Smart Cities and Citizen Adoption

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Gabriel Marín Díaz	--	2175	2023-08-17 10:16:04	\|
2	format correction	Dean Liu	Meta information modification	2175	2023-08-18 02:39:54	\| \|
3	format correction	Dean Liu	Meta information modification	2175	2023-08-28 04:30:13	\|

This entry is adapted from the peer-reviewed paper 10.3390/electronics12163395

Due to the irruption of new technologies in cities such as mobile applications, geographic information systems, internet of things (IoT), Big Data, or artificial intelligence (AI), new approaches to citizen management are being developed. The primary goal is to adapt citizen services to this evolving technological environment, thereby enhancing the overall urban experience. These new services can enable city governments and businesses to offer their citizens a truly immersive experience that facilitates their day-to-day lives and ultimately improves their standard of living. In this arena, it is important to emphasize that all investments in infrastructure and technological developments in Smart Cities will be wasted if the citizens for whom they have been created eventually do not use them for whatever reason. To avoid these kinds of problems, the citizens’ level of adaptation to the technologies should be evaluated. However, although much has been studied about new technological developments, studies to validate the actual impact and user acceptance of these technological models are much more limited.

Smart Cities TAM decision making fuzzy logic Smart Tourism

1. Recommender Systems

A recommender system is a type of computer system designed to suggest or recommend items to users, such as products, services, movies, music, news, or any other type of content, based on their preferences, past behaviors, or user profiles. The main goal of a recommender system is to provide personalized and relevant recommendations, to help users discover new items that may be of interest to them ^[1].

In the literature, different perspectives can be found on the definition of recommender systems. Linked Data comprises a collection of guidelines and practices that facilitate the publication and interlinking of structured data on the Web ^[2]. This work incorporates human cognitive processes, personality, and affective signals into recommendation models ^[3]. In general, articles suggest that recommendation systems are computer tools that use machine learning techniques to model and predict user preferences based on behavioral data, and that there are ongoing efforts to improve their personalization, accuracy, and evaluation.

Recommendation systems applied to Smart Cities are tools that use data and algorithms to provide personalized and relevant recommendations in the context of Smart City services and applications. These systems focus on improving the efficiency and quality of life of citizens by providing suggestions and guidance based on their preferences, needs, and the urban environment ^[4].

Upon investigating the literature related to the search variable TS = (RECOMMENDER SYSTEMS) more than eight thousand publications evaluating this concept were found (Figure 1). The prominence of the field of recommender systems can be underscored by the considerable number of publications generated by academics and practitioners in this domain.

Figure 1. Studies related to the recommender systems in the Smart Cities field.

If the focus of the search is on analyzing recommender systems in a Smart City environment (TS = (RECOMMENDER SYSTEMS)) AND (TS = (SMART CITY) OR TS = (SMART CITIES)), only 91 articles are found, indicating a compelling gap worthy of exploration.

The aim is to showcase scientific publications linked to this concept. To accomplish this, research was conducted using the Web of Science Core Collection, employing specific criteria to select journal articles, covering the entire available period.

In the subsequent section, thorough exploration of the concepts of Smart Cities and Smart Tourism will be conducted.

2. Smart Cities and Smart Tourism

As mentioned in the introduction section, the irruption of new technologies combined with the new challenges of civilization have caused the growth of Smart Cities. In this context, extensive research has been conducted in the domains of Smart Cities.

Smart City technology has a significant impact on the tourism industry. In the following study ^[5], the strategic role of technology in smart tourist destinations is explored. Another study ^[6] examines the relationship between Smart Tourism technology and overall satisfaction in three South Korean cities. In this study, ^[7], it is argued that technology innovations bring stakeholders together in tourism service ecosystems and transform industry structures, processes, and practices. Overall, these papers suggest that Smart City technology can enhance tourism competitiveness and generate new opportunities for the industry.

When conducting research on the publications and citations associated with the search variable TS = (SMART CITY), an extensive body of literature was discovered, consisting of over thirty thousand publications that delve into and study the concept of Smart Cities.

As mentioned, Smart Tourism closely relates to the concept of a Smart City. To study the phenomenon of Smart Tourism, research was conducted on the publications and citations related to the search variable TS = (SMART TOURISM) as shown in Figure 2.

Figure 2. Studies related to Smart Tourism.

To accomplish this objective, research was performed in the Web of Science Core Collection, employing specific criteria to select journal articles, and considering the entire available period for analysis.

Research was conducted on both interconnected concepts, Smart Cities and Smart Tourism, and the following results were obtained. Figure 3 shows an overview of the existent literature associated with the search variables TS = (SMART TOURISM) AND TS = (SMART CITIES). The primary objective is to present the scientific publications relevant to these concepts. To accomplish this objective, research was performed in the Web of Science Core Collection, employing specific criteria to select journal articles and considering the entire available period for analysis.

Figure 3. Studies related to Smart Cities and Smart Tourism.

3. Smart Cities, Smart Tourism, and Technology User Acceptance (TAM)

Having reviewed the literature on the concepts of Smart Tourism and Smart Cities, the research leads to evaluating the studies that connect these terms with the TAM.

Figure 4 provides a thorough summary of the research papers and references related to the search parameters, TS = (SMART TOURISM)) AND ((TS = (TECHNOLOGY ACCEPTANCE MODEL) OR TS = (TAM)). The main objective is to showcase the scientific publications relevant to this concept. To accomplish this objective, research was performed in the Web of Science Core Collection, employing specific criteria to select journal articles and considering the entire available period for analysis.

Figure 4. Studies related to Smart Tourism and TAM.

As observed in these sections, a substantial amount of literature exists on Smart Cities, Smart Tourism, and technology acceptance models. Nevertheless, upon reviewing the literature regarding the integration of these concepts, it becomes evident that there is a noticeable scarcity of scientific studies addressing this aspect as can be seen in Table 1.

Table 1. Studies related to Smart Cities, Smart Tourism and TAM.

Year	Title
2018	Framing a Smart Service with Living Lab Approach: A Case of Introducing Mobile Service within 4G for Smart Tourism in Taiwan ^[8].
2019	The Augmented Reality in Lisbon Tourism Proposal for a AR Technology Adoption Model ^[9].
2020	The Role of Human–Machine Interactive Devices for Post-COVID-19 Innovative Sustainable Tourism in Ho Chi Minh City, Vietnam ^[10].
2022	Using UTAUT-3 to Understand the Adoption of Mobile Augmented Reality in Tourism (MART) ^[11].
2023	Small-Town Citizens’ Technology Acceptance of Smart and Sustainable City Development ^[12].

Given the existing gap in the literature, a functional methodology is presented to develop a recommendation system for categorizing tourists based on their digital proficiency and their utilization of mobile technologies in Smart Cities.

4. Criteria for Measuring Technology User Acceptance

Numerous studies in the literature have identified various criteria for measuring technology user acceptance models. For this work and model proposal, the approach is based on the most frequently utilized TAM criteria, forming the foundation of the research:

Frequency of Mobile Use (FMU): Some studies suggest that the frequency of mobile use is an important factor in technology acceptance models. This study ^[13], revealed that the perceived effectiveness and perceived convenience of mobile applications were impacted by crucial security elements. In this other study ^[14], the research discovered that both the perceived simplicity of usage and the portability aspects had a considerable impact on the perceived usefulness of mobile health services. In ^[15], it was revealed that the perceived level of physical risk and key factors from the TAM were strong indicators of individuals’ intentions to use mobile applications for online transportation services. Finally, in ^[15], it was demonstrated that mobile social media usage exerted a substantial indirect influence on online business models. This influence was mediated by the TAM, emphasizing its critical role in the frequency of mobile use.
Mobile App Usage (MAU): After a deep analysis of the existent literature, it can be highlighted that the impact of the mobile apps usage has directly influenced the TAM and that mobile app usage has had a positive impact on teacher performance and learning capabilities ^[16]. In this other study ^[17], it was observed that the perceived ease of use and perceived usefulness of mobile apps positively affected hotel consumers’ experiences. Additionally, it is highlighted that perceived usefulness, along with user experience, played a significant role in influencing customers’ acceptance of hotel apps. The papers suggest that TAM can be used to investigate the impact of mobile app usage in various contexts, such as education, hospitality, and conferences.
Digital Competence (DC): The literature suggests that digital competence directly affects technology acceptance models. This study ^[18], incorporates technology readiness into the TAM and identifies that the influence of technology readiness on use intention is mediated by the perceptions of usefulness and ease of use. In other words, the impact of an individual’s technological readiness on their intention to use a particular technology is influenced by how they perceive its usefulness and ease of use. In the same vein ^[19], extends the TAM by incorporating two types of perceived usefulness and reveals that perceived near-term usefulness has the most significant influence on the behavioral intention to use a technology. However, it is worth noting that perceived long-term usefulness also exerts a positive impact on the intention to use the technology, albeit to a lesser extent.
Attitude towards Technology (ATT): This criterion is an important factor in technology acceptance models, its significance lies in gauging the emotional and cognitive responses of users, capturing their perceptions, beliefs, and attitudes towards technology. In ^[20], it was found that awareness and perceived risk are external variables that affect the technology acceptance model for mobile banking in Yemen. On another hand, in this study ^[21], the research indicated that attitude served as a significant predictor of university students’ intention to use e-learning, drawing from the TAM;
Perceived Usefulness (PU) and Perceived Ease of Use (PEU): These two variables have been deeply considered and analyzed by authors who collectively suggest that perceived usefulness is an important factor in the TAM. In ^[22], it was discovered that perceived usability holds crucial importance in the TAM and that its presence explains a greater amount of variance in the model compared to its absence. In ^[23], it was found that perceived ease-of-use (PEU) is strongly related to perceived usability, which is a component of the modified TAM (mTAM). However, Ref. ^[24] proposed a theoretical model that suggests perceived ease of use is determined by control, intrinsic motivation, and emotion, and that it adjusts over time to reflect objective usability and perceptions of external control. This suggests that perceived usability may be more complex than simply incorporating it into the TAM. In this arena, many works suggest that perceived usefulness is a key factor in the TAM, but the relationship between perceived usability and TAM may require further investigation.
Social Media Usage (SMU): The literature suggest that the TAM can be used to understand social media usage behavior. In this paper ^[25], interesting results highlighting the relationship between TAM and social media usage were found. The author found that the individual adoption behavior of Facebook can be explained by its perceived ease of use, critical mass, and social networking site capability. In a study conducted by Al-Qaysi ^[26], the author conducted a thorough systematic review, analyzing 57 research articles. The findings indicated that several factors significantly extended the TAM, among the most frequent factors were social media, perceived enjoyment, subjective norm, self-efficacy, perceived critical mass, perceived connectedness, perceived security, and perceived trust. In the same vein, in this other study ^[11], the digital gap that might exist across different generations was delved into and it was uncovered that age plays a significant role in influencing the optimism, innovativeness, and perceived usefulness concerning the adoption of social media. Finally, study ^[27] constructed a comprehensive model to investigate the influence of social-media-use factors on electronic banking adoption. They observed a notable negative impact of the social media factor on the expected efforts, which, in turn, affected the use of electronic banking services.
Previous Experience (PE): The last criterion identified in this study is previous experience. In study ^[28], a meta-analysis of TAM was conducted which found that subjective norm (such as experience) has a significant influence on perceived usefulness and the behavioral intention to use any technology. On the other hand, in study ^[29], out a literature review of the technology acceptance models was carried out, and it was evident that these models play a vital role in comprehending the predictors of human behavior concerning the potential adoption or rejection of innovations and technologies. Finally, and in the same arena, in study ^[30], a research plan dedicated to exploring prospective interventions both before and after IT implementation was outlined, aiming to improve employees’ acceptance and utilization of technology. Moreover, the studies suggest that prior experiences could influence technology acceptance models, and leveraging this criterion can foster technology adoption and utilization.

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Gabriel Marín Díaz

José Luis Galdón Salvador

José Javier Galán Hernández

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Update Date: 28 Aug 2023

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