Abstract
BACKGROUND:
The accumulated knowledge has led to a state of misunderstanding about the precise meanings of digitalization, and a precise framework to define smart airports is still missing.
OBJECTIVE:
This study aims to reveal the current status and future direction of smart airports and digital transformation in the academic literature and to provide a comprehensive definition for smart airports.
METHODS:
The identified keywords were searched in the Web of Science database covering the years 1989-2024 and a total of 372 studies were found. These studies were then analyzed using Bibliometrix (R package).
RESULTS:
We determined that the most influential academic source on the themes is the Journal of Air Transport Management, and the collaboration index in the literature is three. While conferences are the most productive sources in this field, academic journals are mostly cited in studies. Academic studies typically employ and evaluate “performance” and “model,” “impact” and “air,” and “economic development” and “location” in tandem, despite the distinction between technological and managerial issues.
CONCLUSION:
In the light of the findings, the definition of a smart airport can be “an airport ecosystem where personalized service is provided to users by using Industry 4.0 technologies on the basis of big data analysis and real-time sharing between objects; digitalization is turned into a holistic organizational culture starting from top management to cover all personnel; the decision-making process is carried out autonomously within the entire airport operation network; and the main goal of competitive advantage and high-level user experience is provided uninterruptedly.”
Background
The innovations and applications brought about by Industry 4.0 are successfully meeting the technological demands of humanity. Cyber-physical systems play a pivotal role in the 4.0 era within the manufacturing and service sectors. The transportation sector is currently experiencing a growing acknowledgment of the wide range of uses for these systems. Airports, which are a crucial component of the aviation sector and play a substantial role in national economies, have transitioned into a period referred to as Airport 4.0 through the use of 4.0 technologies [3]. The simultaneous advancement of the postponed stages of Airport 4.0 and the stages of Industry 4.0 technologies is noteworthy in this context.
The introduction of the initial mechanized loom during the 1780 s signifies the commencement of the period currently known as Industry 4.0, as seen in Fig. 1. The Industrial Revolution transpired over successive periods of one hundred years.
Industrial revolution stages [1].
The process leading to Airport 4.0 is summarized in Fig. 2. The simultaneous interpretation of Figs. 1 and 2 is crucial in order to elucidate the parallels between the phases of industrial and airport transition. In Industry 1.0 (I1.0), the advent of mechanical machinery in production preceded the initiation of modern aviation. The era, sometimes referred to as Airport 1.0 (A1.0), commenced during the early 1900 s and was characterized by conventional air travel. During this time frame, the current infrastructure and vehicles are utilized for the purpose of landing and taking off aircraft, which serves as the fundamental prerequisite for airport creation. Additionally, foundational equipment is employed on both the ground and air sides. Furthermore, human resources provide the vast majority of airport services. During the period of Airport 2.0 (A2.0), airports have seen several advancements aimed at augmenting passenger capacity and optimizing service efficiency. The primary objective of applications, such as the utilization of computers in baggage claim and passport control, during this era is to enhance the efficiency of the service process and accommodate a larger number of passengers [4]. The initial two stages of the airport transformation phase have traditionally transpired amidst the shift from Industry 2.0 to 3.0, and this process shares comparable objectives with those of industrial enterprises. Industry 3.0 pertains to the advancement of digitalization and the integration of software into formerly manual processes, whereas Airport 3.0 pertains to the enhancement and streamlining of airport service flow through digitalization and diverse programming techniques. It is feasible to enhance the service production process in a reactive manner by leveraging past data. This age is characterized by the implementation of cyber-physical systems at airports and the utilization of modern testing methods [5]. During the period of Industry 4.0, there is a growing correlation between modern technologies and human interfaces, such as big data analytics, artificial intelligence, data mining, augmented reality (AR), virtual reality (VR), cloud computing, Internet of Things, and integrated systems. Airport 4.0 is the implementation of advanced technologies in airports that provide seamless information exchange among all stakeholders involved, ensuring that operational and passenger needs are met through real-time data flows [6]. A major crisis such as COVID-19, the need for more efficient and effective operations, and the need to continuously improve passenger satisfaction have led today’s airports to adopt these technologies [7]. ACI stated that due to the effects of the pandemic or other stated needs, airports’ investments in digitalization have increased since 2020, with investments amounting to approximately USD 6.8 billion in 2022, which will increase further in 2023 [8].
Airport revolution stages [2].
Digital transformation is what drives the Airport 4.0 process. To successfully achieve this comprehensive transformation, airports must consistently modify their operations in terms of technology and organization, aligning with a strategic roadmap. Furthermore, the integration of state-of-the-art technologies into the airport service process necessitates the wholehearted acceptance of digital transformation by all stakeholders within the business [9]. Airports may face challenges such as internal reluctance to change, insufficient investments in technology, and the time needed to bring about a shift in organizational culture [10].
Digital transformation has been defined in numerous ways in the literature due to the inclusion of challenging-to-measure aspects such as organizational structure, strategy, and organizational culture in the digitalization process [11–13]. This scenario gives rise to a state of ambiguity about the concept of digital transformation. In certain studies, digital transformation is elucidated via the lens of technological advancement, while in others, it is explained through management-centric concepts. This issue is also relevant to the level of definitions employed in assessing the stage of digital transformation within businesses. Additionally, the prevalence of research on digital transformation by industry associations and consulting firms within the airport sector emphasizes the necessity of addressing this issue specifically for airports [9].
The aim of this study is to elucidate the current state and future direction of smart airports and digital transformation in the current body of literature, as well as provide a clear definition of airport digital transformation by resolving any ambiguity around the topic. In order to achieve this aim, a comprehensive five-stage bibliographic study was performed on the literature pertaining to digital transformation and smart airports. Bibliometric analysis is a quantitative approach used to examine the bibliographic content of literature or journals pertaining to a specific topic. This method allows for the identification of the current state and future direction of studies within the researched field [14]. The selection of this methodology was based on its appropriateness for analyzing the trajectory of the research domain of interest and its ability to demonstrate the progress and collaboration among authors, countries, and universities in this particular subject [15]. Furthermore, the underutilization of bibliometric analysis in the field of business administration further justifies its preference in this study. The analysis in question utilized the R-based Bibliometrix software, which introduced science mapping analysis to the performance analysis. This analysis was not included in typical bibliometric analysis studies [16]. Therefore, this study aims to highlight the conceptual ambiguity present in the existing literature on digital transformation and smart airports, specifically focusing on the identified keywords. During the initial phase of the study, the Web of Science (WoS) database was queried using the designated keywords. Numerous databases are utilized on a global scale to facilitate access to scholarly research. The reason for selecting WoS for this study is its extensive usage compared to other platforms [17] and its ability to give access to over 50 million documents [18].
Ambiguity regarding the definition of digital transformation within the context of Airport 1.0–4.0
Airport development has been motivated by the insufficiency or enhancement of operations to cater to the needs of many stakeholders. In the era of Airport 1.0, airports were characterized by simplistic facilities solely designed for the purpose of facilitating the landing and take-off of aircraft, lacking automated systems for passenger and luggage management. However, as technology progressed, these airports underwent a gradual transformation, incorporating airside elements that could accommodate larger and heavier aircraft. During the era referred to as Airport 2.0, passengers were provided with passport control areas, baggage claim lanes, and limited Wi-Fi technology [19]. The primary catalyst for the shift to Airport 3.0 was the insufficiency of the current infrastructure during the Airport 2.0 era, characterized by heightened aviation safety and increased passenger demand due to international regulations and technological advancements [20]. The constrained opportunities for the creation of air and landside infrastructure have prompted the exploration of various advancements, including big data, 3D printers, renewable energy, unmanned and semi-autonomous vehicles, cloud computing systems, satellites, internet of things, mobile devices, smart environmental applications, RFID solutions, augmented and virtual reality, as well as data mining and machine learning, to optimize the utilization of current resources [21]. According to Blondel [22], the use of digitalization has led to measures aimed at enhancing passenger satisfaction by mitigating lines and associated flight delays at security and check-in points as well as boarding gates within the terminal.
Airport 3.0 denotes a phase characterized by the seamless exchange of collected data among various stakeholders, the implementation of measures to facilitate the use of unmanned technologies, and the provision of self-service information services within and in the vicinity of the terminal [9]. An additional catalyst for digital transformation during this era involves enhancing non-aeronautical revenue through the enhancement of passenger waiting experiences at airports [23]. The implementation of mobile applications to provide retail store recommendations to travelers upon arrival at the airport, as well as the provision of instructions on navigating the terminal, are among the strategies employed during this era [24]. The key differentiating factor between Airport 3.0 and 4.0 operations is the instantaneous transfer of accumulated large-scale data across stakeholders. According to Ushakov et al. [25], it may be argued that the forthcoming era of Airport 4.0 will be influenced by proactive choices focused on the Internet of Things. The effectiveness and efficiency of airside technical activities and terminal activities for passenger satisfaction can be enhanced by the use of more comprehensive, precise, and expedited analysis of digital data. It might be argued that contemporary airports have not yet achieved a complete transition to the era of Airport 4.0 [26]. In the context of the Fourth Industrial Revolution, the provision of personalized services to users within smart airports can be facilitated through the collection and analysis of personal data. The study conducted by Airlines [27] reveals that travelers exhibit a strong inclination towards utilizing digital services that rely on personal data within airport settings. According to Jarrell [28], the utilization of digital technologies throughout the boarding process enhances passengers’ service experience, resulting in increased expenditure at the terminal and heightened satisfaction upon departure from the airport. Nevertheless, it is crucial to underscore that the implementation of digital transformation should be executed in a manner that avoids discomfort among passengers. The Internet of Things, a key aspect of Airport 4.0, has the potential to enhance the passenger experience, even when connecting flights, by facilitating interconnectivity between airport mobile applications [29].
The ambiguity surrounding the concept of digital transformation can be attributed to the fact that Airport 1.0– 4.0 is commonly referred to as the maturity level within the existing body of literature on digital transformation. In their study on the digitalization of airports in Czechia and Slovakia, Kovacikova et al. [26] found that Bratislava Airport has implemented digital processes to some extent, placing it at the Airport 2.0 level. On the other hand, Prague Airport has reached the Airport 3.0 level and is partially at the Airport 4.0 level. This is attributed to the utilization of technologies such as biometric control, self-service check-in for passengers and baggage, and fully automated baggage sorting. The literature defines digital transformation as the process of technological advancement, alteration of company culture, or a combination of both. The inclusion or exclusion and the sequencing of the terms employed in the definition give rise to ambiguity about the significance of digital transformation. Morakanyane et al. [13] demonstrate the presence of ambiguity in the term through their thorough examination of the literature on digital transformation. The definitions of digital transformation and the concepts highlighted in its content are presented in Table 1.
Definitions of digital transformation and emphasized concepts
Definitions of digital transformation and emphasized concepts
The definitions shown in Table 1 illustrate the varying interpretations of digital transformation in the literature. The absence of widespread agreement on this matter stems from the concept that digital transformation will cater to the distinct requirements of businesses across various industries. Ordieres-Mere et al. [30] evaluated the impact of digital transformation in the manufacturing and service industries on the generation of knowledge that supports sustainability. They also highlighted the importance of innovation in business strategy when defining the term. Consequently, it is asserted that the implementation of digital transformation in the air transportation industry will promote sustainability through the adoption of innovative aircraft technologies, improved digital operations, and the use of sustainable aviation fuel. Digital transformation in the construction industry will enable the establishment of a lean management culture. These beneficial advancements will be attained not just by actively utilizing technology but also by showcasing the leadership attributes of senior executives and enhancing the organization’s capacity to utilize and retrieve information. In a similar vein, Kovacikova et al. [19] provided a definition of digital transformation within the framework of business operations, organizational structures, and operational procedures. Furthermore, they asserted that the process of digitization will enhance the value generated for airport customers and serve as a catalyst for future societal transformations. Airports that are falling behind in digital transformation should expedite their efforts and bridge the gap with their rivals, enhancing their competitive advantage, effectiveness, and efficiency. The significance of identifying the essential Airport 4.0 applications was underlined. The challenge of delineating the concept of digital transformation has prompted several scholarly studies, such as the one conducted by Kane et al. [31], to adopt a more broad and all-encompassing approach in formulating their definitions. One other rationale for maintaining a broad scope of the definition is the presentation of the study as a research report, which is grounded on a case study pertaining to the subject matter.
The inclusion of technology in many definitions is unsurprising, considering its central role in driving digital transformation [9, 32]. Studies that do not include technology in their definitions prioritize cultural and organizational transformation [33], involvement of stakeholders and expansion into new markets [34], adaptation to the environment [35], and enhancement of environmental and economic performance [36]. The examination of the appealing elements of digital transformation in the aforementioned research can offer an impartial portrayal of the matter by juxtaposing it within the context of its pros and cons.
The evolution of airports from Airport 1.0 to Airport 4.0 occurred gradually rather than abruptly. The main justification for this is that the provision of physical elements such as buildings, equipment, people, and software will not be sufficient to speed up the integration of digital transformation into the service process. The adaptation of the business culture and structure to digital transformation is identified as a significant factor in achieving this transformation [37]. Failure to achieve this adaptability will result in restricted and incomplete implementation of structural digitization projects at airports. According to Schuh et al. [10], the attainment of a comprehensive transformation will result in enhanced employee responsiveness to evolving customer needs, leading to a more streamlined, personalized, and smooth airport service process. Currently, it is imperative to allocate resources towards the enhancement of airport personnel’s training and to develop the curriculum for such training in order to foster an organizational culture that is conducive to the process of digitization. One illustrative instance of this imperative is the Singapore Changi Airport Group, renowned for its utilization of digital technologies within its operational procedures. Changi Group has allocated a budget of US$7.2 million for a training program lasting over two years to enhance the digital competencies of 2000 employees in anticipation of digital transformation [38]. According to Halpern et al. [3], the utilization of technologies is considered crucial for achieving digital transformation. They emphasize the significance of technological literacy for effectively employing digital processes that rely on real-time data. In essence, the establishment of a digital technology ecosystem and the presence of technologically proficient personnel are important for smart airports that have successfully undergone significant digital transformation.
The pros and cons of digital transformation are commonly examined within the academic sphere. Cons include the fact that new technologies used in airports are still under development and therefore vulnerable to misdirection [39], the inability of digital technologies to function if the data defined exceeds the upper and lower limits of the control system [40, 41], the high quantity and quality of data required for digital solutions to produce outputs at the desired level and this requires costly additional investments [42], the uncertainty of task sharing in activities based on human-machine interface [43], the difficulty of digital transformation adoption by all stakeholders [44], concerns about distinguishing which data is important and which is not [45], the difficulty of accurately measuring user interest and satisfaction [46], and the added responsibility and cost of securing the personal data collected, creating vendor dependency. To mitigate this reliance, it is common for major airport operators to build innovation laboratories in collaboration with diverse technology firms and academic institutions. These laboratories aim to develop novel technologies that are well-suited for their operational procedures. Examples of such investments are Changi Airport Living Lab, ADP’s Innovation Hub, and Munich Airport LabCampus [9]. Given the significance of digital transformation as a differentiating strategy for established airports, management opts to allocate resources towards this process, notwithstanding the associated costs. The current surge in investments substantiates the perspective that the advantages of digitization in the industry surpass the disadvantages. It is well acknowledged that digital technologies exhibit more cost-effectiveness in comparison to the utilization of 3D printers or robotics. According to the World Economic Forum [47], there is a growing emphasis on investing in intangible technology such as the Internet of Things and mobile/social media applications as opposed to physical hardware. According to existing research, businesses that attain a specific degree of maturity in the process of digital transformation demonstrate superior financial performance compared to their competitors. These businesses demonstrate enhanced efficiency in utilizing their current capacity and manpower. Research has shown that businesses that prioritize transformation management in digital transformation, focusing on organizational culture, tend to produce higher net profit compared to their competitors. This is due to the fact that these businesses employ their workforce to uncover novel prospects and engage people in the process in accordance with the business’s transformational goal [48]. According to Halpern et al. [9], the implementation of digital transformation offers advantages in terms of enhancing operational efficiency and cost-effectiveness, as well as augmenting passenger happiness and expanding revenue generation opportunities. Simultaneously, Merdin et al. [37] highlighted that the incorporation of digital technology into the workforce will result in a competition that is more focused on consumer needs, predictive, and sustainable.
The use of bibliometric analysis in digital transformation and aviation literature
Bibliometric analysis is a method of study employed to assess the qualitative and quantitative influence of scientific publications within an academic domain, as well as the associations between these publications and established methodologies. This analysis offers insights on collaborations among authors, institutions, or countries [49]. Due to the extensive volume of scientific output stored in digital journals and books, it is no longer feasible to utilize human labor using conventional methods. It is crucial to examine these results using sophisticated measurement tools that rely on bibliographic data [50]. The analytical method, which has its roots in the 1930 s and is commonly employed for systematic literature evaluation, has witnessed the development of numerous contemporary tools in recent times [51]. Scholars across many fields have employed the bibliometric analysis method for analyzing studies within distinct scientific domains. In his study, Broadus [52] highlighted the extensive range and variety of terminology pertaining to bibliometrics. In their study, Noyons et al. [53] examined the implementation of the science mapping technique in bibliometric studies, which were previously conducted only through performance analysis. The authors explored both the advantages and disadvantages associated with this integration. The researchers said that this approach was efficacious in offering a more comprehensive and reliable examination of the literature being investigated. Additionally, they suggested that the science mapping technique can be employed to establish a more distinct framework by assigning weights to the fundamental publication data alongside impact data. Utilizing the bibliometric analysis method to examine the relevant literature provides insights into the themes that future research topics in the discipline may focus on. Although all the instruments within this methodology can be employed in a research study, certain studies may utilize only a limited number of tools [54, 55]. Bibliometric analysis enables the examination of publications throughout various academic publication databases pertaining to a specific research domain, such as the present study. Additionally, it facilitates the exploration of publications exclusively found within particular academic journals within such domains [14, 56– 58].
Despite its ancient origins, current studies have elucidated the reapplication of bibliometric analysis. Donthu et al. [16] conducted a comparative examination of the timing and methodology for doing bibliometric analysis, meta-analysis, and systematic literature review approaches. They provided a detailed, step-by-step presentation of these methods. Therefore, our study provides support for the assertion that an advanced bibliometric analysis is conducted using the techniques outlined in our research. According to the researchers, the fundamental components of bibliometric analysis are performance analysis, science mapping, and network analysis, which are employed for more sophisticated analysis. The bibliometric examination of studies pertaining to digital transformation across diverse scientific domains has revealed a notable surge, particularly subsequent to the year 2020. In their study, Pizzi et al. [60] investigated the impact of digital transformation on the internal audit mechanisms of organizations. They found that the existing literature addresses this issue in four specific areas: continuous auditing, fraud detection, data analytics, and technological innovation. In their study, Lee et al. [61] employed a methodology to investigate the progression of digital transformation within the realm of advanced manufacturing and engineering. Their findings revealed that the subject garnered attention across various domains, including smart factories, sustainability and product-service systems, construction digital transformation, public infrastructure-centric digital transformation, techno-centric digital transformation, and business model-centric digital transformation. The method employed by Zhu et al. [62] was utilized to examine the progression of digital transformation in the literature from 2000 to 2020. Based on the analysis of 865 papers inside the WoS database, it was determined that the body of literature pertaining to digital transformation has evolved into three distinct periods and encompasses seven distinct research themes. During the twenty-year period, there has been a significant shift towards digital transformation. This includes the adoption of digital business strategy, strategic action, digital technology, agile digital transformation, digital enterprise architecture, digital transformation of manufacturing, and digital technology enhancement of consulting services. In their study on the impact of digitalization on project management, Marnewick C. and Marnevick A. [63] developed a tool called the “Project management digitalization research agenda cube (PMDRAC).” This tool was created using various analytical techniques, including co-citation networks, co-word networks, and cluster analysis. The researchers analyzed 478 articles to provide guidance for project management experts and academicians. In their study, Charfeddine and Umlai [64] conducted a bibliometric analysis of 166 academic studies conducted between 2000 and 2022 to investigate the relationship between digitalization, the information and communication technology (ICT) sector, and environmental sustainability. They found that climate change, air pollution, telephone usage, and internet usage were commonly used as indicators of environmental sustainability in most of these studies. The most commonly employed econometric models in these studies were the GMM, ARDL, and FE models. Furthermore, the association between ICT/digitization and environmental sustainability was found to be negative and linear. In line with what we said in our research, Oludapo et al.’s [65] bibliometric analysis shows that the terms technology, information systems, and management are often used to group the subject into categories that focus on the main reasons why digital transformation hasn’t been very successful. However, this inclination alone proves inadequate in elucidating the underlying causes of transformation failure.
In recent times, there has been a growing utilization of bibliometric analysis in academic research pertaining to the airline and airport industries. From 1990 to 2012, Bergiante et al. [15] conducted an analysis of 500 papers pertaining to business models and air transportation, which were sourced from the ISI Web of Knowledge. Consequently, there has been a significant surge in the quantity of scholarly papers pertaining to this subject matter over the past five years. Notably, the majority of academics engaged in this field are predominantly situated in the United Kingdom, the United States of America (USA), and Taiwan. Tanrıverdi et al. [66] conducted a comprehensive analysis of 1483 studies published in the Journal of Air Transport Management (JATM) between 2011 and COVID-19. They found that the terms recovery, crisis, and disruption play a crucial role not only in managing safety and economic crises but also in addressing health-related concerns in the post-pandemic era. In a similar vein, Yakath Ali et al. [67] examined the research articles published in JATM, Transportation Research Part A, and Transportation Research Part E, which are three prominent aviation journals, by utilizing Bibliometrix software to assess airline efficiency and effectiveness. Consequently, there is a significant academic interest in this subject. Researchers commonly employ data envelopment analysis and stochastic frontier analysis. The main focus of airline performance monitoring revolves around carbon emissions, flight delays, and accidents. Dixit & Jakhar [68] conducted a comprehensive analysis of the existing literature on airport capacity management (ACM), a crucial aspect of aviation performance. Their findings revealed that the terms airport capacity, congestion, competitiveness, and ground holding concerns are highly prevalent in this field. Furthermore, consistent with the prevailing conclusions in the existing body of literature, it has been determined that JATM holds a prominent position as the foremost journal on ACM. According to Merkert [69], an analysis of the publications in JATM revealed a notable shift in the issues explored in aviation management studies. According to the study’s findings, this publication focuses on the topics of COVID-19, service quality, and discrete choice analysis. Furthermore, the concept of sustainability was consistently highlighted over an extended period. The emergence of advanced air mobility is a prominent theme. Bakır et al. [17] performed a bibliometric analysis using R (Bibliometrix) on 100 papers published in the WoS database from 1975 to 2020. The focus of the analysis was on airport service quality, which has gained increasing attention due to the broad adoption of the smart airport concept in aviation. The study determined that JATM is the foremost journal in terms of publication count, China is the primary contributor to the pertinent literature, and research on this topic predominantly emerges from collaborative efforts among academics. Lahna et al. [70] conducted a bibliometric analysis on airports, examining 489 studies on Maintenance of Airport Infrastructure (MAI) from the WoS database using Bibliometrix. The study identified the most productive authors, institutions, and countries in MAI, as well as the most cited authors, countries, and journals that published the most in MAI. The findings revealed a significant increase in studies on this subject, particularly after 2013. The study made use of the majority of tools from the Bibliometrix package.
The limited utilization of bibliometric analysis in scholarly studies pertaining to airports is noteworthy. When considering this scenario in conjunction with the selected keywords in the research, it can be asserted that this study is necessary for conducting a comprehensive examination of the scientific understanding of digitalization in the aviation industry. Furthermore, it has been assessed that the utilization of R-based software in recent years has the potential to yield more sophisticated results in this particular approach. By adopting this approach, it becomes feasible to elucidate the link between smart airports and digital transformation in a distinctive manner, thereby mitigating the prevailing ambiguity around their definitions.
Methods
This study aims to establish a comprehensive understanding of the concept of digital transformation within the airport industry. To achieve this, the conceptual framework presents an in-depth review of the various definitions of digital transformation that currently exist. This section initially searched the Web of Science (WoS) database for studies on digital transformation and smart airports conducted between 1989 and 2024. The search was limited to the keywords “digital transformation” (topic), “smart airport” (all fields), “aerotropolis” (all fields), and “airport city” (all fields). The obtained references were then analyzed using R-based Bibliometrix (Fig. 3). The utilization of Bibliometrix in the research is justified due to its suitability as an open-source software for thorough and advanced scientific mapping. Additionally, R demonstrates efficiency and adaptability when integrated with other statistical packages (50). Along with performance analysis, this software also lets you do science mapping analysis, which isn’t possible with regular bibliometric analysis studies [16].
Bibliometric flow chart.
The data gathering and processing procedure consisted of four steps, and the final step involved performance and science mapping analysis. Performance analysis provides a numerical assessment of the examined literature, while science mapping visually illustrates the connection between the content of the literature [71]. The performance analysis in this study utilizes various metrics, including a comparative table of the most prolific authors (total studies, h-index, and local total citations), the number of articles and average citations per article according to year, the most relevant and most local cited sources, the local impact of these sources, the most globally and locally cited research, the top contributing countries, a work cloud, a thematic map, and trending topics. The quantification of publications serves as a metric for assessing the productivity of authors, institutions, countries, or journals. However, the quantity of citations received by these publications is a crucial indicator for evaluating their quality [72]. The study also utilized the h-index to identify the authors with the greatest efficacy. The h-index is a significant metric commonly employed to evaluate the excellence and number of publications by authors, institutions, or countries [14, 73]. A word cloud is a valuable tool for visually representing the conceptual structure of a research subject by displaying the most frequently occurring words and their associations. The thematic map is a visual representation tool that categorizes concepts into four distinct quadrants based on their significance and level of advancement within a given dataset. These quadrants include emerging or declining themes, basic themes, niche themes, and motor themes. The trending topics provide an overview of the prevailing concepts of interest in academia based on their frequency of recurrence in research over the years [50].
The study included many analytical techniques, including factor analysis, co-occurrence networks, co-citation networks, a historiograph, authors’ collaboration, and country collaboration indicators, for the purpose of science mapping analysis. Factor analysis groups publications that cover related subjects together. This study employed multiple correspondence analysis (MCA) to examine four clusters that were identified using the Keyword Plus filter. MCA is a method used to analyze multivariate categorical data using both graphical and numerical techniques. Co-occurrence networks are used to build the conceptual framework of a collection of bibliographic material. This is done by mapping and clustering terms that come from keywords, titles, or abstracts. The concept of a co-citation network is employed to depict the relationship between two papers that are referenced by a third document. In this study, co-citation is seen as a viable alternative to bibliographic coupling. The historiograph displays the chronological citation network among sources in the dataset. Collaboration analysis demonstrates the relationship between authors and countries in terms of authorship [50]. The Bibliometrix contains numerous clustering algorithms that primarily aim to identify associations by finding relationships between communities. Members are selected for cluster placement using equivalence criteria or scoring systems. The determination of cluster equivalence and their contents within the network is based on two criteria: (a) the presence of equivalent units that share the same connection pattern with their neighbors, or (b) the existence of equivalent units that share the same or similar connection pattern with various neighbors. Furthermore, every algorithm possesses its own limitations. The formation of clusters in this work is based on the Louvain algorithm. This approach is favored due to its faster computational speed compared to alternative methods and its straightforward implementation [17].
Following the analysis phase, the content pertaining to the stages of digital transformation was examined, including the definitions, similarities, and discrepancies identified in existing research. This analysis provides an explanation of digital transformation, including its definition, implementation methods, triggering factors, and impacts on various business components.
Performance analysis results
The present study involved the retrieval of 372 publications from the WoS database, spanning the time frame of 1989 to 2024. The objective was to examine the existing body of literature on smart airports and digital transformation. Table 2 presents the descriptive data pertaining to the aforementioned publications.
Descriptive statistics
Descriptive statistics
The analysis of Table 2 reveals that a total of 151 sources, including academic journals and other relevant materials, were obtained through the process of scanning and filtering the selected keywords. The publications have an annual growth rate of 3.19% and an average of 5.34 citations per publication. A total of 372 articles, authored by 1021 individuals, were examined, resulting in the citation of 8124 sources and the generation of 709 author keywords. Only 35 of the easily accessible papers have a single author, while 986 of them have multiple authors. The formula for calculating author appearances is to multiply the number of co-authors per document by the total number of publications, which is 3.97 times 372. While the total number of authors is 1021, it is important to note that this figure represents unique names. However, the bibliometric material includes 1477 authors, as some of them have published multiple studies. In instances of this nature, the term “author appearances” pertains to the aggregate count of authors, encompassing the primary co-author, seen within the entirety of publications. The percentage of authors from various countries engaging in collaborative research is 14.78%. The collaboration index can be calculated based on the data provided in Table 2. Divide the total number of authors in publications with multiple authors by the total number of publications with multiple authors to get the aforementioned index. The extant literature indicates that the collaboration index, which is calculated as 986/335, is 2.94. To clarify, the estimated count of authors contributing articles to the literature on “smart airports and digital transformation” is roughly three.
Table 3 displays the quantity of publications each year and the average number of citations per year and article. Approximately 60% of the scholarly articles pertaining to smart airports and digital transformation were published in the year 2019. Notwithstanding the substantial increase in the quantity of publications observed this year, it failed to attain a position among the top 10 in relation to the average number of citations. 2020 had the highest average number of citations per year, with an average of 3.36. On the other hand, 2005 had the highest average number of citations per article, with 47. 2020 is the most productive and influential year overall among the years in all three groups in the top ten (2020, 2015, 2013, 2016).
Number of articles and average citations per articles by year (the top)
Table 4 provides information on author productivity, a significant parameter used in typical bibliometric analysis studies to assess performance. The most extensive body of research on smart airports and digital transformation has been authored by Jiang X. The remaining researchers have a somewhat similar number of publications. There are ten articles each by Wang H and Wang Y, but there are five researchers that have nine publications apiece. The h-index, as proposed by Merigo and Yang [14], is a significant metric utilized in performance analysis to assess the quality and quantity of researchers. It has gained considerable popularity in recent times. Within the dataset comprising 372 articles, Baker D emerges as the researcher with the highest level of influence, as evidenced by their h-index of 5 and their total citation of 24. Given the researcher’s very low ranking in terms of the overall number of publications, it can be inferred that his literature garners significant attention within this particular subject.
Comparative table of the most prolific authors (total studies, h-index, and local total citations)
Table 5 lists the primary sources of publications on smart airports and digital transformation, along with the corresponding impact ratings. Out of the 222 studies included in the top ten sources pertaining to this subject matter, a significant majority of 194 were carried out during the International Conference on Construction, Aviation, and Environmental Engineering (ICCAEE) held in 2018. This conference is notable for its extensive range of studies, which sets it apart. The Journal of Air Transport Management (JATM) demonstrates exceptional performance when assessed based on three criteria pertaining to productivity and effectiveness. In the dataset, JATM ranks second in terms of publication count, with the maximum number of citations (181) and the h-index (6). An additional noteworthy aspect about the quantity of publications is that, despite conferences being a prevalent means of generating articles on smart airports and digital transformation, they do not rank among the most often referenced sources within the dataset. Journals are the most frequently referenced sources.
Most relevant and most local cited sources and sources’ local impact
Table 6 displays the most significant global studies pertaining to the subject matter. Sparks Tc (2020) is the most frequently referenced study (197) on this particular topic. Based on the aforementioned limitations, the aviation study that holds the highest influence (67) in terms of global citations is “The Airport City Phenomenon: Evidence from Large US Airports” by Appold and Kasarda [74] (Appold Sj, 2013). Freestone and Baker [75] conducted a paper titled “Spatial Planning Models of Airport-Driven Urban Development,” which holds the distinction of being the second most prominent study in this field (freestone R, 2011). The paper titled “Fully solar-powered airport: A case study of Cochin International airport” by Sukumaran and Sudhakar [76] ranks third with a total of 52 citations (Sukumaran S, 2017).
The most global cited researches
Table 7 displays the 10 most frequently referenced studies out of the 372 local studies in the dataset. The study with the highest number of citations (15) within the dataset is attributed to Appold and Kasarda [74] (Appold Sj, 2013). The study’s status as the most referenced aviation study worldwide, as indicated in Table 6, along with the results presented in Table 7, demonstrates the research’s efficacy. According to Freestone and Baker [75], their study holds the second position in terms of citation count within the dataset. A comparable finding to the initial study may be drawn for the present study. The papers with seven local citations each cover the following topics: “Human resources in aerotropolis” [77], “Competitiveness of aerotropolis” [78], and “Impact of airports on regional development” [79]. The article titled “Organization of Land Surrounding Airports: The Case of the Aerotropolis” by Flores-Fillol et al. [80] has the highest ratio of local citations to global citations (LC/GC). To clarify, the present study garnered 62.5% of the total global citations within the collection of papers.
The most local cited researches
Table 8 displays a comparative depiction of countries that have had the highest level of productivity in the literature regarding smart airports and digital transformation. China is the most significant contributor to the literature in terms of the quantity of articles, with a total of 238 articles. Following this are Israel, with 28 articles, and Australia, with 17 articles. Single-authored research is prevalent in the first seven countries, while multi-authored research is predominant in Korea, Ghana, and Italy. While China holds the top position in terms of total citation count, the United Kingdom (UK) exerts a far greater influence in terms of the average number of citations per article. China’s performance in terms of average citations per paper falls outside the top ten, indicating a lack of effectiveness in its academic outputs. However, Italy is positioned at the bottom in terms of the overall quantity of studies undertaken, yet it holds the third position in terms of efficacy. This suggests that the academic research carried out in Italy garners significant interest. Upon examining the state of smart airports and digital transformation in the United States of America (USA), it becomes evident that the country ranks fourth with a total of 17 articles. However, it lags significantly behind Italy in terms of the average number of citations per article.
Comparative table of the top contributing countries
Keyword Plus is a tool that shows the themes around which the topics addressed in the relevant literature are mostly gathered. Figure 4 illustrates a word cloud displaying the most commonly used words from the 372 articles examined in this study. Prior to constructing the word cloud, a complete list of keywords was established to ensure the absence of any inconsistencies in the study. Consequently, the terms “aerotropolis,” “airport,” “airports,” “smart,” “transformation,” “digital,” “city,” and “cities” were deliberately omitted from the cloud.
Word cloud of smart airport and digital transformation.
Figure 4 shows that “performance” is the term that appears the most frequently in the literature on smart airports and digital transformation, followed by “impact” and “model.” “Growth” and “economic development” are commonly recurring terms. In this particular context, it can be posited that the subject matter is recurrently addressed through the utilization of terms pertaining to “performance and impact of digital transformation models in smart airports” and “economic growth and development.”
Figure 5 displays the thematic map, a significant instrument utilized for visualizing the overarching topics that constitute the literature within a specific research domain. A thematic map is a coordinate plane that includes an x-axis for centrality and a y-axis for density. The concept of centrality pertains to the degree of association between a cluster located on a certain plane and other clusters. The greater the extent of this relationship, the more it can be deduced that the cluster in question is applicable to a wider range of research domains within the academic realm. Density is a measure that quantifies the degree of interconnectedness among words inside a cluster. Yu and Muñoz-Justicia [59] say that there is a positive relationship between the number of connections between words and the degree to which the study topics linked to the cluster of words make sense. This means that a coherent framework is being formed. The observed motor themes on this plane demonstrate the concentration of the most significant and all-encompassing established themes in organizing the research subject. The centrality and density of the clusters in this quadrant are high. Furthermore, it is acknowledged that the topics presented here are interconnected with other themes. Put simply, inferences can also be made by comparing the themes found in this part with those found in other parts. The themes within the Niche themes quadrant exhibit robust connections in terms of words within the cluster while displaying negligible connections in terms of themes beyond the quadrant. The thematic elements within this quadrant indicate a highly precise conceptual framework for the research subject. The presence of emerging and declining themes can be attributed to the limited interconnectedness of words within a cluster, as well as the lack of relevance in their relationship to concepts in other parts. The topics inside this quadrant represent terms that are either emerging or falling in significance for the study topic. The clusters inside the Basic themes quadrant encompass terms that hold significance within the research domain yet impose constraints on the topic at a more foundational level [81].
Smart airport and digital transformation thematic map.
Figure 5 displays the outcomes based on the initial identification of motor themes in different circumstances. The word frequency per thousand publications is five, the total word count is 130, and the Louvain clustering algorithm is chosen. The cluster named “interpolation-optimization-path” encompasses the motor themes of smart airports and digital transformation. The niche themes related to the topic were categorized into three distinct clusters: “efficacy-antioby-herpesvirus” (purple cluster), “mechanism-extraction-homogeneity” (pink cluster), and “alternate cover test-prism-reliability” (light green cluster). The emerging or declining themes part contained just the “stall inception” cluster, while the “impact-growth-air” and “economic-development-design-location” clusters served as the basic themes that described the overall structure of the topic.
The final step of the performance analysis involved the presentation of a trend topic graph (Fig. 6), which illustrates the pattern of the keywords of interest throughout the studies undertaken in the relevant subject over time. Keyword Plus concepts were favored in the trend topic analysis, and their frequencies were determined. The reason for this choice is that Keyword Plus ideas consist of phrases that are commonly found in the titles and reference lists of the articles in the dataset. These concepts provide a more detailed and comprehensive understanding of the article’s content [82]. In the related literature of 2020, the themes that garnered the highest popularity were “performance” (10), “model” (9), and “impact” (9). The key terms of importance in 2023 were “economic development” (5), “path” (4), and “optimization” (4).
Trending topics by year.
Science mapping is a method employed to visually represent the organizational and evolving elements of research within an academic domain [81]. Bibliometric mapping, often known as this technique, is employed to visually represent the cognitive structure and theme progression of the literature under investigation [53]. Co-citation (Fig. 7) and co-occurrence analysis (Fig. 8) are often employed methodologies for science mapping within a certain study domain [81]. The concept of co-citation, initially proposed by Small [83], quantifies the occurrence rate of two documents that are co-cited within a given dataset. The “biblioNetwork” function in Bibliometrix is utilized to uncover the intellectual framework of an academic domain [51]. Co-occurrence analysis, also known as co-word analysis, is a method used to visually demonstrate the relationship between words in academic papers. Callon et al. [84] were the first to offer this methodology, which involves building word blocks consisting of terms that appear within a specificsubject.
Co-citation network.
Co-occurrence network.
Out of the 372 articles examined in this study, the co-citation link between the articles was clearly demonstrated by setting the number of edges to four. The relationship between the first forty articles was then displayed. The application of the Louvain algorithm for clustering reveals that the co-citation network is organized into four distinct clusters.
The co-citation network map reveals that the articles “charles mb, 2007,” “kasarda j. 2011,” and “appold sj, 2013” have the highest number of co-citations among the articles on the topic of smart airports and digital transformation. The authors “freestone r, 2009” and “stevens n, 2010” were found to have the largest number of co-citations within the blue cluster.
Co-occurrence analysis is an additional methodology employed in the field of science mapping. The co-occurrence study conducted using the Louvain algorithm involved setting the minimum number of linkages at two and examining the relationship between the first 50 nodes. Figure 8 illustrates the thematic framework of smart airports and digital transformation, which revolves around six key clusters. Within the realm of literature, the terms “performance” and “model” hold significant influence and are commonly linked. The terms “impact” and “air” are commonly associated within the same cluster, although “economic development” and “location” are usually used in conjunction. Based on the word and cluster sizes, it may be inferred that other linkages are comparatively less common.
Figure 9 displays the chronological citation network of the initial 24 articles pertaining to the subject matter. The network presented in this study illustrates the interconnectedness of articles within the dataset, highlighting their subject matter and establishing a reference link between them [50]. A total of 24 articles were chosen due to the identification of a second cluster (blue) by setting a minimum of 20 nodes. The initial publication that garners significant attention and can be grouped together about smart airports and digital transformation is “freestone, 2011.” “pereira acc, 2023” is the latest publication inside this cluster. The second cluster was established by the citation of two recent studies (muhsen k, 2022).
Historiogram.
Multiple Correspondence Analysis (MCA), a powerful Bibliometrix analysis method, was used to do a factor analysis and improve the conceptual framework for the topic of smart airports and digital transformation [50]. Based on the analysis shown in Fig. 10, which incorporates Keyword Plus concepts and establishes a minimum of four interrelated clusters, it is feasible to categorize the subject matter into four distinct clusters.
Keyword plus MCA.
Based on the MCA analysis, the examination of smart airports and digital transformation is approached via a technical lens, specifically focusing on the purple cluster. The primary dimension of emphasis is optimization and algorithms. A distinct cluster is formed by the management and effects of air transportation as a service, while the largest cluster is the red cluster bounded by the terms “land use, temperature, and growth.”. The association between “economic development and location” is distinct.
The collaboration between countries (network) studying in the linked literature is depicted in Fig. 11.
Countries’ collaboration network.
It is important to highlight that the scientific collaboration spearheaded by China encompasses a broad geographical range, extending from Canada to the Gulf countries. Although the United Kingdom has engaged in academic collaboration with Ghana, it has not developed any collaborative efforts with the United States, despite the historical and deeply ingrained links between the two countries. The United States conducted extensive research with several developed European countries, including Israel.
Various level definitions have been established in the literature to precisely articulate the current state of airports’ digital transformation. The level definitions show a high degree of similarity, but despite this similarity, the levels have different names. The variation in the deployment of identical technology may be attributed to the sector-specific nature of digital transformation inside businesses. Table 9 presents an overview of the levels and definitions utilized in various investigations, highlighting both shared and distinct characteristics within these categories.
Digital transformation levels, definitions, similar and different aspects
Digital transformation levels, definitions, similar and different aspects
Table 9 illustrates that the definitions of digital transformation levels in the comparative research exhibit shared characteristics consistent with existing literature. The disparities among the levels are illustrated in the “e” column. The airport digital transformation definition in the conclusion section considered these changes, thus broadening the concept’s reach.
This study employed the R-based Bibliometrix software to do an advanced bibliometric analysis. The objective was to assess the current state of smart airports and digital transformation in the literature and to address any existing conceptual ambiguity. The concept of a smart airport or aerotropolis elucidates the ultimate outcome that can be achieved through digital transformation in the airport sector. The search queries conducted on the WoS encompass the keywords (digital transformation), (“smart airport,” “aerotropolis,” and “airport city.”). The integration of Industry 4.0 technologies into the airport service process, with the main emphasis on the seamless exchange of real-time data, particularly across various entities, distinguishes smart airports. According to Halpern et al. [9], the inherent characteristics of this phenomenon impose significant pressure on sensor technology. In addition to the aforementioned technology, the realization of the smart airport concept necessitates a managerial transformation. This necessitates the integration of novel technologies into the service process as well as a fundamental shift in culture from the highest levels to the lowest levels. Put simply, a smart airport is anticipated to involve all parties involved in this procedure, employ cutting-edge technologies, and foster a culture of ongoing change.
A total of 372 scholarly sources pertaining to the subject were accessed as a consequence of the analysis. The publications in these sources experienced an annual growth rate of 3.19%, with an average of 5.34 citations per article. It might be posited that the advancement of scientific knowledge in this particular domain is advancing at a sluggish pace, resulting in a limited influence. According to the calculated collaboration index (2.94), research groups with three different researchers are responsible for the majority of current research. In their bibliometric analysis study, Bakır et al. [17] found that the majority of research groups focused on airport service quality consist of two or three members.
2019 was the most prolific year in terms of publishing output, accounting for about 60% of all existing publications. Nevertheless, in spite of the substantial increase in the quantity of publications observed this year, there was no corresponding effect observed in relation to the average number of citations. According to the average number of citations per year and per article, 2020 and 2005, respectively, showed the highest level of influence. In terms of smart airports and digital transformation, the year 2020 has emerged as particularly productive and influential. This circumstance highlights the generally accepted principle in today’s academic world that “quality, not quantity, is important.” However, it is necessary to evaluate if the publications in the relevant years align with the present trend in terms of subject matter.
In the context of performance analysis, Baker D. emerges as the foremost researcher in terms of influence. Despite having the lowest total number of publications, Baker D. secures the top position in terms of both h-index (5) and local total citation (24), thereby substantiating the aforementioned principle. Table 4 displays the performance of Jiang X, who has conducted the most studies on smart airports and digital transformation. It can be concluded that the assertion made by Gölgeci et al. [85] regarding the positive impact of forming a research group on academic effectiveness is not entirely valid. It is not feasible to address such an enhancement for every member.
The performance evaluation of academic resources in the field of smart airport and digital transformation revealed that the “International Conference on Construction, Aviation and Environmental Engineering (ICCAEE)” organized in 2018 was the most prolific resource in terms of the number of publications in the relevant literature. While it can be argued that this conference is notable for its extensive range of studies, based on a comprehensive review based on three criteria pertaining to productivity and efficacy, JATM emerged as the top-ranked source in the pertinent literature. In the dataset, JATM ranks second in terms of publication count, with the maximum amount of citations (181) and an h-index (6). This finding aligns with the conclusions drawn by Tanrıverdi et al. [66], Bakır et al. [17], and Dixit & Jakhar [68], who have asserted that JATM serves as a valuable resource for scholarly studies within the realm of air transportation. Furthermore, while conferences are frequently utilized as a means of generating knowledge on smart airport and digital transformation, they do not hold significant influence as primary sources. This suggests that scholars mostly depend on articles published in peer-reviewed journals as the primary means of substantiating their academic studies.
Appold and Kasarda [74] conducted a highly influential global study on airports titled “The Airport City Phenomenon: Evidence from Large US Airports.” Another influential study on urban planning is Freestone and Baker’s [75] “Spatial Planning Models of Airport-Driven Urban Development.” The study titled “Fully solar-powered airport: A case study of Cochin International airport” by Sukumaran and Sudhakar [76] ranks third. Upon examining the content of these studies, it becomes evident that the scholarly community has a keen interest in the subject matter of smart airports and digital transformation, particularly in relation to their regional economic implications, urban planning, and energy efficiency. The studies carried out by Antipova and Özdenerol [77], as well as Cidell [79], support the conclusion. The highest LC/GC ratio among the aforementioned studies is that of the Flores-Fillol et al. [80] study. The fact that this particular study garnered 62.5% of its global citations within the dataset suggests a significant connection between the utilization of Bibliometrix and the accurate implementation ofkeywords.
The countries that have made the most significant contributions to research on smart airports and digital transformation are China, Israel, and Australia. In terms of average citations per article, the UK emerges as the most influential country. The prevalence of single-author research is evident in the studies conducted in the first seven countries, while multi-author research is more prevalent in the studies conducted in Korea, Ghana, and Italy. China’s performance in the academic realm can be characterized as ineffective, as it does not rank within the top 10 countries in terms of the average number of citations per publication. In contrast, Italy is positioned at the bottom of the rankings in terms of the overall quantity of studies undertaken; however, it holds the third position in terms of effectiveness. This suggests that academic research produced within Italy garners considerable attention.
The smart airport and digital transformation literature often highlights several key concepts, including “performance,” “impact,” “model,” “growth,” and “economic development.” Additionally, niche themes such as “efficacy-antioby-herpesvirus,” “mechanism-extraction-homogeneity,” and “alternate cover test-prism-reliability” are also frequently emphasized. The basic themes encompassed in this topic are “impact-growth-air” and “economic-development-design-location,” suggesting a broader trend that transcends beyond the realm of smart airport management. The trend can also be assessed by examining the trending terms in 2023 and the co-occurrence network. This scenario provides evidence for the necessity of establishing a precise definition within the context of smart airports and digital transformation, highlighting the importance of doing research that will contribute to the existing body of literature in this area. The current body of literature provides support for research that highlights the significance of environmental and economic development [51]. However, there is a gap in the literature on studies that establish a connection between this issue and cultural and organizational transformation [33]. Currently, an alternate methodology could involve providing an extensive definition of the concept of digital transformation as demonstrated by Kane et al. [86] and examining its connection with the smart airport concept.
Based on the co-citation network map, the studies “charles mb, 2007” (Airport futures: Towards a critique of the aerotropolis model), “kasarda j. 2011” (Aerotropolis: The Way We’ll Live Next), and “appold sj, 2013” (The Airport City Phenomenon: Evidence from Large US Airports) are the most frequently cited publications in the field of smart airports and digital transformation. The publications’ emphasis on the effects of smart airports piques interest in the potential shifts in economic and environmental consequences within regions where the concept of digital transformation is being examined. The effects of airports on the economy, ecology, and society are often examined [87, 88].
The historiograph identifies “freestone R, 2011” as the initial notable article on the subject of smart airports and digital transformation. The fact that the latest publication within this cluster is from 2023, along with the regularity of studies being published on an annual basis, suggests that the subject matter remains an active area of research within the academic sphere and has not yet attained a state of maturity. Many studies in the bibliometric analysis literature [71, 89] do not provide judgments about the trajectory of the study topic using historiograms.
The MCA analysis examines the technical aspects of smart airports and digital transformation, focusing on optimization and algorithms. Although the management and impacts of air transport as a service are considered a distinct cluster, the absence of management or organization concepts even in the largest cluster (the red cluster) and the frequent study of “economic development-location” concepts, which form a separate cluster, highlight the importance of addressing the issue of smart airports and digital transformation within the framework of management and organization.
From the bibliometric analysis and Table 9, it is clear that the literature on smart airports and digital transformation has not yet come up with a solid idea for how to run them or how to use technology to make them smart. One possible explanation for this phenomenon could be that the various stages of digital transformation necessitate the inclusion of sub-levels, similar to the dimensions established in models for measuring digital maturity. Another factor to consider is that the findings pertaining to this matter are derived from sources that are not specifically focused on airport management and organization, even when conducted within a systematic study framework. In this context, a smart airport is defined as “an airport ecosystem where personalized service is provided to users by using Industry 4.0 technologies on the basis of big data analysis and real-time sharing between objects; digitalization is turned into a holistic organizational culture starting from top management to cover all personnel; the decision-making process is carried out autonomously within the entire airport operation network; and the main goal of competitive advantage and high-level user experience is provided uninterruptedly.”
Several studies have been undertaken in the sector of transportation using bibliometric analysis methods [90, 91]. The analysis tools employed in several of these studies were CiteSpace and VOSviewer [92, 93]. There is a scarcity of bibliometric analysis studies pertaining to the airport industry that employ contemporary and sophisticated methodologies [66–68]. As far as the authors are aware, this will be the first study to use advanced bibliometric analysis to examine smart airports and digital transformation, which will close a significant gap in the literature. The addition of performance analysis, science mapping techniques, and network analysis utilizing multiple tools enhances the level of sophistication in bibliometric analysis [16]. With the utilization of MCA factor analysis, a historiogram, and comparative tables, this study is regarded as a significant contribution to the existing body of bibliometric analysis literature. In subsequent studies, it is possible to do a bibliometric analysis of the existing body of literature pertaining to models for measuring digital maturity. This can be accomplished by employing diverse databases and various methods for analysis and visualization. The definitions employed in many industries to elucidate the concepts utilized in academia contribute to the depth of the literature, but they also create potential for ambiguity. Bibliometric analysis studies are widely regarded as a valuable method for addressing these issues.
Ethical approval
This study, as a literature review, is exempt from Institutional Review Board approval.
Informed consent
Not applicable.
Conflict of interest
The authors declare that they have no conflict of interest.
Funding
The authors report no funding.
Footnotes
Acknowledgments
The authors have no acknowledgments.