The role of smart governance in ensuring the success of smart cities: a case of Thailand

Abstract

This study concentrates on the question of what influence does each component of smart governance have on the effectiveness of Thailand’s smart city. The primary objective of this study is to identify the components of smart governance and examine the impact of smart governance components on the effectiveness of smart cities. This study is classified as quantitative research and was conducted through the implementation of a survey for a total of 767 residents in the cities of Chiang Mai and Khon Kaen in Thailand. In the examination of the data, we employed multivariate data techniques, specifically the methodology of Structural Equation Modeling (SEM). To achieve this objective, the measurement of Transparency (TRAN), Collaboration (COL), Partnership (PAN), Participation (PAT), Communication (COM), and Accountability (ACC) was conducted to evaluate the relationships with the effectiveness of smart cities (EFF). Our study finds that the variables include Partnership, Participation, Communication. Accountability, on the other hand, has a negative relationship with Effectiveness. Nevertheless, a negative relationship can be observed between the variables Collaboration, Transparency and Effectiveness. Within the realm of research findings, we emphasize the recognition of governance factors that can assist smart city administrators in formulating public policies and implementing smart city initiatives that actively engage citizens in the pursuit of sustainable development objectives.

Keywords:

• Smart governance
• smart city
• open government
• public policy
• sustainable development
• structural equation model
• Thailand

SUBJECT:

• Statistics for social sciences
• Asian studies
• cultural studies
• public administration & management

1. Introduction

The influence of globalization, industrialization, and urbanization on the socioeconomic transformations of the twenty-first century is generally recognized in scholarly literatures (Blasi et al., 2022; De Guimarães et al., 2020; Liu & Qi, 2022; Yin & Song, 2023). Citizens from various parts of the world relocate to metropolitan areas where are known as drivers for economic development and hubs of innovative activity. In light of the numerous challenges presented by the swift process of urbanization, the concept of ‘smart cities’ has evolved. The concept was introduced by the United Nations with the aim of achieving the Sustainable Development Goals (SDGs), specifically Goal 11, which focuses on the development of sustainable cities and communities. This goal seeks to enhance the inclusivity, safety, resilience, and sustainability of urban areas. Governments worldwide are increasingly implementing smart cities, which aim to improve the quality of citizen’s life through efficient, effective and sustainable resource management (Ben Yahia et al., 2021; Blasi et al., 2022; Tura & Ojanen, 2022).

The concept of the ‘smart city’ is advocated as a strategy to achieve urban sustainability objectives by leveraging advanced information and communication technologies (ICTs), the Internet of Things (IoT), and big data analytics (Meijer & Bolívar, 2016; Stübinger & Schneider, 2020). This concept initially emerged in the 1990s with the main goal of improving economic prosperity and elevating the quality of life for individuals (De Guimarães et al., 2020; Meijer & Bolívar, 2016). Subsequently, a wide range of studies and research endeavors pertaining to smart cities have been undertaken. Nevertheless, the definition of ‘smart cities’ remains a subject of debate, as there is currently no universally accepted definition (Meijer & Bolívar, 2016; Stübinger & Schneider, 2020; Tura & Ojanen, 2022).

Through the research conducted by Meijer and Bolívar (2016), an extensive review of the literature was undertaken to identify and characterize the various dimensions associated with smart cities. The classification of research on smart cities can be divided into three distinct categories: technology-focused research, human resource-focused research, and governance-focused research (Meijer & Bolívar, 2016). When examining the concept of smart cities, a widely recognized aspect involves the use of information technology to improve urban governance by tackling the challenges posed by urbanization and fulfilling the needs of urban residents. In accordance with this categorization, governance is identified as one of the key components that significantly contribute to the realization of smart city goals. This element of smart cities is concerned with the participation of several stakeholders within the urban environment and places a strong emphasis on a user-centric approach.

The concept of ‘smart governance’ is widely recognized as one of the characteristics of a smart city. Giffinger (2010) defined and identified the six-dimensional components of smart cities as follows: smart economy, smart citizens, smart governance, smart mobility, smart environment, and smart living (Giffinger et al., 2010). The categorization of smart cities discussed in this context exhibits distinctions from the classification put forth by Fernandez-Anez et al. (2018). According to his definition, the concept of a smart city encompasses seven dimensions: smart economy, smart people, smart governance, smart mobility, smart environment, smart living, and smart energy (Fernandez-Anez et al., 2018). It could be argued that the concept of smart governance is commonly acknowledged by scholars and researchers, regardless of how smart cities are classified.

The governance aspect holds same significance as the advancement and establishment of information technology (Sucupira Furtado et al., 2023). The concept of smart governance involves a comprehensive range of activities within the realm of state administration, which includes the advancement of technological solutions for the establishment and management of smart cities.

From the standpoint of sustainable development, the presence of smart governance plays a pivotal role in facilitating and achieving the objectives of smart cities (Tura & Ojanen, 2022). The logic behind this is that smart governance prioritizes the principles of transparency and a citizen-centric approach, while also emphasizes the importance of government collaboration and partnership networks involving both the private and public sectors. These lead to the formulation of public policies that effectively respond to the needs and preferences of diverse societal groups. Furthermore, the implementation of smart governance will lead to enhancements in the administration of smart cities, encompassing improvements in both the processes (the speed of decision-making process) and outcomes (for example, economic growth, sustainable development, and community safety) (Meijer, 2016)

Smart governance has the potential to increase public participation in decision-making processes. Public participation in urban development can promote a more democratic and legitimate policy-making process by assisting citizens in becoming better informed about governmental and public affairs. This mechanism enhances citizens’ frequent communication and motivation to participate in public policy and urban development, thereby improving accountability and transparency in governmental affairs. Furthermore, it improves the capacity of governmental organizations to collaborate with other sectors in delivering public services in the smart city. Smart governance is, therefore, a crucial element for achieving success in smart city initiatives (Tomor et al., 2019).

The various elements of smart cities, including economics, transportation, livelihoods, energy, and the environment, have a crucial role in enhancing the overall well-being of urban inhabitants and can be subjected to measurement and evaluation. Nonetheless, assessing and evaluating the effectiveness of smart governance, which is a critical component in the development of smart cities, remains a challenge (Meijer, 2016; Ruhlandt, 2018). The emergence of smart cities in numerous areas has underscored the growing significance of smart governance research. The concept of smart cities has been employed from its beginnings to address rapid challenges in technology, economics, and society. Nevertheless, with the continuous evolution of these dimensions, there is a growing need to explore new approaches for the successful implementation of smart governance. Consequently, there is a growing number of scholarly investigations on smart governance, which is closely linked to the formation and implementation of public policies designed to respond to the demands of local citizens. Additionally, it will contribute to enhancing the effectiveness of smart city operations in multiple dimensions (De Guimarães et al., 2020).

According to contemporary studies on smart governance, the majority of the research focuses on information technology and its applications to smart cities (Lee et al., 2013; Odendaal, 2003; Walravens, 2012). Furthermore, in the governance dimension, the primary focus will be on the implementation of strategies and policies with the objective of transforming cities into smart cities (Lin et al., 2019), as well as an analysis of the positive and negative impacts that will befall smart cities in general. Despite the increasing scholarly interest in smart governance, which focuses on developing and implementing public policies to meet citizens’ demands, there is a lack of research on the specific components of smart governance (Andronie et al., 2021; Dabija & Vătămănescu, 2023; Fernando & Lăzăroiu, 2023; Ruhlandt, 2018). Although De Guimarães et al. (2020) investigated these components, they were only shown to be related to the quality of public life. As a result, no research has been performed to investigate the components of smart governance that relate to the effectiveness of smart cities from both the perspectives of local citizens and administrators (De Guimarães et al., 2020).

Thailand has been actively promoting the smart city initiatives through the development of urban areas that leverage modern and intelligent technologies and innovations. The primary objective is to create a strong digital infrastructure, boost the economy with digital technology, improve quality of life, and move towards a digital and transparent government. The development of smart cities in Thailand encompasses seven primary dimensions, namely: 1) Smart Environment, 2) Smart Mobility, 3) Smart Living, 4) Smart Citizenship, 5) Smart Energy, 6) Smart Economy, and 7) Smart governance (Depa Thailand - Smart City Plan, n.d.). Smart governance specifically pertains to the application of advanced technologies and data-driven methodologies in the administration and decision-making procedures of governmental organizations.

In the initial phase of development, three provinces, namely Khon Kaen, Chiang Mai, and Phuket, were chosen as the pilot locations. Subsequently, an additional four provinces, namely Chonburi, Rayong, Chachoengsao, and Bangkok, have been designated for the implementation of the smart city initiative. By 2019, a total of seven pilot provinces were intended to achieve a goal in advancing the development of smart cities. The Thai government outlined the smart city initiative in its national roadmap. Thailand launched a smart city project by choosing provinces to carry out experiments. The Thai government selected Chiang Mai and Khon Kaen as pilot provinces to establish itself as a digital services hub in the ASEAN region and the center for global connections with other countries. It aims to attract digital workforces, investors, and software companies to Thailand to promote new startups and enhance the digital economy. (Naprathansuk, 2017). Khon Kaen and Chiang Mai provinces have prioritized and implemented smart city policies outlined in their provincial development plan. Both cities demonstrate a strong potential for advanced urban development and urban planning, as evidenced by their current smart city initiatives. Khon Kaen and Chiang Mai received DEPA scores of 72.39 and 72.57 out of 100, respectively, placing them at the top tier of smart city development (Depa Thailand - Smart City Plan, n.d.). Therefore, Khon Kaen and Chiang Mai are relevant to represent the data in this study in order to reveal anticipated results authors aim to have.

In this paper, we, therefore, have selected research sites among those pilot provinces, namely Khon Kaen and Chiang Mai. Both provinces share a common characteristic of engaging in collaborative efforts between the public and the private sectors for the purpose of smart city development. Furthermore, international networks have been established to achieve smart city objectives.

In Thailand, smart governance is also considered a crucial dimension within the broader framework of smart city development. The primary objective of this initiative is to establish a service system that facilitates convenient access to government services for people. This entails the swift expansion of public participation channels, which encompasses initiatives such as granting public access to information and fostering transparency and accountability. In order to attain the objectives, the government intends to establish a management framework and implement a process of public involvement, as well as provide support for the long-term viability of the initiative. In the context of smart governance, the evaluation criteria have been established to measure the percentage of citizens’ access to informative online content as well as the percentage of citizen participation in public development services (Depa Thailand - Smart City Plan, n.d.). Consequently, the emphasis on communication, transparency, and participation as the components of smart governance can be observed. Nevertheless, the relationship between the effectiveness of smart cities and smart governance remains unobservable and unestablished.

Consequently, our focus lies in the analysis of the several elements of smart governance that impact the effectiveness of smart cities. The research questions are as follows: What influence does each component of smart governance have on the effectiveness of Thailand’s smart city?

2. Theoretical framework and research hypotheses

Governance is a concept that has gained widespread acceptance in the fields of political science and public administration. It originated from the New Public Management (NPM) concept, which first arose during the 1980s. Its function is connected to the operational aspects of the governmental organization. This entails the involvement of both central and local governments in enhancing the effectiveness of public services and systems with a focus on outcomes and responsibility (Bolívar, 2018; Meijer & Bolívar, 2016).

The term ‘governance’, as defined by Lynn et al. (2000), encompasses several components, including organizational structures, administrative processes, systems of incentives and rules, philosophies of administration, or a combination of these components (Lynn et al., 2000). Thus, the term ‘governance’ is frequently employed within the realm of organizational management as an essential aspect. Furthermore, in the concept of governance in cities, Odendaal (2003) introduced an additional definition that encompasses the management of local governments with the aim of achieving economic growth, equitable income distribution, and efficient public administration (Odendaal, 2003). According to his definition, the primary focus is on the roles and responsibilities of governmental entities. Governance in the context of urban affairs can be defined as a collaborative effort between governmental and non-governmental stakeholders to formulate public policies for the city (Nesti, 2020). However, the existing definition lacks the inclusion of public participation in governance and fails to acknowledge the utilization of technology in the context of governance. Hence, the concept of ‘smart governance’ is a comprehensive idea that incorporates the constraints.

Smart governance is a crucial element within the framework of smart cities, as conceptualized by Giffinger (2010). Smart governance involves the using of ICTs to enhance citizen engagement in decision-making processes. It may help citizens become more knowledgeable about public and social services, as well as more proficient in communication. This interaction enhances citizens’ readiness and enthusiasm to participate in public policy and urban development, thereby increasing governmental transparency. Within this framework, smart governance pertains to the utilization of information technology by governmental bodies to enhance the provision of public services. The primary emphasis lies on the proactive participation of citizens and the promotion of accountability and transparency (Giffinger et al., 2010).

Scholl et al. (2009) underscored the importance of smart cities in the context of governance and suggested that it is essential for cities to embrace smart city systems, and that public policies should be adjusted accordingly to align with the strategies toward becoming smart cities. Moreover, urban areas should demonstrate a cultural environment that promotes creativity and innovation while also offer a high quality of life, support economic development, ensure stability and security, and facilitate sustainable progress in social, economic, and environmental aspects (Scholl et al., 2009). Furthermore, Meijer & Bolívar (2016) emphasized the importance of smart governance by clarifying the concept of smart cities through a three-fold framework. The dimensions under consideration involve smart cities as cities using smart technologies, smart cities as cities with smart people, and smart cities as cities with smart collaboration (Meijer & Bolívar, 2016). The final dimension centers on governance and underscores the interaction among numerous stakeholders within smart cities. In this smart city, citizens would be seen as the focal point of administrative activities. Consequently, these interactions will facilitate the exchange of knowledge among citizens and other stakeholders, ultimately fostering the development of innovation hubs (Kourtit et al., 2012).

Within the framework of smart governance, open government can also provide citizen with the opportunity to access governmental information and documents. This enhances public participation in the processes and activities related to the administration of government. According to Harrison et al. (2012), the achievement of a prosperous open government necessitates the presence of three fundamental elements: transparency, participation, and collaboration, which are often regarded as the main principles of public administration (Harrison et al., 2012).

Open government in the smart city context ensures that data and information are readily available to all citizens who require it. All citizens can utilize that information for any purpose that might improve their quality of life. (Goldsmith & Crawford, 2014; Lnenicka & Saxena, 2021; Ubaldi, 2013). This means that open government enables the public, private sector, and citizens to utilize governmental data through technological advancements, ensuring that the data is openly accessible and usable. Considering the public values of open government, it will strengthen democratic process and enhance the effectiveness of smart cities by promoting transparency, accountability, participation, and collaboration (Anthopoulos et al., 2022; Jetzek et al., 2013; Pereira et al., 2017).

Furthermore, open government as a key element of smart governance significantly improves the quality of life for residents in smart cities by ensuring effective management and governance. Delivering precise and timely information and services to citizens enhances citizen engagement and enhances the government’s capacity to provide public services to all segments of society. (Neves et al., 2020; Rana et al., 2017). Through the concept of open government, smart cities can also enhance efficiency, effectiveness, and transparency in managing and providing public services. (Lodato et al., 2021; Nam & Pardo, 2011; Pereira et al., 2017). Additionally, Pereira et al. (2017) suggest that open government can establish six mechanisms: efficiency, effectiveness, intrinsic enhancements, transparency, participation, and collaboration. (Pereira et al., 2017). The literature supports the idea that implementing open government in the context of smart cities can enhance their effectiveness.

Chourabi et al. (2012) identified various dimensions of smart governance including collaboration, leadership and championing, participation and partnership, communication, data exchange, service and application integration, accountability, and transparency. (Chourabi et al., 2012). This framework is widely employed in the analysis of progressively sophisticated administrative systems. Nevertheless, several studies have failed to comprehensively examine all eight dimensions of smart governance as outlined by Chourabi’s framework.

An illustrative example can be observed in the study conducted by De Guimarães et al. (2020), wherein they employ a set of five dimensions, namely collaboration, participation and partnership, communication, accountability, and transparency, to examine the relationship between these components of smart governance and the overall quality of life of citizens residing in smart cities (De Guimarães et al., 2020).

This study extended and applied the six components of smart governance as outlined by De Guimarães et al. (2020), namely transparency, collaboration, participation, partnership, communication, and accountability. These components were employed as a conceptual framework to analyze the relationship between them and the effectiveness of smart cities.

2.1. Transparency and effectiveness of smart cities

Transparency serves as a means of empowering citizens by enabling them to scrutinize government entities. The use of this approach has the potential to encourage government entities to transparently develop policies that are more responsive to the demands and requirements of the average citizen. The oversight of state activities should be entrusted to stakeholders who have a vested interest in the outcomes of state actions. This process additionally addresses the undesirable conduct demonstrated by government officials through the implementation of legislative and regulatory actions (Liu & Qi, 2022; Meijer, 2013).

The connection between transparency and the concept of smart cities can be established by considering people as the core focus of smart city governance. To uphold the principle of transparency, it is imperative that people are afforded the opportunity to actively participate in the inspection of government information and administration (Schware & Deane, 2003). The practice of transparency is widely recognized as essential for cultivating a democratic environment, aiming to develop policies that support open government (De Guimarães et al., 2020; Harrison et al., 2012). Furthermore, transparency is also closely associated with public participation, as it enables citizens to utilize government-provided information as a foundation for collectively formulating public policies that address the public needs (Jiang, 2021).

Transparency is one of the essential indicators of the government’s effectiveness, as it facilitates citizen participation in the management process, mitigates corruption, and fosters accountability between the government and its citizens (Chourabi et al., 2012). According to literatures and theoretical studies, there is evidence to suggest that public satisfaction with government services can be influenced by the transparency of government (Ben Yahia et al., 2021; Meijer, 2013; Tomor et al., 2019). Thus, the first Hypothesis was developed.

H1: Transparency is positively related to the effectiveness of smart cities.

2.2. Collaboration and effectiveness of smart cities

Collaboration has emerged as an achievable approach for addressing the challenges that arise in urban contexts. By fostering cooperative relationships between governmental agencies and individuals, collaboration enables effective problem-solving and resolution of these issues. Collaboration can take various forms, including cooperation between two parties, or ‘dyads’, as well as multi-party cooperation (Osborne, 2010). Additionally, collaboration can extend to cooperation at the worldwide level. The emerging focal point in the field of government administration pertains to the establishment of public-private partnerships (PPPs), wherein the concerted efforts of pertinent stakeholders exemplify collaborative efforts such as public procurement or project financing (Nesti, 2020; Pianezzi et al., 2023).

The integration of collaboration within the realm of public policymaking is widely recognized as an essential element in the successful management of smart cities (Giffinger et al., 2010; Yin & Song, 2023). In addition, the effectiveness of public management relies on the ability to cultivate collaboration among a wide range of stakeholders (Ben Yahia et al., 2021; Chourabi et al., 2012; Scholl et al., 2009).

The examination of potential outcomes of public-private sector partnerships with the government in this study places significant emphasis on the consideration of collaboration as a crucial factor. This entails the development of arguments pertaining to diverse public matters. Instead of focusing on urban challenges, this collaboration has the potential to promote sustainable development that exemplifies the effectiveness of smart cities (Ben Yahia et al., 2021; Pereira et al., 2017). The second Hypothesis was developed.

H2: Collaboration is positively related to the effectiveness of smart cities.

2.3. Partnership and effectiveness of smart cities

Government agencies have the potential to establish partnerships with other entities, such as the commercial sector, educational institutions, communities, and other relevant organizations. Furthermore, the establishment of partnerships facilitates the development of trust among agencies. Partnership can enhance the effectiveness of public services that has an impact on the public interest (Osborne, 2010).

Moreover, the concept of partnership is deeply connected with the concept of smart governance. This aligns with the findings of Awoleye et al. (2014), who suggest that establishing partnerships with several agencies can effectively enhance the operational effectiveness of state agencies (Awoleye et al., 2014). Moreover, partnerships can also facilitate networking among local stakeholders to promote innovative public policy (Nesti, 2020). Given the current circumstances, it is imperative to acknowledge that state agencies are unable to operate in isolation (Ben Yahia et al., 2021). Consequently, the attainment of effective administration necessitates the establishment of collaborative and coordinated efforts among key sectors. The third Hypothesis was developed.

H3: Partnership is positively related to the effectiveness of smart cities.

2.4. Participation and effectiveness of smart cities

The participation of citizens in the governance process enhances the government’s ability to address the needs and concerns of the population, as it stems from individuals who experience the direct impact of governmental actions. Citizens’ participation in the political process enables them to provide valuable and constructive recommendations to the local government, thereby facilitating the development of more effective public policies that directly address the needs and concerns of the populace (Liu & Qi, 2022). It can also enhance the democratic legitimacy for policy-making process by helping citizens become more knowledgeable about government and public affairs and allowing citizens to scrutinize governmental affairs.(Tomor et al., 2019). Furthermore, it has the potential to bolster the government’s ability to address urban challenges, thereby enhancing the overall efficacy of smart cities (Przeybilovicz et al., 2022). The fourth Hypothesis was developed.

H4: Participation is positively related to the effectiveness of smart cities.

2.5. Communication and effectiveness of smart cities

Communication has been accepted as a crucial element in the domain of smart governance. Community communication has its role in fostering partnerships and cooperation between governments and citizens (Lodato et al., 2021; Odendaal, 2003). The process of communication plays a vital part in promoting transparency within government administration, as it facilitates the dissemination of information and enhances public understanding of the activities carried out by government agencies (Chourabi et al., 2012; Tomor et al., 2019). Moreover, effective communication has a significant impact on the overall effectiveness of smart cities. The fifth Hypothesis was developed.

H5: Communication is positively related to the effectiveness of smart cities.

2.6. Accountability and effectiveness of smart cities

Accountability has a fundamental connection to the degree of commitment exhibited by a governor in fulfilling their obligations to the public within the domain of governance. The administration of public resources, including financial assets, has been entrusted to public authorities (De Guimarães et al., 2020). In alternative terms, one could argue that any action carried out by a governmental entity is deemed to have implications for the general populace. As a result, it is the state authorities who are accountable for the outcomes that arise as a result of their actions.

Accountability is crucial for contemporary public management as it involves aligning organizational goals and promoting cooperation. Responsibility is intrinsically associated with the stakeholders of the state, each of whom exerts an influence on the welfare of the general population (König, 2021; Osborne, 2010). Therefore, by implementing accountability measures, effective governance can be established to deal with urban challenges, facilitate economic growth, and enhance the quality of citizens’ life (De Guimarães et al., 2020). The sixth Hypothesis was developed.

H6: Accountability is positively related to the effectiveness of smart cities.

2.7. Effectiveness

The effectiveness of smart city management covers the capacity to deal with challenges that arise from urban growth, including those related to densely populated communities, economic competitiveness, and environmental concerns, among others. Additionally, it encompasses the procedure of fulfilling the particular needs of the local community and enhancing the overall quality of life of its residents (Al Farsi & Achuthan, 2018). This can be evaluated through the achievement of superior results, such as enhanced economic progress, sustainable advancement, or heightened stability in livelihoods (Meijer, 2016).

Assessing the effectiveness of government agencies may pose greater challenges compared to evaluating the performance of private-sector entities, which can be quantified through earnings or profits (Meijer, 2016). Nevertheless, Pereira et al. (2018) proposed that measuring the effectiveness of smart cities can be accomplished by considering various factors, such as cost reductions, decreased staff numbers, enhanced staff efficiency and performance, and the implementation of a more systematic management system (Pereira et al., 2018). All hypotheses are formed and represented below in Figure 1.

Figure 1. PartLabel-upper Theoretical framework proposed.

3. Methodology

3.1. Survey design and data collection

This research entails a quantitative survey aimed at examining the correlation between smart governance and administrative effectiveness within the context of smart cities. The present study employed the structural equation modeling (SEM) technique, as recommended by Kline, 2015. SEM is a robust methodology that allows for the examination of interdependencies among observable variables and latent variables (constructs). In this study, SEM was utilized to investigate the relationship between smart governance and the effectiveness of smart cities in Thailand. Confirmatory Factor Analysis (CFA) is employed to establish the relationship between observable variables and latent variables, enabling the Structural Equation Modeling (SEM) technique to establish paths of relationship and impact among the modeled constructs. The questionnaires that were utilized in this study were adapted or adjusted from previously validated constructs to assess the research constructs. Several questionnaire items were modified to align with the specific research context. However, it is worth noting that some items were able to be incorporated immediately due to their relevance in previous studies pertaining to the smart governance context. The informed consent for participation in the study has been obtained verbally as well as the written one.

This study’s protocol was reviewed and approved by the Khon Kaen University Ethics Committee for Human Research based on the Belmont Report and GCP in Social and Behavioral Research, No. HE653160.

The questionnaire has been divided into three distinct sections to ensure the collection of data that aligns most closely with the research objectives. The first section consisted of a set of five demographic inquiries pertaining to the participant’s gender, age, occupation, educational achievement, and income. In addition, to validate the efficacy of the survey, respondents were queried regarding their present domicile and duration of residency.

The second section comprised 29 inquiries pertaining to the viewpoints of the participants on seven aspects of smart governance and the effectiveness of smart cities. These dimensions encompass transparency, collaboration, partnership, participation, communication, accountability, and effectiveness. The seven dimensions were derived from scholarly sources, including peer-reviewed literature and research on smart governance. The last section of our survey comprised a single inquiry regarding participants’ recommendations for the implementation of smart governance shown in Table 1.

Table 1. Construct and observable variables.

Transparency (TRAN)
TRAN1	Disclosure of information to citizens promotes greater transparency in the decision-making processes of government agencies.
TRAN2	The use of technology increases the transparency of government agencies’ operations.
TRAN3	Transparency in the information and actions of government agencies reduces the occurrence of corruption.
TRAN4	Transparency in the information and actions of government agencies empowers the civic sector.
Collaboration (COL)
COL1	The collaboration between citizens and the government agencies generates the effectiveness of smart cities.
COL2	Government agencies that consider public opinion on urban governance issues typically carry out their duties more effectively.
COL3	Public participation in the budget administration of state agencies fosters greater cooperation between citizens and state agencies.
COL4	Strong community leaders can represent the citizens in effective collaboration with state agencies.
Partnership (PAN)
PAN1	Partnership with educational institutions for technological development increases the technological skill for technology users.
PAN2	Partnership with educational institutions for technological development can develop the economy
PAN3	Partnerships with other organizations, such as non-governmental organizations, associations, cooperatives, religious institutions, and so on, are essential to the city’s technological development.
PAN4	The partnership between local, regional, and central governments is essential to effectively applying technology to city development.
Participation (PAT)
PAT1	Public participation promotes greater transparency in governmental processes.
PAT2	The use of technology enables greater citizen participation in government agencies’ operations.
PAT3	Public participation increases the effectiveness of government administration.
PAT4	Public participation enhances communication between the public and private sectors.
Communication (COM)
COM1	Communication between government agencies and citizens promotes transparency in decision-making.
COM2	The use of technology enhances the effeciency of communication between government agencies and citizen.
COM3	Tools that facilitate government-to-citizen communication should be considered as priority.
COM4	Social networks are practical and efficient tools for government-to-citizen communication.
Accountability (ACC)
ACC1	Transparency in the operation of government agencies helps prevent them from being held accountable for misconduct.
ACC2	Government agencies’ responsibility improves government administration.
ACC3	Accountability of government agencies reduces the occurrence of corruption.
ACC4	The accountability of government agencies will result in a stronger electoral base for executives, thereby ensuring the continuity of government project implementation.
Effectiveness of smart cities (EFF)
EFF1	The establishment of a network between the public and private sectors fosters innovation, which is essential to the effectiveness of a smart city.
EFF2	The technology, which includes security solutions and big data, is the instrument that will enable a smart city to operate more efficiently.
EFF3	Transparency of government agencies enable a smart city to operate more efficiently
EFF4	Collaboration between government agencies and the citizens through the use of relevant technology promotes more efficient smart city management.
EFF5	Public participation combined with effective communication promotes more efficient smart city management.

The research framework presented in this study comprises a set of seven variables. The initial set of six variables comprises transparency (TRAN), collaboration (COL), partnership (PAN), participation (PAT), communication (COM), and accountability (ACC). Each variable is associated with a set of four sub questions. The effectiveness of smart cities (EFF) is further examined through the addition of five sub questions. Most of the questions have been derived and modified from the scholarly works of De Guimarães et al. (2020), Harrison et al. (2012) and Odendaal (2003) and have been modified to fit the research purposes. Hence, the questionnaire will consist of a comprehensive set of 29 questions covering seven constructs. These constructs will be measured using a seven-point Likert-type scale, providing a range of response options for participants that spans from ‘strongly disagree’ (one) to ‘strongly agree’ (seven).

Data was collected from questionnaires in two specific areas, namely the Khon Kaen city municipality and the Chiang Mai city municipality. According to Kline’s (2015) formula, the number of questions in the questionnaire times a factor of 10 determines the necessary sample size. Approximately 290 samples are collected in each area, resulting in a total of at least 580 samples required for the two areas combined (Kline, 2015).

The structured questionnaire-type survey was administered over a period of three months, spanning from January to April 2023. The questionnaires were distributed to a total of 374 residents residing in the municipal area of Chiang Mai city, as well as 393 citizens residing in the municipal area of Khon Kaen city, Thailand. The distribution was done in both digital formats using Google Forms and standard paper format. The survey sample was randomly obtained without consideration for probability. The participants were approached through face-to-face interactions through Google Forms application.

A comprehensive dataset consisting of 772 responses was collected, with 374 originating from the Chiang Mai area and 398 from the Khon Kaen province. After eliminating the missing data from a total of 772 responses, we were left with 767 valid observations for the purpose of conducting statistical analysis. These observations consisted of 374 responses from Chiang Mai and 393 responses from Khon Kaen. The response rate that was deemed genuine for the sampling survey met the requirements, reaching a value of 99.35%. that are sufficient to evaluate the hypotheses.

Adapted from De Guimarães et al. (2020), Harrison et al. (2012) and Odendaal (2003).

4. Results

The sample data of 767 valid observations are shown in Table 2. In this research study, we investigated the demographic profiles of two segments - Segment 1 (KKC) and Segment 2 (CNX) - to identify any significant differences between the two groups. In particular, we looked at gender, age, level of education, occupation, monthly income, years of residence, and area of living.

Table 2. Descriptive statistics.

Demographic Variable	Category	Segment 1 (KKC)		Segment 2 (CNX)		Total		Significant Chi-Square Test
		n	%	n	%	n	%
Segment Size		393	51.2	374	48.8	767	100
Gender	Male	149	19.4	73	9.5	222	28.9
	Female	244	31.8	301	39.2	545	71.1	<.001
Age	18–30 years old	126	16.4	255	33.2	381	49.7
	31–40 years old	129	16.8	36	4.7	165	21.5
	41–50 years old	79	10.3	36	4.7	115	15
	51–60 years old	40	5.2	27	3.5	67	8.7
	Above 60 years old	19	2.5	20	2.6	39	5.1	<.001
Level of Education	Elementary school	13	1.7	12	1.6	25	3.3
	Secondary education	45	5.9	19	2.5	64	8.3
	High vocational certificate	75	9.8	58	7.6	133	17.3
	B.A. (Bachelor of Arts)	238	31	258	33.6	496	64.7
	M.A. (Master of Arts) or above	22	2.9	27	3.5	49	6.4	<.009
Occupation	Student	73	9.5	199	25.9	272	35.4
	Business owner	115	15	75	9.8	190	24.8
	Government officer	78	10.2	25	3.3	103	13.5
	Office worker	98	12.8	41	5.3	139	18.1
	Retired	12	1.6	2	0.3	14	1.8
	Unemployed	17	2.2	32	4.2	49	6.4	<.001
Monthly Income	Less than 5,000 THB	29	3.8	107	14	136	17.7
	5,001–10,000 THB	59	6.9	134	17.5	193	24.4
	10,001–15,000 THB	78	10.2	40	5.2	118	15.4
	15,001–20,000 THB	75	9.8	40	5.2	115	15
	20,001–25,000 THB	56	7.3	30	3.9	86	11.2
	Above 25,000 THB	102	13.3	23	3	125	16.3	<.001
Years of Residence	0-5 years	76	9.9	226	29.5	302	39.5
	6-10 years	122	15.9	58	7.6	180	23.5
	Above 10 years	193	25.2	90	11.8	283	37	<.001
Area of Living	In Municipal Area	387	50.5	358	46.7	745	97.1
	Outside of Municipal Area	6	0.8	16	2.1	22	2.9	0.022

Source: data adapted from authors (2023).

Descriptive Statistics: Segment 1 (KKC) had 393 respondents, making up 51.2% of the total sample, while Segment 2 (CNX) had 374 respondents, making up 48.8% of the total sample. Gender: Of the total sample, 222 (28.9%) were male and 545 (71.1%) were female. A significantly higher proportion of females was found in both Segment 1 (31.8%) and Segment 2 (39.2%). The chi-square test showed a statistically significant difference in gender distribution between the two segments (p < .001). Age: The majority of respondents were in the 18-30 years age group (49.7%), followed by the 31-40 years age group (21.5%). The distribution of age groups varied significantly between the two segments. In Segment 1, the 18-30 years age group was more common (33.2%), while in Segment 2, the 31-40 years age group was more common (16.8%). The chi-square test showed a statistically significant difference in age distribution between the two segments (p < .001). Level of Education: Of the total sample, most respondents held a B.A. degree (64.7%), followed by a high vocational certificate (17.3%). The distribution of education levels varied significantly between the two segments. In Segment 1, most respondents held a B.A. degree (31.0%), while in Segment 2, most held a high vocational certificate (33.6%).

The chi-square test showed a statistically significant difference in education level distribution between the two segments (p < .009). Occupation: Of the total sample, most respondents were students (35.4%), followed by business owners (24.8%). The distribution of occupations varied significantly between the two segments. In Segment 1, most respondents were students (25.9%), while in Segment 2, most were business owners (15.0%). The chi-square test showed a statistically significant difference in occupation distribution between the two segments (p < .001). Monthly Income: Of the total sample, most respondents had a monthly income of ‘Less than 5,000 THB’ (17.7%), followed by ‘5,001 – 10,000 THB’ (24.4%). The distribution of monthly income varied significantly between the two segments. In Segment 1, most respondents had a monthly income of ‘Above 25,000 THB’ (13.3%), while in Segment 2, most had a monthly income of ‘5,001 – 10,000 THB’ (17.5%). The chi-square test showed a statistically significant difference in monthly income distribution between the two segments (p < .001). Years of Residence: Of the total sample, most respondents had lived in their current location for more than 10 years (37.0%). The distribution of years of residence varied significantly between the two segments. In Segment 1, most respondents had lived in their current location for more than 10 years (25.2%), while in Segment 2, most had lived in their current location for 6 -10 years (29.5%). The chi-square test indicated that this difference was statistically significant (p < .001). Area of Living: Almost all respondents lived in a municipal area (97.1%). However, there was a significant difference between the two segments in terms of area of living. In Segment 1, half of respondents lived in a municipal area (50.5%), while in Segment 2, almost half lived in a municipal area (46.7%). This difference was statistically significant (p = .022). Overall, these descriptive statistics for demographic profiles show that there are significant differences between the two segments in terms of gender, age, level of education, occupation, monthly income, years of residence and area of living.

4.1. The goodness of fit (GOF)

Based on our confirmatory factor analysis (CFA), the goodness of fit (GOF) measures reprented in Table 3 indicated an acceptable model fit (p < 0.001, CMIN/df = 2.988, TLI = 0.941, CFI = 0.948, IFI = 0.948, RMSEA = 0.051), as recommended by Hu & Bentler (1999). These results suggest that the measurement model demonstrates adequate internal consistency, reliability, convergent validity, and discriminant validity, as all constructs were connected with covariances and involved their manifest variables before testing. Furthermore, allowing covariances among errors within the same construct helped improve the GOF of the entire relationship (Kline, 2018).

Table 3. The goodness of the fit of the measurement model.

Fit index	Value	Threshold	Assessment
p-value	0.000		Acceptable for complex model
CMIN/df	2.988	<3.00	Passed
TLI	0.941	>0.90	Passed
CFI	0.948	>0.90	Passed
IFI	0.948	>0.90	Passed
RMSEA	0.051	<0.10	Passed

Note. that CMIN/df = Chi-square/degree of freedom; TLI = Tucker–Lewis index; CFI = comparative fit index; IFI = incremental fit index; RMSEA = root mean square error approximation.

4.2. Convergent validity

The present study aimed to assess the convergent and discriminant validity of the measurement model used to evaluate the effectiveness of smart cities. As shown in Table 4, all constructs demonstrated good convergent validity, with indicator loadings greater than 0.7 and composite reliability (CR) values exceeding the threshold of 0.7. The average variance extracted (AVE) values were also above the recommended level of 0.5, indicating adequate convergent validity (Abraham & Barker, 2015). All indicators of each construct loaded significantly on their respective construct, with factor loadings ranging from 0.625 to 0.835. Furthermore, the Cronbach’s alpha coefficients for each construct exceeded the threshold value of 0.7, indicating high internal consistency reliability.

Table 4. Convergent validity.

Construct	Indicator	Loading	Cronbach α	AVE	CR
Transparency	TRAN1	0.799	0.829	0.551	0.831
	TRAN2	0.750
	TRAN3	0.704
	TRAN4	0.713
Collaboration	COL1	0.764	0.828	0.549	0.839
	COL2	0.804
	COL3	0.709
	COL4	0.680
Partnership	PAN1	0.835	0.829	0.548	0.827
	PAN2	0.735
	PAN3	0.625
	PAN4	0.750
Participation	PAT1	0.748	0.843	0.575	0.844
	PAT2	0.770
	PAT3	0.807
	PAT4	0.704
Communication	COM1	0.757	0.855	0.594	0.854
	COM2	0.805
	COM3	0.760
	COM4	0.760
Accountability	ACC1	0.721	0.822	0.538	0.823
	ACC2	0.792
	ACC3	0.743
	ACC4	0.674
Effectiveness of smart cities	EFF1	0.740	0.885	0.606	0.885
	EFF2	0.777
	EFF3	0.803
	EFF4	0.790
	EFF5	0.781

Note. that AVE = average variance extracted; CR = composite validity.

4.3. Discriminant validity

For the next stage, the HTMT method and the criterion are applied to determine the measurement’s model discriminant validity (Fornell & Larcker, 1981), as shown in Table 5 (Henseler et al., 2015). The following table below presents the Fornell & Larcker, 1981’s determinant for the latent variables, which was employed to examine discriminant validity, which was partially satisfied. According to cross-loadings between the related latent variables, several off correlations show that it is larger than the diagonal values (square root AVEs), indicating the presence of discriminant validity. The Fornell and Larker criterion, nevertheless, has been condemned for its inability to demonstrate the distinctness of the latent variables in the analysis of measuring the model (Fornell & Larcker, 1981; Henseler et al., 2015).

Table 5. Discriminant validity.

Fornell & Larker Criterion
Construct	CR	AVE		EFF		ACC	COM		PAT		PAN	COL		TRAN
EFF	0.831	0.551		0.778
ACC	0.839	0.549		0.695		0.734
COM	0.827	0.548		0.907		0.683	0.771
PAT	0.844	0.575		0.901		0.728	0.914		0.758
PAN	0.854	0.594		0.86		0.636	0.85		0.844		0.740
COL	0.823	0.538		0.771		0.651	0.779		0.83		0.706	0.741
TRAN	0.885	0.606		0.778		0.784	0.802		0.828		0.766	0.829		0.742
HTMT Ratio Approach
EFF	-		-		-			-		-			-		-
ACC	0.54		-		-			-		-			-		-
COM	0.73		0.52		-			-		-			-		-
PAT	0.72		0.55		0.71			-		-			-		-
PAN	0.67		0.47		0.46			0.64		-			-		-
COL	0.64		0.49		0.60			0.63		0.53			-		-
TRAN	0.61		0.59		0.62			0.63		0.58			0.62		-

Source: data adapted from authors (2023).

The heterotrait–monotrait (HTMT) ratio technique by Henseler et al. (2015) was also used in this study to assess discriminant validity. The HTMT ratio technique was utilized to obtain more dependable findings because the reliability of the (Fornell & Larcker, 1981) criteria in addressing uniqueness among latent variables was questioned. Since all of the HTMT ratios in Table 5 were lower than 0.85 or 0.90, the HTMT ratio technique recommended better discriminant validity in this study (Kline, 2018).

4.4. Structural equation modeling (SEM)

4.4.1. The goodness of fit of the structural model

After examining the measurement model, we developed a structural model incorporating all relevant constructs. Furthermore, the structural model with the primary objective allowed us to investigate the factors (Hu & Bentler, 1999). The goodness of fit (GOF) criteria results in Table 6 were positive and showed how the constructs supported each other. All GOF indices were within the acceptable thresholds.

Table 6. The goodness of fit of the structural model (SEM).

Fit index	Value	Threshold	Assessment
p-value	0.000		Acceptable for complex model
CMIN/df	2.988	<3.00	Passed
TLI	0.941	>0.90	Passed
CFI	0.948	>0.90	Passed
IFI	0.948	>0.90	Passed
RMSEA	0.051	<0.10	Passed

Note. that CMIN/df = Chi-square/degree of freedom, TLI = Tucker-Lewis Index, CFI = Comparative Fit Index, IFI = Incremental Fit Index, RMSEA = Root Mean Square Error of Approximation.

Table 7 summarizes all six hypotheses testing results from the structural model. H3 to H5 had a positive relationship among variables that supported the hypotheses, whereas H1, H2, and H6 relationship variables were rejected.

Table 7. Test results from the structural model.

Hypothesis	Endogenous variable	Exogenous variable	Model	Path coefficients (β)	P-value	Result
H1	Transparency	Effectiveness of Smart Cities	TRAN→ EFF	0.001	.233	Rejected
H2	Collaboration	Effectiveness of Smart Cities	COLL→ EFF	0.078	.257	Rejected
H3	Partnership	Effectiveness of Smart Cities	PAN → EFF	0.376	***	Supported
H4	Participation	Effectiveness of Smart Cities	PAT→ EFF	0.269	.01**	Supported
H5	Communication	Effectiveness of Smart Cities	COM → EFF	0.207	***	Supported
H6	Accountability	Effectiveness of Smart Cities	ACC → EFF	0.090	.07	Rejected

Noted: *** p-value < 0.001, ** p-value < 0.01.

Multigroup Moderation Analysis (MGA) was conducted to investigate the measurement invariance of a questionnaire between two groups of people that live in Khon Kaen and Chiang Mai in Thailand. The measurement invariance analysis included testing for configural invariance, metric invariance, and scalar invariance. The results of the analysis showed that the CMIN/df values of configural invariance, metric invariance, and scalar invariance were all below the threshold of 3.00, indicating an acceptable fit. In addition, the TLI, CFI, IFI, and RMSEA fit indices all met the threshold for acceptability, with the exception of TLI for scalar invariance, which was below the threshold. Therefore, partial measurement invariance was supported, allowing for the comparison of factor loadings between the two groups.

These findings are consistent with previous research which has demonstrated the importance of testing for measurement invariance in the multigroup analysis (Byrne et al., 1989; Chen, 2007; Cheung & Rensvold, 2002). By establishing measurement invariance, researchers can be confident that the same construct is being measured consistently across different groups, and that any observed differences between groups are not due to measurement bias (Vandenberg & Lance, 2000).

Furthermore, partial measurement invariance illustrated in Table 8 allows for the examination of factor loadings between groups, which can provide insights into the relative importance of different items in the questionnaire for each group (Vandenberg & Lance, 2000). This information can be used to tailor interventions or messages aimed at promoting sustainable behavior to different groups of consumers.

Table 8. Measurement invariance.

Fit Index	Configural Invariance (Unconstrained Model)	Metric Invariance (Equal Factor Loading)	Scalar Invariance (Equal Intercept)	Threshold	Assessment
p-value	0.000
CMIN/df	2.058	2.089	2.707	<3.00	Acceptable
TLI	0.936	0.934	0.897	>0.90	(Partial)
CFI	0.944	0.94	0.903	>0.90	Acceptable
IFI	0.944	0.941	0.904	>0.90	Acceptable
RMSEA	0.037	0.038	0.047	<0.10	Acceptable

Source. Data adapted from authors (2023).

In the present study, we aimed to investigate the loading differences of six exogenous variables (Transparency, Collaboration, Partnership, Participation, Communication, and Accountability) on the endogenous variable (Effectiveness of smart cities) using the Z-test. The Z-test is a widely used statistical test to compare two sample means and determine if they differ significantly. The critical ratio was set at 1.96, and the significance level was set at p < 0.05.

The results of the Z-test in Table 9 indicated that there were significant loading differences between the exogenous variables and the endogenous variable. Specifically, Transparency had a negative loading on the Effectiveness of smart cities (H1; Standardized Loading = -0.035; Critical Ratio = |0.117|), whereas Collaboration had a positive loading but not significant (H2; Standardized Loading = 0.057; Critical Ratio = |-0.207|).

Table 9. Test results from loading differences.

Hypothesis	Endogenous variable	Exogenous variable	Model	Standardized Loading		Critical Ratio Difference	Threshold
				(KKC)	(CNX)
H1	Transparency	Effectiveness of Smart Cities	TRAN→ EFF	−0.035	−0.008	|0.117|	|1.96|
H2	Collaboration	Effectiveness of Smart Cities	COLL→ EFF	0.057	−0.006	|-0.207|	|1.96|
H3	Partnership	Effectiveness of Smart Cities	PAN → EFF	0.337***	0.311	|-0.039|	|1.96|
H4	Participation	Effectiveness of Smart Cities	PAT→ EFF	0.306**	0.308	|-0.098|	|1.96|
H5	Communication	Effectiveness of Smart Cities	COM → EFF	0.253**	−0.123	|-0.628|	|1.96|
H6	Accountability	Effectiveness of Smart Cities	ACC → EFF	0.064	0.548***	|2.895|*	|1.96|

Source: Data adapted from authors (2023). Note. *** p-value < 0.001, ** p-value < 0.01, * p-value < 0.05.

In contrast, Partnership (H3; Standardized Loading = 0.337***; Critical Ratio = |-0.039|) and Participation (H4; Standardized Loading = 0.306**; Critical Ratio = |-0.098|) had significant positive loadings on the Effectiveness of smart cities. Furthermore, Communication also had a significant positive loading on the Effectiveness of smart cities (H5; Standardized Loading = 0.253**; Critical Ratio = |-0.628|).

Finally, Accountability had a positive loading on the Effectiveness of smart cities (H6; Standardized Loading = 0.548***), and it was the only variable that had a significant loading difference with the exogenous variable (Critical Ratio = |2.895|*).

5. Discussion

The study’s results reveal significant findings regarding the impact of smart governance on the effectiveness of smart cities, specifically in relation to Transparency (TRAN), Collaboration (COL), Partnership (PAN), Participation (PAT), Communication (COM) and Accountability (ACC), in terms of public opinion.

The findings show that not all elements of smart governance contribute to the effectiveness of smart cities. This study has provided empirical evidence to support the existence of positive relationships between variables among PAN, PAT, COM, and EFF. However, there exists a negative relationship between TRAN, COL, ACC and EFF.

The findings indicate that there exists a negative relationship between transparency (TRAN) and the effectiveness of smart cities (EFF), which contrasts with the research conducted by De Guimarães et al., 2020, where a positive relationship was observed between transparency and quality of life. The reason is that local governments that aspire to be ‘open government’ frequently encounter resource constraints when it comes to the allocation and provision of budgets, personnel, and administrative capacity for the purpose of disseminating public information. This assertion holds validity in the context of utilizing modern technology to improve transparency, especially the Information and Communication Technologies (ICT). So, investing in transparency may potentially diminish work effectiveness, which corroborates with the studies of Schmidthuber et al., 2018. Additionally, to adequately assess the effectiveness of smart city deployments in public spaces, it is imperative for these systems to exhibit transparency. This entails ensuring transparency in the governance processes that have facilitated their implementation, as well as promoting transparency in the functionality of the systems and devices encompassed by these processes. It is imperative that the purpose and actual operations of these devices are easily discernible and comprehensible, while also identifying the responsible parties for their actions where accountability lies.

The construct of Collaboration (COL) demonstrated a negative correlation with the EFF. This outcome can be partially attributed to the involvement of people in the decision-making process and in the development of the smart city, enabling the local government to gain insights into the needs and offer viable solutions to urban challenges. Collaboration facilitates the attainment of efficiency and effectiveness in the realm of urban governance. The implementation of smart governance, therefore, has the potential to improve the effectiveness of public resource allocation through collaborations with private and non-governmental organizations (Capdevila & Zarlenga, 2015; De Guimarães et al., 2020; Harrison et al., 2012). The establishment of collaborative efforts among different levels of government is essential in addressing challenges that arise from geographical and jurisdictional boundaries, which is a core aspect of smart city conceptualizations. The necessity of intergovernmental collaboration for the attainment of smart city effectiveness was exemplified in the case of CNX and KKC, where local governments demonstrated their collaborative efforts. Significantly, our study findings highlight the crucial role of the city government in fostering an inclusive and cooperative atmosphere among stakeholders, as well as in effectively coordinating the efforts of collaborating parties towards the attainment of shared smart city objectives.

On the PAN and PAT construct, it was also possible to confirm the high intensity in the positive relationship of influence on the EFF in the smart city, which corroborates with the studies of De Guimarães et al. (2020). According to Tomor et al. (2019), both PAN and PAT provide valuable recommendations that can assist governments in making more informed policy decisions (Tomor et al., 2019). Furthermore, Evdorides & Shoji (2013) have emphasized the connection between the PAN and PAT in relation to the effectiveness of smart cities (EFF), which ultimately contribute to an enhanced quality of life for citizens (Evdorides & Shoji, 2013). These factors contribute to various benefits, including but not limited to economic growth, the provision of social services, the development of infrastructure, and other related advantages. The findings of this study suggest that individuals hold the belief that the interaction between public and private entities has the potential to yield substantial advantages for the inhabitants of an intelligent urban environment.

The hypothesis test provided empirical evidence supporting the existence of a positive correlation between communication (COM) and effectiveness (EFF). According to Tomor et al. (2019), communication plays a crucial role in facilitating citizen engagement in the policy realm by reducing barriers and enabling citizens to establish connections with the government (Tomor et al., 2019). Furthermore, communication facilitates the process of democratizing the ability of citizens to participate in the development of urban areas. This, in turn, enhances the assurance of effectively providing public services to society and ultimately contributes to the improved effectiveness of smart cities (De Guimarães et al., 2020). The fundamental components of the smart city encompass terminal devices that bear resemblance to those found on the Internet of Things. The integration of information and communication technologies (ICT) within the infrastructure of Smart Cities is evident in various domains such as intelligent mobility, energy management, and service provision. These technologies not only facilitate the effective functioning of other technological systems but also foster collaborative opportunities for their combined benefits. Simultaneously, they serve as a medium for facilitating information dissemination and fostering communication between urban or municipal administration and the populace.

A negative correlation between ACC and EFF was observed within the framework of smart cities. According to De Guimarães et al. (2020), the implementation of accountability enables the achievement of efficient governance (De Guimarães et al., 2020). According to Healey (2006), accountability plays a key role in public management as it encompasses important values such as honesty, commitment to the efficient utilization of public resources, the mitigation of corruption, and the preservation of strategic government initiatives (Healey, 2006). Moreover, the establishment of accountability mechanisms can foster public trust and confidence, enabling the government to carry out its duties and deliver public services at the highest level of quality, thereby enhancing overall effectiveness.

Based on the findings of the study, it can be concluded that the dimensions of Collaboration (CO), Partnership (PAN), Participation (PAT), Communication (COM), and Accountability (ACC) within the framework of smart cities have a positive impact on the Effectiveness of smart cities (EFF) as perceived by citizens. In this particular context, it has been established that the constructs and observable variables investigated in this study have a positive impact on enhancing the governance process in urban areas. These factors encompass social, economic, and environmental dimensions, thereby promoting sustainable urban development. The implementation of various components of smart governance facilitates increased interaction and collaboration between the government and citizens, leading to the provision of effective public services within the context of a smart city.

The implementation of data analysis, incorporating statistical techniques to validate the scale, and conducting tests to assess the measurement model and the structural model enable us to assert that most of the research hypotheses were confirmed. It is important to highlight that the scope of the study was restricted to the assessment of specific aspects and variables constituting each construct. However, it should be acknowledged that there are additional pertinent factors that may influence the effectiveness of smart cities. One of the limitations inherent in this study concerns its geographical scope, which is restricted to the population residing solely within the cities of Chiang Mai and Khon Kaen in Thailand. This limitation raises the possibility that the findings may not be generalizable to other areas, potentially yielding divergent results when compared to studies conducted in different locations.

6. Implications and conclusion

6.1. Practical and policy implications

6.1.1. The significance of citizen participation in society

Successful smart cities recognize the importance of citizen participation in influencing the process of urban development. Through the adoption of participatory approaches, cities enable their inhabitants to actively engage in decision-making processes, thereby empowering them to contribute effectively. The utilization of digital platforms can serve as a notable illustration. This digital platform enables individuals to propose ideas, offer feedback, and participate in discussions pertaining to diverse urban initiatives. The platform facilitates a transparent and participatory discourse between individuals and policymakers, fostering a collective sense of ownership and mutual accountability. In order to enhance citizen participation, future smart cities may consider investigating novel approaches such as gamification and immersive technologies. The implementation of gamification strategies has the potential to stimulate citizen engagement by employing mechanisms such as rewards, challenges, and interactive experiences. Immersive technologies, such as virtual reality (VR) and augmented reality (AR), have the potential to offer citizens immersive simulations of urban projects under consideration. This capability allows individuals to provide feedback and gain a visual understanding of the potential consequences associated with different design choices.

6.1.2. Collaborative governance and partnerships

The establishment of strategic collaborations among the public and private sectors, academia, and community organizations plays a crucial role in promoting innovation and comprehensive approaches in the development of smart cities. By facilitating the participation of various stakeholders such as businesses, residents, and city officials. The adoption of a collaborative approach has led to the emergence of inventive solutions to urban challenges. In order to augment strategic alliances, forthcoming smart cities may prioritize the establishment of open innovation platforms. These platforms function as central hubs for the exchange of knowledge, pooling of resources, and fostering collaboration among various stakeholders. Open data initiatives have the potential to facilitate the dissemination of valuable information, thereby empowering businesses, researchers, and policymakers to make well-informed and evidence-based decisions. It is imperative for governance structures and frameworks to effectively address the legal, ethical, and privacy considerations that arise from the sharing of data.

6.1.3. Exploring the capabilities of nascent technological advancements

The potential for emerging technologies to significantly transform stakeholder engagement and strategic partnerships within smart cities is considerable. An illustration can be found in the application of artificial intelligence (AI) and machine learning (ML) algorithms to analyze extensive volumes of data derived from citizen interactions. These technological advancements facilitate the extraction of valuable insights and patterns within urban environments, thereby supporting decision-making processes that are grounded in empirical evidence. In the realm of urban development, forthcoming smart cities have the potential to harness the capabilities of the IoT in order to collect instantaneous data from sensors and devices that are integrated within the urban infrastructure. The utilization of this data has the potential to augment stakeholder engagement through the provision of precise and punctual information pertaining to diverse facets, including traffic patterns, air quality, and energy consumption. Furthermore, the implementation of blockchain technology has the potential to enhance transparency, security, and traceability in the realm of data sharing and transactions, thereby cultivating a sense of trust among various stakeholders involved.

6.1.4. Promoting inclusivity and equity in engagement

In order for stakeholder engagement to achieve effectiveness, it is imperative that it adopts an inclusive approach and takes into account the diverse needs of all members within the community. The effective implementation of strategies can be achieved through collaborative partnerships with local community organizations, with the aim of addressing various social and economic challenges. These collaborative alliances enable underprivileged communities, granting them a medium to express their grievances and engage actively in the process of making decisions. In order to enhance inclusivity, it is imperative for forthcoming smart cities to prioritize the mitigation of the digital divide. This objective can be accomplished through the implementation of strategies such as facilitating equitable access to digital platforms, allocating resources towards digital literacy initiatives, and guaranteeing the affordability of Internet services. Additionally, the utilization of social media platforms can facilitate cities in expanding their audience reach, thereby ensuring the inclusion of a wide range of perspectives.

6.1.5. The preservation and safeguarding of data privacy and security

The preservation of data privacy and security is of utmost importance in smart cities, given their reliance on extensive data. Stakeholders require reassurance regarding the safeguarding of their personal information and the provision of a secure environment for their participation. It is imperative for smart cities to incorporate stringent data protection protocols, uphold privacy-by-design principles, and establish explicit frameworks for data sharing and accessibility. In the realm of urban development, forthcoming smart cities have the potential to investigate decentralized data management models, wherein individuals possess authority over their data and possess the ability to bestow access on a selective basis. Privacy-enhancing technologies, such as differential privacy, have the capability to anonymize data while simultaneously enabling analysis and facilitating decision-making processes.

6.2. Conclusion

This study contributes to the understanding of the effectiveness of smart cities by confirming that the elements of smart governance have an influence on the effectiveness of smart city initiatives. The findings of the integrated model yield significant insights into the impact of public governance on the effectiveness of smart cities, specifically in relation to Transparency (TRAN), Collaboration (COL), Partnership (PAN), Participation (PAT), Communication (COM) and Accountability (ACC). These elements demonstrate a mostly positive relationship with the effectiveness of smart cities as perceived by citizens.

In addition to other constructs examined in this research, it is noteworthy to mention that TRANS is the only construct that exhibits a negative influence on EFF. The aforementioned outcome can be attributed to the limited availability of resources for facilitating the transparency process, which has the potential to undermine the effectiveness of smart cities.

One of the key findings of the study indicates that the variables PAN and PAT and COM exhibit the strongest influence on the variable EFF. The findings underscore the significance of governmental engagement with both public and private entities, as well as the inclusion of citizens in policy-making procedures, in order to enhance the effectiveness of public services within the framework of smart cities.

The analysis of smart governance holds significant importance in both academic and practical contexts. Essentially, this analysis will serve to either validate established theories or generate innovative concepts that hold academic value.

In a practical setting, smart governance is commonly acknowledged as the dimension that prioritizes the public’s interests. This is due to its direct connection to public policy and administration, aligning with the initial concept of building smart cities that prioritize the participation of citizens as stakeholders, and ensure a people-centric smart city development approach. Gaining a comprehensive understanding of the smart governance components would facilitate the enhancement of policies aimed at addressing the requirements of local residents, as well as identifying strategies that align with the locality to promote enduring effectiveness in managerial procedures and foster sustainable development.

Based on the results and findings of the conducted research, it is recommended that future studies be undertaken to explore matters pertaining to the recognition of additional variables that impact the effectiveness of public governance within the framework of smart cities. Another significant aspect to consider in the advancement of new research is the examination of various regional contexts. This is because the structural context of a city plays a crucial role in shaping the implementation of smart city practices.

Authors’ contributions

Thawatchai Worrakittimalee: Research conception and design, data collection, paper drafting, result discussion and analysis. Teerapong Pienwisetkaew: Data interpretation and analysis. Phaninee Naruetharadhol: Paper drafting and critically revising for intellectual content, and final approval of the version to be published. All authors agree to be accountable for all aspects of the work.

Disclosure statement

All authors declare no conflicts of interest.

Data availability statement

The data will be available upon request. Authors agree to make available data and materials supporting the results or analyses in our paper.

Additional information

Funding

This research was funded by the Young Researcher Development Project of Khon Kaen University Year 2022.

Notes on contributors

Thawatchai Worrakittimalee

Thawatchai Worrakittimalee, PhD. is a lecturer in International Affairs department at Khon Kaen University International College. His research areas are East Asian Studies, Japanese Politics and International Relations, Comparative Politics.

Teerapong Pienwisetkaew

Teerapong Pienwisetkaew is a lecturer in Business Administration Division at Khon Kaen University International College. His research areas are Consumer intentions and behaviors, Food waste management, Product development, Quantitative study, Functional Foods, UX/UI, Business Processes.

Phaninee Naruetharadhol

Phaninee Naruetharadhol, Ph D. is a director at Center for Sustainable Innovation and Society, Khon Kaen University International College (KKUIC). She received diamond researcher award 2022. Her research interests are Organizational Behavior, Innovation Management, Consumer Behavior and Financial Planning shown at her ongoing research articles. E-mail: [email protected]