bolstering more robust, dynamic local economies and indirectly uplifting the community’s living standards and wellness (Li and Liao, 2018). Besides, the notion of positive change valence, an openness to adapt to new technologies and innovations, is prevalent among citizens in the context of smart cities. Their willingness to use and derive benefits from smart city initiatives like intelligent transportation, e-governance and digital health services is heightened (Wang et al., 2023a). This openness towards technological change eases adaption processes, lowers anxiety tied to new technologies and boosts the overall acceptance and use of innovations that ameliorate daily life. Ultimately, this change valence fosters an improved quality of life, as people become skilled at harnessing these technologies for individual and communal advantages. The smart city initiative advocates the construction of new communities, providing convenient services for citizens while also giving rise to new social relationships, values and cohesion among citizens (Orejon-Sanchez et al., 2022). In the new pattern community, a good social atmosphere is the foundation for the perfect integration of technology and citizen life (Sonta and Jiang, 2023). Social cohesion is one of the important indicators for testing smart city initiatives, while also bringing tangible benefits to citizens (Miao et al., 2020; Sonta and Jiang, 2023). This study confirms the relationship between social cohesion and quality of life, which to some extent indicates the importance of social relationships and social assets for citizens of emerging smart cities. The finding complements past research (Shee et al., 2021) that showed insignificant effect of smart city social dimensions on economic growth. Our study implies that social cohesion, like how well people get along and support each other, are important for making cities a good place to live. This, in turn, may indirectly helps economic development in the long run, explaining why there is no significant effect of social factors on economic growth detected in past research.
The present study uses three dimensions (i.e. technological readiness, organizational readiness and environmental readiness) to explain the readiness of smart cities and its effect on quality of life. Technological, organizational and environmental readiness play critical roles in enhancing the living experience and quality of life in urban settings (Ullah et al., 2021). Different from El Barachi et al. (2022), which showcased the insignificant impact of technology readiness inhibitors on citizen satisfaction, our study suggests otherwise. Technological readiness improves quality of life by providing enhanced communication, better access to information and services, improved digital healthcare and overall ease and efficiency in day-to-day activities. This could be attributed to the fact that the sample used in the current study is composed of citizens of smart cities, different from El Barachi et al. (2022), which used mainly early adopters who are early adopters of smart citizenship, resulting in different baseline for satisfaction. Organizational readiness emphasizes the importance of well-structured and responsive governance in delivering better services, solving problems effectively and promoting community engagement, subsequently improving living conditions (Rjab et al., 2023). The critical role of a sustained, safe and well-maintained environment is highlighted by the strong influence of environmental readiness, contributing to healthier living spaces and long-term sustainability. These factors collectively shape a more livable, efficient and fulfilling living environment.
Technology has been deeply integrated into people lives, which might render technology anxiety less relevant as a moderator. While Park et al. (2019) found that technology anxiety can negatively impact perceived benefits derived from technology usage, our contradicting finding suggests that the effect may be overridden when citizens have gained sufficient usage experience and when the technology, such as smart cities, meets their expectations. In terms of the insignificant role of empowerment, the result seemingly contradicts that of Zhu and Alamsyah (2022), suggesting a different operating mechanism of empowerment. Although empowerment is important in shaping satisfaction, there is a plausible reason why it does not play a moderating role. In smart cities, the rules and regulations, as well as citizens’ roles are set in advance, so it might be unlikely for one to feel empowered by the city governance.
Theoretical implications
Previous studies have highlighted the contribution of smart technology to the medical, education, transportation and logistics industries (Shami et al., 2022). However, the implementation of the smart city initiative is considered to be a social change and needs to be paid attention to more relevant factors (Obringer and Nateghi, 2021). This study substantially enhances our theoretical understanding of smart cities by evaluating and explaining the favorable impact of change valence on quality of life, offering critical guidance for subsequent research. It particularly emphasizes the requirement for citizens to show a keen interest in actively engaging with smart city ventures in order to fully enjoy their advantages (Lam and Yang, 2019). This discovery not only highlights the significance of citizen involvement in these endeavors but also expands our knowledge of individual perceptions and reactions to societal changes in the context of smart cities.
In the context of smart cities, the contribution of social cohesion tends to be forgotten. Previous studies have typically focused on the impact of smart technologies on government performance and the lives of citizens, yet neglecting the significant impact of smart city initiatives on citizens’ social resources and relationship networks (Antwi-Afari et al., 2021; Miao et al., 2020). Smart city initiatives may bring new ways of communication, new topics and new ways of team entertainment to citizens, and thus revolutionize consumer social patterns (Camboim et al., 2019; Rjab et al., 2023). As a member of smart cities, citizens may have more opportunities than ever to expand and maintain their social networks, reduce social costs, access social resources and ultimately improve their quality of life (Sonta and Jiang, 2023).
Additionally, the study investigates the correlation between smart economic development, such as economic expansion, improvement in citizens’ income and job creation, showcasing the construction of an economic model embedded with smart technology (Dash, 2022). A notable contribution of this research is the precedent it sets in proving that smart economy development positively affects quality of life. This offers a fresh viewpoint for understanding how smart economy advancements impact the daily living of citizens. By creating this connection, the study offers a groundwork for continued analysis of the effects of smart economic development and citizen welfare, consequently enriching smart city studies.
This study of the TOE framework, with the combination of socio-technical systems theory, empowerment theory and fear acquisition theory in relation to smart cities represents a significant theoretical contribution to past research models in smart city literature. For example, past studies have used TAM to examine urban technology acceptance in smart cities (Choi, 2022; Sepasgozar et al., 2019). This study enhances the existing framework by expanding the technology-centric view to include social and economic perspectives as important considerations in the smart city context. On the other hand, De Guimarães et al.'s (2020) framework emphasizes smart governance and quality of life in smart cities. Our study extends by incorporating a richer community perspective as well as identifying the effect of quality of life on citizenships. This comprehensive examination is vital for addressing urban challenges in smart cities, and represents a critical component of the sustainable development of smart cities.
Practical implications
This study provides effective advice for decision-makers of government and smart city planner so that they can clearly understand what readiness needs to be made to implement the smart city initiative. In addition, it also makes them clear the importance of social factors, they need to build a smart city-friendly social foundation, which is beneficial to improving the quality of life of citizens. For designers of smart cities and third-party organizations, this study enables them to understand the importance of improving citizens’ quality of life. In addition, the results of this study give them some hints, that is, technology anxiety or passive authorization may be harmful to civic consciousness. For the decision-makers of third-party organizations, the enlightenment of this study is to enable organizations to actively participate in social change, actively promote the development of a smart economy and provide more jobs for citizens. Notably, our study on quality of life and citizenship is consistent with SDG 11 (Sustainable Cities and Communities), which seeks to make cities more safe and sustainable (Sharifi et al., 2024). The finding suggests potential initiatives for enhancing smart city living standards. For example, stakeholders (e.g. policymakers and urban planners) are encouraged to develop a smart economy that generates employment and fuels economic expansion, as well as preparing organizations to offer optimal smart city services.
Limitations
There are some limitations that need to be addressed in future research. First, this study did not explain the relationship between a sense of participation and engagement, as well as the relationship with other outcomes (e.g. behavioral intentions). Future research could explore this area to understand how participation and engagement translate into concrete actions or behavioral changes among citizens, such as their willingness to participate in community initiatives in the smart city. Second, this study lacks an explanation for the dark side of smart cities, as these factors may affect the quality of citizens’ lives. Future research could look into factors such as privacy concerns, digital divide and increased surveillance, that potentially affect the quality of life of citizens. Thirdly, in addition to the readiness of the government and their publicity (third-party organizations), and relevant social foundations, individuals’ psychographics such as digital literacy, openness to change and self/lifestyle congruence may also affect smart city outcomes (Basha et al., 2022). Fourthly, due to the fact that smart city implementations can vary widely in scope, scale and nature across different urban contexts, citizens in different cities may have different perceptions of smart cities. Future research could utilize data mining techniques to uncover determinant attributes of smart city outcomes (Fernando and Aw, 2023).
Figures
Figure 1
Research model
Demographic profile
| Demographic | Frequency | Percentage (%) |
|---|---|---|
| Gender | ||
| Male | 159 | 56.79 |
| Female | 121 | 43.21 |
| Age | ||
| 18–25 years old | 58 | 20.71 |
| 26–35 years old | 132 | 47.14 |
| 36–45 years old | 53 | 18.93 |
| 46–55 years old | 27 | 9.64 |
| 56 years old and over | 10 | 3.57 |
| Education | ||
| High School | 16 | 5.71 |
| Junior College | 22 | 7.86 |
| Bachelor | 204 | 72.86 |
| Master | 33 | 11.79 |
| PhD | 5 | 1.79 |
| Work status | ||
| Student | 41 | 14.64 |
| Businessman | 180 | 64.29 |
| Private employee | 17 | 6.07 |
| Government employee | 27 | 9.64 |
| Unemployed | 15 | 5.36 |
| Income per month | ||
| Less than CNY2000 | 31 | 11.07 |
| CNY2001-CNY4000 | 21 | 7.50 |
| CNY4001-CNY6000 | 42 | 15.00 |
| CNY6001-CNY8000 | 63 | 22.50 |
| CNY8001-10000 | 66 | 23.57 |
| CNY10001 and Above | 57 | 20.36 |
| Total | 280 | 100.00 |
Source(s): Authors’ own creation
Construct reliability and convergent validity
| Latent construct | Items | Loadings | CR | rhoA (ρA) | Cronbach’s alpha | AVE |
|---|---|---|---|---|---|---|
| CS | CS1 | 0.830 | 0.918 | 0.893 | 0.907 | 0.736 |
| CS2 | 0.874 | |||||
| CS3 | 0.868 | |||||
| CS4 | 0.860 | |||||
| CV | CV1 | 0.885 | 0.939 | 0.923 | 0.920 | 0.793 |
| CV2 | 0.890 | |||||
| CV3 | 0.890 | |||||
| CV4 | 0.896 | |||||
| ER | ER1 | 0.832 | 0.946 | 0.935 | 0.942 | 0.714 |
| ER2 | 0.865 | |||||
| ER3 | 0.826 | |||||
| ER4 | 0.852 | |||||
| ER5 | 0.845 | |||||
| ER6 | 0.849 | |||||
| ER7 | 0.847 | |||||
| OR | OR1 | 0.866 | 0.922 | 0.906 | 0.900 | 0.747 |
| OR2 | 0.837 | |||||
| OR3 | 0.881 | |||||
| OR4 | 0.871 | |||||
| QL | QL1 | 0.912 | 0.930 | 0.888 | 0.901 | 0.815 |
| QL2 | 0.904 | |||||
| QL3 | 0.892 | |||||
| SC | SC1 | 0.866 | 0.936 | 0.911 | 0.926 | 0.785 |
| SC2 | 0.909 | |||||
| SC3 | 0.885 | |||||
| SC4 | 0.883 | |||||
| SED | SED1 | 0.858 | 0.932 | 0.906 | 0.920 | 0.773 |
| SED2 | 0.888 | |||||
| SED3 | 0.879 | |||||
| SED4 | 0.891 | |||||
| TR | TR1 | 0.868 | 0.945 | 0.935 | 0.937 | 0.71 |
| TR2 | 0.85 | |||||
| TR3 | 0.817 | |||||
| TR4 | 0.825 | |||||
| TR5 | 0.866 | |||||
| TR6 | 0.849 | |||||
| TR7 | 0.824 |
Source(s): Authors’ own creation
Fornell-Larcker criterion
| Latent variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|
| 1. Citizenship | 0.858 | |||||||
| 2. Change Valence | 0.478 | 0.890 | ||||||
| 3. Environmental readiness | 0.384 | 0.459 | 0.845 | |||||
| 4. Organizational readiness | 0.438 | 0.448 | 0.369 | 0.864 | ||||
| 5. Quality of life | 0.485 | 0.493 | 0.494 | 0.468 | 0.903 | |||
| 6. Social cohesion | 0.312 | 0.336 | 0.256 | 0.243 | 0.341 | 0.886 | ||
| 7. Smart economic development | 0.424 | 0.422 | 0.426 | 0.44 | 0.515 | 0.304 | 0.879 | |
| 8. Technological readiness | 0.459 | 0.439 | 0.486 | 0.396 | 0.521 | 0.285 | 0.423 | 0.843 |
Source(s): Authors’ own creation
Structural model
| Hypotheses | PLS paths | Std. Beta | T statistics |
|---|---|---|---|
| H1 | Smart economy development – > Quality of life | 0.204 | 0.000*** |
| H2 | Change valence – > Quality of life | 0.144 | 0.006** |
| H3 | Social cohesion – > Quality of life | 0.094 | 0.017* |
| H4 | Technological readiness – > Quality of life | 0.207 | 0.000*** |
| H5 | Organizational readiness – > Quality of life | 0.149 | 0.007 ** |
| H6 | Environmental readiness – > Quality of life | 0.162 | 0.003** |
| H7 | Quality of life – > Citizenship | 0.485 | 0.000*** |
| H8 | Technology anxiety*Quality of life – > Citizenship | −0.011 | 0.346ns |
| H9 | Empowerment*Quality of life – > Citizenship | −0.009 | 0.378ns |
Note(s): ***p < 0.001; **p < 0.01; *p < 0.05; ns Not significant
Source(s): Authors’ own creation
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Acknowledgements
This work was supported by the 2021 Science and Technology Plan Project of Chongzuo City: Construction and planning response of resilient and healthy city evaluation system under the new coronavirus epidemic.