Integration of Smart City Technology and Business Model Innovation

Abstract

At present, the progress of business model stages supported by a variety of technology categories may unclear, and the integration of technology application and business model innovation (BMI) is hard due to operational practices in various industries and research areas, which block the view of the integration of smart city technology (SCT) and BMI for further development. Thus, this paper aims to explore the current research on the integration of SCT and BMI and identify the current research status and hotspots, assisting in indicating the status of these technologies in the business model stages in order to determine future trends, and adopts a mixed research methodology including a macro-quantitative method based on bibliometrics and a micro-qualitative method using content analysis. The results reveal a 17-year (2007 to 2023) integration of SCT and BMI enhanced by cloud computing, big data, Internet of Things, and artificial intelligence in recent years, with the most current studies focusing on the ideation and design stages of the business model stages with an emphasis on using digital capabilities to refine, formulate, and design the corresponding business model while defining the strategy through internal and external factors. Interestingly, Industry 4.0 and digital servitization are currently the key trends.

1. Introduction

The growth of urbanization is inspiring a new aspiration to integrate technology into the design of urban services, creating the essence of the “smart city” [1]. Cities have integrated smart city technology (SCT) into service production, delivery, and governance to solve persistent urban and environmental problems [2]. SCT has been used as a new enabling approach for companies to create and capture value [3], in which business model innovation (BMI) has acted an important role in realizing the competitive advantage of companies, and considered critical for companies to adopt in order to meet the challenges of the current business environment [4]. The integration of SCT and BMI is considered, as a way to offer new solutions in order to address the problems of smart cities [5]. SCT includes “digital twin”, “Internet of Things”, “big data”, “blockchain”, “cloud computing”, “artificial intelligence”, and “machine learning”. However, there is still no agreement on a common definition, although there are numerous methodologies and definitions of the business models stages. The business models stages are defined and devised based on the literature [6,7,8], in this paper listed as five stages: Ideation (strategy definition), Design, Testing, Verification, and Implementation.

At present, attention has been paid to the relationship between SCT and BMI, such as the contribution of BMI to the development of new food supply chains [9], the integration of the circular economy and digital technology [5], and the application of artificial intelligence to innovation processes [10]. Existing studies adopt a variety of perspectives from the circular economy [5,11]; innovation and management [10,12,13,14,15]; Industry 4.0 [16,17,18,19,20]; and life cycle [21] to study the demand–supply relationship between SCT and BMI in certain industries and certain technologies. These industries include food [9]; software engineering and financial services [22]; healthcare [23]; and services [18]. The technology categories mainly include artificial intelligence [10,12,13,14,15,21,24] and big data [25]. However, existing studies fail to establish a holistic relationship between SCT and BMI as they tend to compare the application status and trends of different technical systems, and variations in technology and business model innovation brought about by different disciplinary fields or industries. There is still a deficiency in research examining the comprehensive correlation between SCT and BMI. Interestingly, a recent review study explores the relationship between digital technology and BMI and provides an explanatory framework focusing on the theory and management of digital-driven BMI [3], which excludes the field of engineering science and is limited to the perspectives of business and management.

Hence, the integration of technology application and BMI in the current literature is not universally established due to the different operational practices in various industries and research areas. These challenges block the view of SCT and BMI for further development. As such, a holistic view of the key topics such as the multidisciplinary research status and business models stages covered in the relationship between SCT and BMI need to be clearly established. Therefore, this paper aims to explore the current research on the integration of SCT and BMI and identify the current research status and hotspots based on key topics and areas, which assist in indicating the application status and context structure of each SCT in relation to BMI stages in order to determining future trends. As such, this paper addresses the following four research questions (RQs) that form the research rationale, as shown in Figure 1:

• What is the research background and current research status in the integration of SCT and BMI?
• What is the multidisciplinary research status in the integration of SCT and BMI?
• What are the main context structures in the integration of SCT and BMI in the business models stages and application areas?

2. Materials and Methods

This paper adopts a mixed research methodology that includes a macro-quantitative method based on bibliometrics and a micro-qualitative method using content analysis to investigate the integration of SCT and BMI. Bibliometric analysis is a popular and rigorous method for analyzing large amounts of scientific data, which can be applied to explore the evolutionary nuances of a particular field and draw suggestions on a particular area of knowledge to the research topic, resulting in clarifying emerging areas of the research direction of the topic. The macro-quantitative method explores the research background and current status of integrating SCT and BMI. This is achieved through descriptive statistics, analysis of the structural distribution of research hotspots, and examination of research hotspot and continuation trend in the field over various time periods, effectively addressing research objective 1. However, due to the multifaceted and multilevel complexity of the research problem, it is necessary to analyze the data from a micro perspective using qualitative methods in light of the results of the previous quantitative methods to explore multidisciplinary research status (research objective 2) and business model stages analysis (research objective 3). A flow chart of the research methodology has six phases, which are shown in Figure 2: (1) identification of research objectives, (2) database selection, (3) data collection, (4) definition of selection criteria for data quality, (5) macro-quantitative data analysis, and (6) follow-up micro-qualitative analysis.

2.1. Phase 1: Identification of Research Objectives

This paper aims to answer the RQs in Section 1—how to investigate the research on SCT and BMI integration by examining the current research status and hotspots in terms of key topics and focused areas, for which the following three objectives (Objs) needed addressing:

Obj1: Explore the research background and current research status in the integration of SCT and BMI;

Obj2: Explore the multidisciplinary research status in the integration of SCT and BMI;

Obj3: Explore the main contributions and relationships of SCT- and BMI-integrated fields to business models stages and application fields.

2.2. Phase 2: Database Selection

Since Web of Science is widely regarded as a robust and reliable data source for bibliometric approach [26], this paper adopted the Web of Science Core Collection (WoSCC) as the source for data collection.

2.3. Phase 3: Data Collection

The keywords “smart city technology” and “business model innovation” were used for the search query as TS = (“business model innovation” AND “smart city technology”) in the Web of Science Core Collection, as illustrated in

Table 1. Search query and literature quantity.

Search Query	Literature Quantity
TS = (“business model innovation” AND (DT or “digital twin”))	8
TS = (“business model innovation” AND (IOT or “Internet of Thing*”))	63
TS = (“business model innovation” AND “big data”)	119
TS = (“business model innovation” AND “blockchain”)	24
TS = (“business model innovation” AND “cloud computing”)	16
TS = (“business model innovation” AND (AI or “artificial intelligence”))	69
TS = (“business model innovation” AND “machine learning”)	17

“*” indicates any character group, including null characters.

. The keyword “smart city technology” was further defined as certain technologies such as ‘DT or digital twin’, ‘IOT or Internet of Thing’, big data, blockchain, cloud computing, ‘AI or artificial intelligence’, and machine learning to enhance the search results. The results are exported as ‘Plain text file’ and the exported information as ‘Complete record’, including the year of publication, language, journal, title, author, affiliation, keywords, document type, abstract, and number of citations.

2.4. Phase 4: Definition of Selection Criteria for Data Quality

Since the obtained data, which are relevant to the research topic, may be included in the process of automatic retrieval and could cause bias to the data, the following criteria have been set to filter out the obtained data to ensure data quality:

• Repeated literature;
• Literature unrelated to the application of SCT to BMI;
• The main body of research is not the business model innovation;
• Literature that does not mention any type of SCT application;
• Literature not in English;
• Literature type other than ‘Article’, ‘Review article’, or ‘Proceeding Paper’.

2.5. Phase 5: Macro-Quantitative Data Analysis

The adopted macro-quantitative data analysis aims to solve research objective 1. Scientific cartography is one of the mainstream methods of bibliometric analysis, which can be used to explore the structure and evolution of the research field [27], which is facilitated by software tools, such as VOSviewer (1.6.18) and CiteSpace (6.2.R4). VOSviewer (1.6.18) is a tool for generating and visualizing bibliometric networks based on journals, researchers, individual publications, bibliographic coupling, or co-authorship [28]. CiteSpace (6.2.R4) is an information visualization software tool that is mainly used for the measurement and analysis of scientific knowledge data [29]. The obtained raw data are imported into VOSviewer (1.6.18) and CiteSpace (6.2.R4) to generate the visualization of knowledge structure and reveal the relationship between SCT and BMI. The macro-quantitative analysis is set to address (Obj1): (1) descriptive statistics of research background from two perspectives: publication years and research areas; and (2) the current research status in the field via bibliometric tools, for which the VOSviewer software tool is used for keyword network visualization to explore the structural distribution of research hotspots in the field of SCT and BMI integration followed by implementing CiteSpace (6.2.R4) software tool for keyword burst detection and keyword time zone chart to show the research hotspot and continuation trend in the field over various time periods.

2.6. Phase 6: Follow-Up Micro-Qualitative Analysis

The follow-up micro-qualitative analysis has been used to address research objectives 2 and 3, and explore the key subject areas that are carried out through keyword co-occurrence visualization based on the classification of “research areas” in the results of previous macro-quantitative data analysis (Obj2), and a screening of SCT and BMI integration throughout business models stages for identifying the application field and relationships (Obj3).

3. Results

3.1. Results of Macro-Quantitative Analysis

3.1.1. Background Statistics

The number of publications is an important indicator reflecting the direction of the research development trend. The results of the number of publications on SCT and BMI, which are shown in Figure 3, indicate that the research initially surfaced in the year 2007, and a total of 237 articles have been published over past 17 years from the year 2007 to 2023, which have been divided into the following three phases:

• Phase 1 (initial stage): Only one article was published in each year during the initial seven years from the year 2007 to 2013 (eight years);
• Phase 2 (progressive stage): There were 65 published articles (27.43 percent of all the articles) during the year 2014 and 2019 (six years), indicating a noteworthy upward trend;
• Phase 3 (period of rapid growth): The number of publications, 167, accounting for 70.46 percent of all the articles, has increased dramatically over the last four years (the year 2020 to 2023) to a total of 167, reaching its peak in the year 2022 at 56 published articles.

The exponential trend line, the blue line in Figure 3, shows an average annual growth rate of 15.73 percent for cumulative publications over the last 15 years from the year 2007 to 2023, as suggested by using the following formula:

y = 0.3417e0.3008x

(1)

The above results indicate that the topics of SCT and BMI integration have developed at a high speed and have been gaining sustained attention.

Figure 4, exploring popular research areas of publications related to SCT and BMI by using the “Analyze Results” tool in the WoSCC database, indicates the top ten hot research areas in the publications. These include Business Economics (117 articles), Engineering (54 articles), Computer Science (41 articles), Science Technology Other Topics (37 articles), Environmental Sciences Ecology (35 articles), Telecommunications (21 articles), Operations Research Management Science (17 articles), Public Administration (15 articles), Information Science Library Science (13 articles), and Social Sciences Other Topics (8 articles).

3.1.2. Keyword Network Visualization

The obtained data have been imported into the VOSviewer (1.6.18) software tool for a keyword co-occurrence analysis in line with the VOSviewer (1.6.18) cluster algorithm in order to a generate cluster map that is a keyword co-occurrence network visualization on SCT and BMI as depicted in Figure 5. In the latter, circles and labels constitute a unit; different colored cells form distinct clusters, representing various research directions; the size of the node indicates the frequency of keyword occurrence; the frequency of the same keyword appearing in various articles increases with the node size; the distance between two nodes indicates the correlation between keywords; the link is stronger the closer the distance is; and the width of the line shows the intensity of the relationship between the keywords.

Figure 5 shows five clusters that are highlighted in red, green, blue, yellow, and purple. Cluster 1 (red) focuses on the design stage of the business model, including a total of 21 keywords such as “big data”, “internet”, “framework”, “value creation”, “circular economy”, “challenges”, and “industry 4.0”, whose relation relate to the digital transformation of smart cities based on the technology and framework. Industry 4.0 is the application of intelligence in the manufacturing industry, which, together with smart cities, constitutes a smart society. In addition, the keywords “value creation” and “circular economy” highlight the most recent advancements in value creation theory and business model innovation. Further, the keyword “challenges” indicates that the research in SCT and BMI integration is facing challenges due to its rapid development and has received extensive attention from scholars. Cluster 2 (green), with a total of 18 keywords including “management”, “construction”, “IoT”, and “smart buildings”, represents aspects of BMI with SCT in the context of sustainable development, such as artificial intelligence, IoT, blockchain, and machine learning, as well as their management issues across BMI stages. Cluster 3 (blue) includes keywords “digital”, “technology”, and “servitiztion”. The SCT continues to drive the digital transformation and servitization of manufacturing. Cluster 4 (yellow) focuses on the digital transformation process of smart cities, including the keywords “digitalization”, “dynamic capabilities”, and “co-creation”, which emphasizes the co-creation and dynamic capability of enterprises in the process of digitization. Cluster 5 (purple) is facilitated with the role of BMI in its briefing stage. The keywords “SMES”, “firm performance”, and “sustainability” indicate that that enterprises, especially small and medium enterprises (SMEs), have participated in SCT and BMI.

The findings of a total of 20 high-frequency keywords obtained by the keyword co-occurrence analysis via VOSviewer (1.6.18) software, which are shown in

Table 2. High-frequency keywords for research on smart city technology and business model innovation from 2007 to 2023 (17 years) (generated by the authors).

Color	Cluster	Keyword	Occurrences	Total Link Strength	Links
	2	business model innovation	153	730	72
	1	big data	61	394	70
	1	industry 4.0	36	285	65
	3	technology	36	264	61
	5	innovation	40	220	62
	3	digital transformation	31	210	62
	2	management	29	208	57
	1	framework	27	204	61
	5	business model	41	193	62
	2	artificial intelligence	40	192	59
	1	internet	24	177	50
	4	dynamic capabilities	24	175	61
	1	value creation	24	165	57
	5	strategy	23	165	53
	2	impact	24	161	58
	2	performance	23	143	51
	1	challenges	22	142	45
	1	future	24	140	48
	3	servitization	17	139	53
	4	digitalization	21	136	53

, have been listed, including information on color, cluster, keyword, occurrences, and total link strength, in which the showing color is consistent with the color of the keyword co-occurrence network visualization map. Keywords with a higher Total Link Strength, Links, and Occurrences have a greater impact. Excluding the same keywords as the search query, such as “big data”, “artificial technology”, “ business model innovation”, “business”, and “innovation”, the remaining top keywords are “Industry 4.0”, “digital transformation”, “management”, “framework”, “dynamic capabilities”, “value creation”, “strategy”, “impact”, “performance”, “challenges”, “future”, “servitization”, and “digitalization”, of which the keywords “Industry 4.0”, “digital transformation”, “servitization”, and “digitalization” show the digitalization and service transformation of intelligent manufacturing in the context of Industry 4.0. In addition, the keywords “dynamic capabilities”, “value creation”, and “management” reflect the integration of BMI and stakeholder management, collaboration, and capabilities in smart cities. Further, the keywords “strategy” and “impact” are broadly associated with the BMI stages, indicating that a further in-depth analysis of each BMI stage is required.

The top keywords are from five clusters:

• Cluster 1 (Occurrences: 218, Total Link Strength: 1507): big data, industry 4.0, framework, internet, value creation, challenges, and future;
• Cluster 2 (Occurrences: 269, Total Link Strength: 1434): business model innovation, management, artificial intelligence, impact, and performance;
• Cluster 3 (Occurrences: 84, Total Link Strength: 613): technology, digital transformation, and servitization;
• Cluster 4 (Occurrences: 45, Total Link Strength: 311): dynamic capabilities and digitalization;
• Cluster 5 (Occurrences: 104, Total Link Strength: 578): innovation, business model, and strategy.

3.1.3. Keyword Burst Detection

Based on the burst detection algorithm [30], the CiteSpace (6.2.R4) software tool detects the sudden occurrence of nodes and obtains the burst words to determine the research hotspot in a certain period. If a burst period of a node persists into the present period, this circumstance will persist for a certain amount of time for forecasting the research trend. Figure 6 depicts the top 19 keywords with the strongest citation bursts from the year 2007 to 2023: “Year” denotes the year when the keyword first appeared, “Strength” represents the strength of the burst, “Begin” and “End” show the year when the burst started and ended, and the red line indicates the duration of the keyword burst, whilst the blue line suggests the duration of the study.

According to Figure 6, the keyword “cloud computing” had the longest burst from the year 2009 to 2019 among all the 19 highly cited keywords. This was followed by the keywords “big data”, “machine learning”, “internet of things”, and “data mining” in the field of SCT burst between the year 2015 and 2018. The overall strength of the burst remains at a high level, demonstrating the significance of the field of SCT. Additionally, the keywords “capturing value” and “digital business model” highlight the significance of digitalization and value capture in the context of developing new business models for smart cities. At the same time, the keyword “competitive advantage” shows the positive role of the integration of SCT and BMI in improving the competitiveness of smart cities. The burst of the keyword “design” from 2020 to 2021 triggers the analysis of research in the field of SCT and various BMI stages. The keyword “business model” has the highest burst strength among all the keywords. The keywords “barriers”, “product”, and “future” burst out simultaneously in 2020 and 2021, and indicate the real-world products of smart city technology present both opportunities and barriers in future research. Since 2021, the burst of the keywords “internet” and “digital servitization” highlight the transformation from commodity-led logic to service-led logic in Industry 4.0 that has attracted research attention across the world.

3.1.4. Keyword Time Zone Map

The CiteSpace software tool introduces the time factor into the network to obtain the time zone map that can be used to identify emerging research problems in the field and to determine how a particular research field evolved by breaking into distinct periods and a correlation analysis. As shown in Figure 7, each circle represents a keyword, and the bottom timeline indicates the year in which the keyword initially appeared. The circle progressively enlarges to reflect the growing frequency of the keyword occurrence over time. The connecting line indicates that two keywords appear in the same or several articles. Hence, the research hotspots in SCT and BMI integration from the overall perspective and the period of initial keyword appearances from the year 2007 to 2023 have been mapped, as shown in Figure 7.

Figure 7 illustrates that the research on SCT and BMI began in year 2009. The research scenario with “cloud computing” as the core appears for the first time in the research as the first technology category in the field. In the year 2014, “BMI” was first proposed via an application of “big data”, leading to explosive development in the field, which was followed by the notion of “big data analytics” in 2015, representing a significant advancement in the use of big data for BMI. Subsequently, business model innovation was faced with information and service challenges in 2016 with the most recent iteration of the technology category in the field, which led to more sophisticated research methods, e.g., “multiple case study” and “expert interview” used in the context of Industry 4.0 in 2017 and 2018 to address management issues by focusing on advanced technological approaches such as “machine learning” and “Internet of Things”. At the same time, the concept of the life cycle has been introduced, concerning the strategy definition stage and design stage of business models. Additionally, an emerging area of management issues in the field, “knowledge management”, complemented and enriched the related study since the year 2018. The keyword “artificial intelligence” emerged in 2019, which has become an important technological approach as a means of advancing the digital transformation of the field. In 2020, the framework research on the BMI design stage became a new research hotspot, while the circular economy appeared with the conceptual framework. In the last three years (the year 2021 to 2023), the research has been focused on the testing, validation, and implementation stages of the business models stages driven by the trend of digital servitization and sustainable development, whilst the issue of “big data analytics” has come to the forefront again after the year 2015, with a greater emphasis on demonstrating the capabilities of the technology.

3.2. Results of Micro-Qualitative Analysis

3.2.1. Main Research Areas on Smart City Technology and Business Model Innovation

The results of the top 10 research areas in the published studies from 2007 to 2023 have been listed, in line with the data obtained from the above micro-qualitative analysis, which are shown in Figure 8. Of these, six areas, including Business Economics, Engineering, Science Technology Other Topics, Environmental Science Ecology, Operations Research Management Science, and Public Administration, were used for micro-analysis by importing the data from each research area into the VOSviewer (1.6.18) software tool for analysis. The keyword mapping of the six areas could assist in revealing the current state of multidisciplinary research and identify gaps and trends across the areas.

• Business Economics

As shown in Figure 9, the keyword co-occurrence network visualization of “Business Economics” for SCT and BMI has been mapped, including four clusters highlighted in red, yellow, blue, and green. The red cluster (16 keywords) is strongly associated with the application of AI across BMI stages, which mostly consists of the keywords “artificial intelligence”, “impact”, and “performance”. In the yellow cluster (11 keywords), the keywords “digital transformation” and “digital servitization” are heavily weighted, focusing on the role of SCT in the process of the digitization and servitization of BMI. The keywords “big data”, “Industry 4.0”, and “value creation” in the blue cluster (11 keywords) illustrate the innovation of value creation in business model innovation in the context of the big data and Industry 4.0 era. The green cluster (14 keywords) is associated with the strategies and challenges of “BMI” and “technology” in terms of management. In addition, the keywords “management”, “big data”, “value creation”, “digital transformation” and “artificial intelligence” are strongly related to the topic keywords “BMI” and “technology” in Figure 9, which indicates that the research hotspots in “Business Economics” for SCT and BMI focus on business model innovation value co-creation, smart city technology management, and digital transformation.

• Engineering

As reflected in Figure 10, the keyword co-occurrence network visualization for SCT and BMI in Engineering” includes five clusters. The keyword “BMI” in the purple cluster has the largest weight, of which the central keyword “framework” connects with the keywords “artificial intelligence”, “digital transformation”, “big data”, and “Industry 4.0” in the red and green clusters. In the red cluster of 16 keywords, “internet”, “value creation”, “value capture”, “artificial intelligence”, and “digital transformation” are the keywords that closely relate to “framework” and “BMI”. In the green cluster (11 keywords), “Industry 4.0” is most closely related to “framework” and “BMI”. “Big data” plays an important role in driving the circular economy in the context of Industry 4.0. The results indicate that the research hotspot in “Engineering” for SCT and BMI is associated with the technology and industrial study related to the framework development process of BMI.

• Science Technology Other Topics

“Science Technology Other Topics” for SCT and BMI have three clusters: green, red, and blue, which are shown in Figure 11. “Big data” occupies the connecting center of the map of the keyword co-occurrence network visualization, which is associated with “circular economy”, “product service system”, and “Industry 4.0”, focusing on the issues of “design”, “management”, “framework”, “challenge”, and “sustainability” in BMI. The keywords “BMI”, “big data”, and “sustainability” take up a large proportion of the green cluster, indicating that big data and BMI are key issues for organizations to consider in order to achieve sustainable development. The red cluster consists of 12 keywords related to the innovation during the BMI design stage, the product system, and sustainable development. The blue cluster (8 keywords) mainly includes the keywords “circle economy”, “management”, “Industry 4.0”, “servitization”, and “framework”, which have a close relationship with SCT and BMI. In addition, this cluster emphasizes the role that Industry 4.0 plays in business model innovation design and management by enabling servitization, and supporting the development of circular economy models that generate value through servitization. As such, the research hotspots in “Science Technology Other Topics” for SCT and BMI concentrate on the application of technologies such as big data, in the design stage of business models and sustainable development within the context of Industry 4.0.

• Environmental Sciences Ecology

The results of the network visualization of keyword co-occurrence in the research area of “Environmental Sciences Ecology” including four clusters are shown in Figure 12. The central keyword “sustainable development” shares close relationships with the following clusters—“big data”, “management”, and “systems” in the red cluster, “industry 4.0” and “circular economy” in the blue cluster, and “BMI” and “sustainability” in the green cluster—which emphasize the close connections between sustainability and the research fields. Hence, the keywords are closely associated with sustainable development, and management and circular economy issues as current research hotspots.

• Operations Research Management Science

“Operations Research Management Science” in the field of SCT and BMI has four clusters (green, red, blue, and yellow) with a total of 29 keywords, which are shown in Figure 13. The green cluster (eight keywords) is centered around the keywords “big data” and “BMI”, addressing the issue of privacy in smart cities based on a circular economy business model framework. The red cluster (nine keywords) shows the design stage of Industry 4.0 and manufacturing driven by “big data analytics”. The strategic and managerial performance of businesses involved in the digitalization and servitization of manufacturing is the main emphasis of the blue cluster (seven keywords). The yellow cluster (five keywords) focuses on the application of artificial intelligence in innovation. Thus, the results indicate that “Operations Research Management Science” concerns the privacy of citizens in smart cities and the role of big data and artificial intelligence in the process of digitization and servitization.

• Public Administration

As illustrated in Figure 14, the findings of the keyword co-occurrence network visualization for “Public Administration” contain 25 keywords, including big data, Industry 4.0, fourth Industrial Revolution, service, research agenda, digital transformation, technology, systems, entrepreneurship, innovation, servitization, ecosystem, impact, firms, digital technology, dynamic capability, BMI, things, industrial internet, artificial intelligence, review, business model, internet, framework, and IoT. “Big data” is the core topic of public administration, which focuses on industrial manufacturing innovation based on Industry 4.0, BMI, servitization, and digitalization.

Therefore, the above results suggest that “Science Technology Other Topics” and “Environment Sciences Ecology” have shown a relation that improves sustainable development, focusing on issues related to the design stage of the business models stages and circular economy, respectively. These two areas address management issues, of which “Business Economics” focuses on technology management, and “Information Science Library Science” concerns corporate performance and management. At the same time, digital servitization is a hot trend in the field of SCT and BMI integration, especially in “Business Economics” and “Social Sciences Other Topics”. In addition, the areas of “Business Economics”, “Engineering”, “Computer Science”, and “Telecommunications” are known for their respective areas of expertise: business model innovation value co-creation, framework development, technology integration, and business model ecosystem. The fields of “Public Administration” and “Operations Research Management Science” focused on industrial manufacturing innovation and citizen privacy, respectively. Further, to achieve structure and investigate scientific management techniques, by mapping key subject areas, we highlight a need for BMI to be screened in different stages, which facilitates the alignment of studies on SCT and BMI integration with the stages of the business models.

3.2.2. Analysis of Business Models Stages

As mentioned in Section 1, this paper adopts the stages Ideation (strategy definition), Design, Testing, Verification, and Implementation as the five-stage business models stages.

• Ideation (strategy definition) Stage

Table 3. Research on smart city technology and business model innovation that are closely related to the ideation (strategy definition) stage of the business models stages.

Year	Research Method	Topic
2023	modeling	Enterprise big data business ecology model decision-making and planning [31]
2023		Digital technology use and business model innovation for enterprises [30]
2023		Innovative service models for the data industry [32]
2023		Digital platforms, innovation capabilities, and strategic alignment [33]
2022		Impact of big data technology skills on new business model innovation [34]
2022		IoT technologies, digital servitization, and business model innovation [35]
2020		A comprehensive and unified paradigm for business model innovation [36]
2019		Convergence of servitization and Industry 4.0 for digital transformation of enterprises [37]
2019		Lean production and technological innovation in manufacturing [38]
2023	case study	Enterprise digital transformation [39]
2020		Business model innovation based on IoT applications [40]
2014		Sensing business models [41]
2014		Business model innovation and duality [42]
2023	literature review	Business model roadmap [43]
2022		Blockchain as a cutting-edge technology impacting business [44]
2021		The role of servitization in transformation [45]
2018		Transformative sustainable business models [46]
2023	action research	Motivations and challenges of using IoT sensors to reduce food waste [47]
2023	mixed study	The interplay of artificial intelligence, the IoT edge, and impact resilience [48]
2022	questionnaire	Technology startup business models utilizing knowledge management systems [49]
2022	interview	Business model innovation in the energy sector [50]

shows that, since 2014, studies closely related to the ideation (strategy definition) stage of the business models stages employ the methods of modeling, case study, literature review, action research, mixed study, questionnaire, and interview to investigate SCT and BMI, and this stage focuses on enterprises (especially traditional manufacturing industries), applies digital technologies such as big data, IoT, blockchain, and artificial intelligence to the decision-making and planning phase of business models, comprehensively considers industry-specific motivations and challenges, and defines a roadmap for business model innovation.

• Design Stage

The findings of research relating to design stage, which are shown in

Table 4. Research on smart city technology and business model innovation that are closely related to the design stage of the business models stages.

Year	Research Method	Topic
2022	case study	Blockchain and value co-creation [51]
2021		The interplay between business models and digital technologies [52]
2021		Business model innovation and transformation enabled by AI [53]
2020		Precision medicine business model [54]
2020		Industrial IoT business model in machine environment [55]
2020		Sustainable business model [56]
2017		The impact of industrial IoT on business models [57]
2023	literature review	Artificial intelligence and enterprise innovation [14]
2023		The role of IoT in business transformation [58]
2018		AI and deep learning support business model innovation [59]
2023	modeling	Business model transparency affects digital service adoption [60]
2023		The impact of digitalization on business performance [28]
2022	systematic literature review	Sustainable supply chain management
2021	design science research	Digital innovation-driven business model regeneration [61]
2020	interview	Blockchain and business model innovation [62]

, indicate that the methods of case study, literature review, modeling, systematic literature review, design science research, and interview have been used to study SCT and BMI in the design stage since 2017, which focuses on the application of technology, operational performance, feasibility factors, and business transformation in the business model design framework to improve sustainability performance and enhance supply chain management.

• Testing, Verification, and Implementation Stage

As reflected in

Table 5. Research on smart city technology and business model innovation that are closely related to the testing, verification, and implementation stages of the business models stages.

Year	Research Method	Topic
2023	modeling	Service and digital transformation of manufacturing enterprises [63]
2022		The role of psychological expectation in driving business model innovation [64]
2021		Equity investment drives business model innovation [65]
2021		Assess innovative project management capabilities [66]
2021		New service delivery model in telecommunication industry [67]
2021		Automotive company functional areas implement Industry 4.0 solutions [68]
2018		Manufacturing enterprise business model innovation [69]
2022	case study	Artificial intelligence business model innovation [70]
2022		Digital, data-driven dynamic capabilities and innovation [71]
2022		Industrial digital platforms transform Industry 4.0 business models [72]
2022		IoT driven business model innovation [73]
2023	literature review	Big data innovates business models [25]
2021		Circular business model [74]
2023	interview	Smart transportation based on the Internet of Things [75]
2022	design science research	Conceptualize and evaluate the value of IoT solutions [76]
2019	empirical research	Business model innovation practice improves digital performance of SMEs [77]

, studies on SCT and BMI since 2018 employ the modeling, case study, literature review, interview, design science research, and empirical research methods during the testing, verification, and implementation stages, which emphasizes the application of SCT in the testing, verification, and implementation stages to incorporate psychological expectation management capabilities, value creation and value capture, dynamic capabilities, performance, and other metrics into the test program, and implement accurate monitoring and evaluation to ensure the smooth implementation of BMI in certain industries.

• Across All Stages

The studies on SCT and BMI implement the case study, literature review, deductive-inductive approach, design research, and modeling methods since the year 2018 to identify the BMI framework process and the application of various technologies across all the stages of the business models, which are detailed in

Table 6. Research on smart city technology and business model innovation that are closely related to the business models across all stages.

Year	Research Method	Topic
2023	case study	Big data drives business model innovation [28]
2023		Business model innovation based on the Internet of Things [78]
2023		Conceptualize business model characteristics [79]
2023	literature review	The application of artificial intelligence to innovation [10]
2021	deductive-inductive approach	The Internet of Things drives business model innovation [80]
2020	design research	Tools for developing circular innovation ecosystems [81]
2019	modeling	Business model innovation based on cloud computing [82]

. In the definition stage, AI has been used to anticipate demand, streamline the innovation process, and reduce costs. In turn, creating virtual prototypes and comparing their performance assist in determining the most efficient and cost-effective design. Ultimately, machine learning has been adopted to improve the customer experience by engaging customers in user journeys, local product development, and customization, to enhance marketing and promotions.

4. Discussion

This paper matches various SCTs with business models stages and refines the distinctions between technology adoption and BMI across various industry operating practices, for which the results are further discussed in the following Section 4.2.

4.1. Application and Contribution of Smart City Technology in the Field of Business Model Innovation

The results indicate that “big data” and “artificial intelligence” are two technology research areas that are most relevant to BMI in SCT, and suggest that Industry 4.0 and digital servitization are important development trends in the field of SCT and BMI integration.

4.1.1. Big Data

The results, which are shown in the research area of “Engineering” for SCT and BMI, indicate that the technical implementation of big data influences new BMI through mediating resource integration and moderating environmental uncertainty roles. Big data analytics provides new operational paths for managers such as a positive effect on innovative performance management via big data analytics capability [83]; healthcare facility projects relying on big data to guarantee the quality and timeliness of information in investments [84]; and an improvement in corporate sustainability and value creation for the company through efficient supply chain network design management [47]. In addition, the innovative big data techniques proposed in recent studies for the conceptual modeling of the Internet of Things [31] and the duality mechanism driven by big data for BMI [85] have initiated new research directions.

Moreover, the results presented in the research area of “Business Economics” for SCT and BMI suggest that “big data” is crucial to the advancement of the circular economy within the framework of Industry 4.0, of which simulation and data analytics are hot topics. Rather than conceptualizing data analytics as simulation and optimization techniques [86], the focus is on engineering analytics [87,88] and business analytics [57,89]. Big data analytics technology is expected to facilitate fundamental improvements in industrial processes from product design to manufacturing and delivery, in order to achieve Industry 4.0 and circular economy integration in sustainable supply chain management [90]. In addition, the results of the research area of “Operations Research Management Science” fpr SCT and BMI illustrate that big data analytics are seen as drivers for Industry 4.0 and manufacturing intelligence in the business models design stage, in which continuous digitization such as “Industry 4.0”, “cloud computing”, “smart manufacturing”, and “augmented reality” contribute to the development of sensing, communication, data processing, and visualization.

Furthermore, managers and policymakers have become more aware of the benefits and downsides of big data in recent years. The results of the research area of “Science Technology Other Topics” for SCT and BMI reveal the increased cost of database creation, management, and analysis [91], security issues [92], and the degradation of the quality of decisions due to data abundance [93,94]. Additionally, the results of the research area of “Operations Research Management Science” for SCT and BMI consider challenges relating to quality, legality, and trust [95], in which experts pay the most attention to maintaining personal and business privacy security during the use of data, since data de-identification is the most concerning issue [96]. In a recent study, a business model framework has been proposed to overcome these barriers through a three-stage multidimensional pilot with design, validation, and institutionalization [79].

4.1.2. Artificial Intelligence

The results of the research area of “Business Economics”, “Engineering”, and “Operations Research Management Science” for SCT and BMI reveal the application of artificial intelligence in BMI. The results of the research area of “Business Economics” for SCT and BMI demonstrate how artificial intelligence (AI) is upending business in a variety of industries and how the related research is expanding to almost every field of economics. However, the current research relating to SCT and BIM is still in the early stages of the business models stages, focusing on strategic thinking, algorithms and the humanization of the digital robot, data collection, and market value creation [97]. In addition, the results of the research area of “Engineering” for SCT and BMI emphasize the importance of AI in innovation, including AI-enabled digital innovation and a sustainable business model [12]. However, the results of the research area of “Operations Research Management Science” for SCT and BMI indicate that there has not been much research carried out on innovation management. Instead, studies on SCT and BMI have mostly concentrated on implementation barriers [98,99], supporting strategies for organizational process [37], decision-making [100,101], operations [102], and attaining organizational goals [103].

4.1.3. Industry 4.0 and Digital Servitization

The results show that Industry 4.0 and digital servitization are key trends in the development of SCT and BMI. Moreover, the results of the research hotspots and continuation trends in the field over various time periods further emphasize the shift from a commodity-driven approach to a service-driven approach in the era of Industry 4.0, attracting sustainable attention from a wide range of industries. In addition, the results of the research area of “Business Economics” for SCT and BMI suggest that, over the last two decades, an increasing number of manufacturers have started to pursue servitization by adding customer-oriented services (e.g., customized solutions) to their existing products [104,105,106,107]. Manufacturers are using digital technologies to better manage their product and service operations and develop new value propositions, so-called smart products, services, and solutions [108,109]. Due to the high degree of interdependence, studies have referred to the convergence of these shifts as “digital servitization” and begun to explore them together [110,111].

Further, the results presented in the research area of “Science Technology Other Topics”, “Environmental Sciences Ecology”, and “Public Administration” for SCT and BMI demonstrate that servitization and Industry 4.0 are widely acknowledged as the two most recent developments in the transformation of industrial firms. Servitization is primarily concerned with providing value to customers (demand-pull), whereas Industry 4.0 is generally linked to enhancing the manufacturing process (technology-push). Scholars and practitioners are actively adhering to the circular economy paradigm by adopting key technologies to improve the operational management efficiency and financial performance of smart cities [112], and to improve transparency and accountability [30], and civic engagement in urban governance [113].

4.2. Application of Smart City Technology in Business Models Stages

The results of the structural distribution of research hotspots reveal that the issue of BMI and its management at different stages is closely related to sustainable development. In addition, the results of the research area of “Environmental Sciences Ecology” for SCT and BMI show that both corporate profitability and environmental benefits are recommended in future research as being related to the circular economy driven by big data technology.

The following sections discuss the current status and differences in the application of SCT in the business models stages.

4.2.1. Overview of the Pulse of Smart City Technology and Business Models Stages

The results of the keyword time zone map and business models stages analysis suggest that cloud computing was the first technology category to be analyzed for SCT and the business models stages in 2009. Ten years later, a model of enterprise BMI drivers was introduced based on cloud computing to further investigate the drivers of enterprise development [82]. Big data became an emerging technology in 2014, mainly used in the ideation (strategy definition), and testing, verification, and implementation stages. Big data can be used to assist in the planning and decision-making process for business model innovation at the ideation (strategy definition) stage to consider the incentives and difficulties unique to the industry and establish a BMI roadmap. During the testing, verification, and implementation stages, big data facilitates accurate monitoring and evaluation based on metrics to ensure the successful implementation of BMI in specific industries [114]. Subsequently, big data analytics has been applied to the decision-making and planning of business model innovation, especially the innovation of service models [115]. In addition, Industry 4.0, which emerged in 2017, was closely associated with the business models stages of testing, verification, and implementation for industrial digital platforms and automotive firms’ functional areas [68,72]. At the same time, the IoT was applied across business models’ stages and closely related to digital servitization in the ideation (strategy definition) stage, such as the motivation and challenges of using IoT sensors to reduce food waste [47]. In the design stage, studies explored the role of industrial IoT business models in machine environments and business model transformation [55]. Eventually, as IoT-based smart transportation potentially surfaced during the testing, verification, and implementation stages, studies started delving into the importance of conceiving and assessing IoT solutions [75,116]. In 2019, AI emerged as a significant technological advancement in this area [116]. Studies have examined how AI has interacted with the IoT and influenced resilience during the ideation (strategy formulation) stage [117]. The technology is now used more in the design stage, where studies focus on enterprise innovation and integrating it with deep learning to jointly promote BMI [59]. Subsequently, it influences the value creation and value capture of the business model innovation during the testing, verification, and implementation stages [70]. In 2020, frameworks for the design stage of the business models stages became an emerging research hotspot in the field. Case studies and interviews were used to explore the industrial IoT business model and healthcare business model in machine environments and to propose a sustainable business model, in which blockchain technology can then be applied to the BMI process [35]. In the last three years (2021 to 2023), studies have focused more on the business models testing, verification, and implementation stages, driven by the trend of digital servitization and sustainability [118]. Particularly, in 2022, a new business model has been proposed for technology startups utilizing knowledge management systems [49].

4.2.2. Ideation (Strategy Definition) Stage

The results of the data sheet of the ideation (strategy definition) stage analysis suggest that studies in the ideation (strategy definition) stage of the business model focus on internal factors and external factors. The internal factors are mainly related to strategic decision-making [31,36,37], goal-driven [38,45], and organizational restructuring [33]. The external factors are associated with new market opportunities [47,50], institutional change [44], technological innovation [31,35,43], and other perspectives of the industry [34]. These studies are associated with the energy sector [50], manufacturing companies [30], family businesses [48], and technology start-ups [49].

Meanwhile, studies were connected to new market opportunities through organizational structure adjustment, institutional change (e.g., the three-subjects value creation model) [44], and technological innovation to complete the definition stage—for example, the incorporation of IoT into overall strategic business planning and decision-making at all levels of the BMI program [31]. Simultaneously, business model roadmaps were proposed and used as a strategic planning tool for BMI in academia and in practice [43], which is also used to assist in visually codified basic strategic initiatives, and better understand the different strategic situations that companies are currently facing [35].

4.2.3. Design Stage

The results of the data sheet of the design stage analysis indicate the development of frameworks for a digitally enabled BMI system to incorporate digital capabilities to refine, formulate, and design the appropriate business model. Early studies on the adoption of technology have demonstrated that IoT influences value propositions, internal infrastructure management, and customer connections to drive innovation in the industrial business model [57]. The use of AI and the IoT to support enterprise BMI has been confirmed by recent studies [14,58].

Equally, sustainability issues were recently given more attention in the design stage of business models. A sustainable business model analysis framework was proposed for companies to design their business model based on an in-depth understanding of the social, political, legal, and market factors in the business environment [56]. In addition, stakeholders play an important role in the design stage, for which a value co-creation perspective was incorporated in the design of the business model [51]. User awareness may influence the value proposition design, especially when multiple customer segments are on the same platform [60]. As such, recommendations were provided for stakeholders to explore their roles in the transformation of the manufacturing business model. For instance, government incentive schemes will influence current enterprises’ research practices in a favorable way [53]. Furthermore, corporate strategy managers could utilize the process model to evaluate the possible effects of digital innovations on their current business model innovation leading to a competitive advantage in response to technological developments [61]. Therefore, the findings of this paper indicate that stakeholders play an important role in the design stage, providing a new perspective on SCT and BMI integration, which is advocated in order to integrate the perspective of value co-creation into the design of business model. To keep the business model competitive, future studies should better consider the awareness and actions of users, governments, research centers, and corporations.

4.2.4. Testing, Verification, and Implementation Stages

The results of the data sheet of the testing, verification, and implementation stages analysis show that a large number of scales and assessment models have been developed to quantify business model innovation in various domains, namely, the business dimension of manufacturing maintenance services [69], the ability of companies in the technology industry to undertake and manage disruptive innovation project [66], the functional areas of the organization [68], the impact of technological connectivity and compatibility on market perception, resource integration, and resource reconfiguration [63], and the assessment of national and local antitrust regulation monitoring [71]. Hence, it is necessary to design an implementation program corresponding to the business model innovation from the perspective of technological innovation and verify its network carrying capacity and significance to the business value of the enterprise through simulation experiments [67]. From a knowledge management perspective, econometrics and machine learning techniques can be utilized to confirm whether equity investment can influence BMI [65]. Ultimately, the implementation of the business models stages is also an important part of the process. The equally important roles played by business model experimentation and strategy implementation were identified as important factors in improving performance through a fuzzy set qualitative comparative analysis (fsQCA) [77]. The relationship between the transformation and psychological dynamics were deemed the main areas of emphasis for BMI implementation in the context of big data [64].

4.3. Application and Contribution of Smart City Technology in Urban Planning

• Urban Regeneration

Urban regeneration is recognized as a policy for the sustainable development of cities, utilizing a multidisciplinary approach [119]. The results of Section 3.2 suggest the current state of multidisciplinary research and reveal strategies for integrating SCT into urban regeneration. Studies have discovered that SCT can significantly influence the behavior of urban participants and their capacity to lead sustainable lives. For example, smart infrastructures leverage sensor and IoT technologies to enable real-time data acquisition, analysis, and communication. Consequently, these technologies can be applied across various domains of urban planning, including energy, water management, transportation, and building systems [120]. Moreover, in urban spaces rich with cultural resources, AR has been seamlessly integrated with these resources into the physical infrastructure of the city [121]. However, practical studies that effectively describe the relationship between smart cities and citizens’ lives in urban regeneration remain scarce [122].

• Transportation Systems

The results of Section 3.2 highlight that SCT has impacts on urban planning, particularly within the transportation system. The IoT has been extensively used in applications across various industries, with the transportation sector being one of the most significantly impacted [75]. The integration of intelligence and the IoT represents an inevitable trend in the development of intelligent logistics. In addition, AI technology has been significantly promoted in the upgrading and transformation of supply chain logistics enterprises, particularly in logistics infrastructure, production tools, and operational processes [123]. Further, the role of technology-driven and market-pulled mechanisms has been further explored through a case analysis of the Port of Barcelona, thereby demonstrating the significance of the intelligence of transportation systems in various urban scenarios [124].

• Energy Management

As demonstrated in the Table 2, “management” is a high-frequency keyword in the field of SCT and BMI in the macro-quantitative analysis, while “energy” frequently appears in the micro-qualitative analysis. Thus, energy management is a crucial application of the SCT in urban planning and warrants increased attention. Smart energy technology, enabled by the IoT, is ushering in novel business models in the energy sector, which progresses, coupled with decentralized renewable energy production, market liberalization, and evolving consumer demands, and fosters ongoing innovation within the energy sector [125]. Recent studies have turned their attention to blockchain and energy management, including scalability, data privacy, cost of change, market dynamics, user experience, skills, multi-stakeholder governance, and regulatory change [50], of which the contemporary studies predominantly focus on the technical aspects of blockchain-enhanced energy applications. However, it is imperative for both corporate strategies and public policy making for energy management in urban planning to incorporate an investigation on user behavior, innovations in business models, and regulatory experimentation to comprehensively grasp and effectively implement the blockchain to integrate with other emerging technologies [126].

• Environmental Sustainability and Social Equity

The results of current research trends indicate that current studies are predominantly focusing on the intersection of environmental and societal concerns. Among the top ten research areas depicted in Figure 4, three are focused on environmental and societal issues, that is, Environmental Sciences Ecology, Public Administration, and Social Sciences Other Topics. The potential of SCT in fostering inclusivity and environmental sustainability while promoting social equity has been extensively explored.

Moreover, the results of Section 3.2.1 demonstrate that technology enhances the dynamic capabilities of public administration. SCT has been involved in urban metabolism for its efficiency in furnishing policymakers, urban managers, and planners with valuable tools for gathering, monitoring, analyzing, and assessing the circularity of environmental, social, and economic resources to enhance their efficacy and quality [113]. In addition, a number of studies and research initiatives underscore the significance of information technology in advancing the circular economy. Key examples include the utilization of artificial intelligence, IoT, big data, and online platforms [81]. However, SCT enhances environmental sustainability, which may also result in adverse effects, such as an increased energy consumption for digital infrastructure [127]. As such, the introduction and proliferation of emerging lifestyles and accompanying technologies will profoundly influence social equity, contingent upon planning practices and policy formulation [128]. Further, AI has enabled the proliferation of social welfare programs, concentrating on public health and health equity, framing systemic social and environmental issues through a technology-driven lens [129].

4.4. Contributions and Limitations

This paper examines how SCT has been integrated with BMI over the past 17 years, which sheds light on the interplay between technological advancements and the evolving business strategies that cities and enterprises use to enhance urban living, with a possible explanation being the field of SCT and BMI convergence has made significant progress in interdisciplinary and full-stage business model research, demonstrating its research value and continuing research trends. This interpretation assists in understanding the responses to the above-mentioned research questions by revealing practical technology-driven urban solutions, citizen-centric approaches, and data-driven decision-making across all the business model stages.

In terms of research content, previous studies have generated valuable insights into the relationship between SCT and BMI, such as the application of AI in innovation management [13] and the use of big data analytics to empower strategic alliances [130]. However, the high degree of fragmentation of study impedes a comprehensive understanding of the subject, which has become a major obstacle to the advancement in the field. This paper is the first to thoroughly examine the present research status, hotspots, and future development trends of SCT and BMI at the theoretical and application levels based on the research background, general research status, multidisciplinary analysis, and analysis of business models stages.

Instead of using the systematic literature review (SLR), modeling, and case study approaches that are typically employed in previous research, this paper adopts a mixed research methodology to explore the integration of SCT and BMI from a macro-quantitative perspective and a micro-qualitative perspective, respectively. Bibliometric visualization tools, i.e., VOSviewer (1.6.18) and CiteSpace (6.2.R4), are used to conduct keyword network visualization, keyword burst detection, and a keyword time zone map to identify the context, hotspots, and evolution of the field, as well as to add to the body of knowledge on the research themes. However, all the related studies in the field are hard to obtain by relying solely on the Web of Science Core Database as the bibliometric data source. Moreover, the search date was set to November 2023, and some portions of the studies in the field may be excluded from the retrieval date to the publication date due to the continuous updating of the database. The keywords appear mainly in the title or abstract when the field label is defined as “TS”; thus, relevant valid keywords in the body text may be missed. Further, the comprehensive keyword-based analysis via bibliometric tools which was carried out in this paper may lead to the misinterpretation and omission of specific elements of the literature. Additionally, although bibliometrics reveals the trends and important research areas in a certain field through the collection of literature, it is difficult to provide precise answers to scenario and logical issues, which should be complemented by primary research and data collection. Furthermore, while bibliometrics unveils trends and significant research domains within a specific field by gathering data on the literature, the data collection was confined to a specific web-based database, making it challenging to offer precise responses to scenario and logical issues, thereby inadequately capturing the intricate nuances of specific subjects of the smart city such as urban planning.

In terms of research value, since exploring the relationship between SCT and BMI requires a comprehensive consideration of factors in engineering, business, urban development, and other fields, this paper employs a multi-dimensional and comprehensive perspective based on business models stages and multi-disciplines to provide theoretical and practical guidance for the sustainable development of smart cities, which is helpful for city administrators and planners who monitor, evaluate, and assess the capacity of municipal services and offer plans for bettering the environment, society, and economy. Further, the findings of this paper offer innovative and focused incentive schemes for businesses looking to innovate and boost productivity, which assist in developing business model innovation and encourage the development of values in business model innovation application.

5. Conclusions

This paper reveals a 17-year (2007 to 2023) investigative view of the integration of SCT and BMI, which explores the application of SCT in BMI in conjunction with business models stages and a multidisciplinary analysis to identify future trends.

The results of this paper suggest that there have been three distinct change phases comprising an initial stage (2007 to 2013), a progressive stage (2014 to 2019), and a period of rapid growth since 2020 in terms of publications on SCT and BMI during the last 17 years. The integration of SCT and BMI has developed quickly and gained continuous attention and wide recognition in recent years. Cloud computing is the longest-running research keyword in the field, while the keywords internet and digital servitization are still being explored at a high frequency. Big data and AI are two technologies most closely related to BMI, which affects business operations in practically every industry and field of economics, albeit to different degrees. Big data technology relied on resource integration and environmental regulation to affect the process of BMI and promote the achievement of a circular economy in the era of Industry 4.0. AI plays an important role in the innovation ecosystem through strategic thinking, algorithms, data acquisition, and market value creation. By and large, current studies focus on the ideation (strategy definition) and design stages of the business models stages, with an emphasis on using digital capabilities to refine, formulate, and design the corresponding business model while defining the strategy through internal and external factors. The IoT and AI as key technologies are incorporated into enterprise BMI applications, affecting value propositions, facility management, and customer relationships. Further, Industry 4.0 and digital servitization are currently the key trends in the field. The creation of stakeholder value has been pursued in the Industry 4.0 era based on the digital technology and service-oriented approach, which support the integration of the circular economy and Industry 4.0 into operational practices to regulate cost structures and availability, thus facilitating the digitalization and servitization of industrial companies. Finally, the sustainable development orientation was emphasized in this field to address management issues at different stages of the business model, helping companies improve financial performance and smart city sustainability. SCT and BMI are increasingly recognized as pivotal elements in the intricate landscape of urban planning, deserving further attention in forthcoming endeavors.

This paper contributes to the existing knowledge via three main areas: theory, multidisciplinary perspective, and applications. 1. Within the theoretical aspect, the time periods and business models stages for SCT and BMI integration are clarified by conducting a systematic analysis of the business models stages. 2. This paper addresses the high degree of fragmentation of the research in the field by adopting a multidisciplinary perspective to promote a thorough understanding of the subject by researchers and advance the development of research and practice. 3. This paper compares the trends presented due to the differences in research areas and practices across industries, providing an exploratory view of the field in terms of practical applications.

Future research could consider employing other research methods, and multiple sources and databases, including multiple types of data, to enrich the topic, and innovative technological tools, such as combining industrial databases with in-depth case studies to further investigate the research questions. In addition, future research could focus on SCT in relation to the testing, verification, and implementation stages in the business models stages, extending into applied research on technologies such as digital twin, BIM, IoT, blockchain, cloud computing, and machine learning, which could assist in achieving a digitally enabled sustainable business model. Finally, the relationship between BMI stages and their corresponding innovation strategies in the context of urban planning need further investigation.