Sociotechnical Characteristics of Conceptually Related Smart Cities’ Services from an International Perspective

Abstract

Sustainable smart cities (SSCs) have developed various services and technologies with multi-stakeholderism under multiple names. The characteristics of SSCs are specified by implementing conceptually related smart cities (CRSCs), which are ICT-based transformative cities. Many scholars point out that in-depth empirical studies of CRSCs are necessary to clarify the nature of the sociotechnical transition of SSCs while avoiding the oversimplified narratives of techno-utopia. Utilizing a periodic matrix taxonomy, this study aims to examine empirical characteristics of CRSCs services’ socio-technical transformation from international perspectives. The target cities were sampled using cluster sampling through three screening steps based on four representative documents reflecting the critical aspects of the operational definition of SSCs. The city-level data were collected using Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol and preprocessed with coding and weighting to create a periodic matrix taxonomy. The outcomes are the commonalities and different services’ sociotechnical transitions of sampled European cities from perspectives of multi-stakeholderism. The outcomes have managerial implications demonstrating empirically the sequences of service transformation of European megacities. Theoretical implications for the existing theories also arise through empirical analysis of historical real-city data and specification of stakeholders’ partnerships in conceptually related smart cities.

1. Introduction

Sustainable smart cities have developed through technologies and services in a paradigm of a socio-technology transition, which is the co-development nature of society, technologies, and human innovation, under various names to achieve urban sustainability [1,2,3,4,5,6,7]. The sustainable smart cities’ transformations have been influenced by miscellaneous stakeholders’ participation and partnerships [3]. According to Komninos et al. (2019), the governance and investment from international public and global private sectors in the first circuit transform the first circuit of establishing core technologies such as backbone internet, virtual communities, and cyberspaces using Advanced Research Project Agency Network to a second circuit, which is also transformed by citizen behavior based on urban awareness of efficiently using urban infrastructure into third one that emphasizes the improved local government systems based on strong participation of local entities [3]. Subsequently, conceptually related smart cities are developed as an innovative platform to gather diverse stakeholders to transform society into sustainable smart cities.

The two concepts of sustainability and smart cities have distinctive characteristics that compensate for their counterparts’ shortcomings [8]. Sustainability has distinct indicators of citizen participation, social inclusion, future diversity, and GDP, which are more related to the Sustainable Development Goals (SDGs). At the same time, smart cities are more oriented toward ICT to improve the efficiency of urban management and services, citizens’ quality of life, and urban competitiveness so as to satisfy the needs of present and future generations [9,10,11,12,13].

Conceptually related smart cities are subordinate to sustainable smart cities. Related studies have synthesized the two concepts to contribute to sustainable development in smart cities [14,15,16,17,18,19]. The definitions of sustainable smart cities have arisen from five academic disciplines as follows: (1) a combination of sustainability and smart cities, (2) a compound word emphasizing urban resource management, (3) a unified, innovative index, (4) a transformative process of urban services based on ICT, and (5) places of transformative sociotechnical innovative transitions to satisfy current and future local needs and improve urban ecosystems. The latter supports a multilevel perspective framework and the middle range of sustainable smart cities [5]. Diversely named smart cities are largely understood in the last concept of Sustainable smart cities when analyzed in the existing literature [8,20,21,22,23]. They emphasize the need for urban planning, data, and infrastructure to appropriately guide the direction of ICT and data analytic technologies for developing sustainable smart cities [8]. By following the last concept [5,8,24], this study conceives conceptually related smart cities as ICT-based transformative communities and cities, including cyber cities, virtual cities, Internet cities, wireless broadband cities, intelligent cities, digital cities, ubiquitous cities, and smart cities [8]. They have been introduced, advanced, and expired in the history of sustainable smart cities. Subsequently, conceptually related smart cities have been developed and implemented in the transformative sociotechnical framework of sustainable smart cities in cooperation with diverse players, based on the advancement history of ICT and applied urban services.

The qualitative literature review method was utilized to identify the characteristics of studies on smart cities in relation to other topics and unresearched fields within smart city studies. The related study adopts a scoping review [25] to extract the relevant data from each piece of literature as possible, including the methods, findings, and variables, because the method aims to provide an overview of what has been done [26]. A systematic literature review is largely utilized in the field of sustainable smart cities to provide the standards for applying the term “smart city” in the view of knowledge management since 2010 [27]. A systematic literature review has been utilized to identify key services or elements to understand the key notion of smart cities in relation to sustainability [14,28,29,30,31]. The Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA) is utilized based on a systematic literature review to identify how disruptive technologies transform cities in relation to the fields of environment, health, and social services [32,33,34,35,36,37,38]. The data for conducting PRISMA are not limited to white literature, but they utilize data extracted from social network services, blogs, news, and other informal online written documents to analyze public opinion [39]. Theoretical research has been conducted on smart cities using meta-reviews to identify a holistic view of smart city development, which creates advanced ICT to promote sustainability and participatory urban governance [40,41]. Some studies have used bibliographic analysis to identify the effect of smart infrastructure on e-governance and e-participation [42]. This study adopts PRISMA to collect related data on three conceptually related smart cities based on white literature, grey literature, and unofficial written documents in line with existing literature on smart cities.

A lack of in-depth empirical studies of sociotechnical transitions in conceptually related smart cities affects the oversimplified narratives [43]. Since the new technologies have been emphasized in the development of conceptually related smart cities, it has resulted in the absence of in-depth empirical studies of the sociotechnical transition and oversimplified the narratives of techno-utopia by ignoring the urban sustainability, diverse urban systems, and stakeholders’ capabilities [43]. The challenges of the recent development of conceptually related smart cities give rise to the necessity of empirical studies to identify the different characteristics of real cities’ cases of sustainable smart cities in sociotechnical transformation. Based on the comprehensive research question “what are the sociotechnical characteristics of Conceptually Related Smart Cities services from an international perspective”, this study aims to identify the empirical characteristics of conceptually related smart cities services’ socio-technical transformation led by different stakeholders’ cooperation for service implementation from international perspectives.

Considering the study’s aim illustrated in Figure 1, this paper is structured as follows. Section 2 reviews the concept of sustainable smart cities and establishes a theoretical background based on a literature review. Section 3 describes the framework of the proposed method. Section 4 describes the study process and explains its three outcomes. Section 5 discusses and expands our findings using existing theories, and the last section presents concluding remarks.

2. Literature Review

2.1. Background of Conceptually Related Smart Cities

Empirical characteristics of conceptually related smart cities have yet to be thoroughly studied from a holistic perspective using several real-city cases. Scholars have conceptually typified names to identify the classified heterogeneities and features, as shown in

Table 1. The names of Conceptually Related Smart Cites in the existing literature.

Citation	Author (Year)	Title	Conceptually Related Smart Cites
[20]	Nam et al. (2011)	Conceptualizing smart city with dimensions of technology, people, and institutions	a technological dimension (the digital city, intelligent city, ubiquitous city, wired city, hybrid city, and information city), a people dimension (a creative city, learning city, a human city, and knowledge city), and a community dimension (smart community)
[21]	Guo et al. (2019)	A Bibliometric Diagnosis and Analysis of Smart Cities	smart city, digital city, information city, intelligent city, knowledge-based city, and ubiquitous city
[22]	D’Auria et al. (2018)	Modern Conceptions of Cities as Smart and Sustainable and Their Commonalities	technology-led digital city, human capital emphasized smart cities, cyber cities, and virtual cities
[8]	Kim&Yang (2021)	Characteristics of Conceptually Related Smart Cities (CRSCs) Services from the Perspective of Sustainability	cyber city, virtual city, internet city, wireless broadband city, intelligent city, digital city, ubiquitous city, and smart city
[23]	Anthopoulos et al. (2017)	The rise of the smart city	A cyber-based city, a web city, a virtual city, a knowledge base, a broadband city, a wireless/mobile city, a smart city, a digital city, a ubiquitous city, and an eco-city.

. The term of conceptually related smart cities was first suggested by Kim and Yang (2021) to analyze the different names of smart cities in holistic manners [8]. It encompasses all the names of smart cities developed by the public, private, and public sectors, as illustrated in Table 1. Meanwhile, Research by Anthopoulos [23] has shortcomings in describing cities’ characteristics as it unifies the concepts related to conceptually related smart cities based on the real cities’ projects even if the research ushered in new approaches to smart city research based on real cities’ transformation. Therefore, it is necessary to clarify the meaning of various terminologies using real-city cases by focusing on the local distinctiveness and sustainable implementation of ICT to deal with smart city challenges of developing local sustainability.

Conceptually related smart cities are regarded as innovative platforms in cooperation with diverse stakeholders’ partnerships. Technology and society have developed through the formulation of correlated relationships. The technologies are influenced by economic, social, environmental, and political urban resources to satisfy different needs in each period while untethering a flood of social changes in modern society [44]. Those sociotechnical communities are the conceptually related smart cities developed in the five stages of IT transformative history, beginning from pre-telecommunications, telegraph, telephony, computing, and ICT [45,46,47]. In the history of transitions, ICTs have influenced better decisions and policymaking at the city level. It has empowered citizens’ participation by opening up the data and training them to become intelligent citizens, encouraged multi-stakeholder partnerships by breaking down traditional silos, and enhanced city sustainability by managing social, economic, and environmental resources, even though the technologies seem to be emphasized in the initial implementation stages. Sociotechnical advancement based on different stakeholders’ partnerships diversifies and evolves conceptually related smart cities in innovative trajectories. Their endogenous elements emphasize the role of stakeholders, geographical characteristics, and dynamic urban services [5,48,49,50]. The ICT-based transformative communities and cities are, aforementioned, cyber cities, virtual cities, Internet cities, wireless broadband cities, intelligent cities, digital cities, ubiquitous cities, and smart cities [8]. Through the interaction of these three factors, cities become innovative laboratories to develop place-based technologies [47,48] through exploration and exploitation activities immersed in the urban services’ interaction, especially in cooperation with the public, private, people, academia, and NGO sectors [51].

Overall, the related literature insists on the importance of multi-stakeholder partnerships and place-based urban services to make conceptually related smart cities sustainable. However, few studies have examined the empirical development of sustainable smart cities according to different stakeholder partnerships [43]. This study typified sociotechnical service developments of conceptually related smart cities according to different stakeholders’ cooperation by adopting the comprehensive concept of multi-stakeholderism. Meanwhile, this study differs from previous sustainable smart cities studies with respect to empirically clarifying three types of service sustainability by focusing on different stakeholders’ partnerships.

2.2. Concept of Sustainable Smart Cities

The concept of sustainable smart cities is neither a general nor fixed term. It has been discussed disparately by combining two terms: smart cities and sustainability. The concept of sustainable smart cities is based on (1) the concept of sustainability or sustainable development goals (SDGs) as a vision in a government plan [52,53,54,55] which the United Nations’ SDGs influence, 2030 Agenda, the New Urban Agenda to improve indiscreet urbanization, and the rapid growth of urban populations [56,57,58]. (2) Some studies define it as a compound term for managing urban resources by mobilizing ICT [21,59,60]. (3) It is regarded as a complex and innovative index for achieving urban sustainability [53,61,62,63,64]. (4) Other studies consider sustainability as a transformative process of urban services sustainability based on the transformative concept of ICT and multi-stakeholder partnership [51,65,66,67]. (5) Established from the transformative concept of sustainable smart city services, others conceive it in transformative sociotechnical innovative transitions to improve urban ecosystems and meet local needs [3,11]. In particular, Mora et al. (2021) adopt a middle-range of sustainable smart city transitions, which bridge the theoretical preposition with subsequent empirical testing based on the multilevel perspective framework to bring new theoretical insights beyond techno-utopianism [5]. Kim and Yang (2021) defined sustainable smart cities as complex initiatives aiming to develop and implement sustainably and integratively diverse urban services from social, technological, governancial, environmental, and managemental aspects [8]. Tcholtchev and Schieferdecker (2021) describe sustainable smart cities as those that fulfill SDGs in a sustainable process based on ICT, including IT, to boast various fields sustainably based on their development history [24].

In this study, sustainable smart cities are defined as transformative initiatives for developing and implementing integrative social, technological, governance, economic, environmental, and management urban services sustainably by extending the ideas of Tcholtchev and Schieferdecker (2021) [24], Kim and Yang’s (2021) concepts [28], and Mora et al.’s (2021) framework [5].

2.3. Literature Review of Socio-Technical Transition

The theory of a multilevel perspective of sociotechnical transitions was first framed by Geels [68] and influenced by various multilevel transition concepts. The field began its study by understanding technological changes and shifts by scholars in evolutionary economics. Nelson-Winter’s model is a representative study for identifying technological transitions using technological regimes and organizational and cognitive routines [69]. The technological regime focuses on the attention of engineers to provide strong guidance on the tactics for probing in that direction [70]. The regime results from the organizational and cognitive routines that engineers and firms share. It results in technological trajectories through innovative activities and R&D towards incremental improvements based on the stability of the technological regime [68,69]. Rip and Kemp widened the concept of the technological regime by conceiving sociological and economic perspectives [71]. Having the foundation of these two concepts, Geels combines various concepts by adopting Hughes’ large technological systems and the relationship between sociology, institutions, and rules [72]. This notion is supported by multilevel perspectives [73]. Malerga’s sectoral systems of innovation [74] and Carlsson’s technological systems [75] frame the sociotechnical transition from multilevel perspectives [68,76]. Rotmans et al. (2001) collaborated with Geels on different research levels of aggregation, including sociotechnical landscapes, regimes, and niches [77]. Geels was influenced by existing sociotechnical transition theories, including transformative economic, institutional economic, and middle-range frameworks, to conceptualize overall dynamic patterns in sociotechnical transitions [78].

The multilevel perspectives on sociotechnical transitions include three levels: niche innovations, sociotechnical regimes, and sociotechnical landscapes. Niche innovations are developed by small networks of various social groups and actors to build internal momentum based on visions, expectations, and the learning process. It is an expanded concept that embeds the concept of Dosi’s (1982) technological trajectories [79] with social groups and engineering communities, such as a knowledge base, engineering practice, corporate governance structures, manufacturing processes, and product characteristics [68,79]. As an extended version of the technological regime in Nelson and Winter’s theory, the sociotechnical regime acts as a window of opportunity for niche innovation by stabilizing the uncertainty of dominant design, which results in technological trajectories and cognitive routines. The socio-technical landscape, which is an exogenous context, places pressure on the existing regime and opens up opportunities for novelty.

Multilevel perspectives become robust when more empirical studies with diverse cases in varied time-periods and sectors [68]. The perspectives have been supported by historical case studies and typological studies [68]. The real society has different phases from adopting emerging technologies. As there is a lack of research on conceptually related smart cities from a holistic multilevel perspective that considers technological trajectories, technological regimes, local contexts, and different stakeholders’ characteristics and participation, it is not easy to conceptualize smart cities visions and definitions, and develop conceptually related smart cities sustainably [80,81]. Empirical research has been conducted in specific fields or areas, such as the Metaverse, which has recently been researched as socially constructed, politically driven, economically conditioned, and historically situated to overcome its problematic nature [82]. As the technologies have consistently been considered in the local context and the distinct characteristics of stakeholders, in-depth empirical studies of the sociotechnical transition are necessary to clarify diverse urban systems and stakeholders’ capabilities to make cities sustainable by avoiding the oversimplified narratives of techno-utopia [2,43]. This study has a gap in terms of identifying the holistic development of technological trajectories, regimes, and participants from multilevel perspectives. Sophisticatedly, this study is different from other sociotechnical transition researchers adopting multilevel perspectives in terms of clarifying “which services are developed mainly by whom in which cities” by reflecting the different conditions of local context and historical trends in each period.

3. Materials and Methods

A periodic matrix taxonomy was utilized based on the literature-reviewed data after cluster sampling, as illustrated in Figure 2. The cluster sampling method is helpful when the frame of the study population is not fully available [83], especially in the case of ongoing projects in smart cities. Multi-stage cluster sampling is a probability sampling method in which researchers list the study population as

Table 2. A process of sampling cities.

Study Population	First Screening Result	Second Screening Result	European Cities
Singapore, Tokyo, New York, London, San Francisco (Oakland), Paris, Hong Kong, Osaka, Los Angeles-Long, Beach-Santa Ana, Chicago, Barcelona, Moscow, Stockholm, Seoul, Munich, Stuttgart, Boston, Madrid, Shenzhen, Frankfurt am Main, Philadelphia, Toronto, Taipei, Houston, Miami, Berlin, Melbourne, Rome, Shanghai, Seattle, Manchester, Atlanta, San Jose, Cleveland, Sydney, Hiroshima, Birmingham, Beijing, Milan, Montreal, Dallas-Fort Worth, Buenos Aires, Vienna, Tel Aviv-Yafo, Denver-Aurora, Hamburg, Zurich, Nagoya, Baltimore, Kitakyushu-Fukuoka, Copenhagen, Hannover, Salt Lake City, San Diego, Perth, Washington D.C., Incheon, Suzhou, Raleigh, Kuala Lumpur, Vancouver, Amsterdam, Astana, Geneva, Brussels, Detroit, Guangzhou, Austin, Orlando, West Yorkshire, Cologne, Helsinki, Daejeon, Istanbul, Ulsan, Richmond, Valencia, Jerusalem, Columbus, Sao Paulo, Bridgeport Stamford, Phoenix-Mesa, Nanjing, Doha, Haifa, Antwerp, Hartford, Riyadh, Sapporo, Gwangju, Busan, Naples, Xiamen, Milwaukee, Glasgow, Adelaide, Dubai, Daegu, Santiago de Chile, Malaga, Athens, Wuxi, Dortmund, Louisville, Pretoria, Essen, Tianjin, Foshan, Taichung, Brisbane, Auckland, Dresden, Saint Petersburg, Virginia Beach, Calgary, Las Vegas, Bogota, Medina, Dongguan, Wuhan, Lima, Kaohsiung, Dusseldorf, Tampa-St., Petersburg, Belfast, Jedda, Worcester, Hangzhou, Lyon, New Haven, Leipzig, Dublin, Hamilton, Hague, Buffalo, Charlette, Liege, Zaragoza, Torino, Colorado Springs, Chengdu, Qingdao, Nashville-Davidson, Macao, Rio de Janeiro, San Antonio, Zhongshan, Minneapolis-Saint Paul, Sendai, Lisbon, Silo, Ningbo, Lille, Liverpool, Provo-Orem, Changzhou, Zhengzhou, Amman, Venice, Dammam, Rotterdam, Tainan, Changsha, Leicester, Tehran, San Juan, Providence, Shizuoka-Hamamatsu M.M.A., Verona, Johannesburg, Baton Rouge, Bangkok, New Orleans, Gold Coast, Ottawa-Gatineau, Bologna, Leon, Solfa, Indianapolis, Shenyang, Pittsburgh, Ogden, Florence, Kansas City, Budapest, Montevideo, Zhuhai, Honolulu, Barcelona-Puerto La Cruz, Oklahoma City, Dallin, Minsk, Porto, Mecca, Xi’an, Ahvaz, Hefei, Marseille-Aix-en Provence, San Francisco, Tallinn, Roma, São Paulo, Mexico City, Warsaw, Prague, Almaty	Singapore, New York, Stockholm, Seoul, Shanghai, Amsterdam, Helsinki, San Francisco, Chicago, Copenhagen, Barcelona, Melbourne, London, Tokyo, Paris, Moscow, Madrid, Toronto, Berlin, Bogota, Buenos Aires, Istanbul, Brussels, Dubai, Mexico City, Sydney, Johannesburg, Lisbon, Athens, Kuala Lumpur, Seattle, Austin, Vienna, Beijing, Shenzhen	Singapore, New York, San Francisco, Chicago, Barcelona, Melbourne, London, Tokyo, Berlin, Dubai, Mexico City, Seoul	Barcelona, London, Berlin
207 cities	36 cities	12 cities	3 cities

, divide them into clusters, and randomly select data from among them. The selected clusters were listed to conduct stratified sampling to determine the final samples randomly [84]. This method is usually utilized to select cities or people in social sciences, medical research, and agricultural studies for interviews or surveys [85,86,87,88,89,90]. Some systematic literature review researches use adaptive cluster sampling by clustering sources into three overarching steps of sample selection [91,92]. In this study, sampled European cities were selected using cluster sampling from all lists of smart cities (population), combined with lists of sustainable smart cities (study population), while considering the concept’s sectoral keywords, including social, technological, governance, economic, environmental, and management elements. The second step was to compile relevant conceptually related smart cities data through longitudinal PRISMA and sort them by criteria to create a dataset for further analysis. The next step was data preprocessing by coding the projects and providing weights to the services depending on the number of stakeholders. The fourth step was to create a periodic matrix taxonomy using coded services and years. It comprises the year category on the top of the x-axis and the category of services on the left side of the y-axis. The vision and names of cities are positioned below the y-axis services and are marked as identical to the services. The cells are shaded if there is a service in a corresponding year, even if identically categorized services are developed within a year. Periodic matrix taxonomy provides two results from an international perspective within the framework of sociotechnical transitions. The following paragraphs elaborate on methodologies and outcomes in a detailed manner.

The first step in framing the methods was started by casting the research question. The question is correlated with the study aim and concept of conceptually related smart cities in the concept of sustainable smart cities, which contain measurable indicators to establish the study frame, as illustrated in Figure 2. As mentioned above, in the concept of sustainable smart cities, the factors of six aspects and one keyword are narrowed down by adopting suitable research materials to sample sustainable smart cities to reflect the concept. The key concept in this study is the sustainability of urban services, and the six aspects are social, technological, governancial, economic, environmental, and managemental elements.

The cluster sampling in this study proceeded through three filtering steps. The cluster sampling needs to list the population to cluster them, and the population for this study is 221 cities, according to Smart City Tracker 1Q18 [93]. The study population combines three rankings representing the sectoral aspects of the sustainable smart cities concept: the United Nations-Habitat Global Urban Competitiveness Report, McKinsey Company’s Smart Cities: Digital Solutions for a More Livable Future, and the United Nations E-Government Survey 2020 [94,95,96]. The first report represents the social, economic, environmental, and managemental aspects. The second report indicates the aspect of technology, while the third one represents the aspects of governance. The first screening results came from the combination of three sustainable smart cities rankings, mentioned more than twice in the three groups of selected cities based on the Sustainable Smart Cities concept, which resulted in 36 cities. The second sampling is conducted to select high-performing sustainable smart cities by integrating the first screen result and the top 20 smart cities from the rank of smart cities performance published by Juniper Research, which included 12 cities. Among the result of the second screening, European cities were selected, and Berlin, London, and Barcelona are the sampled cities representing European samples of the operational concepts’ keywords.

Longitudinal PRISMA is conducted to compile the year of occurrence, related events, stakeholders on duty, and related government plans from the written documents that describe events starting from 1968 to 2021, considering the emergence of social ICT from the Advanced Research Project Agency, a pioneer of the Internet. As illustrated in

Table 3. Search Keywords of Conceptually Related Smart Cities.

Conceptually Related Smart Cities	Search Keywords
Cyber City and Virtual City	Advanced Research Project Agency, file transfer protocol, packet switching, ethernet, telnet, e-mail, backbone networking, web-based community, video chat, audio chat, Bulletin Boards Systems, list server, news lists, Usenet, Bitnet, Computer Mediated Community, Internet Relay Chat, and Multiuser Dungeons.
Internet City	Transmission Control Program/Internet Protocol, modem, and World Wide Web.
Broadband City	digital subscriber, and fiber optic data communication.
Wireless Broadband City	packet radio, local area network, Packet Radio Network, Wave LAN, Wi-Fi, 1G, 2G, 3G, 4G, LTE, and 5G.
Intelligent City	Intelligent Cities are related with keywords of intelligent city, intelligent transport systems, artificial intelligence, intelligent government and intelligent building system.
Digital City	digital city, Geographic Information System, cloud computing, one-stop services, digital inclusion, digital education, digital economy, digital participation, crowd funding, crowdsourcing, digital plan, and data network.
Ubiquitous City	3A(anywhere, anyone, anytime), and manage city resources using ubiquitous, computing, sensors and microchips such as ubiquitous computing, operation center, clouding computer, Radio-Frequency Identification, Near Field Communication, Quick Response code, CCTV, ubiquitous city, ubiquitous plan and u-services.
Smart City	smart city, smart citizens, smart services, Internet of Things, Internet of Everything, robotics, 3D printing, neuro-technologies, quantum computing, solar desalination, zero-carbon natural gas, nanotechnology-cloud computing, energy storage, predicting future and proacting to the disasters and climate changes, cyber-physical system, digital twin, microdata, super connectivity, 5G, Big Data, data security, blockchain, Vehicle To Grid, autonomous vehicle, health passport, DNA computing and storage, carbon based transistors, and other emerging technologies.

, the search keywords refer to the existing literature in Section 2 and Kim and Yang’s study because they typify the elements of conceptually related smart cities by combining the indicator of sustainable smart cities and conceptually related smart cities based on transformative concepts using a systematic literature review [8]. The search keywords were used to collect related data from Google and Google Scholars for each city.

Collecting relevant government, company, NGO, academic, and international organization publications is a fundamental step in constructing a dataset for further work. The data features for analysis are shown in

Table 4. Compiled Data Features.

Name of Cities	The Number of Participators	The Number of Conceptually Related Smart Cities	The Number of Implemented Years	Implemented Projects
Barcelona	182	3	34	179
London	110	5	44	132
Berlin	64	3	25	73

. After searching for the documents with keywords in Table 3, it resulted in 182 documents concerning Barcelona, 110 documents regarding London, and 64 documents regarding Berlin. Non-English documents, PPT slides, duplications, blog posts, board posts, book reviews, book introductions, and magazines were excluded. As organized in Appendix A, the final data sources for analyzing three cities were 47 documents for Barcelona [97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143], 40 sources for London [65,106,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181], and 38 sources for Berlin [161,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218]. As Table 4, the 179 Barcelona projects were implemented with 182 participants over 34 years continuously and in an ad hoc manner. The 132 London projects are implemented with 110 players in London under the five names of the conceptually related smart cities for 44 years, mostly continuous plans. Berlin has implemented 73 projects with 64 participants under three names of conceptually related smart cities in 25 years in an ad hoc manner.

The compiled data were organized into the categories of the year of occurrence, related projects, stakeholders on duty, and related government plans. Since the compiled data are usually long sentences or paragraphs that are difficult to analyze systematically, it is challenging to identify the flow of conceptually related smart city services’ sustainable development. The data were divided into service, vision, naming, and reference categories by extracting relevant data from long sentences following the progress shown in Figure 3.

The compiled text data were transformed into numeric data for the analysis as Appendix A. The first step for data preprocessing is to code them using sustainable smart cities service categories in Figure 4 by referring to Kim and Yang’s study because of their similar study aims and concepts [8]. This study adopts 17 service indicators, including social, tour, safety, environment, economy, health, transport, government, waste, architecture, energy, education, history, media and art, standardization, data, and infrastructure, while excluding urban planning vision and including three subordinate elements of hard infrastructure from Kim and Yang’s study [8]. The coding index of vision consists of extracting frequently mentioned words from the data documents of the three cities. Following the progress illustrated in Figure 5, the services are codified to create a periodic matrix taxonomy. The codes are marked on the top of the x-axis, referring to the year categories, and service categories are marked on the left side of the y-axis. The vision and names of the cities are marked below the y-axis services based on the government plan.

The second step for data preprocessing is to give weights to each service and stakeholder to count them equally if they correspond in the same years. The weighting process is a crucial step to identify which services are more developed by whom in individual cities. Some projects, which describe only the vision and absent action plans or activities, were excluded from making a weighted service table as this study purposed to identify the characteristics of implemented or planned activities. The services involved in each stakeholder are divided by services’ number from the assigned weights of the individual stakeholders. As each year weights “1” equally, the weight is divided by the number of participating stakeholders in that year to distribute the weight to the participated stakeholders equally. The services get weights individually through dividing the assigned weights for stakeholders by the number of implemented services. The point is that the total weight of services in the same year needs to be “1”, as shown in Appendix A. Each service is included in some stakeholders each year because this is counted equally, and stakeholders are vice versa in the same year.

The key players are illustrated distinctively in

Table 5. Three types of services’ sustainability by different stakeholders’ partnerships.

	Barcelona	London	Berlin
The weights of publicly driven services	26.82	23.64	9.58
The weights of privately driven services	4.3	12.99	13.47
The weights of people-driven services	1.92	0.33	1.95
The weights of academic-driven services	0.89	4.87	0
The weights of NGO-driven services	0.08	2.17	1
Total year of service implementation	34	44	25
	Barcelona	London	Berlin
The portion of public-driven services	0.79	0.54	0.34
The portion of private-driven services	0.13	0.3	0.54
The portion of people-driven services	0.06	0.01	0.08
The portion of academic-driven services	0.03	0.11	0
The portion of NGO-driven services	0	0.05	0.04
Total	1	1	1

and Figure 6. The number of individual stakeholders’ driven services is deducted by adding weights corresponding to each city’s stakeholders’ services. The portion of services results from dividing the number of stakeholder services by the entire year of service implementation in individual cities. When the result is interpreted with the major service-leading stakeholders, the public and private sectors, separate from the others, it reveals that the private and people partnership has developed in Berlin. By contrast, Barcelona’s smart cities have comparatively more weight in the public and people sectors, and London’s smart cities are relatively strong in public, academic, and NGO partnerships.

4. Results

The periodic matrix taxonomy provided two important commonalities and three different empirical characteristics from an international perspective through cluster sampling, PRISMA, and data preprocessing of coding and weighting. The first commonality among the three cities is the comparatively higher weights for publicly and privately driven services. The two stakeholders have led the projects since the smart cities and their essential technologies were developed. After the public and international private sectors develop built environments, core technologies, and infrastructure with their large funds in the initial stage, the frontier and emerging technologies are publicized by national companies in support of small and medium companies. After spreading across nations and cities, innovation has emerged in cooperation with multiple stakeholders, such as public-academics-NGO, private-people, or public-people partnerships. In other words, the major players in the development of smart cities are the public and private sectors. The other commonality is sustaining the development and implementation of services after approximately 2015. This phase is influenced by the public sector’s leadership at the national level and the launch of SDGs at the international level. Specifically, London launched a London Infrastructure in 2015 to advance supportive technologies after planning the Smart London Plan in 2013. Barcelona planned municipal action plans after the smart city project was launched after Mayor Trias’ decided to enhance citizens’ quality of life and reduce the cost of government operations in 2012 [101]. The Berlin government launched a “Smart city makes the people and citizens smarter” plan with ten goals. The plans correlate with the SDGs to make smart and existing cities sustainable. It deals with current challenges based on active participation in innovative ideas, advanced, resilient infrastructure to reduce resource consumption, and interactive, diverse urban services to encourage smart networks. Consequently, the three cities have commonalities in stakeholder cooperation and service sustainability.

The period matrix taxonomy discovers three different characteristics of service development in socio-technical transitions concerning different stakeholder partnerships, as illustrated in Figure 7. The primary focus of the public-people-driven city in Barcelona was used to develop infrastructure, government, economic, and social services for the preparation of the Olympics by implementing the @BCN Plan beginning in 1985 with 500 km of communication infrastructure of fiber optic cables [98]. As an Internet city, they have mobilized Internet networks in cooperation with academia and citizens, such as the Guifi.net platform [105], to educate citizens to allow anyone to make anything. Additionally, it implemented transportation and environmental services in 2000 when the government launched programs such as electric motorcycle charging stations, water management facilities, and hybrid bus prototypes and regulated new buildings to install solar energy sources [100]. In 2013, Barcelona provided health and well-being services and ensured the availability and sustainable management of water and sanitation [102,105,107,112,219]. Other current services and related projects are sustainably developed and posted in the municipality website (e.g., 5G-related projects, eight Barcelona Open Data Challenge projects, the makers’ fair Barcelona, innovative solutions for dependent senior citizens project led by I. Lab, the mobile youth festival, remote surgeons, and other public-people partnership projects). They generally focus on participatory urban planning led by public-people partnerships in developing conceptually related smart cities.

Berlin is a private-people-driven smart city. Its first computer networking was implemented in 1981 by networking with the Stanford Research Institute to produce the German Ministry for Research and Technology status report on computer networking in the intended federal project “German Research Network” [182]. The Internet network was publicized in cooperation with the government and private sectors until Deutsche Telekom was established in 1995. Although Deutsche Telekom became a government corporation, the required services for operating companies were still developed in an ad hoc manner until 2011, including transportation (e.g., Door2door and on-demand air taxi), health (e.g., farming tech and Plantix), and social (e.g., Internet café and Internet Relay Chat) services. However, cooperation between the public and private sectors began when Silicon Allee was implemented through urban planning in 2011 to build a start-up community using meetups, events, and an English-language blog [189]. The government and companies branded Silicon Allee in Berlin as a creative city through urban planning, which implied a vision of the future with urban planners. The plan aimed to instill an ideal future in the city by attracting new creative classes, ideas, investments, businesses, and money [220]. Public plans (e.g., “Smart City makes the people and citizens smarter,” and “Digital Strategy 2025”) led to other types of services, including social, public, energy, safety, and education services, which have been sustained with previously developed services.

Public-academic-NGO-driven services are provided in London. It has a long history of developing data transmission infrastructure in cooperation with governments and researchers since Donald Davies conceived the idea of packet switching for data communications chosen by the Advanced Research Project Agency Network to serve as an interface message processor [44]. The government and private companies have provided social services since the public view data service was opened by Prestel [175]. Health services were provided for research purposes in 1986 (e.g., NYSERNet) and were served within the government project by creating a set of locally sustainable and nationally scalable digital inclusion initiatives in 2005 [221]. As broadband technology was implemented, more people used the Internet [175], and commercial ISPs (e.g., Pipex) and nonprofit ISPs (e.g., GreenNet) were introduced. As an inclusive and innovative city, the government launched free services (e.g., Mapping service, Tool kits, and other Scalable and Sustainable Local Projects) and private sectors (e.g., TalkTalk and Sky TV) while NGOs raised their voices to secure digital rights (e.g., Open Rights Group). The digital city emphasizes the digital economy, cyberspace security, and digital content [156]. Other services, including energy, the environment, and transportation, have been implemented through the London Infrastructure 2050 Plan by supplying energy to homes and businesses and unlocking housing growth through transport infrastructure [148].

5. Discussion

Conceptually related smart cities have evolved in the history of sociotechnical transitions to realize SDGs for urban sustainability in cooperation with diverse stakeholders’ partnerships. Some scholars point out that some service implementation standards are far from real-cities, because they lack a holistic strategic framework of service development and are drawn from related research, mostly on the conceptual characteristics of smart cities or the empirical characteristics of technological fields [81,222,223]. In this regard, this study identified the sociotechnical characteristics of conceptually related smart cities services from international perspectives by using the periodic matrix taxonomy under the research question of “What are the sociotechnical characteristics of conceptually related smart cities services from international perspectives?”. The empirical characteristics are based on three sampled European sustainable smart cities using a periodic matrix taxonomy. This section elaborates on the findings and implications of their relations with the existing literature.

The first outcome concerns two commonalities of European conceptually related smart cities development in the history of sociotechnical transitions from an international perspective. The development process, from a macro perspective, analyzes the European empirical cases in correlation to the existing study, which uses city data from Manchester (UK) and Boston, Massachusetts, and San Diego (US) [224]. Subsequently, this study has implications in terms of providing a headstone for making a global consensus reflecting on the local sustainable smart cities context regarding Schiavoand Magalhães’ research [81] for establishing the global sustainable smart cities’ urban service standardization. Specifically, this study clarifies that initial services, including infrastructure, economy, government, society, and education, are fundamental to develop smart cities. The services are either shifted or diversified to meet needs in the current period, including safety, health, and transportation services. These transitions are based on an Extended Metabolism Model by Newman, demonstrating how livability is integrated with the flow of resources [225] and supports smart metabolism, which assesses cities with three critical approaches to increase livability involving the use of sensors, real-time data, and informing different stakeholders through the use of pervasive technologies [80,226]. The transitions of magnified services in each period have shifted smart metabolism to increase livability. Using the internal combustion engine automobile for e-mobility is an example of the smart metabolism of sustainable smart cities service development. The smart metabolism can be empirically identified, given that the sensor and big data analytic device analyze the related spatial-temporal and longitudinal data. The second commonality is that public or private stakeholders initialize and sustain them with various local services, in line with the SDG plan after 2015. It compensates for the issues raised by Parra-Domínguez et al. [18], who demonstrate the necessity of smart city development by incorporating the SDGs set by the 2030 Agenda with technology implementation. However, it would be challenging for the local indicators and themes to be used or reflected in conjunction with global smart city indicators and local context, and vice versa in a holistic and systemic view [81].

The other outcome is the development of three empirical characteristics of sustainable smart cities according to different stakeholder partnerships, which are divided into public-academic-NGO, public-people, and private-people partnerships. This study empirically demonstrates the multi-level perspectives in socio-technical transitions raised by Geel and expands the technological transitions by Nelson-Winter’s model. It contributes to the discourse on multi-stakeholderism in conceptually related smart cities by specifying partnerships based on the development of services and local physical characteristics. The multi-stakeholderism for developing sustainable smart cities has been discussed in several previous studies empirically from the perspective of sociotechnical transitions by mobilizing ICT, which serves as a gluing component to interact with different services and engage stakeholders’ partnerships by responding resiliently to upcoming challenges through technological advancement [24]. The quadruple-helix framework helps to understand the different characteristics of service implementation based on the local context in sociotechnical history and multi-stakeholderism [227]. It has been researched in several models, such as strategic niche management [71], technological innovation system [228], and multilevel perspective models [2,68]. The middle range theory, based on multilevel perspective frameworks, provides a scientific foundation by empirically examining the patterns, regularities, and stylized mechanisms of diverse sustainable smart city transition processes and using general conceptual schemes [5]. However, related empirical studies have not yet been conducted using transformative city-level data. Therefore, this study expands the existing literature based on the middle-range theory framework to identify the empirical characteristics of sustainable smart cities development from the perspective of stakeholder roles, innovation, and sustainable smart city planning.

6. Conclusions

Sustainable smart cities have evolved through sociotechnical transitions in cooperation with diverse stakeholders to achieve future sustainability. In-depth empirical studies of the sociotechnical transition based on conceptual studies would prevent one-size-fits-all narratives [43] and provide a fundamental node for planning sustainable smart cities. This study aims to identify the empirical characteristics of the service developments of conceptually related smart cities services’ socio-technical transitions from an international perspective. The empirical characteristics were identified based on the real-city data sources of three sampled cities, which are selected by cluster sampling using the representative ranks of keywords in the concept of sustainable smart cities. The outcomes were measured using periodic matrix taxonomy. It results in two outcomes: two commonalities from international perspectives and three empirical characteristics of the development of sustainable smart cities. The two commonalities of the conceptually related smart cities service development of the three cities resulted in the identification of leading stakeholders and the starting year of cooperation between the local level of sustainable smart cities implementation and the international level of sustainable smart cities development. The other outcome is the three empirical characteristics of sociotechnical transitions in sustainable smart cities development according to different stakeholders’ partnerships, resulting in conceptually related smart cities services development in cooperation with public-academic-NGOs, public people, and private-people partnerships.

These results provide managerial contributions. The first outcome provides the entire sequence of developing smart cities services for achieving sustainability in correlation to existing studies. Specifically, the initial services such as infrastructure, economy, government, society, and education are fundamental to develop smart cities. At the same time, the services diversify other services satisfying the local context and the needs in the era including safety, health, and transportation services. This transformation in the history of developing smart city services could be considered as one of the socio-economic development processes [229] but also intelligent metaphoric approaches for upcoming challenges. In the current transformation, the local authorities and citizens’ active participation would be the determinant factors in adjusting cities to the new environment mobilizing new technologies, digitalization, data, information, and vision. Meanwhile, cities could develop contextualized or advanced services based on geographical, cultural, political, economic, and environmental data drawn from measuring cities’ status using sensors, data analytic technologies, and alerting devices. The second result empirically clarifies that smart cities’ development emphasizes place-based development rather than the disappearance of time and space by extending Sassen [229]. The place-based traits, which are intrinsically inherited in the history of developing cities mobilizing ICTs, influence smart city services’ development and stakeholders’ cooperation characteristics.

Moreover, the outcome has theoretical contributions to smart metabolism that empirically clarify how urban resources and services have been transformed or evolved in the history of sustainable smart cities with the advancement of ICTs. It would be empirically identified given that the sensor and big data analytic device analyze the related spatial-temporal and longitudinal data. It could contribute to the conceptualization of establishing smart cities’ history by analyzing the commonalities of several mega smart city services’ transformation so as to provide a fundamental principle to newly participated sectors and to deal with the development from the perspectives of the technological utopia. The second outcome contributes to the discourse on multi-stakeholderism in conceptually related smart cities by specifying partnerships based on the development of services and local characteristics. Empirical models of sustainable smart cities have been discussed from the middle-range theory based on multilevel perspective frameworks [2,68]. It thoroughly examines the sustainable smart city transition process and the general conceptual scheme using diverse empirical smart city-related concepts and models [5]. However, this field has not used data from transformative cities. Therefore, this study expands the middle-range theory framework to identify the empirical transformative characteristics of sustainable smart cities through the lens of stakeholders’ roles, innovation, and smart city planning.

This study had some limitations. First, the unit of analysis, which is entirely related to documents describing conceptually related smart cities, could have the possibility to omit some relevant documents, even though the research result provides managerial and theoretical implications by explaining the priorities of developing conceptually related smart cities’ services according to different stakeholders’ partnerships and local contexts. Second, a lack of background knowledge makes it difficult to explain the development of different services based on multi-stakeholderism in a sophisticated manner. To deal with these issues, it updates and applies the theory and related research on multiple stakeholders to further studies.

Nevertheless, the outcomes have managerial implications of empirically demonstrating the different sequences of service transformation for European megacities depending on diverse stakeholders’ partnerships, and theoretical implications to the existing theories, such as smart metabolism, multilevel perspectives, and the middle range theory, through the empirical analysis of historical real-city data and the specification of stakeholders’ partnerships in conceptually related smart cities to identify the successful element of establishing sustainable smart cities from the urban sustainability perspective.