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Rev. Gest. Soc. Ambient. | Miami | v.18.n.8| p.1-39 | e06038 | 2024.
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RGSA – Revista de Gestão Social e Ambiental
ISSN: 1981-982X
Data de submissão: 01/15/2024
Data de aceite: 03/15/2024
DOI: https://doi.org/10.24857/rgsa.v18n8-050
Organização: Comitê Científico Interinstitucional
Editor Chefe: Christian Luiz da Silva
Avaliação: Double Blind Review pelo SEER/OJS
SMART CITY DEVELOPMENT IN EASTERN ECONOMIC CORRIDOR FROM
THE PERSPECTIVE OF INDUSTRIAL SECTOR
Apisit Phahisuk
1
Pairat Pornpundejwittaya
2
Thanin Silpcharu
3
ABSTRACT
Objectives: This research aims to study the development of smart cities in the Eastern Economic Corridor (EEC)
area from the perspective of the industrial business sector and develop a structural equation model.
Theoretical framework: The study is based on the 20-year National Strategy, which emphasizes smart city
development as an approach to distribute economic, social, and technological prosperity, particularly in areas of
economic importance to the country, such as the EEC.
Methods: The research was conducted using both qualitative and quantitative methods. Quantitative data was
collected from questionnaires administered to 500 executives in the target industrial businesses of the EEC,
employing descriptive statistics, inferential statistics, and multivariate statistics.
Results and Conclusion: The research findings reveal that the development priorities are ranked into four
components: 1) Enterprise Management (= 4.34), 2) Community Operation (= 4.33), 3) Public Administration (=
4.32), and 4) City Regulation (= 4.26). The hypothesis testing results show that existing high-potential industrial
businesses (First S-Curve) and future industrial businesses (New S-Curve) place significantly different importance
on the development of smart cities in the EEC area at a statistical significance level of 0.05. The developed structural
equation model meets the assessment criteria and is consistent with the empirical data.
Implications of the research: The findings of this study can inform policymakers and stakeholders in the EEC
area about the key components and priorities for smart city development from the perspective of the industrial
business sector. The structural equation model can serve as a framework for guiding smart city development efforts
in the region.
Originality/value: This research contributes to the understanding of smart city development in the context of the
EEC area in Thailand, focusing on the perspective of the industrial business sector. The developed structural
equation model provides a novel approach to analyzing the relationships between various components of smart city
development in the region.
Keywords: Structural Equation Model, Smart City, Eastern Economic Corridor, Target Industrial Businesses, Bio-
Circular-Green Economy (BCG).
DESENVOLVIMENTO DE CIDADES INTELIGENTES NO CORREDOR ECONÔMICO ORIENTAL
A PARTIR DE A PERSPECTIVA DO SETOR INDUSTRIAL
RESUMO
Objetivos: Esta pesquisa tem como objetivo estudar o desenvolvimento de cidades inteligentes na área do
Corredor Econômico Oriental (CEE) na perspectiva do setor empresarial industrial e desenvolver um modelo de
equação estrutural.
1
King Mongkut's University of Technology North Bangkok, Thailand. E-mail: s6214011950162@kmutnb.ac.th
Orcid: https://orcid.org/0009-0003-0398-317X
2
King Mongkut's University of Technology North Bangkok, Thailand. E-mail: pairut.p@fba.kmutnb.ac.th
Orcid: https://orcid.org/0009-0003-4235-2442
3
King Mongkut's University of Technology North Bangkok, Thailand. E-mail: thanin.s@fba.kmutnb.ac.th
Orcid: https://orcid.org/0000-0001-9503-2379
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Estrutura teórica: O estudo é baseado na Estratégia Nacional de 20 anos, que enfatiza o desenvolvimento urbano
inteligente como uma abordagem para distribuir prosperidade econômica, social e tecnológica, particularmente em
áreas de importância econômica para o país, como a CEE.
Métodos: A pesquisa foi realizada utilizando métodos qualitativos e quantitativos. Dados quantitativos foram
coletados a partir de questionários administrados a 500 executivos nas empresas industriais-alvo da CEE,
empregando estatísticas descritivas, estatísticas inferenciais e estatísticas multivariadas.
Resultados e Conclusão: Os resultados da pesquisa revelam que as prioridades de desenvolvimento estão
classificadas em quatro componentes: 1) Gestão Empresarial (= 4,34), 2) Operação Comunitária (= 4,33), 3)
Administração Pública (= 4,32) e 4) Regulamento do Município (= 4,26). Os resultados dos testes de hipóteses
mostram que as empresas industriais de alto potencial existentes (Primeira Curva-S) e as empresas industriais
futuras (Nova Curva-S) atribuem uma importância significativamente diferente ao desenvolvimento de cidades
inteligentes na área da CEE, com um nível de significância estatística de 0,05. O modelo de equação estrutural
desenvolvido cumpre os critérios de avaliação e é consistente com os dados empíricos.
Implicações da pesquisa: Os resultados deste estudo podem informar os formuladores de políticas e as partes
interessadas na área da CEE sobre os principais componentes e prioridades para o desenvolvimento de cidades
inteligentes da perspectiva do setor empresarial industrial. O modelo de equação estrutural pode servir como uma
estrutura para orientar os esforços de desenvolvimento de cidades inteligentes na região.
Originalidade / valor: Esta pesquisa contribui para a compreensão do desenvolvimento de cidades inteligentes
no contexto da área CEE na Tailândia, com foco na perspectiva do setor empresarial industrial. O modelo
desenvolvido de equação estrutural fornece uma nova abordagem para analisar as relações entre vários
componentes do desenvolvimento de cidades inteligentes na região.
Palavras-chave: Modelo de Equação Estrutural, Cidade Inteligente, Corredor Econômico Oriental, Negócios
Industriais-Alvo, Economia Biorcircular-Verde (BCG).
DESARROLLO DE CIUDADES INTELIGENTES EN EL CORREDOR ECONÓMICO ORIENTAL
DESDE LA PERSPECTIVA DEL SECTOR INDUSTRIAL
RESUMEN
Objetivos: Esta investigación tiene como objetivo estudiar el desarrollo de ciudades inteligentes en el área del
Corredor Económico Oriental (CEE) desde la perspectiva del sector empresarial industrial y desarrollar un modelo
de ecuación estructural.
Marco teórico: El estudio se basa en la Estrategia Nacional de 20 años, que enfatiza el desarrollo de ciudades
inteligentes como un enfoque para distribuir la prosperidad económica, social y tecnológica, particularmente en
áreas de importancia económica para el país, como la CEE.
Métodos: La investigación se llevó a cabo utilizando métodos cualitativos y cuantitativos. Se recogieron datos
cuantitativos a partir de cuestionarios administrados a 500 ejecutivos en las empresas industriales objetivo de la
CEE, utilizando estadísticas descriptivas, estadísticas inferenciales y estadísticas multivariadas.
Resultados y conclusión: Los resultados de la investigación revelan que las prioridades de desarrollo se clasifican
en cuatro componentes: 1) Gestión Empresarial (= 4,34), 2) Operación Comunitaria (= 4,33), 3) Administración
Pública (= 4,32), y 4) Regulación Urbana (= 4,26). Los resultados de las pruebas de hipótesis muestran que las
empresas industriales de alto potencial existentes (First S-Curve) y las futuras empresas industriales (New S-
Curve) otorgan una importancia significativamente diferente al desarrollo de ciudades inteligentes en el área de la
CEE con un nivel de significación estadística de 0,05. El modelo de ecuación estructural desarrollado cumple con
los criterios de evaluación y es consistente con los datos empíricos.
Implicaciones de la investigación: Los resultados de este estudio pueden informar a los responsables políticos y
a las partes interesadas en el área de la CEE sobre los componentes clave y las prioridades para el desarrollo de
ciudades inteligentes desde la perspectiva del sector empresarial industrial. El modelo de ecuación estructural
puede servir como marco para guiar los esfuerzos de desarrollo de ciudades inteligentes en la región.
Smart City Development in Eastern Economic Corridor from the Perspective of Industrial Sector
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Originalidad/valor: Esta investigación contribuye a la comprensión del desarrollo de ciudades inteligentes en el
contexto del área de la CEE en Tailandia, centrándose en la perspectiva del sector empresarial industrial. El modelo
de ecuación estructural desarrollado proporciona un enfoque novedoso para analizar las relaciones entre varios
componentes del desarrollo de ciudades inteligentes en la región.
Palabras clave: Modelo de Ecuación Estructural, Ciudad Inteligente, Corredor Económico Oriental, Negocios
Industriales Objetivo, Economía Biocircular-Verde (BCG).
RGSA adota a Licença de Atribuição CC BY do Creative Commons (https://creativecommons.org/licenses/by/4.0/).
1 INTRODUCTION
Currently, the direction of Thailand's development, following the government's
Thailand 4.0 initiative, is outlined in the 11th National Economic and Social Development Plan
(2012-2016) and the 2nd National Information and Communication Technology Master Plan
(2009-2013). These plans aim to promote the development of information technology
infrastructure to cover the entire country and improve public services through information
technology systems. The objective is to create opportunities for citizens to access services
comprehensively and equitably. Smart City development is, therefore, a crucial part of
Thailand's strategy. The government's policy to drive Smart Cities is a step in the right direction,
utilizing technology to solve problems. However, this issue is too large for any single agency
to handle alone. The government should play a role in supporting legislation and investment,
while the private sector can assist with know-how and technology to help increase energy
efficiency and reduce CO2 emissions. Consequently, all parties must work together in
coordination.
According to the IESE Cities in Motion Index 2023 report, published by the IESE
Business School University of Navarra, Spain, which ranks smart cities in 183 countries
worldwide, Thailand's ranking has been continuously declining. In 2023, Thailand ranked
122nd globally (IESE, 2023).
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Figure 1
Ranking of Bangkok's smart cities from the IESE City in Motion Index (IESE, 2022)
Source : IESE City in Motion Index (IESE, 2022)
An examination of the rankings within the ASEAN group from the IESE Cities in
Motion Index report for the year 2023 (IESE, 2023), specifically focusing on the 6 ASEAN
countries included in the ranking - Singapore, Malaysia, Thailand, Philippines, Vietnam, and
Indonesia - reveals noteworthy findings. The results of the 2023 report indicate that Singapore
has achieved the highest ranking among the ASEAN countries, securing the 6th position.
Malaysia follows at a considerably lower rank of 107, while Thailand is positioned at 122.
Vietnam and Indonesia occupy the 130th and 135th ranks, respectively, and the Philippines is
ranked 175th.
Table 1
Smart City Ranking of ASEAN Countries from the IESE Cities in Motion Index Report,
Comparing the Years 2022-2023
ASEAN Country
2022
Ranking
2023
Ranking
Change 2022/2023
Singapore
7
6
+ 1
Malaysia
105
107
- 2
Thailand
118
122
- 4
Vietnam
134
130
+ 4
Indonesia
152
135
+ 17
Philippines
176
175
+ 1
Source: IESE Cities in Motion Index 2023 (IESE, 2023)
2018 2019 2020 2021 2022
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These rankings underscore the imperative for Thailand to allocate substantial resources
and expedite development initiatives in fundamental areas that will bolster the nation's
potential. Moreover, the establishment of an effective management system is crucial for driving
and fostering sustainable competitiveness for the country as a whole. The disparities in rankings
among the ASEAN countries highlight the need for concerted efforts and strategic planning to
bridge the gaps and enhance Thailand's position in the global arena.
According to data from the Office of the National Economic and Social Development
Council in 2019, the Eastern region had the highest GRP per capita in the country at 502,492
baht per year. Meanwhile, the three provinces in the Eastern Economic Corridor (EEC) had
GPP per capita among the top five in the country. Rayong province had a GPP per capita of
988,748 baht per year (1st in the country), Chonburi province had 571,234 baht per year (3rd
in the country), and Chachoengsao province had 459,005 baht per year (5th in the country).
Therefore, the EEC is a crucial area that clearly generates economic value and is an industrial
business area included in the 20-year National Strategy.
The development of smart cities has become a crucial aspect of Thailand's 20-year
National Strategy, particularly in areas of strategic importance such as the Eastern Economic
Corridor (EEC). Despite the government's efforts to promote smart city development,
Thailand's ranking in the IESE Cities in Motion Index has declined in recent years, highlighting
the need for a more focused and collaborative approach to smart city development. Moreover,
while the EEC region has the highest GRP per capita in the country and is home to several key
industrial sectors, there is a lack of research on how industrial businesses in the region perceive
and contribute to smart city development.
This study aims to address these gaps by examining the factors that influence smart city
development in the EEC region from the perspective of industrial businesses. By understanding
the structure and operations of target industrial businesses in the EEC and their views on smart
city development, this research seeks to develop a structural equation model that can inform
future policy and planning efforts.
The rationale for this study is twofold. First, it recognizes the importance of engaging
key stakeholders, such as industrial businesses, in the smart city development process to ensure
that it meets the needs and priorities of the local community. Second, it acknowledges the
unique economic and strategic importance of the EEC region and the potential for smart city
development to drive innovation, competitiveness, and sustainable growth in the area. In line
with the objectives of the National Strategy to develop smart cities with the participation of
businesses and citizens, ensuring that the development of smart cities truly meets the needs of
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residents and stakeholders in the area, this research incorporates various factors to study a new
conceptual framework for smart city development. The study focuses on the development of
smart cities in the Eastern Economic Corridor (EEC) area from the perspective of industrial
business operators in the EEC to jointly drive the development of smart cities in accordance
with the National Strategy and ensure sustainability.
1.1 RESEARCH OBJECTIVES
Based on the identified research problems, the objectives of this study are as follows:
1) To examine the structure and operational characteristics of target industrial businesses
in the Eastern Economic Corridor (EEC) area.
2) To investigate the development of smart cities in the EEC area from the perspective of
industrial businesses.
3) To develop a structural equation model for smart city development in the EEC area
based on the views of industrial businesses.
By achieving these objectives, this study aims to contribute to the knowledge base on
smart city development in Thailand and provide valuable insights for policymakers, planners,
and businesses involved in the EEC region. The findings of this research can help to inform
future strategies and initiatives for smart city development that are more inclusive, sustainable,
and responsive to the needs of local stakeholders.
2 THEORETICAL FRAMEWORK
2.1 SMART CITY
A smart city is a city that utilizes technology or innovation to enhance the efficiency of
service delivery to citizens and city management, including citizen participation in urban
development (Mueadkhunthod, 2023). The Smart City Office under the supervision of the
Digital Economy Promotion Agency has defined seven dimensions for smart city development:
1) Smart Mobility, which focuses on increasing convenience, efficiency, safety, and
environmental friendliness in transportation; 2) Smart People, which aims to develop citizens'
knowledge and ability to apply technology for economic and lifestyle benefits, create an
environment that fosters creativity and informal learning, and promote social diversity; 3) Smart
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Living, which emphasizes improving quality of life through technology; 4) Smart Economy,
which aims to increase business efficiency and flexibility, create business connections and
collaboration, and apply innovation for business transformation; 5) Smart Governance, which
focuses on developing service systems for easy and fast access to government services,
increasing citizen participation channels, and providing transparency and accountability; 6)
Smart Energy, which aims to increase the city's energy efficiency or use clean, renewable
energy sources; and 7) Smart Environment, which focuses on improving the quality and
efficiency of environmental management and monitoring systems.
These seven dimensions highlight the comprehensive approach needed for successful
smart city development, encompassing various aspects of urban life, from transportation and
governance to economic growth and environmental sustainability. By leveraging technology
and innovation, smart cities aim to create a more livable, efficient, and inclusive urban
environment that benefits all citizens.
2.2 CITY REGULATION
City regulation refers to the rules governing the city, including the structuring of
government agencies and city organizations, as well as defining the scope of authority and
responsibility to enable agencies to administer in accordance with city policies aimed at
providing services or meeting the needs of citizens.
Heurkens (2012), in a study of private sector-led urban development projects in the
Netherlands and the United Kingdom, highlights the importance of defining the proactive role
of the public sector and creating a conducive environment for urban development. The private
sector must also play a leading role in co-management with the public sector, including a clear
division of roles and responsibilities between the two sectors. It is undeniable that legal
institutional mechanisms play a crucial role in enabling each party to operate efficiently based
on their own goals or interests.
Lima et al. (2020), in a study of smart cities in Brazil, state that city property use
regulations are a fundamental tool for urban development. However, most of the norms that
guide general guidelines for urban policy predate the changes that the smart city concept has
brought about in how cities are appropriately perceived and understood by society. Even today,
studies on how these regulations work together to make cities smarter and more sustainable
prioritize the five most important aspects of the city's constitution: 1) guaranteeing rights in a
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sustainable city; 2) urban planning; 3) providing urban equipment, community transportation,
and public services; 4) adequacy of tools and public spending; and 5) preventing the
conservation and restoration of the natural and built environment.
Shwedeh Fanar (2021) researched the impact of smart city policies and technology
readiness on the performance of Dubai's smart city. The function of policy is to drive and
stimulate citizens to exhibit specific desired behaviors under regulations. The research clearly
concludes that policy and technology readiness play a significant role in the development of
Dubai's smart city. The study also cites Alan Wiig (2015), who argues that smart city policies
are necessary for private sector activities and foreign investor investments. However, an earlier
study by Vojnovic (2014) suggests that smart city policies must be seriously implemented with
a clear mission to be successful.
These studies underscore the importance of city regulations and policies in creating an
enabling environment for smart city development. The public and private sectors must work
together, with clearly defined roles and responsibilities, to ensure that regulations and policies
are aligned with the goals of smart city development. Moreover, regulations and policies must
be adaptable to the changing needs and perceptions of society, prioritizing key aspects such as
sustainability, urban planning, and public services.
2.3 PUBLIC ADMINISTRATION
Public administration refers to the formulation and implementation of strategies and
public policies for the public interest, within the context of social, economic, political, and
technological factors. This includes the structure, processes, and behavior of the public sector.
Public administration is a field of study that examines and understands the processes and
activities involved in government administration, including planning, implementing, and
controlling state resources to achieve societal goals and needs, with an emphasis on public
service delivery and efficient operations. One of the key principles of public administration is
providing public services that respond to the needs and requirements of citizens, as well as
planning and implementing measures to increase service efficiency. Moreover, public
administration should focus on transparency in government operations, presenting information
to the public, and involving citizens in decision-making processes. Public administration should
also be flexible in adapting to societal changes, including the use of technology and innovation
to adjust to changing environments.
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Arif Budy Pratama and Satria Aji Imawan (2019) present factors that influence smart
city development in dimensions beyond technology, focusing on the crucial role of public
administration and bureaucracy in driving the success of smart cities. A significant
transformation process is organizational transformation to adapt to promoting the development
of higher and better quality of life.
Principale et al. (2023) state that the role of public administration is crucial for smart
city development. Many scholars emphasize the relationship between public administration and
smart cities. Research has identified various aspects of public administration, such as urban
planning, sustainable development, public sector governance approaches, the Internet of Things
(IoTs), and communication technology.
Filho et al. (2018) focus on municipal management through information and
communication technology in creating smart city models. They present a smart city
management model using information technology resources to demonstrate the process of
structuring an integrated system, analyze the benefits of modeling local government activities,
and introduce new measures for restructuring local government organizations. The researchers
suggest managing the city using information and communication technology, from which
millions of people will benefit the most from such projects. The researchers studied public
administration within the framework of technology, innovation, and public activities.
Zhao and Xu (2020) discuss the relationship between smart cities and public services,
with a demand for gradually increasing the level of public service delivery. The creation of
public services in smart cities should consider important issues such as individual differences
and infrastructure improvement. Smart city public services should focus on increasing
efficiency for public benefit, adhering to the basic principle of people-centeredness, realizing
broad and intense citizen participation, and responding to people's needs for improving public
services to lay a good foundation for future urban development.
Yu et al. (2020) discuss smart cities and Perceived Smart Public Administration (PSPA),
which positively affect citizens' emotional well-being by increasing convenience and life
satisfaction. The study finds that the more public administration can facilitate citizens, the more
positive impact it will have on their satisfaction and emotional well-being.
Gesa Franke (2023) examines how local governments are increasingly investing in
'smart city' projects. "Smart cities" are cities that use new technologies (e.g., sensors, cameras,
algorithmic tools) to collect data, automate infrastructure, and shape policymaking to address
issues such as sustainability or security. A case study in Amsterdam demonstrates how the
municipality uses strict legal tools (e.g., procurement) and also experiments with collaboration
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(e.g., Urban Living Labs) and regulation (e.g., soft law) to control and collaborate in smart city
projects.
These studies highlight the central role of public administration in smart city
development. Public administration must adapt to the changing needs of society and leverage
technology and innovation to improve public service delivery, increase citizen participation,
and promote transparency. Organizational transformation and the integration of information
and communication technology are crucial for public administration to effectively manage
smart city development. Moreover, public administration must focus on people-centered
approaches, considering individual differences and responding to citizens' needs to lay a strong
foundation for future urban development.
2.4 ENTERPRISE MANAGEMENT
Enterprise management refers to the methods organizations use to efficiently utilize
resources to create value and generate profits in a competitive market. Business organizations
must manage various factors related to production and add value to goods and services for
export to society. Business organizations are economic entities with independence, property
rights, decision-making rights, and self-management rights (Enterprise Management, 2022).
The definition of enterprise management includes planning, organizing, directing, and
controlling various business operations and related factors. It encompasses the functions of
human resource management, production, marketing, accounting, and finance, as well as the
social context and influence on business operations and management in an international
environment (Woraphot, 2022).
Tyas et al. (2019), in a study of Indonesian cities, found that organizations can leverage
smart cities by using IoT and ICT technologies for business operations, such as information
retrieval, customer communication, and marketing purposes. The availability of high-speed
internet and digital connectivity in smart cities enables seamless organizational communication
and collaboration, allowing organizations to reach a wider customer base and explore new
markets. Smart cities also foster innovation and entrepreneurship by providing a supportive
ecosystem, access to funding, and opportunities for collaboration and networking among
enterprises.
Umar (2022) suggests that organizations can benefit from smart cities by integrating
knowledge from information systems engineering, technology management, and emerging
digital technologies to gain a competitive advantage. Smart cities can deliver seamless and
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personalized customer experiences through technologies such as IoT, AI, and data analytics,
enabling organizations to deliver customized products and services, thus having a competitive
edge. Smart cities have an interconnected ecosystem of digital infrastructure, data, and services,
which can help organizations access valuable resources and information for innovation and
growth. Furthermore, smart cities introduce advanced technologies and infrastructure that can
enhance organizational operational efficiency, such as intelligent transportation systems and
optimized logistics, leading to cost savings and increased productivity.
Van den Buuse and Kolk (2019) conducted a study on renowned multinational
technology companies such as IBM, Cisco, and Accenture regarding their involvement in smart
city projects worldwide. These organizations offer various technologies suitable for smart
cities, such as IBM Smarter Cities, Cisco Smart + Connected Communities, and Accenture
Intelligent Cities. Collaboration between businesses, governments, and communities is crucial
in developing and implementing smart city initiatives.
These studies emphasize the importance of enterprise management in the context of
smart city development. Businesses can leverage the technologies and infrastructure provided
by smart cities to improve their operations, reach new markets, and gain a competitive
advantage. Smart cities create a supportive ecosystem for innovation and entrepreneurship,
enabling businesses to access valuable resources and collaborate with other stakeholders. To
fully benefit from smart city development, businesses must integrate knowledge from various
domains, such as information systems engineering and technology management, and adapt to
emerging digital technologies. Collaboration between businesses, governments, and
communities is essential for the successful implementation of smart city initiatives.
2.5 COMMUNITY OPERATION
The concept of community operation refers to the management and implementation of
activities related to the community or civil society, emphasizing participation and collaboration
among community members. The importance of this concept lies in creating a space where
citizens can be part of the decision-making process, problem-solving, and resource
development in their own communities. Civil society refers to non-state, not-for-profit
organizations. Examples of civil society organizations include trade associations, labor unions,
professional organizations, religious belief groups, and advocacy groups for various rights.
Manfreda, Ljubi, and Groznik (2021) conducted research on the use of Autonomous
Vehicles (AVs) for smart cities and found that the perception of both personal and societal
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benefits significantly affects the demand for AV usage, which will determine the direction of
businesses related to smart cities.
Zhu et al. (2023) discovered that the smart city community governance system is based
on the integration of online and offline services, using rapid communication, image recognition
algorithms, and big data services. The implementation of combined online and offline services
in cities not only improves the city's economic system but also increases the city's GDP by up
to 12%. Furthermore, the online and offline community governance model accelerates the
process of democratization and improves the people's happiness index.
Gurick and Felger (2022) found that the relationship between smart cities and
communities is based on how organizations leverage technology to impact the quality of life in
cities, states, or countries. In particular, revenue generation, technology utilization, and policy
formulation will be used to manage planning related to smart city infrastructure. Additionally,
leadership is found to be another critical strategic challenge in smart city development. The
research also concludes that there are links between smart cities and communities, challenges
associated with smart cities, requirements for modern leadership, and opportunities for smart
cities to contribute to community improvement.
Riki et al. (2020) conducted a study that found smart cities have a significant impact on
society, the economy, and the environment, and prioritize the benefits of citizens by providing
better and more efficient services. Smart governance is an essential aspect of smart cities,
involving various stakeholders in decision-making and public services, emphasizing citizen-
centric approaches and transparent operational processes through central government ICT
technologies. The concept of smart cities promotes active community participation and citizen-
centric methods in city management, leading to dynamic and close interactions between citizens
and service providers. This interaction aims to create a comfortable and flexible city that can
respond quickly to changes.
These studies highlight the crucial role of community operation in smart city
development. Smart cities must prioritize citizen participation and collaboration, creating
spaces for community members to be involved in decision-making, problem-solving, and
resource development. The integration of online and offline services can improve the city's
economic system and increase the happiness index of its citizens. Smart governance, which
involves various stakeholders and emphasizes citizen-centric approaches, is essential for
successful smart city development. Moreover, the relationship between smart cities and
communities is based on how organizations leverage technology to impact the quality of life in
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cities, requiring modern leadership and the ability to address challenges associated with smart
city development.
2.6 SYNTHESIS OF COMPONENTS
From the concepts and theories analyzed by the researcher, the development of smart
cities in the Eastern Economic Corridor (EEC) area from the perspective of the industrial
business sector can be summarized into four components: City Regulation, Public
Administration, Enterprise Management, and Community Operation.
Based on the research objectives and related literature, the researcher has formulated
research hypotheses according to theoretical principles, which can be summarized as a
conceptual framework in the research, as shown in Figure 2, and can be summarized into six
research hypotheses as follows:
H1: The City Regulation component directly influences the Public Administration
component. This hypothesis is consistent with the research of Karataş (2022), who studied the
role of regulatory analysis on public administration and found that the serious public policy
issues currently faced by the administrations of various countries include environmental
problems, consumer rights, improving living standards, property rights, controlling new
technologies, and sustainable growth. These policies generally consist of regulatory issues. It
is now recognized that the function of the state and the public is not to row the boat but to steer
the stern, implying that regulatory approaches have a significant impact on public
administration work. The quality of regulations plays a crucial role in the success of public
sector work.
This finding aligns with the research of Astrid (2022), which states that the public sector
manages smart city projects using existing powers in various areas, such as city planning and
environmental protection. The government will work with private companies and other partners
to introduce new technologies and concepts, and the public sector must ensure that all sectors
work well together and comply with the rules. Furthermore, city governments must find ways
to use money wisely to support smart city projects and try new methods to make the city better
for everyone. For example, the city of Amsterdam has been working on smart city projects and
learning how to deal with these challenges. The smart city program in Amsterdam is about
using technology to make the city work better, like adding a brain to the city's roads, buildings,
and services to make them more effective and less damaging. The city of Amsterdam is trying
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to make a smart city by using new tools and ideas to help people travel easily, keep the air clean,
and make city life better. To achieve this goal, the city government, businesses, and people
living in the city must be brought together as one. This project not only uses advanced
technological devices but also ensures that the city is a good place to live in the present and the
future.
H2: The City Regulation component directly influences the Enterprise Management
component. This hypothesis is consistent with the research of Marchesani et al. (2023), who
studied various cities in Italy over an 11-year period and found that government policies can
create a favorable environment by providing tax incentives, and good city regulations will
support private companies in considering investing and expanding their businesses in the city.
Conversely, if city regulatory policies are stringent, they may hinder business growth and
discourage private investment. The researchers concluded that a thorough analysis should be
conducted to achieve a balance between regulations and supporting business growth.
Yang et al. (2021) studied the impact of environmental regulations on organizations'
green innovation policies and found that city policy controls stimulate green innovation in
organizations within the area. The study's conclusion proves that strict environmental policies
can promote the effectiveness of the city's sustainable development plans. In other words,
environmental regulations have a positive relationship with organizations' green innovation,
which, if cooperated with organizations in the target area, could help create a smart city.
H3: The City Regulation component directly influences the Community Operation
component. This hypothesis is consistent with the research of Lebrument et al. (2021), who
studied the factors affecting public participation in smart city development and found that the
ethics and integrity of government agencies influence the satisfaction and support of civil
society for smart city development. Yang et al. (2022) studied the use of policy regulations to
control vehicles. In recent decades, the number of cars in cities across China has increased
significantly, leading to numerous problems related to pollution and traffic, which are essential
components of being a smart city, namely smart mobility and smart environment. The study
found that the city's vehicle control administrative regulations help reduce the number of private
cars without impacting the city's economic system and do not affect the amount of taxes
collected by the state.
H4: The Knowledge Management component directly influences the Information
Management component. This hypothesis is consistent with the research of Nicolas et al.
(2020), who studied 50 smart cities worldwide to analyze the structural equation in smart city
development. They found that the important components include technological infrastructure,
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governance, smart community, and innovative economy. When these four components are
integrated, they create a successful smart city. Kusumastuti et al. (2022) studied the searching
and sharing of information about smart cities by citizens. The researchers view that an essential
component of a smart city is the people or civil society. Therefore, they studied the factors
influencing the search and sharing of information about smart cities on digital platforms from
eight smart cities in Indonesia. The research results showed that social factors (sense of
ownership and reputation seeking) play a crucial role in determining citizens' intention to seek
smart city information on digital platforms. Conversely, the intention to seek information is a
significant factor that affects residents' intention to share information on smart city platforms
and social media. Thus, the researchers suggest that the government or public sector should
create digital platforms to encourage citizen participation in disseminating information about
smart cities.
H5: The Community Operation component directly influences the Enterprise
Management component. This hypothesis is consistent with the research of Bammens and
Hünermund (2023), who studied companies' responses to environmental policies or green
innovation resulting from the community. They found that the community has a significant
impact on the activities and decisions of private companies, especially concerning green
innovation policies. Smart environment is one of the critical components of smart city
development and should receive cooperation from the community, private companies, and the
public sector. This finding aligns with the research of Pinochet et al. (2019), who studied the
intention to live in a smart city among the younger generation, differentiated by personal
characteristics. They found that for smart cities, digital technology is highly suitable for
facilitating community operations to drive communities to share resources and decentralize.
This opens up opportunities for technology and innovation entrepreneurs in the civil society
sector to help develop solutions using technology. It is a social challenge that leverages the
collaboration of the public and citizens. These technologies will make it easier to disclose large-
scale data, create more transparency, and serve as information for knowledge creation. Smart
cities will benefit from this process.
H6: The overall level of importance of smart city development in the Eastern Economic
Corridor area differs when classified by industrial groups. This hypothesis addresses the
difference between the existing industrial groups, including modern automotive industry, smart
electronics industry, high-income tourism and health tourism industry, agriculture and
biotechnology industry, and food processing industry, and the new industrial groups, including
industrial robotics, aviation and logistics industry, biofuel and biochemical industry, digital
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industry, and comprehensive medical industry. The concept of the emergence of new industrial
groups stems from the fact that the existing industrial groups that drive Thailand's economy
with high economic value have experienced a decline in growth rates. Therefore, it is necessary
to have new industrial groups or what can be called future industrial groups that focus on the
use of modern technology and innovation, with a high probability of continued growth. It can
be seen that the new industrial groups tend to focus on technology and high investment.
The formulation of these research hypotheses is based on a comprehensive review of
existing literature and theoretical principles, addressing various aspects of smart city
development, such as city regulation, public administration, enterprise management,
community operation, knowledge management, and information management. These
hypotheses aim to explore the relationships between different components of smart city
development and their impact on the overall success of smart cities in the Eastern Economic
Corridor area of Thailand.
3 METHODOLOGY
This research aims to create new knowledge (Inductive Research) by using a mixed
research method consisting of three parts: qualitative research using in-depth interview
techniques, quantitative research using survey techniques, and qualitative research using focus
group discussions to confirm the accuracy of this research model.
3.1 QUALITATIVE RESEARCH
The sample used in this research consists of nine experts, selected using purposive
sampling. Purposive sampling is a non-probability sampling technique in which the researcher
relies on their judgment when choosing members of the population to participate in the study
(Etikan, Musa, & Alkassim, 2016). The qualifications of the experts are determined by the
Doctor of Business Administration Program in Industrial Business Administration, Faculty of
Business Administration, King Mongkut's University of Technology North Bangkok. The
experts are divided into three groups: three entrepreneurs or executives in business
organizations, three from the government and related agencies, and three from academia. This
diverse group of experts ensures that the research captures insights from various perspectives,
allowing for a comprehensive understanding of the topic (Merriam & Tisdell, 2015).
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Qualitative research using focus group discussion techniques to certify the model.
The population used in this research consists of 11 experts in the business sector,
selected using purposive sampling. The qualifications of the experts are determined by the
Doctor of Business Administration Program in Industrial Business Administration, Faculty of
Business Administration, King Mongkut's University of Technology North Bangkok. These
experts are different from those in the qualitative research using in-depth interview techniques
to avoid bias and ensure the validity of the research findings (Creswell & Creswell, 2017).
3.2 QUANTITATIVE RESEARCH
The population used in this research is defined as organizational executives or
entrepreneurs in the industrial business sector in the Eastern Economic Corridor (EEC) area,
registered with the Ministry of Industry. They are divided into two groups: 250 from the First
S-curve industrial group and 250 from the New S-curve industrial group. This stratified
sampling approach ensures that the sample is representative of the two main industrial groups
in the EEC area (Sekaran & Bougie, 2016). The First S-curve industries are the existing
industries that drive Thailand's economy, while the New S-curve industries are emerging
industries that focus on technology and innovation (Thailand Board of Investment, 2021).
3.3 RESEARCH INSTRUMENTS
The research instrument is a rating scale questionnaire with a 5-point Likert scale, which
provides appropriate options for the respondents (Joshi, Kale, Chandel, & Pal, 2015). The Likert
scale is a widely used psychometric scale in research involving questionnaires, as it allows
respondents to express their level of agreement or disagreement with a given statement (Likert,
1932).
The researcher submitted the draft questionnaire and evaluation form to five experts
with knowledge and experience in the field of study to assess the questionnaire and determine
the quality of the instrument by examining the Index of Item-Objective Congruence (IOC). The
IOC is a measure of the content validity of the questionnaire items, ensuring that they are
relevant to the research objectives (Turner & Carlson, 2003). The results of the IOC
examination ranged from 0.80-1.00, with an appropriate value of 0.50 or higher (Turner &
Carlson, 2002).
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The researcher then conducted a try-out of the questionnaire with 30 people who have
similar characteristics to the target population. The data were analyzed to determine the
discrimination power for checklist questions and rating scale questions using standard deviation
(S.D.), and for rating scale questions using correlation analysis. Discrimination power refers to
the ability of a questionnaire item to differentiate between respondents with high and low levels
of the measured trait (Ebel, 1954). The reliability of the questionnaire was also assessed using
Cronbach's alpha, which measures the internal consistency of the questionnaire items
(Cronbach, 1951). The discrimination power ranged from 0.31-1.80, and the reliability of the
entire questionnaire was 0.99, which is considered excellent if greater than 0.80 (George &
Mallery, 2003). The instrument was then used to collect data by requesting the sample group to
complete the questionnaire.
3.4 DATA ANALYSIS
The data analysis employed both descriptive statistics and inferential statistics using the
SPSS software package. Descriptive statistics, such as means and standard deviations, were
used to summarize and describe the data, while inferential statistics, such as t-tests and analysis
of variance (ANOVA), were used to test the research hypotheses (Field, 2018).
For multivariate statistical analysis and the development of structural equation models
(SEM), the AMOS software package was used. SEM is a powerful statistical technique that
allows for the examination of complex relationships between multiple variables, including
latent variables that cannot be directly observed (Kline, 2015). The criteria for evaluating the
goodness-of-fit of the model (Evaluating the Data-Model Fit) used in the consideration
consisted of four values, as shown in Table 1 below (Silpcharu, 2024). These criteria ensure
that the developed structural equation model fits the empirical data well and can be used to
draw meaningful conclusions (Hu & Bentler, 1999).
Table 1
Criteria for evaluating the harmony of the model.
Evaluating the Data–Model Fit
Acceptable Criteria
Chi-square probability (CMIN-P)
Value > 0.05
Relative chi-square (CMIN/DF)
Value < 2.00
Goodness of fit index (GFI)
Value > 0.90
Root mean square error of approximation (RMSEA)
Value < 0.08
Source : (Silpcharu, 2024)
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4 RESULTS
The results of the research on the development of smart cities in the Eastern Economic
Corridor (EEC) area from the perspective of the industrial business sector can be summarized
as follows:
4.1 QUALITATIVE RESEARCH USING IN-DEPTH INTERVIEWS
In-depth interviews and content analysis from experts regarding the structural equation
model for the development of smart cities in the EEC area, as viewed by the industrial business
sector, revealed that there are four components of smart city development: City Regulation,
Public Administration, Enterprise Management, and Community Operation. The results are
presented in Table 2.
Table 2
Eastern Economic Corridor area from the perspective of the industrial business sector, ranked
by overall significance level
Components of smart city development in the Eastern Economic
Corridor area from the perspective of the industrial business sector
Mean
S.D.
Significance
Level
Overall Significance Level
4.31
0.42
High
1. Enterprise Management
4.34
0.44
High
2. Community Operation
4.33
0.44
High
3. Public Administration
4.32
0.44
High
4. City Regulation
4.26
0.48
High
Source: Prepared by the authors (2023)
Table 2 shows the mean and standard deviation of the components of smart city
development in the EEC area, as perceived by the industrial business sector, ranked by overall
significance level. The overall significance level was high, with a mean of 4.31. When
analyzing the significance level by component, all components were found to have a high level
of importance. The components, ranked from highest to lowest importance, are as follows: 1)
Enterprise Management (mean = 4.34), 2) Community Operation (mean = 4.33), 3) Public
Administration (mean = 4.32), and 4) City Regulation (mean = 4.26).
Considering the items with the highest importance within each component, the most
important item for Enterprise Management was promoting innovation in organizations to
extend businesses (= 4.40). For Community Operation, the most important item was promoting
ethics in the coexistence of the civil society to support the growth of smart cities (= 4.39). In
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the Public Administration component, the most important item was digitizing database systems
and eliminating the use of paper in government transactions (= 4.36). Finally, for City
Regulation, the most important item was enacting laws to promote businesses related to the
Bio-Circular-Green Economy (BCG) in the EEC (= 4.35).
4.2 ANALYSIS OF THE OPTIMISED STRUCTURAL EQUATION MODEL FOR THE
SMART CITY DEVELOPMENT IN EASTERN ECONOMIC CORRIDOR FROM THE
PERSPECTIVE OF INDUSTRIAL SECTOR
Figure 2
Structural equation model for the Smart City Development in Eastern Economic Corridor from
The Perspective of Industrial Sector, in standardised estimate mode, after model optimisation.
Source: Prepared by the authors (2023)
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Table 3
Mean and standard deviation of the components of smart city development in the Statistical
values obtained from the analysis of the structural equation model after model modification.
Variables
Estimate
Variance
C.R.
P
Standard
Unstandard
City Regulation
0.23
Public Administration
0.39
0.36
0.91
0.02
4.03
***
Community Operation
0.88
0.62
0.78
0.03
9.77
***
Enterprise Management
0.40
0.37
0.92
0.02
3.94
***
Community Operation
0.60
0.03
Public Administration
0.59
0.78
0.91
0.02
5.44
***
Enterprise Management
0.59
0.79
0.92
0.02
5.26
***
City Regulation
0.02
CR06
0.68
1.00
0.46
0.28
CR07
0.65
0.99
0.42
0.31
13.20
***
CR08
0.61
0.89
0.38
0.31
12.49
***
CR11
0.72
1.10
0.52
0.26
14.49
***
CR13
0.60
0.85
0.35
0.30
12.17
***
CR14
0.67
0.96
0.45
0.26
13.52
***
CR15
0.59
0.87
0.35
0.32
12.11
***
CR18
0.63
0.91
0.40
0.28
12.90
***
Public Administration
0.91
0.02
PA07
0.64
1.00
0.40
0.29
PA08
0.67
1.12
0.45
0.29
13.01
***
PA09
0.70
1.11
0.48
0.26
13.34
***
PA10
0.63
0.99
0.40
0.29
12.31
***
PA14
0.66
1.05
0.43
0.28
12.74
***
PA15
0.67
1.05
0.45
0.27
12.91
***
***Statistical significance of 0.001
Source: Prepared by the authors (2023)
From Figure 2 and Table 3, it is evident that the structural equation model for the
development of smart cities in the Eastern Economic Corridor (EEC) area according to the
perspective of the industrial business sector, after model adjustment, comprises 4 latent
variables. These include 1 exogenous latent variable, namely the City Regulation component,
and 3 endogenous latent variables, namely the Public Administration component, the Enterprise
Management component, and the Community Operation component.
The City Regulation component directly influences the Community Operation
component with a standardized regression weight of 0.880, which is statistically significant at
the 0.001 level. The coefficient of determination (R²) is 0.78 and the variance is 0.03. It also
directly influences the Enterprise Management component with a standardized regression
weight of 0.40, statistically significant at the 0.001 level, with an R² of 0.37 and a variance of
0.02. Furthermore, it directly influences the Public Administration component with a
standardized regression weight of 0.388, statistically significant at the 0.001 level, with an R²
of 0.91 and a variance of [missing value].
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The Community Operation component directly influences the Enterprise Management
component with a standardized regression weight of 0.59, statistically significant at the 0.001
level, with an R² of 0.91 and a variance of 0.02. It also directly influences the Public
Administration component with a standardized regression weight of 0.59, statistically
significant at the 0.001 level, with an R² of 0.91 and a variance of 0.018.
The City Regulation component consists of 8 observed variables, sorted in descending
order of standardized regression weights as follows:
1) Car Free Zone (CR11): standardized regression weight 0.72, statistically significant at
0.001 level, R² 0.52, variance 0.26.
2) Legislation to use state land to create special digital economic zones for SMEs and
startups, with privileges 2 times greater than those from the Board of Investment (BOI)
(CR06): standardized regression weight 0.68, statistically significant at 0.001 level, R²
0.46, variance 0.28.
3) Legislation to promote the use of renewable energy and control energy use for 25 years
(CR14): standardized regression weight 0.67, statistically significant at 0.001 level, R²
0.45, variance 0.26.
4) Clear Transit Oriented Development (TOD) plans around transit stations (CR07):
standardized regression weight 0.65, statistically significant at 0.001 level, R² 0.42,
variance 0.31.
5) Legislation to protect the use of applications, including conversations from city
residents and reports of various occurrences on the City Data Platform (CR18):
standardized regression weight 0.634, statistically significant at 0.001 level, R² 0.40,
variance 0.28.
6) Designation of zero greenhouse gas emission zones (CR08): standardized regression
weight 0.61, statistically significant at 0.001 level, R² 0.38, variance 0.31.
7) Plan to improve quality, expand coverage, and reduce prices of internet services,
providing accessible internet throughout the EEC (CR13): standardized regression
weight 0.60, statistically significant at 0.001 level, R² 0.35, variance 0.30.
8) Legislation to develop clean energy community housing projects for low-income
individuals, with a budget of 1 million baht per unit (CR15): standardized regression
weight 0.59, statistically significant at 0.001 level, R² 0.35, variance 0.32.
The Community Operation component consists of 7 observed variables, sorted in
descending order of standardized regression weights as follows:
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1) Teaching community members about cybercrime prevention (CO14): standardized
regression weight 0.685, statistically significant at 0.001 level, R² 0.47, variance 0.27.
2) Building an investment network for solar power projects in the community (CO08):
standardized regression weight 0.68, statistically significant at 0.001 level, R² 0.46,
variance 0.26.
3) Promoting rainwater harvesting in households for a 3-day reserve (CO07): standardized
regression weight 0.64, statistically significant at 0.001 level, R² 0.41, variance 0.29.
4) Providing cultural and educational parks with learning activities and cultural
dissemination (CO21): standardized regression weight 0.60, statistically significant at
0.001 level, R² 0.36, variance 0.29.
5) Installing adequate closed-circuit television (CCTV) cameras throughout the
community (CO06): standardized regression weight 0.55, statistically significant at
0.001 level, R² 0.30, variance 0.33.
6) Developing a community living application to monitor and report air, noise, and
disruptive sound pollution, as part of the Smart Citizen project for participatory city
management (CO12): standardized regression weight 0.53, statistically significant at
0.001 level, R² 0.29, variance 0.36.
7) Establishing Student IT Corners or information and communication technology
knowledge centers within the community for the general public and the elderly (CO04):
standardized regression weight 0.50, statistically significant at 0.001 level, R² 0.25,
variance 0.34.
The Enterprise Management component consists of 7 observed variables, sorted in
descending order of standardized regression weights as follows:
1) Reducing fossil fuel consumption by 50% and using renewable energy (EM07):
standardized regression weight 0.68, statistically significant at 0.001 level, R² 0.46,
variance 0.27.
2) Using smart water meters to detect leaks (EM08): standardized regression weight 0.67,
statistically significant at 0.001 level, R² 0.45, variance 0.26.
3) Using smart sensor systems to detect water and air quality, alerting operators to
immediately stop pollution releases (EM03): standardized regression weight 0.64,
statistically significant at 0.001 level, R² 0.41, variance 0.29.
4) Recycling chemicals from waste through urban mining (EM20): standardized regression
weight 0.64, R² 0.41, variance 0.28.
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5) Collaborating with the government to create a platform for startups to conduct business,
access funding, and obtain city data easily, promoting investment in the EEC (EM06):
standardized regression weight 0.63, statistically significant at 0.001 level, R² 0.40,
variance 0.29.
6) Developing personnel to learn, apply, and build upon digital technologies, and preparing
for new technologies (EM15): standardized regression weight 0.61, statistically
significant at 0.001 level, R² 0.38, variance 0.29.
7) Supporting the use of innovations developed from domestic research and educational
institutions to create a supply chain of Thai innovations (EM17): standardized
regression weight 0.57, statistically significant at 0.001 level, R² 0.32, variance 0.33.
The Public Administration component consists of 9 observed variables, sorted in
descending order of standardized regression weights as follows:
1) Using a smart traffic light system with image processing technology to detect vehicle
density (PA09): standardized regression weight 0.70, statistically significant at 0.001
level, R² 0.48, variance 0.26.
2) Providing electric public buses with dedicated lanes capable of charging the buses
(PA08): standardized regression weight 0.67, statistically significant at 0.001 level, R²
0.45, variance 0.29.
3) Collaborating with the business sector to develop a transportation system supporting an
aging society in the next 15 years (PA15): standardized regression weight 0.67,
statistically significant at 0.001 level, R² 0.45, variance 0.27.
4) Disclosing the status of various government permit applications, allowing transparent
tracking through government websites (PA14): standardized regression weight 0.66,
statistically significant at 0.001 level, R² 0.43, variance 0.28.
5) Adjusting work processes to provide one-stop services for public services (PA07):
standardized regression weight 0.64, statistically significant at 0.001 level, R² 0.40,
variance 0.29.
6) Implementing smart street and walkway lighting systems to reduce energy consumption
by 50% (PA10): standardized regression weight 0.63, statistically significant at 0.001
level, R² 0.40, variance 0.29.
7) Installing personal digital assistants (PDAs) connected to the City Data Platform in all
police patrol cars, ambulances, and fire trucks (PA15): standardized regression weight
0.62, statistically significant at 0.001 level, R² 0.38, variance 0.31.
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8) Using face recognition technology for public transportation fare payment through a
business sector financial platform (PA24): standardized regression weight 0.60,
statistically significant at 0.001 level, R² 0.36, variance 0.32.
9) Providing public access to the city's wireless internet (Wi-Fi) connection without a
password (PA16): standardized regression weight 0.58, statistically significant at 0.001
level, R² 0.33, variance 0.29.
4.3 ANALYSIS OF THE GOODNESS-OF-FIT OF THE STRUCTURAL EQUATION
MODEL PRIOR TO AND AFTER MODEL OPTIMISATION
Table 4
Statistical measures applied to evaluate the goodness-of-fit of the structural equation model,
comparing pre- and post-model optimisation results.
Statistical Measures
Statistical Criteria
Before Model
Optimisation
After Model
Optimisation
CMIN–
)Chi-square probability level(
> 0.05
0.000
0.057
CMIN/DF )Normed chi-square(
< 2.00
1.407
1.110
GFI )Goodness-of-fit index(
> 0.90
0.795
0.942
RMSEA
)Root mean square error of
approximation(
< 0.08
0.029
0.015
Source: Prepared by the authors (2023)
Based on Figure 2 and Table 4, which present the statistical values assessing the
goodness-of-fit of the structural equation model before model modification, it was found that
the relative chi-square (CMIN/DF) was 1.407, and the root mean square error of approximation
(RMSEA) was 0.029. These values met the criteria for evaluating the model's fit with the
empirical data. However, the chi-square probability level (CMIN-P) was 0.000, and the
goodness-of-fit index (GFI) was 0.795, which did not meet the criteria for evaluating the
model's fit with the empirical data.
Consequently, the researcher proceeded to modify the model by considering the
Modification Indices, following the recommendations of Arbuckle (2016). This process
involved examining the values obtained from the software package along with theoretical
principles to remove inappropriate observed variables one at a time. The model was then re-
evaluated, and this process was repeated until a model was obtained that met all four statistical
criteria, indicating that the structural equation model was complete and consistent with the
empirical data.
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After the model modification was completed, it was found that the chi-square
probability (CMIN-P) was 0.057, which is greater than 0.05. The relative chi-square
(CMIN/DF) was 1.110, which is less than 2.00. The goodness-of-fit index (GFI) was 0.942,
which is greater than 0.90. Lastly, the root mean square error of approximation (RMSEA) was
0.015, which is less than 0.08. Therefore, it can be concluded that all four statistical values met
the evaluation criteria. As a result, the structural equation model for the development of smart
cities in the Eastern Economic Corridor area from the perspective of the industrial business
sector, after modification, is consistent with the empirical data.
4.4 HYPOTHESIS TESTING RESULTS
The hypothesis testing results for analyzing the causal influence between latent variables
in the structural equation model for the development of smart cities in the Eastern Economic
Corridor area from the perspective of the industrial business sector include five hypotheses.
Research hypothesis for testing the causal influence between latent variables, item 1 H1:
The City Regulation component has a direct influence on the Public Administration component.
The hypothesis testing results show that the City Regulation component has a direct influence
on the Public Administration component with a statistical significance level of 0.001 and a
Standardized Regression Weight of 0.39, which supports the research hypothesis.
Research hypothesis for testing the causal influence between latent variables, item 2 H2:
The City Regulation component has a direct influence on the Enterprise Management
component. The hypothesis testing results show that the City Regulation component has a direct
influence on the Enterprise Management component with a statistical significance level of
0.001 and a Standardized Regression Weight of 0.40, which supports the research hypothesis.
Research hypothesis for testing the causal influence between latent variables, item 3 H3:
The City Regulation component has a direct influence on the Community Operation component.
The hypothesis testing results show that the City Regulation component has a direct influence
on the Community Operation component with a statistical significance level of 0.001 and a
Standardized Regression Weight of 0.88, which supports the research hypothesis.
Research hypothesis for testing the causal influence between latent variables, item 4 H4:
The Community Operation component has a direct influence on the Public Administration
component. The hypothesis testing results show that the Community Operation component has
a direct influence on the Public Administration component with a statistical significance level
of 0.001 and a Standardized Regression Weight of 0.59, which supports the research hypothesis.
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Research hypothesis for testing the causal influence between latent variables, item 5 H5:
The Community Operation component has a direct influence on the Enterprise Management
component. The hypothesis testing results show that the Community Operation component has
a direct influence on the Enterprise Management component with a statistical significance level
of 0.001 and a Standardized Regression Weight of 0.59, which supports the research hypothesis.
Hypothesis for testing the difference in the overall importance level of smart city
development in the Eastern Economic Corridor area, classified by industrial groups: existing
high-potential industries (First S-Curve) and future industries (New S-Curve) H6: The overall
importance level of smart city development in the Eastern Economic Corridor area differs when
classified by industrial groups.
The research results, when comparing the components of the smart city development
approach in the Eastern Economic Corridor area according to the perspective of the industrial
business sector, categorized into existing high-potential industries (First S-Curve) and future
industries (New S-Curve), show that there is a statistically significant difference at the 0.05
level.
5 DISCUSSION
Based on the research findings regarding the development of smart cities in the Eastern
Economic Corridor (EEC) area from the perspective of the industrial business sector, the
proposed approach aims to create efficient smart cities by understanding the principles and
factors that influence successful management. This approach will benefit organizations,
provincial industries, and the nation, aligning with the 20-year National Strategy, the Thailand
4.0 initiative, and the 12th National Economic and Social Development Plan (2017-2021). The
researcher has discussed the results to draw conclusions, supported or contradicted by relevant
research documents, which can be summarized into five points as follows:
The development of smart cities in the EEC area from the industrial business sector's
perspective, specifically in the aspect of Enterprise Management, has the highest mean score of
4.34. From the industrial business sector's viewpoint, enterprise management is a collaborative
effort in developing smart cities that can be carried out by the industrial business sector itself,
while also adapting to various situations that arise (Rungroj & Chainas, 2022). Despite the
COVID-19 situation, various industries still need to solve problems and continue their business
operations as usual, potentially altering business models to capitalize on emerging opportunities
Udayakumar et al., 2023)
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Digital transformation is one of the development approaches for the survival of
organizations in the present day (Kotarba, 2018; Vial, 2019). It involves applying digital
technology to all aspects of the business, resulting in changes from the foundation of the
business to the delivery processes for customers. It is a strategic organizational change that
utilizes technology to increase efficiency and keep pace with the economic world. The
development of innovative business management capabilities should focus on improving
strategies, products, and services (Orbaningsih et al., 2024). Organizations should promote the
development of knowledge and the paradigm of executives to have a vision and a systematic
business planning process. Innovative business management requires novelty, economic
benefits, and the use of knowledge and creativity in its implementation (Oslo Manual, 2018).
Therefore, it is crucial for organizational leaders to have a vision to continuously develop new
and interesting products and services that are in demand, creating competitive advantages.
Considering the individual items of smart city development in the EEC area from the
industrial business sector's perspective, it was found that promoting innovation development
within organizations to extend businesses had the highest mean score of 4.40. This is consistent
with the Digital Economy Promotion Agency (2023), which emphasizes the importance of
innovation for smart cities. Smart cities utilize modern and intelligent technology and
innovation to enhance the efficiency of city services and management, reduce costs and
resource consumption for the city and its target population, with a focus on good design and the
participation of the business and public sectors in city development. From the industrial
business sector's perspective, innovation development is considered crucial in creating smart
cities (Takahashi et al., 2024). The development of smart cities can be carried out according to
the authority and responsibilities that should be undertaken to improve businesses and the
quality of life of the people efficiently and appropriately for the local context, or according to
the readiness in terms of human resources and budget. Regarding the aspect of Innovation
Organizations, they can be created because human resources drive innovation, as follows
(Dessler, 2003): 1) Quality of Human Resources (HR Innovation): For an organization to be
successful, it must have quality resources and recruit quality people. Existing employees must
be developed and retained, and quality people must be utilized. "Quality people" start from the
selection of "people" who come to work and must have an imagination for innovation. 2)
Quality of Organization: When quality people come together in an organization, that
organization will be a quality organization capable of creating innovation continuously. 3)
Quality of Product or Service: A quality organization will be an innovative organization that
can create quality product or service innovations. Especially for managing human resources to
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be able to create innovation, leaders must create an organizational atmosphere that fosters a
learning organization. This is consistent with Vittayaprapat et al. (2021), who stated that to
upgrade work standards towards the smart electronics industry, priority should be given to
operational approaches, starting with the integration and application of innovation and
technology to improve work standards. This leads to higher management efficiency and
progress towards the smart electronics industry, resulting in the organization's ability to
compete in the global market. The National Innovation Agency (2023) mentioned that
organizations that will survive and grow in the future must be able to consistently develop
innovations that create value from the perspective of their target groups. The innovations
presented by the organization will lead to business models that can generate returns in terms of
finance and good reputation for extending products or services, as well as building brand
confidence. Taksin et al. (2022) also discussed that innovation creation is a crucial component
in developing personnel and technology to support the growth of the industrial sector.
The hypothesis testing results revealed that the City Regulation component has the
highest direct influence on the Community Operation component, with a standardized
regression weight of 0.88. This empirical evidence demonstrates that, similar to the private
sector, the civil society sector is entirely affected by the government's laws or regulations. The
well-being and safety of the public largely depend on the city's regulations. For example, the
brightness of electric lights in various areas, the installation of closed-circuit television (CCTV)
cameras in urban areas, and the provision of adequate knowledge to the community are all
influenced by city regulations. Concrete public policies and action plans that facilitate the
public, save energy, and manage the environment contribute to the success of smart city
development (Nirintr et al., 2019). This is consistent with Smita et al. (2020), who mentioned
that policies and measures promoting public services in cities create cooperation from the civil
society sector, resulting in the rapid growth of smart city development. The concept of smart
cities is an urban development approach that applies technology and innovation to city planning,
making cities livable and capable of accommodating urban expansion. The smart city concept
plays a crucial role in determining strategies, policies, plans, and monitoring the results of smart
city development to create truly intelligent cities (Suthee, 2020). The process of implementing
smart city policies is a form of decentralization from top to bottom, systematically driving
policy plans according to authority and responsibilities. The policy implementation involves
receiving smart city policies from the government and translating them into the operations of
management agencies through individual projects for development planning (Mukunda, 2018).
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The hypothesis testing results showed that the highest total influence was the City
Regulation component's overall influence on the Enterprise Management component, with a
standardized regression weight of 0.92. This empirical evidence indicates that the government's
laws or regulations significantly impact the private sector's operations. For example, the
enactment of tax reduction laws or promotional measures from the Board of Investment (BOI)
clearly stimulates various activities in the industrial business sector. Lima et al. (2020) stated
that regulations and rules are essential foundations for rapid urban development. This is
consistent with the research of Smita et al. (2020), which found that local governments will
have measures to promote the private business sector in creating technology businesses (Tech
Startups) to play a role in building a technological ecosystem, which is a crucial mechanism for
smart city development. Woradej (2009), the author of "Theory of Policy Implementation,"
emphasized the importance of driving policies to be recognized, understood, accepted, and
creating a positive attitude towards policy practitioners. Policy givers and recipients must be
clear and able to interpret the meaning of the policy correctly, implementing it in accordance
with the policy requirements to facilitate learning reform and improve the efficiency of
government administration. Policy implementation is a crucial component of the policy process
that affects the success of the policy. It is a process of managing and coordinating activities to
bring about desired changes and achieve the intended outcomes of the policy (Nantharat, 2021).
Policy implementation is the process of driving established policies to achieve the desired
results and objectives. The policy options chosen by decision-makers for implementation may
be in the form of laws, orders, cabinet resolutions, or ministerial announcements, which the
relevant government agencies are responsible for implementing successfully (Dunn, 2018).
Policies are translated into clear and concrete plans or projects that can be put into practice
(Viennet & Pont, 2017), affecting business operators. Kanchanamai et al. (2023) also
highlighted the importance of establishing flexible regulatory bodies that accept
recommendations on legal issues, rules, and regulations, which is a significant benefit in
improving work processes to enhance competitiveness and attract more investors.
When comparing the components of the smart city development approach in the EEC
area from the perspective of the industrial business sector, categorized into existing high-
potential industries (First S-Curve) and future industries (New S-Curve), the research results
showed that there was a statistically significant difference at the 0.05 level. The existing high-
potential industries (First S-Curve) placed more importance on the smart city development
approach in the EEC area compared to the future industries (New S-Curve). Considering the
First S-Curve group, which involves investing in existing industries in the country to increase
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the efficiency of production factors, while the New S-Curve group represents investment in new
industries to change product forms and technology (Chanette & Nanthanit, 2021), the First S-
Curve group has an average business operation duration of 11.18 years, residing in the area
longer than the New S-Curve group, which has an average business operation duration of 5.10
years. The First S-Curve group places importance on the positive changes in smart city
development. The First S-Curve industries are industries with production expertise and the
potential to create substantial economic value and trade value. However, without further
development using modern technology, these industries will reach a saturation point and have
low growth potential. Therefore, it is necessary to use new technologies and innovations to help
these industries continue to grow (Sasima, 2019). Thailand's progress towards 4.0 is the current
national policy that aims to drive Thailand towards change to achieve the vision of "a stable,
prosperous, and sustainable Thailand, a developed country based on the sufficiency economy
philosophy." The policy intends to use an innovation-driven economic model to develop
towards Thailand 4.0, which seeks to transform the economic structure into a "Value-Based
Economy" by focusing on using innovation and technology to enhance the efficiency of
domestic operations. This affects the country's driving strategies in various aspects to shift from
the manufacturing sector to the creation of new innovations. When combining the concept of
digital transformation, it is consistent with Pasu (2019), who stated that digital transformation
is a process that changes the mindset of an organization, from the level of work, management,
marketing, interaction between people in the organization, organizational culture, to the
delivery of product and service values using digital technology. Applying digital technology in
organizations accelerates activities, processes, capabilities, and business models to take
advantage of technological changes, creating opportunities for businesses to build
competitiveness in a rapidly changing technological environment. Traditional manufacturing
organizations need to systematically manage digital technology to quickly impact
organizational success (Avirutha, 2018; Vial, 2019).
6 CONCLUSION
Based on the research paper titled "Smart City Development in Eastern Economic
Corridor from The Perspective of Industrial Sector", the following conclusions can be drawn:
The development of smart cities in the Eastern Economic Corridor (EEC) area from the
perspective of the industrial business sector comprises four key components, ranked in order of
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importance: Enterprise Management, Community Operation, Public Administration, and City
Regulation.
The structural equation model developed in this research reveals that the City
Regulation component has the highest direct influence on the Community Operation component,
followed by its influence on the Enterprise Management and Public Administration components.
The overall importance level of smart city development in the EEC area differs
significantly between existing high-potential industries (First S-Curve) and future industries
(New S-Curve), with the First S-Curve industries placing more importance on the smart city
development approach.
Promoting innovation development within organizations to extend businesses was
identified as the most crucial item within the Enterprise Management component for smart city
development in the EEC area.
The research highlights the need for collaboration between the government, businesses,
and civil society in establishing appropriate city regulations and policies that align with smart
city development goals and garner cooperation from all sectors.
Industrial business entrepreneurs should prioritize promoting innovation, encouraging
the use of public transportation, and employing graduates from local educational institutions to
foster a balance in smart city development.
Future research should explore avenues for collaboration between various stakeholders
in producing high-quality human resources for future industries and consider employing mixed-
methods approaches to gain a more comprehensive understanding of smart city development in
the EEC area.
6.1 SUGGESTIONS
1) The research found that city regulations are an exogenous latent variable that
significantly influences the development of smart cities in the Eastern Economic
Corridor (EEC) area from the perspective of the industrial business sector. The Eastern
Economic Corridor Policy Committee Office should establish clear directions and
policies for smart city development in the EEC area. This will enable the issuance of
appropriate city regulations that align with smart city development, garnering
cooperation from all sectors, including the government, businesses, and civil society.
Furthermore, it will facilitate effective and targeted solutions to the problems faced by
the local population. To achieve this, the committee should engage in extensive
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consultation with various stakeholders, such as local authorities, industry associations,
academic institutions, and community representatives, to gather insights and ensure that
the regulations are practical, inclusive, and beneficial to all parties involved (Kummitha
& Crutzen, 2017; Nilssen, 2019).
2) The research findings suggest that industrial business entrepreneurs should prioritize the
following three aspects of smart city development in enterprise management: promoting
innovation development within organizations to extend businesses, encouraging
employees to use public transportation and reduce the use of personal vehicles, and
promoting the employment of graduates from local educational institutions to foster a
balance in smart city development in terms of the environment and civil society
(Hasayotin et al., 2024). The development of organizational innovation should focus on
meeting consumer needs or solving the pain points of the local population (Yaya, et al.,
2024), simultaneously promoting business growth and smart city development.
Organizations can achieve this by adopting user-centric design approaches, engaging in
open innovation practices, and collaborating with local stakeholders to identify and
address the most pressing challenges (Appio et al., 2019; Peeters & Schuilenburg, 2022).
Additionally, businesses should invest in employee training and development programs
to foster a culture of innovation and equip their workforce with the necessary skills to
drive smart city initiatives (Sepasgozar et al., 2019).
3) Future research should explore avenues for collaboration between the government,
businesses, and educational institutions in producing high-quality human resources for
future industries across various economic zones in the country, aligning with the
government's smart city development policies. This collaborative approach can help
bridge the skills gap and ensure that the workforce is well-prepared to meet the demands
of emerging industries and contribute to the success of smart city initiatives (Nicholds
et al., 2021; Thite, 2022). Researchers can examine best practices from other countries,
identify key success factors, and propose frameworks for effective public-private
partnerships in human resource development. Additionally, future studies can
investigate the impact of such collaborations on regional economic growth, social well-
being, and environmental sustainability in the context of smart city development
(Caragliu et al., 2021; Yigitcanlar & Cugurullo, 2022).
4) To further enhance the understanding of smart city development in the EEC area, future
research should consider employing a mixed-methods approach, combining quantitative
and qualitative data collection and analysis techniques. This approach can provide a
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more comprehensive and nuanced understanding of the perspectives and experiences of
various stakeholders, including government officials, business leaders, and community
members (Creswell & Creswell, 2018). Qualitative methods, such as in-depth interviews
and focus group discussions, can help uncover the underlying factors, challenges, and
opportunities associated with smart city development, while quantitative methods can
help validate and generalize the findings to a larger population (Shukla & Bibri, 2021).
5) Future research should also examine the role of emerging technologies, such as artificial
intelligence, blockchain, and the Internet of Things, in enabling and accelerating smart
city development in the EEC area and beyond. These technologies have the potential to
revolutionize various aspects of urban life, from transportation and energy management
to healthcare and governance (Allam & Dhunny, 2019; Ismagilova et al., 2020).
Researchers can explore the adoption, implementation, and impact of these technologies
in the context of smart city initiatives, as well as the associated risks, challenges, and
ethical considerations (Kitchin, 2022; Zambetti et al., 2021). By doing so, future studies
can contribute to the development of evidence-based policies and strategies for
harnessing the power of technology to create more livable, sustainable, and resilient
cities.
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